TY - JOUR M3 - Article in Press Y1 - 2023 SP - 104801 SN - 1879-176X JF - Journal of dentistry JO - J Dent UR - https://www.embase.com/search/results?subaction=viewrecord&id=L643020786&from=export U2 - L643020786 C5 - 38097035 DB - Medline U3 - 2023-12-21 L2 - http://dx.doi.org/10.1016/j.jdent.2023.104801 DO - 10.1016/j.jdent.2023.104801 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=1879176X&id=doi:10.1016%2Fj.jdent.2023.104801&atitle=Application+of+omics+technologies+in+Cariology+research%3A+a+critical+review+with+bibliometric+analysis&stitle=J+Dent&title=Journal+of+dentistry&volume=&issue=&spage=104801&epage=&aulast=Zhang&aufirst=Josie+Shizhen&auinit=J.S.&aufull=Zhang+J.S.&coden=&isbn=&pages=104801-&date=2023&auinit1=J&auinitm=S A1 - Zhang, J.S. A1 - Huang, S. A1 - Chen, Z. A1 - Chu, C.-H. A1 - Takahashi, N. A1 - Yu, O.Y. M1 - (Zhang J.S.; Huang S.; Chu C.-H.; Yu O.Y., ollieyu@hku.hk) Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China M1 - (Chen Z.) Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China M1 - (Takahashi N.) Division of Oral Ecology and Biochemistry, Tohoku University Graduate School of Dentistry, Sendai, Japan T1 - Application of omics technologies in Cariology research: a critical review with bibliometric analysis LA - English KW - artificial intelligence KW - bibliometrics KW - clinical significance KW - dental caries KW - digital technology KW - human KW - major clinical study KW - metabolomics KW - metagenomics KW - microflora KW - multiomics KW - pharmacology KW - prevention KW - proteomics KW - review KW - risk assessment KW - systematic review KW - transcriptomics KW - Web of Science N2 - OBJECTIVES: To review the application of omics technologies in the field of Cariology research and provide critical insights into the emerging opportunities and challenges. DATA & SOURCES: Publications on the application of omics technologies in Cariology research up to December 2022 were sourced from online databases, including PubMed, Web of Science and Scopus. Two independent reviewers assessed the relevance of the publications to the objective of this review. STUDY SELECTION: Studies that employed omics technologies to investigate dental caries were selected from the initial pool of identified publications. A total of 922 publications with one or more omics technologies adopted were included for comprehensive bibliographic analysis. (Meta)genomics (676/922, 73%) is the predominant omics technology applied for Cariology research in the included studies. Other applied omics technologies are metabolomics (108/922, 12%), proteomics (105/922, 11%), and transcriptomics (76/922, 8%). CONCLUSION: This study identified an emerging trend in the application of multiple omics technologies in Cariology research. Omics technologies possess significant potential in developing strategies for the detection, staging evaluation, risk assessment, prevention, and management of dental caries. Despite the numerous challenges that lie ahead, the integration of multi-omics data obtained from individual biological samples, in conjunction with artificial intelligence technology, may offer potential avenues for further exploration in caries research. CLINICAL SIGNIFICANCE: This review presented a comprehensive overview of the application of omics technologies in Cariology research and discussed the advantages and challenges of using these methods to detect, assess, predict, prevent, and treat dental caries. It contributes to steering research for improved understanding of dental caries and advancing clinical translation of Cariology research outcomes. ER - TY - JOUR M3 - Article Y1 - 2023 VL - 14 IS - 1 SN - 2041-1723 JF - Nature Communications JO - Nat. Commun. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2022074447&from=export U2 - L2022074447 C5 - 36922516 DB - Embase DB - Medline U3 - 2023-04-17 U4 - 2023-07-10 L2 - http://dx.doi.org/10.1038/s41467-023-36983-2 DO - 10.1038/s41467-023-36983-2 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=20411723&id=doi:10.1038%2Fs41467-023-36983-2&atitle=An+integrated+single+cell+and+spatial+transcriptomic+map+of+human+white+adipose+tissue&stitle=Nat.+Commun.&title=Nature+Communications&volume=14&issue=1&spage=&epage=&aulast=Massier&aufirst=Lucas&auinit=L.&aufull=Massier+L.&coden=&isbn=&pages=-&date=2023&auinit1=L&auinitm= A1 - Massier, L. A1 - Jalkanen, J. A1 - Elmastas, M. A1 - Zhong, J. A1 - Wang, T. A1 - Nono Nankam, P.A. A1 - Frendo-Cumbo, S. A1 - Bäckdahl, J. A1 - Subramanian, N. A1 - Sekine, T. A1 - Kerr, A.G. A1 - Tseng, B.T.P. A1 - Laurencikiene, J. A1 - Buggert, M. A1 - Lourda, M. A1 - Kublickiene, K. A1 - Bhalla, N. A1 - Andersson, A. A1 - Valsesia, A. A1 - Astrup, A. A1 - Blaak, E.E. A1 - Ståhl, P.L. A1 - Viguerie, N. A1 - Langin, D. A1 - Wolfrum, C. A1 - Blüher, M. A1 - Rydén, M. A1 - Mejhert, N. M1 - (Massier L.; Jalkanen J.; Elmastas M.; Zhong J.; Frendo-Cumbo S.; Bäckdahl J.; Subramanian N.; Kerr A.G.; Tseng B.T.P.; Laurencikiene J.; Rydén M., mikael.ryden@ki.se; Mejhert N., niklas.mejhert@ki.se) Department of Medicine Huddinge (H7), Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 83, Huddinge, Sweden M1 - (Wang T.; Wolfrum C.) Laboratory of Translational Nutrition Biology, Institute of Food, Nutrition and Health, Department of Health Sciences and Technology, ETH Zurich, Schwerzenbach, Switzerland M1 - (Nono Nankam P.A.; Blüher M.) Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany M1 - (Sekine T.; Buggert M.; Lourda M.) Center for Infectious Medicine, Department of Medicine Huddinge (H7), Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 52, Huddinge, Sweden M1 - (Lourda M.) Childhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska Institutet, SE-171 77, Stockholm, Sweden M1 - (Kublickiene K.) Department of Clinical Science, Intervention & Technology (CLINTEC), Unit of Renal Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86, Huddinge, Sweden M1 - (Bhalla N.; Andersson A.; Ståhl P.L.) Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, SE-171 65, Solna, Sweden M1 - (Valsesia A.) Department of Metabolic Health, Nestle Institute of Health Sciences, Nestle Research, Lausanne, Switzerland M1 - (Astrup A.) Department of Obesity and Nutritional Sciences, The Novo Nordisk Foundation, Hellerup, Denmark M1 - (Blaak E.E.) Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre(+), Maastricht, Netherlands M1 - (Viguerie N.; Langin D.) Institute of Metabolic and Cardiovascular Diseases (I2MC), Institut National de la Santé et de la Recherche Médicale (Inserm), Université Toulouse III - Paul Sabatier (UPS), Université de Toulouse, Toulouse, France M1 - (Viguerie N.; Langin D.) Franco-Czech Laboratory for Clinical Research on Obesity, Third Faculty of Medicine, Charles University, Prague and Université Toulouse III - Paul Sabatier (UPS), Toulouse, France M1 - (Langin D.) Laboratoire de biochimie, Centre Hospitalier Universitaire de Toulouse, Toulouse, France M1 - (Langin D.) Institut Universitaire de France (IUF), Paris, France M1 - (Blüher M.) Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany AD - M. Rydén, Department of Medicine Huddinge (H7), Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 83, Huddinge, Sweden AD - N. Mejhert, Department of Medicine Huddinge (H7), Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 83, Huddinge, Sweden T1 - An integrated single cell and spatial transcriptomic map of human white adipose tissue LA - English KW - NCT01727245 KW - NCT01785134 KW - high throughput sequencer KW - abcd2 protein KW - acacb protein KW - ackr1 protein KW - apod protein KW - CD16 antigen KW - CD34 antigen KW - CD36 antigen KW - cell adhesion molecule KW - chemerin KW - CXCL14 chemokine KW - CXCL2 chemokine KW - decay accelerating factor KW - diacylglycerol acyltransferase 2 KW - fcer1g protein KW - gpam protein KW - hacd2 protein KW - high density lipoprotein cholesterol KW - interleukin 16 KW - interleukin 8 KW - leptin KW - lipoprotein KW - monocyte chemotactic protein 1 KW - peroxisome proliferator activated receptor gamma KW - phosphodiesterase 3beta KW - phosphodiesterase III KW - platelet derived growth factor alpha receptor KW - platelet derived growth factor beta receptor KW - protein KW - tissue inhibitor of metalloproteinase 1 KW - tnfrsf1a protein KW - tnfrsf1b protein KW - transcription factor RUNX2 KW - triacylglycerol KW - tumor necrosis factor KW - tyrobp protein KW - unclassified drug KW - wdpcp protein KW - adipocyte KW - adipogenesis KW - adult KW - article KW - body weight change KW - CD4+ T lymphocyte KW - CD8+ T lymphocyte KW - cell communication KW - cell differentiation KW - cell interaction KW - cell subpopulation KW - cohort analysis KW - comparative study KW - controlled study KW - deconvolution KW - dendritic cell KW - dyslipidemia KW - endothelial progenitor cell KW - female KW - fibroblast KW - hematopoietic cell KW - human KW - human cell KW - human tissue KW - immunocompetent cell KW - in vitro study KW - insulin resistance KW - insulin sensitivity KW - k nearest neighbor KW - lipid metabolism KW - lipolysis KW - M1 macrophage KW - M2 macrophage KW - macrophage KW - major clinical study KW - male KW - marker gene KW - mast cell KW - metabolism KW - monocyte KW - natural killer cell KW - natural killer T cell KW - perivascular adipose tissue KW - principal component analysis KW - proadipocyte KW - single cell RNA seq KW - stromal vascular fraction KW - subcutaneous fat KW - transcriptomics KW - upregulation KW - vascular cell line KW - waist hip ratio KW - white adipose tissue N2 - To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells. Using these results, we deconvolved spatial and bulk transcriptomic data from nine additional cohorts to provide spatial and clinical dimensions to the map. This identified cell-cell interactions as well as relationships between specific cell subtypes and insulin resistance, dyslipidemia, adipocyte volume, and lipolysis upon long-term weight changes. Altogether, our meta-map provides a rich resource defining the cellular and microarchitectural landscape of human WAT and describes the associations between specific cell types and metabolic states. ER - TY - JOUR M3 - Review Y1 - 2023 VL - 4 SN - 2772-4425 JF - Healthcare Analytics JO - Healthc. Anal. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2028484289&from=export U2 - L2028484289 DB - Embase U3 - 2023-12-12 U4 - 2023-12-14 L2 - http://dx.doi.org/10.1016/j.health.2023.100282 DO - 10.1016/j.health.2023.100282 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=27724425&id=doi:10.1016%2Fj.health.2023.100282&atitle=A+comprehensive+survey+of+deep+learning+algorithms+and+applications+in+dental+radiograph+analysis&stitle=Healthc.+Anal.&title=Healthcare+Analytics&volume=4&issue=&spage=&epage=&aulast=Bhat&aufirst=Suvarna&auinit=S.&aufull=Bhat+S.&coden=&isbn=&pages=-&date=2023&auinit1=S&auinitm= A1 - Bhat, S. A1 - Birajdar, G.K. A1 - Patil, M.D. M1 - (Bhat S., suvarna.bhat@vit.edu.in; Birajdar G.K., gajanan.birajdar@rait.ac.in; Patil M.D.) Department of Electronics Engineering, Ramrao Adik Institute of Technology, D.Y. Patil deemed to be University, Nerul, Maharashtra, Navi Mumbai, India AD - G.K. Birajdar, Department of Electronics Engineering, Ramrao Adik Institute of Technology, D.Y. Patil deemed to be University, Nerul, Maharashtra, Navi Mumbai, India T1 - A comprehensive survey of deep learning algorithms and applications in dental radiograph analysis LA - English KW - artificial intelligence KW - automation KW - clinical decision support system KW - computer assisted diagnosis KW - convolutional neural network KW - deep learning KW - dental caries KW - dental examination KW - dental procedure KW - dentist KW - dentistry KW - human KW - image processing KW - image segmentation KW - machine learning KW - review KW - systematic review KW - tooth disease KW - tooth radiography KW - X ray analysis N2 - The Integration of machine learning and traditional image processing in dentistry has resulted in many applications like automatic teeth identification and numbering, caries, anomaly, disease detection, and dental treatment prediction. They have a broad scope in different applications observed in the dentistry literature review. This study reviews the literature on deep learning and dental radiograph analysis. We present an overview of machine learning algorithms in different areas of dentistry: tooth identification and numbering, Dental disease detection, and dental predictive treatment models. The methods under each area are briefly discussed. The dental radiograph data set required for performing experiments is summarized from the available literature. The study concludes by discussing new research opportunities and initiatives in this field. This paper offers a comprehensive overview of this innovative, challenging, and growing area in dentistry. ER - TY - JOUR M3 - Review Y1 - 2023 VL - 178 SN - 1872-8243 SN - 1386-5056 JF - International Journal of Medical Informatics JO - Int. J. Med. Informatics UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2026430478&from=export U2 - L2026430478 C5 - 37595373 DB - Embase DB - Medline U3 - 2023-08-22 U4 - 2023-08-30 L2 - http://dx.doi.org/10.1016/j.ijmedinf.2023.105170 DO - 10.1016/j.ijmedinf.2023.105170 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=18728243&id=doi:10.1016%2Fj.ijmedinf.2023.105170&atitle=Machine+learning+techniques+for+periodontitis+and+dental+caries+detection%3A+A+narrative+review&stitle=Int.+J.+Med.+Informatics&title=International+Journal+of+Medical+Informatics&volume=178&issue=&spage=&epage=&aulast=Radha&aufirst=&auinit=R.C.&aufull=Radha+R.C.&coden=IJMIF&isbn=&pages=-&date=2023&auinit1=R&auinitm=C A1 - Radha, R.C. A1 - Raghavendra, B.S. A1 - Subhash, B.V. A1 - Rajan, J. A1 - Narasimhadhan, A.V. M1 - (Radha R.C., radharc.207ec502@nitk.edu.in; Raghavendra B.S.; Narasimhadhan A.V.) Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India M1 - (Subhash B.V.) Department of Oral Medicine and Radiology, DAPM R V Dental College, Bengaluru, India M1 - (Rajan J.) Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India AD - R.C. Radha, Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India T1 - Machine learning techniques for periodontitis and dental caries detection: A narrative review LA - English KW - alveolar bone loss KW - dental caries KW - diagnostic accuracy KW - human KW - machine learning KW - meta analysis KW - periodontitis KW - point of care testing KW - review KW - systematic review N2 - Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction. ER - TY - JOUR M3 - Article Y1 - 2023 VL - 52 IS - 7 SP - 20230284 SN - 0250-832X JF - Dento maxillo facial radiology JO - Dentomaxillofac Radiol UR - https://www.embase.com/search/results?subaction=viewrecord&id=L642182983&from=export U2 - L642182983 C5 - 37665008 DB - Medline U3 - 2023-09-08 U4 - 2023-10-02 L2 - http://dx.doi.org/10.1259/dmfr.20230284 DO - 10.1259/dmfr.20230284 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=0250832X&id=doi:10.1259%2Fdmfr.20230284&atitle=Applications+of+artificial+intelligence+in+the+analysis+of+dental+panoramic+radiographs%3A+an+overview+of+systematic+reviews&stitle=Dentomaxillofac+Radiol&title=Dento+maxillo+facial+radiology&volume=52&issue=7&spage=20230284&epage=&aulast=Turosz&aufirst=Natalia&auinit=N.&aufull=Turosz+N.&coden=&isbn=&pages=20230284-&date=2023&auinit1=N&auinitm= A1 - Turosz, N. A1 - Chęcińska, K. A1 - Chęciński, M. A1 - Brzozowska, A. A1 - Nowak, Z. A1 - Sikora, M. M1 - (Turosz N.) Institute of Public Health, Jagiellonian University Medical College, Poland M1 - (Chęcińska K.) Department of Glass Technology and Amorphous Coatings, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Poland M1 - (Chęciński M.) Department of Oral Surgery, Preventive Medicine Center, Poland M1 - (Brzozowska A.) Preventive Medicine Center, Poland M1 - (Nowak Z.) Department of Temporomandibular Disorders, Medical University of Silesia in Katowice, Katowice, Poland M1 - (Sikora M.) Department of Maxillofacial Surgery, Hospital of the Ministry of Interior, Wojska Polskiego, Poland M1 - (Sikora M.) Department of Biochemistry and Medical Chemistr, Pomeranian Medical University, Poland T1 - Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews LA - English KW - alveolar bone loss KW - artificial intelligence KW - dental caries KW - human KW - panoramic radiography KW - systematic review (topic) N2 - OBJECTIVES: This overview of systematic reviews aimed to establish the current state of knowledge on the suitability of artificial intelligence (AI) in dental panoramic radiograph analysis and illustrate its changes over time. METHODS: Medical databases covered by the Association for Computing Machinery, Bielefeld Academic Search Engine, Google Scholar, and PubMed engines were searched. The risk of bias was assessed using ROBIS tool. Ultimately, 12 articles were qualified for the qualitative synthesis. The results were visualized with timelines, tables, and charts. RESULTS: In the years 1988-2023, a significant development of information technologies for the analysis of DPRs was observed. The latest analyzed AI models achieve high accuracy in detecting caries (91.5%), osteoporosis (89.29%), maxillary sinusitis (87.5%), periodontal bone loss (93.09%), and teeth identification and numbering (93.67%). The detection of periapical lesions is also characterized by high sensitivity (99.95%) and specificity (92%). However, due to the small number of heterogeneous source studies synthesized in systematic reviews, the results of this overview should be interpreted with caution. CONCLUSION: Currently, AI applications can significantly support dentists in dental panoramic radiograph analysis. As systematic reviews on AI become outdated quickly, their regular updating is recommended. PROSPERO registration number: CRD42023416048. ER - TY - JOUR M3 - Review Y1 - 2023 VL - 13 IS - 15 SN - 2075-4418 JF - Diagnostics JO - Diagn. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2024861271&from=export U2 - L2024861271 DB - Embase U3 - 2023-08-18 U4 - 2023-11-14 L2 - http://dx.doi.org/10.3390/diagnostics13152512 DO - 10.3390/diagnostics13152512 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=20754418&id=doi:10.3390%2Fdiagnostics13152512&atitle=Deep+Learning+in+Diagnosis+of+Dental+Anomalies+and+Diseases%3A+A+Systematic+Review&stitle=Diagn.&title=Diagnostics&volume=13&issue=15&spage=&epage=&aulast=Sivari&aufirst=Esra&auinit=E.&aufull=Sivari+E.&coden=&isbn=&pages=-&date=2023&auinit1=E&auinitm= A1 - Sivari, E. A1 - Senirkentli, G.B. A1 - Bostanci, E. A1 - Guzel, M.S. A1 - Acici, K. A1 - Asuroglu, T. M1 - (Sivari E.) Department of Computer Engineering, Cankiri Karatekin University, Cankiri, Turkey M1 - (Senirkentli G.B.) Department of Pediatric Dentistry, Baskent University, Ankara, Turkey M1 - (Bostanci E.; Guzel M.S.) Department of Computer Engineering, Ankara University, Ankara, Turkey M1 - (Acici K.) Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara, Turkey M1 - (Asuroglu T., tunc.asuroglu@tuni.fi) Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland AD - T. Asuroglu, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland T1 - Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review LA - English KW - X ray film KW - alexnet KW - alveolar bone KW - alveolar bone loss KW - ameloblastoma KW - amelogenesis imperfecta KW - area under the curve KW - artificial intelligence KW - bone cyst KW - class activation mapping KW - classification algorithm KW - classifier KW - cleft lip KW - cleft palate KW - clinical evaluation KW - cone beam computed tomography KW - controlled study KW - convolutional neural network KW - data base KW - data extraction KW - deep learning KW - densenet121 KW - dental apical lesion KW - dental caries KW - dental enamel hypomineralization KW - dental fluorosis KW - dental health KW - dentigerous cyst KW - dentin KW - dentistry KW - diagnostic accuracy KW - diagnostic test accuracy study KW - dice similarity coefficient KW - efficientdetd3 KW - enamel KW - enamel breakdown KW - false negative result KW - gingivitis KW - granuloma KW - human KW - hybrid neural network KW - hypoplasia KW - image analysis KW - image segmentation KW - inceptionresnetv2 KW - keratocyst KW - long short term memory network KW - mandible fracture KW - maxillary canine impaction KW - maxillary first molar KW - maxillary sinus KW - mesioden KW - microdontia KW - molar incisor hypomineralization KW - object detection KW - odontogenic cyst KW - odontoma KW - osteoarthritis KW - outcome assessment KW - panoramic radiography KW - performance indicator KW - periodontal disease KW - periodontally compromised teeth KW - periodontitis KW - positivity rate KW - predictive value KW - radicular cyst KW - residual root KW - resnet18 KW - resnext 101 KW - review KW - segmentation algorithm KW - segmentation task KW - sensitivity analysis KW - squeezenet KW - supernumerary tooth KW - support vector machine KW - systematic review KW - temporomandibular joint disorder KW - third molar impacted teeth KW - tooth disease KW - tooth eruption KW - tooth impaction KW - tooth malformation KW - tooth periapical disease KW - tooth plaque KW - transfer of learning KW - treatment planning KW - unit network KW - vertical root fracture KW - white spot lesion KW - you only look once N2 - Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics. ER - TY - JOUR M3 - Conference Abstract Y1 - 2023 VL - 79 SP - 943 SN - 1421-9697 JF - Annals of Nutrition and Metabolism JO - Ann. Nutr. Metab. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L642185663&from=export U2 - L642185663 DB - Embase U4 - 2023-09-08 L2 - http://dx.doi.org/10.1159/000530786 DO - 10.1159/000530786 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=14219697&id=doi:10.1159%2F000530786&atitle=Establishing+online+dietary+assessment+models+to+facilitate+strategies+for+reducing+sugar+intake&stitle=Ann.+Nutr.+Metab.&title=Annals+of+Nutrition+and+Metabolism&volume=79&issue=&spage=943&epage=&aulast=Chen&aufirst=Chien+Yin&auinit=C.Y.&aufull=Chen+C.Y.&coden=&isbn=&pages=943-&date=2023&auinit1=C&auinitm=Y A1 - Chen, C.Y. A1 - Kuo, P.H. A1 - Yang, S.H. M1 - (Chen C.Y.; Yang S.H.) Taipei Medical University, Taiwan M1 - (Kuo P.H.) National Taiwan University, Taiwan AD - C.Y. Chen, Taipei Medical University, Taiwan T1 - Establishing online dietary assessment models to facilitate strategies for reducing sugar intake LA - English KW - carbohydrate KW - sugar KW - sweetening agent KW - adult KW - artificial intelligence chatbot KW - caloric intake KW - calorie KW - child KW - chronic disease KW - conference abstract KW - dental caries KW - education KW - experimental design KW - female KW - health KW - health hazard KW - health status KW - healthy lifestyle KW - human KW - incidence KW - Japan KW - literacy KW - male KW - meta analysis KW - metabolic disorder KW - motivation KW - network meta-analysis KW - nutrient KW - nutritional assessment KW - obesity KW - online social network KW - practice guideline KW - prevalence KW - public health KW - substitution reaction KW - sugar intake KW - Taiwan KW - thinking KW - United States KW - Viet Nam KW - World Health Organization N2 - Obesity is a major challenge to global public health. According to “Taiwan Nutrition and Health Status Change Survey 2013-2016” showed that the prevalence of adult overweight and obesity reached 45.4%. Poor dietary quality could lead to lots of metabolic diseases. Excessive intake of sugary foods leads to an increased risk of obesity and its derived chronic diseases. However, an increase in the intake of sugary foods will result in an increase in total calories consumed and a decrease in the intake of other essential nutrients. The World Health Organization published the “Guidelines on Sugar Intake for Adults and Children” in 2015. It is recommended that the intake of free sugars in the diet should be kept below 10% of the total daily caloric intake, which can reduce the incidence of overweight, obesity and tooth decay. Therefore, the trend of sugar reduction has been attached great importance. More and more commercial products use sugar substitute, due to their low cost and sweeter taste. First, we conduct the systematic review and network meta-analysis to evaluate the effect of low-calorie sweeteners (LCS) on weight management. Second, establish an online dietary database, combined Taiwan, United States, Japan and Vietnam database. To collect different group of their dietary records used to analyze the daily sugar consumption. Third, design an interventional experiment, using design thinking model to create an active sugar reduction plan. The experimental design has a total of four weeks of experience. The online social network platform is equipped with AI chatbot interactive technology. It interacts with participants daily to set tasks challenge, reflect on the body's feelings and daily health hazards of taking too much refined sugar, and then increase the motivation to reduce sugar. Integrate online sugar reduction education to effectively establish healthy literacy concepts, improve physical health, achieve a healthy lifestyle of sugar reduction program!. ER - TY - JOUR M3 - Conference Abstract Y1 - 2023 VL - 79 SP - 943 SN - 1421-9697 JF - Annals of Nutrition and Metabolism JO - Ann. Nutr. Metab. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L642440690&from=export U2 - L642440690 DB - Embase U4 - 2023-10-12 L2 - http://dx.doi.org/10.1159/000530786 DO - 10.1159/000530786 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=14219697&id=doi:10.1159%2F000530786&atitle=Establishing+online+dietary+assessment+models+to+facilitate+strategies+for+reducing+sugar+intake&stitle=Ann.+Nutr.+Metab.&title=Annals+of+Nutrition+and+Metabolism&volume=79&issue=&spage=943&epage=&aulast=Chen&aufirst=Chien+Yin&auinit=C.Y.&aufull=Chen+C.Y.&coden=&isbn=&pages=943-&date=2023&auinit1=C&auinitm=Y A1 - Chen, C.Y. A1 - Kuo, P.H. A1 - Yang, S.H. M1 - (Chen C.Y.; Yang S.H.) Taipei Medical University, Taiwan M1 - (Kuo P.H.) National Taiwan University, Taiwan AD - C.Y. Chen, Taipei Medical University, Taiwan T1 - Establishing online dietary assessment models to facilitate strategies for reducing sugar intake LA - English KW - carbohydrate KW - sugar KW - sweetening agent KW - adult KW - artificial intelligence chatbot KW - caloric intake KW - calorie KW - child KW - chronic disease KW - conference abstract KW - dental caries KW - education KW - experimental design KW - female KW - health KW - health hazard KW - health status KW - healthy lifestyle KW - human KW - incidence KW - Japan KW - literacy KW - male KW - meta analysis KW - metabolic disorder KW - motivation KW - network meta-analysis KW - nutrient KW - nutritional assessment KW - obesity KW - online social network KW - practice guideline KW - prevalence KW - public health KW - substitution reaction KW - sugar intake KW - Taiwan KW - thinking KW - United States KW - Viet Nam KW - World Health Organization N2 - Obesity is a major challenge to global public health. According to 'Taiwan Nutrition and Health Status Change Survey 2013-2016' showed that the prevalence of adult overweight and obesity reached 45.4%. Poor dietary quality could lead to lots of metabolic diseases. Excessive intake of sugary foods leads to an increased risk of obesity and its derived chronic diseases. However, an increase in the intake of sugary foods will result in an increase in total calories consumed and a decrease in the intake of other essential nutrients. The World Health Organization published the 'Guidelines on Sugar Intake for Adults and Children' in 2015. It is recommended that the intake of free sugars in the diet should be kept below 10% of the total daily caloric intake, which can reduce the incidence of overweight, obesity and tooth decay. Therefore, the trend of sugar reduction has been attached great importance. More and more commercial products use sugar substitute, due to their low cost and sweeter taste. First, we conduct the systematic review and network meta-analysis to evaluate the effect of low-calorie sweeteners (LCS) on weight management. Second, establish an online dietary database, combined Taiwan, United States, Japan and Vietnam database. To collect different group of their dietary records used to analyze the daily sugar consumption. Third, design an interventional experiment, using design thinking model to create an active sugar reduction plan. The experimental design has a total of four weeks of experience. The online social network platform is equipped with AI chatbot interactive technology. It interacts with participants daily to set tasks challenge, reflect on the body's feelings and daily health hazards of taking too much refined sugar, and then increase the motivation to reduce sugar. Integrate online sugar reduction education to effectively establish healthy literacy concepts, improve physical health, achieve a healthy lifestyle of sugar reduction program! . ER - TY - JOUR M3 - Article Y1 - 2023 VL - 17 IS - 2 SP - 783 EP - 786 SN - 1996-7195 JF - Pakistan Journal of Medical and Health Sciences JO - Pak. J. Med. Health Sci. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2024788759&from=export U2 - L2024788759 DB - Embase U3 - 2023-06-06 U4 - 2023-06-12 L2 - http://dx.doi.org/10.53350/pjmhs2023172783 DO - 10.53350/pjmhs2023172783 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=19967195&id=doi:10.53350%2Fpjmhs2023172783&atitle=A+Systematic+Review+on+Artificial+Intelligence+Applications+in+Restorative+Dentistry&stitle=Pak.+J.+Med.+Health+Sci.&title=Pakistan+Journal+of+Medical+and+Health+Sciences&volume=17&issue=2&spage=783&epage=786&aulast=Fatima&aufirst=Sayyeda+Tatheer&auinit=S.T.&aufull=Fatima+S.T.&coden=&isbn=&pages=783-786&date=2023&auinit1=S&auinitm=T A1 - Fatima, S.T. A1 - Akbar, S.M.A. A1 - Ahmed, A. A1 - Asghar, S.K. A1 - Hussain, M. A1 - Iqbal, S.S. M1 - (Fatima S.T., drtatheer_fatima@hotmail.com) HBS Medical and Dental College, Islamabad, Pakistan M1 - (Akbar S.M.A.; Ahmed A.) RDS - Islamic International Dental College, Islamabad, Pakistan M1 - (Asghar S.K.) Islamabad Medical and Dental College, Polyclinic Hospital, Islamabad, Pakistan M1 - (Hussain M.; Iqbal S.S.) Department of Community and Preventive Dentistry, Karachi Medical and Dental College, Pakistan AD - S.T. Fatima, HBS Medical and Dental College, Islamabad, Pakistan T1 - A Systematic Review on Artificial Intelligence Applications in Restorative Dentistry LA - English KW - X ray film KW - dental material KW - article KW - artificial intelligence KW - artificial neural network KW - checklist KW - classifier KW - clinical assessment KW - clinical effectiveness KW - clinical evaluation KW - convolutional neural network KW - data base KW - decision tree KW - deep neural network KW - dental caries KW - dental restoration KW - dental surgery KW - diagnostic accuracy KW - fuzzy logic KW - human KW - k nearest neighbor KW - multi layered perceptron KW - probabilistic neural network KW - prognosis KW - quasi experimental study KW - regression analysis KW - restorative dentistry KW - sensitivity and specificity KW - support vector machine KW - systematic review KW - tooth fracture KW - treatment failure KW - treatment indication N2 - Statement of problem: Artificial intelligence (AI) applications are increasingly used in restorative process. But existing dentistry restoration effectiveness as well as growth and IA applications have not yet been systematically analyzed or documented. Purpose: The goal of such comprehensive evaluation is to discover & assess the abilities associated with artificial intelligence models in restorative dentistry for the analysis of caries as well as vertical tooth fracture, evaluate margins in preparing tooth, and analyze reconstructive failures. Methods: A systematic electronic review of 5 databases was carried out: PubMed/ MEDLINE, World of Science, EMBASE, Scopus and Cochrane. The investigation was carried out manually as well. Research using AI models was chosen on the basis of 4 criterion: dental caries diagnostics, diagnostics, vertical tooth fracture, tooth preparation recognition, & cause of failure of restoration. Both researchers rated the quality of the study for Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). The 3rd author was asked for resolving the dispute. Results: 34 researches were made the part of this analysis: from which 29 contains artificial intelligence techniques including the diagnostics or treatment related to caries and its causes according to sensitivity models, 2 to diagnose vertical tooth fractures, 1 to prepare the teeth. Among the studied analysis, the accuracy of caries diagnosis in the AI models was tested from 76-88.3%, sensitivity from 73-90%, as well as specificity from 61.5-93%. In the study, the accuracy of predicted caries ranges from 83.6-97.1%. The performed research showed the accuracy of the analysis of a vertical tooth fracture from 88.3-95.7%. The study, which uses AI models to find a destination, had details with the range of 90.6-97.4%. Conclusions: AI models are a powerful tool for diagnosing caries as well as vertical tooth fractures, recognizing preparation margins and predicting restoration failures. But, the dental use of AI models continues to evolve. More research is needed to evaluate the clinical effectiveness of artificial intelligence models in restorative dentistry. ER - TY - JOUR M3 - Article Y1 - 2023 VL - 23 IS - 1 SP - 101837 SN - 1532-3390 JF - The journal of evidence-based dental practice JO - J Evid Based Dent Pract UR - https://www.embase.com/search/results?subaction=viewrecord&id=L640581305&from=export U2 - L640581305 C5 - 36914305 DB - Medline U3 - 2023-03-22 U4 - 2023-03-23 L2 - http://dx.doi.org/10.1016/j.jebdp.2023.101837 DO - 10.1016/j.jebdp.2023.101837 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=15323390&id=doi:10.1016%2Fj.jebdp.2023.101837&atitle=ARTIFICIAL+INTELLIGENCE+%28AI%29+AS+AN+AID+IN+RESTORATIVE+DENTISTRY+IS+PROMISING%2C+BUT+STILL+A+WORK+IN+PROGRESS&stitle=J+Evid+Based+Dent+Pract&title=The+journal+of+evidence-based+dental+practice&volume=23&issue=1&spage=101837&epage=&aulast=Alqutaibi&aufirst=Ahmed+Yaseen&auinit=A.Y.&aufull=Alqutaibi+A.Y.&coden=&isbn=&pages=101837-&date=2023&auinit1=A&auinitm=Y A1 - Alqutaibi, A.Y. A1 - Aboalrejal, A.N. M1 - (Alqutaibi A.Y.; Aboalrejal A.N.) T1 - ARTIFICIAL INTELLIGENCE (AI) AS AN AID IN RESTORATIVE DENTISTRY IS PROMISING, BUT STILL A WORK IN PROGRESS LA - English KW - artificial intelligence KW - dentistry KW - human N2 - ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Artificial intelligence applications in restorative dentistry: A systematic review. Revilla-León, M., Gómez-Polo, M., Vyas, S., Barmak, A. B., Özcan, M., Att, W., & Krishnamurthy, V. R. J Prosthet Dent 2021 SOURCE OF FUNDING: Not reported. TYPE OF STUDY/DESIGN: Systematic review. ER - TY - JOUR M3 - Review Y1 - 2023 VL - 21 IS - 1 SP - 53 EP - 70 SN - 1179-1896 SN - 1175-5652 JF - Applied Health Economics and Health Policy JO - Appl. Health Econ. Health Policy UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2019056537&from=export U2 - L2019056537 DB - Embase U3 - 2022-09-20 U4 - 2023-05-12 L2 - http://dx.doi.org/10.1007/s40258-022-00758-5 DO - 10.1007/s40258-022-00758-5 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=11791896&id=doi:10.1007%2Fs40258-022-00758-5&atitle=Economic+Evaluations+of+Preventive+Interventions+for+Dental+Caries+and+Periodontitis%3A+A+Systematic+Review&stitle=Appl.+Health+Econ.+Health+Policy&title=Applied+Health+Economics+and+Health+Policy&volume=21&issue=1&spage=53&epage=70&aulast=Nguyen&aufirst=Tan+Minh&auinit=T.M.&aufull=Nguyen+T.M.&coden=AHEHA&isbn=&pages=53-70&date=2023&auinit1=T&auinitm=M A1 - Nguyen, T.M. A1 - Tonmukayakul, U. A1 - Le, L.K.-D. A1 - Calache, H. A1 - Mihalopoulos, C. M1 - (Nguyen T.M., tan.nguyen@deakin.edu.au; Tonmukayakul U.; Calache H.) Deakin Health Economics, Institute of Health Transformation, Deakin University, Level 3, Building BC, 221 Burwood Highway, Burwood, Melbourne, VIC, Australia M1 - (Nguyen T.M., tan.nguyen@deakin.edu.au; Le L.K.-D.; Mihalopoulos C.) Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia AD - T.M. Nguyen, Deakin Health Economics, Institute of Health Transformation, Deakin University, Level 3, Building BC, 221 Burwood Highway, Burwood, Melbourne, VIC, Australia T1 - Economic Evaluations of Preventive Interventions for Dental Caries and Periodontitis: A Systematic Review LA - English KW - tooth crown KW - chlorhexidine KW - fissure sealant KW - fluoride varnish KW - interleukin 1 KW - mouthwash KW - xylitol KW - adult KW - artificial intelligence KW - checklist KW - child KW - clinical attachment level KW - cost KW - cost benefit analysis KW - cost control KW - cost effectiveness analysis KW - dental caries KW - dental procedure KW - disability-adjusted life year KW - economic evaluation KW - education KW - fluoridation KW - genetic screening KW - health education KW - health status KW - human KW - interrater reliability KW - mouth hygiene KW - periodontitis KW - prophylaxis KW - quality adjusted life year KW - review KW - societal cost KW - sugar-sweetened beverage KW - systematic review KW - telehealth KW - tooth brushing KW - tooth extraction KW - tooth fracture KW - tooth root canal KW - Willingness To Pay N2 - Objectives: To critically examine the methods used for full economic evaluations of preventive interventions for dental caries and periodontitis. Methods: Published literature post-2000 was searched to April 2021. Based on a developed intervention classification framework for dental caries and periodontitis, only universal, selective or indicated interventions were included in this review. The Drummond 10-point checklist was used for quality appraisal. Results: Of 3,007 unique records screened for relevance, 73 studies were reviewed. Most model-based studies (61/73) used cost-effectiveness analysis (49%) or cost-benefit analysis (28%). Trial-based studies (16/73) commonly used cost-effectiveness analysis (59%). Four studies used both economic evaluation methods. Sixty-four papers (88%) were on dental caries, eight papers (11%) focused on periodontitis, and one paper (1%) included both oral diseases; 72% of model-based and 82% of trial-based studies were of good quality. The most frequently investigated dental caries preventive interventions were water fluoridation (universal intervention; cost-saving or cost-effective), fissure sealant and fluoride varnish (selective and indicated interventions; cost-effectiveness outcomes were inconsistent). Supportive periodontal therapy with oral health education (indicated intervention; cost-effective) was the most frequently evaluated preventive intervention for periodontitis. Thirty percent of studies with a time horizon > 1 year did not apply an appropriate discount rate and 26% did not comprehensively discuss other important considerations beyond the technical analysis. Conclusions: Generic health outcome measures should be incorporated for economic evaluations on preventive interventions for dental caries and periodontitis, and an increased focus to prevent periodontitis using economic evaluation methods is needed to inform resource allocation and policy decision-making. ER - TY - JOUR M3 - Article in Press Y1 - 2023 SN - 1601-0825 SN - 1354-523X JF - Oral Diseases JO - Oral Dis. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2024122574&from=export U2 - L2024122574 C5 - 37392423 DB - Embase DB - Medline U3 - 2023-07-11 L2 - http://dx.doi.org/10.1111/odi.14659 DO - 10.1111/odi.14659 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=16010825&id=doi:10.1111%2Fodi.14659&atitle=Detecting+dental+caries+on+oral+photographs+using+artificial+intelligence%3A+A+systematic+review&stitle=Oral+Dis.&title=Oral+Diseases&volume=&issue=&spage=&epage=&aulast=Moharrami&aufirst=Mohammad&auinit=M.&aufull=Moharrami+M.&coden=ORDIF&isbn=&pages=-&date=2023&auinit1=M&auinitm= A1 - Moharrami, M. A1 - Farmer, J. A1 - Singhal, S. A1 - Watson, E. A1 - Glogauer, M. A1 - Johnson, A.E.W. A1 - Schwendicke, F. A1 - Quinonez, C. M1 - (Moharrami M., faraz.moharrami@mail.utoronto.ca; Farmer J.; Singhal S.; Watson E.; Glogauer M.; Quinonez C.) Faculty of Dentistry, University of Toronto, Toronto, ON, Canada M1 - (Moharrami M., faraz.moharrami@mail.utoronto.ca; Schwendicke F.) Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland M1 - (Singhal S.) Health Promotion, Chronic Disease and Injury Prevention Department, Public Health Ontario, Toronto, Canada M1 - (Watson E.; Glogauer M.) Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, ON, Canada M1 - (Glogauer M.) Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, ON, Canada M1 - (Johnson A.E.W.) Oral Diagnostics, Digital Health and Health Services Research, Charité – Universitätsmedizin Berlin, Berlin, Germany M1 - (Schwendicke F.) Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada M1 - (Quinonez C.) Schulich School of Medicine and Dentistry, Western University, London, ON, Canada AD - M. Moharrami, Faculty of Dentistry, University of Toronto, 124 Edward Street, Toronto, ON, Canada T1 - Detecting dental caries on oral photographs using artificial intelligence: A systematic review LA - English KW - adult KW - algorithm KW - artificial intelligence KW - camera KW - deep learning KW - dental caries KW - diagnostic test accuracy study KW - Embase KW - female KW - human KW - intraoral camera KW - machine learning KW - male KW - Medline KW - performance indicator KW - photography KW - Quality Assessment of Diagnostic Accuracy Studies KW - review KW - risk assessment KW - Scopus KW - smartphone KW - systematic review KW - teledentistry N2 - Objectives: This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. Methods: Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. Results: Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%–94.3% and 42.8%–95.4%, respectively. Irrespective of the task, F1-scores were 68.3%–95.4% for professional cameras, 78.8%–87.6%, for intraoral cameras, and 42.8%–80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. Conclusion: Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions. ER - TY - JOUR M3 - Article Y1 - 2023 VL - 11 SP - 1286595 SN - 2296-2565 JF - Frontiers in public health JO - Front Public Health UR - https://www.embase.com/search/results?subaction=viewrecord&id=L642881360&from=export U2 - L642881360 C5 - 38026419 DB - Medline U4 - 2023-12-07 L2 - http://dx.doi.org/10.3389/fpubh.2023.1286595 DO - 10.3389/fpubh.2023.1286595 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=22962565&id=doi:10.3389%2Ffpubh.2023.1286595&atitle=Estimation+of+alkali+dosage+and+contact+time+for+treating+human+excreta+containing+viruses+as+an+emergency+response%3A+a+systematic+review&stitle=Front+Public+Health&title=Frontiers+in+public+health&volume=11&issue=&spage=1286595&epage=&aulast=Oishi&aufirst=Wakana&auinit=W.&aufull=Oishi+W.&coden=&isbn=&pages=1286595-&date=2023&auinit1=W&auinitm= A1 - Oishi, W. A1 - Sano, D. M1 - (Oishi W.; Sano D.) Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan M1 - (Sano D.) Department of Frontier Science for Advanced Environment, Graduate School of Environmental Studies, Tohoku University, Sendai, Japan T1 - Estimation of alkali dosage and contact time for treating human excreta containing viruses as an emergency response: a systematic review LA - English KW - calcium oxide KW - disinfectant agent KW - chemistry KW - human KW - sewage KW - virus KW - wastewater N2 - Water, sanitation, and hygiene provisions are essential during emergencies to prevent infectious disease outbreaks caused by improper human excreta management in settlements for people affected by natural disasters and conflicts. Human excreta disinfection is required when long-term containment in latrines is not feasible on-site. Alkali additives, including lime, are effective disinfectants for wastewater and faecal sludge containing large amounts of solid and dissolved organic matter. The aim of this study was to determine the minimum dose and contact time of alkali additives for treating virus-containing human excreta in emergency situations. We used literature data collected by searching Google Scholar and Web of Science. The date of the last search for each study was 31th May 2023. Only peer-reviewed articles that included disinfection practices in combination with quantitative data for the physicochemical data of a matrix and viral decay were selected for data extraction. Two reviewers independently collected data from each study. We extracted datasets from 14 studies that reported quantitative information about their disinfection tests, including viral decay over time, matrix types, and physicochemical properties. Three machine learning algorithms were applied to the collected dataset to determine the time required to achieve specified levels of virus inactivation under different environmental conditions. The best model was used to estimate the contact time to achieve a 3-log10 inactivation of RNA virus in wastewater and faeces. The most important variables for predicting the contact time were pH, temperature, and virus type. The estimated contact time for 3 log inactivation of RNA virus was <2 h at pH 12, which was achieved by adding 1.8 and 3.1% slaked lime to wastewater and faeces, respectively. The contact time decreased exponentially with the pH of the sludge and wastewater. In contrast, the pH of the sludge and wastewater increased linearly with the slaked lime dosage. Lime treatment is a promising measure where long-term containment in latrine is not feasible in densely populated areas, as 1 day is sufficient to inactivate viruses. The relationship we have identified between required contact time and lime dosage is useful for practitioners in determining appropriate treatment conditions of human waste. ER - TY - JOUR M3 - Article Y1 - 2022 VL - 130 IS - 12 SP - 763 EP - 777 SN - 1600-0463 SN - 0903-4641 JF - APMIS JO - APMIS UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2019167315&from=export U2 - L2019167315 C5 - 36050830 DB - Embase DB - Medline U3 - 2022-09-27 U4 - 2022-12-06 L2 - http://dx.doi.org/10.1111/apm.13272 DO - 10.1111/apm.13272 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=16000463&id=doi:10.1111%2Fapm.13272&atitle=Meta-analysis+of+caries+microbiome+studies+can+improve+upon+disease+prediction+outcomes&stitle=APMIS&title=APMIS&volume=130&issue=12&spage=763&epage=777&aulast=Butcher&aufirst=Mark+C.&auinit=M.C.&aufull=Butcher+M.C.&coden=APMSE&isbn=&pages=763-777&date=2022&auinit1=M&auinitm=C A1 - Butcher, M.C. A1 - Short, B. A1 - Veena, C.L.R. A1 - Bradshaw, D. A1 - Pratten, J.R. A1 - McLean, W. A1 - Shaban, S.M.A. A1 - Ramage, G. A1 - Delaney, C. M1 - (Butcher M.C.; Short B.; Veena C.L.R.; McLean W.; Shaban S.M.A.; Ramage G., gordon.ramage@glasgow.ac.uk; Delaney C., christopher.delaney@glasgow.ac.uk) Oral Sciences Research Group, Glasgow Dental School, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom M1 - (Bradshaw D.; Pratten J.R.) R&D Innovation, Haleon, Weybridge, United Kingdom AD - G. Ramage, Oral Sciences Research Group, Glasgow Dental School, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom AD - C. Delaney, Oral Sciences Research Group, Glasgow Dental School, School of Medicine, Dentistry and Nursing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom T1 - Meta-analysis of caries microbiome studies can improve upon disease prediction outcomes LA - English KW - DNA purification kit KW - genetic analyzer KW - nucleic acid isolation kit KW - Actinomyces KW - Aggregatibacter KW - article KW - carbohydrate metabolism KW - carbon metabolism KW - classifier KW - dental caries KW - diagnostic accuracy KW - diagnostic value KW - disease simulation KW - DNA extraction KW - human KW - KEGG KW - machine learning KW - meta analysis KW - mouth flora KW - prediction KW - predictive value KW - random forest KW - receiver operating characteristic KW - Selenomonas KW - Shannon index KW - Simpson index KW - Treponema KW - upregulation N2 - As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta-analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta-analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non-biased, reproducible pipeline, which can be applied to microbiome meta-analyses of multiple diseases, but importantly we have derived from our meta-analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants. ER - TY - JOUR M3 - Article Y1 - 2022 VL - 23 IS - 1 SN - 1474-760X SN - 1474-7596 JF - Genome Biology JO - Genome Biol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2020214737&from=export U2 - L2020214737 C5 - 36419176 DB - Embase DB - Medline U3 - 2022-11-29 U4 - 2022-12-05 L2 - http://dx.doi.org/10.1186/s13059-022-02811-x DO - 10.1186/s13059-022-02811-x LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=1474760X&id=doi:10.1186%2Fs13059-022-02811-x&atitle=The+genetic+and+biochemical+determinants+of+mRNA+degradation+rates+in+mammals&stitle=Genome+Biol.&title=Genome+Biology&volume=23&issue=1&spage=&epage=&aulast=Agarwal&aufirst=Vikram&auinit=V.&aufull=Agarwal+V.&coden=GNBLF&isbn=&pages=-&date=2022&auinit1=V&auinitm= A1 - Agarwal, V. A1 - Kelley, D.R. M1 - (Agarwal V., vikram.agarwal@sanofi.com; Kelley D.R., drk@calicolabs.com) Calico Life Sciences LLC, South San Francisco, CA, United States M1 - (Agarwal V., vikram.agarwal@sanofi.com) Present Address: mRNA Center of Excellence, Sanofi Pasteur Inc., Waltham, MA, United States AD - V. Agarwal, Present Address: mRNA Center of Excellence, Sanofi Pasteur Inc., Waltham, MA, United States AD - D.R. Kelley, Calico Life Sciences LLC, South San Francisco, CA, United States T1 - The genetic and biochemical determinants of mRNA degradation rates in mammals LA - English KW - messenger RNA KW - 3' untranslated region KW - animal cell KW - article KW - biochemical analysis KW - cell specificity KW - codon KW - cohort analysis KW - consensus KW - controlled study KW - convolutional neural network KW - deep learning KW - gene control KW - gene regulatory network KW - genetic model KW - genetic variability KW - half life time KW - human KW - human cell KW - luciferase assay KW - mammal cell KW - mouse KW - nonhuman KW - prediction KW - prevalence KW - RNA degradation KW - RNA sequence KW - RNA splice site KW - RNA stability KW - RNA-binding domain KW - statistical model N2 - Background: Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. Results: We establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. Conclusions: Our work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome. ER - TY - JOUR M3 - Article Y1 - 2022 VL - 22 IS - 4 SP - 101772 SN - 1532-3390 JF - The journal of evidence-based dental practice JO - J Evid Based Dent Pract UR - https://www.embase.com/search/results?subaction=viewrecord&id=L639742616&from=export U2 - L639742616 C5 - 36494110 DB - Medline U3 - 2022-12-15 U4 - 2022-12-21 L2 - http://dx.doi.org/10.1016/j.jebdp.2022.101772 DO - 10.1016/j.jebdp.2022.101772 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=15323390&id=doi:10.1016%2Fj.jebdp.2022.101772&atitle=DEEP+LEARNING+ALGORITHMS+SHOW+SOME+POTENTIAL+AS+AN+ADJUNCTIVE+TOOL+IN+CARIES+DIAGNOSIS&stitle=J+Evid+Based+Dent+Pract&title=The+journal+of+evidence-based+dental+practice&volume=22&issue=4&spage=101772&epage=&aulast=Hegde&aufirst=Shwetha&auinit=S.&aufull=Hegde+S.&coden=&isbn=&pages=101772-&date=2022&auinit1=S&auinitm= A1 - Hegde, S. A1 - Gao, J. M1 - (Hegde S.; Gao J.) T1 - DEEP LEARNING ALGORITHMS SHOW SOME POTENTIAL AS AN ADJUNCTIVE TOOL IN CARIES DIAGNOSIS LA - English KW - algorithm KW - dental caries KW - dental procedure KW - human N2 - ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Mohammad-Rahimi H, Reza Motamedian S, Hossein Rohban M, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F, Deep learning for caries detection: A systematic review, J Dent, 2022,122, 104115. ISSN 0300-5712 https://doi.org/10.1016/j.jdent.2022.104115. SOURCE OF FUNDING: Information not available TYPE OF STUDY/DESIGN: Systematic review. ER - TY - JOUR M3 - Review Y1 - 2022 VL - 128 IS - 5 SP - 867 EP - 875 SN - 1097-6841 JF - The Journal of prosthetic dentistry JO - J Prosthet Dent UR - https://www.embase.com/search/results?subaction=viewrecord&id=L634790542&from=export U2 - L634790542 C5 - 33840515 DB - Medline U3 - 2021-04-22 U4 - 2022-12-13 L2 - http://dx.doi.org/10.1016/j.prosdent.2021.02.010 DO - 10.1016/j.prosdent.2021.02.010 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=10976841&id=doi:10.1016%2Fj.prosdent.2021.02.010&atitle=Artificial+intelligence+applications+in+restorative+dentistry%3A+A+systematic+review&stitle=J+Prosthet+Dent&title=The+Journal+of+prosthetic+dentistry&volume=128&issue=5&spage=867&epage=875&aulast=Revilla-Le%C3%B3n&aufirst=Marta&auinit=M.&aufull=Revilla-Le%C3%B3n+M.&coden=&isbn=&pages=867-875&date=2022&auinit1=M&auinitm= A1 - Revilla-León, M. A1 - Gómez-Polo, M. A1 - Vyas, S. A1 - Barmak, A.B. A1 - Özcan, M. A1 - Att, W. A1 - Krishnamurthy, V.R. M1 - (Revilla-León M.) Assistant Professor and Assistant Program Director AEGD Residency, Department of Comprehensive Dentistry, College of Dentistry, Texas A&M University, Dallas, Texas; Affiliate Faculty Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Researcher at Revilla Research Center, Madrid, Spain M1 - (Gómez-Polo M.) Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain M1 - (Vyas S.) Graduate Research Assistant, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, Dallas, TX, United States M1 - (Barmak A.B.) Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY, United States M1 - (Özcan M.) Professor and Head, Division of Dental Biomaterials, Clinic for Reconstructive Dentistry, Center for Dental and Oral Medicine, University of Zürich, Zürich, Switzerland M1 - (Att W.) Department of Prosthodontics, Tufts University School of Dental Medicine, Mass, Boston, United States M1 - (Krishnamurthy V.R.) J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, United States T1 - Artificial intelligence applications in restorative dentistry: A systematic review LA - English KW - artificial intelligence KW - dental caries KW - dental restoration KW - dentistry KW - human KW - procedures KW - tooth fracture N2 - STATEMENT OF PROBLEM: Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS: An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS: AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry. ER - TY - JOUR M3 - Review Y1 - 2022 VL - 13 SN - 1664-042X JF - Frontiers in Physiology JO - Front. Physiol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2019605596&from=export U2 - L2019605596 DB - Embase U3 - 2022-11-07 U4 - 2023-01-31 L2 - http://dx.doi.org/10.3389/fphys.2022.952709 DO - 10.3389/fphys.2022.952709 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=1664042X&id=doi:10.3389%2Ffphys.2022.952709&atitle=Deep+learning+techniques+for+cancer+classification+using+microarray+gene+expression+data&stitle=Front.+Physiol.&title=Frontiers+in+Physiology&volume=13&issue=&spage=&epage=&aulast=Gupta&aufirst=Surbhi&auinit=S.&aufull=Gupta+S.&coden=&isbn=&pages=-&date=2022&auinit1=S&auinitm= A1 - Gupta, S. A1 - Gupta, M.K. A1 - Shabaz, M. A1 - Sharma, A. M1 - (Gupta S.; Gupta M.K.) Department of Computer Science and Engineering Department, SMVDU, Jammu, India M1 - (Gupta S.; Shabaz M., bhatsab4@gmail.com) Model Institute of Engineering and Technology, Jammu, India M1 - (Sharma A.) School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India AD - M. Shabaz, Model Institute of Engineering and Technology, Jammu, India T1 - Deep learning techniques for cancer classification using microarray gene expression data LA - English KW - microarray kit KW - acute lymphoblastic leukemia KW - Adaptive Gradient Optimizer KW - Adaptive Momentum KW - algorithm KW - artificial intelligence KW - artificial neural network KW - AutoEncoder with Cox regression network KW - backpropagation through time KW - bioinformatics KW - biological study KW - breast invasive carcinoma KW - cancer classification KW - cancer diagnosis KW - clear cell renal cell carcinoma KW - colon adenocarcinoma KW - comprehensibility KW - convolutional neural network KW - cross entropy KW - cytogenetics KW - data base KW - Deep Belief Neural Network KW - deep learning KW - DNA microarray technology KW - DNAJC2 gene KW - EBSCO database KW - Embase KW - gene KW - gene expression KW - gene mutation KW - gene selection KW - GMPPA gene KW - high throughput sequencing KW - human KW - Kaplan Meier method KW - long short term memory network KW - lung adenocarcinoma KW - mathematical computing KW - meta analysis KW - microarray analysis KW - MMRN2 gene KW - MMRN2 gene KW - multilayer perceptron KW - Neuro Fuzzy method KW - oncology KW - performance KW - Preferred Reporting Items for Systematic Reviews and Meta-Analyses KW - process optimization KW - prostate adenocarcinoma KW - review KW - RNA sequence KW - Root Mean Squared Propagation KW - stochastic gradient descent KW - systematic review KW - training KW - Web of Science KW - ZNF560 gene N2 - Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods. ER - TY - JOUR M3 - Article Y1 - 2022 VL - 56 IS - 17 SP - 12106 EP - 12115 SN - 1520-5851 SN - 0013-936X JF - Environmental Science and Technology JO - Environ. Sci. Technol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2019994898&from=export U2 - L2019994898 C5 - 35984692 DB - Embase DB - Medline U3 - 2022-09-12 U4 - 2022-10-04 L2 - http://dx.doi.org/10.1021/acs.est.1c07552 DO - 10.1021/acs.est.1c07552 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=15205851&id=doi:10.1021%2Facs.est.1c07552&atitle=Meta-Analysis+Addressing+the+Implications+of+Model+Uncertainty+in+Understanding+the+Persistence+of+Indicators+and+Pathogens+in+Natural+Surface+Waters&stitle=Environ.+Sci.+Technol.&title=Environmental+Science+and+Technology&volume=56&issue=17&spage=12106&epage=12115&aulast=Dean&aufirst=Kara&auinit=K.&aufull=Dean+K.&coden=ESTHA&isbn=&pages=12106-12115&date=2022&auinit1=K&auinitm= A1 - Dean, K. A1 - Mitchell, J. M1 - (Dean K.; Mitchell J., jade@msu.edu) Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, East Lansing, MI, United States AD - J. Mitchell, Department of Biosystems and Agricultural Engineering, Michigan State University, 524 S. Shaw Lane, East Lansing, MI, United States T1 - Meta-Analysis Addressing the Implications of Model Uncertainty in Understanding the Persistence of Indicators and Pathogens in Natural Surface Waters LA - English KW - surface water KW - article KW - bacteriophage KW - environmental factor KW - environmental indicator KW - experimental design KW - feces microflora KW - health hazard KW - pH KW - predation KW - protozoon KW - quantitative analysis KW - random forest KW - risk assessment KW - sunlight KW - temperature KW - turbidity KW - virus KW - water management KW - water monitoring KW - water quality N2 - This study evaluates the impact persistence model selection has on the prediction of persistence values of interest and the identification of influential water quality and environmental factors for microorganisms in natural surface waters. Five persistence models representing first-order decay and nonlinear decay profiles were fit to a comprehensive database of 629 data sets for fecal indicator bacteria (FIB), bacteriophages, bacteria, viruses, and protozoa mined from the literature. Initial periods of minimal decay and decay rates tapering off over time were often observed, and a two-parameter model, based on the logistic probability distribution, provided the best fit to the data most frequently. First-order decay kinetics provided the best fit to less than 20% of the analyzed data. Using the best fitting models in this analysis, T90 and T99 metrics were calculated for each data set and used as the dependent variable in a variety of exploratory factor analyses. Random forest methods identified temperature and predation as some of the most important water quality factors influencing persistence, and the protozoa target type differed the most from FIB. This analysis further confirmed the interactions between temperature and predation and suggests that pH and turbidity be more frequently documented in persistence studies to further elucidate their impact on target persistence. The findings from this analysis and the calculated persistence metrics can be used to better inform quantitative microbial risk assessments and may lead to improved predictions of human health risks and water management decisions. ER - TY - JOUR M3 - Article Y1 - 2022 VL - 166 IS - 2 SP - 334 EP - 343 SN - 1095-6859 SN - 0090-8258 JF - Gynecologic Oncology JO - Gynecol. Oncol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2018898091&from=export U2 - L2018898091 C5 - 35738917 DB - Embase DB - Medline U3 - 2022-06-30 U4 - 2022-08-19 L2 - http://dx.doi.org/10.1016/j.ygyno.2022.06.010 DO - 10.1016/j.ygyno.2022.06.010 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=10956859&id=doi:10.1016%2Fj.ygyno.2022.06.010&atitle=Predictive+biomarker+for+surgical+outcome+in+patients+with+advanced+primary+high-grade+serous+ovarian+cancer.+Are+we+there+yet%3F+An+analysis+of+the+prospective+biobank+for+ovarian+cancer&stitle=Gynecol.+Oncol.&title=Gynecologic+Oncology&volume=166&issue=2&spage=334&epage=343&aulast=Keunecke&aufirst=Carlotta&auinit=C.&aufull=Keunecke+C.&coden=GYNOA&isbn=&pages=334-343&date=2022&auinit1=C&auinitm= A1 - Keunecke, C. A1 - Kulbe, H. A1 - Dreher, F. A1 - Taube, E.T. A1 - Chekerov, R. A1 - Horst, D. A1 - Hummel, M. A1 - Kessler, T. A1 - Pietzner, K. A1 - Kassuhn, W. A1 - Heitz, F. A1 - Muallem, M.Z. A1 - Lang, S.M. A1 - Vergote, I. A1 - Dorigo, O. A1 - Lammert, H. A1 - du Bois, A. A1 - Angelotti, T. A1 - Fotopoulou, C. A1 - Sehouli, J. A1 - Braicu, E.I. M1 - (Keunecke C., carlotta@keunecke.de; Kulbe H.; Chekerov R.; Pietzner K.; Kassuhn W.; Muallem M.Z.; Sehouli J.; Braicu E.I.) Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Gynecology, Augustenburger Platz 1, Berlin, Germany M1 - (Keunecke C., carlotta@keunecke.de; Kulbe H.; Taube E.T.; Chekerov R.; Horst D.; Pietzner K.; Kassuhn W.; Heitz F.; Muallem M.Z.; Vergote I.; du Bois A.; Fotopoulou C.; Sehouli J.; Braicu E.I.) Tumor Bank Ovarian Cancer, ENGOT Biobank, Charité Medizinische Universität Berlin, Augustenburger Platz 1, Berlin, Germany M1 - (Dreher F.; Kessler T.) Alacris Theranostics GmbH, Max-Planck-Straße 3, Berlin, Germany M1 - (Taube E.T.; Horst D.; Hummel M.; Lammert H.) Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Pathology, Augustenburger Platz 1, Berlin, Germany M1 - (Heitz F.; du Bois A.) Department of Gynecology and Gynecologic Oncology, Evang. Kliniken Essen-Mitte, Henricistrasse 92, Essen, Germany M1 - (Vergote I.) Department of Gynecologic Oncology, University Hospitals Leuven, Herestraat 49, Leuven, Belgium M1 - (Lang S.M.; Dorigo O.; Braicu E.I.) Department of Obstetrics and Gynaecology, Division of Gynaecologic Oncology, Stanford University School of Medicine, Stanford, CA, United States M1 - (Angelotti T.) Department of Anaesthesiology, Perioperative and Pain Medicine, 300 Pasteur Drive H3580, Stanford, CA, United States M1 - (Fotopoulou C.) Imperial College, London, United Kingdom AD - C. Keunecke, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Gynecology, Campus Virchow Clinic, Augustenburger Platz 1, Berlin, Germany T1 - Predictive biomarker for surgical outcome in patients with advanced primary high-grade serous ovarian cancer. Are we there yet? An analysis of the prospective biobank for ovarian cancer LA - English KW - antineoplastic agent KW - biological marker KW - CA 125 antigen KW - decay accelerating factor KW - fatty acid binding protein 4 KW - messenger RNA KW - platinum KW - protein Patched 1 KW - transcription factor RUNX2 KW - transcriptome KW - adult KW - advanced cancer KW - aged KW - area under the curve KW - article KW - ascites KW - biobank KW - cancer chemotherapy KW - cancer grading KW - cancer survival KW - carcinomatosis KW - clinical decision making KW - cohort analysis KW - computer model KW - cytoreductive surgery KW - differential gene expression KW - down regulation KW - female KW - hierarchical clustering KW - histology KW - human KW - human tissue KW - immunohistochemistry KW - immunoreactivity KW - International Federation of Gynecology and Obstetrics KW - leave one out cross validation KW - major clinical study KW - median survival time KW - meta analysis (topic) KW - minimal residual disease KW - morbidity KW - ovary cancer KW - overall survival KW - pathologist KW - postoperative period KW - primary tumor KW - progression free survival KW - prospective study KW - surgical mortality KW - survival rate KW - systemic therapy KW - time of death KW - tissue microarray KW - treatment outcome KW - upregulation N2 - Background: High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer and is associated with high mortality rates. Surgical outcome is one of the most important prognostic factors. There are no valid biomarkers to identify which patients may benefit from a primary debulking approach. Objective: Our study aimed to discover and validate a predictive panel for surgical outcome of residual tumor mass after first-line debulking surgery. Study design: Firstly, “In silico” analysis of publicly available datasets identified 200 genes as predictors for surgical outcome. The top selected genes were then validated using the novel Nanostring method, which was applied for the first time for this particular research objective. 225 primary ovarian cancer patients with well annotated clinical data and a complete debulking rate of 60% were compiled for a clinical cohort. The 14 best rated genes were then validated through the cohort, using immunohistochemistry testing. Lastly, we used our biomarker expression data to predict the presence of miliary carcinomatosis patterns. Results: The Nanostring analysis identified 37 genes differentially expressed between optimal and suboptimal debulked patients (p < 0.05). The immunohistochemistry validated the top 14 genes, reaching an AUC Ø0.650. The analysis for the prediction of miliary carcinomatosis patterns reached an AUC of Ø0.797. Conclusion: The tissue-based biomarkers in our analysis could not reliably predict post-operative residual tumor. Patient and non-patient-associated co-factors, surgical skills, and center experience remain the main determining factors when considering the surgical outcome at primary debulking in high-grade serous ovarian cancer patients. ER - TY - JOUR M3 - Review Y1 - 2022 VL - 122 SP - 104115 SN - 1879-176X JF - Journal of dentistry JO - J Dent UR - https://www.embase.com/search/results?subaction=viewrecord&id=L637673030&from=export U2 - L637673030 C5 - 35367318 DB - Medline U3 - 2022-04-11 U4 - 2022-06-14 L2 - http://dx.doi.org/10.1016/j.jdent.2022.104115 DO - 10.1016/j.jdent.2022.104115 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=1879176X&id=doi:10.1016%2Fj.jdent.2022.104115&atitle=Deep+learning+for+caries+detection%3A+A+systematic+review&stitle=J+Dent&title=Journal+of+dentistry&volume=122&issue=&spage=104115&epage=&aulast=Mohammad-Rahimi&aufirst=Hossein&auinit=H.&aufull=Mohammad-Rahimi+H.&coden=&isbn=&pages=104115-&date=2022&auinit1=H&auinitm= A1 - Mohammad-Rahimi, H. A1 - Motamedian, S.R. A1 - Rohban, M.H. A1 - Krois, J. A1 - Uribe, S.E. A1 - Mahmoudinia, E. A1 - Rokhshad, R. A1 - Nadimi, M. A1 - Schwendicke, F. M1 - (Mohammad-Rahimi H.) Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Computer Engineering, Sharif University of Technology, Tehran, Iran M1 - (Motamedian S.R.) Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran M1 - (Rohban M.H.) Department of Computer Engineering, Sharif University of Technology, Tehran, Iran M1 - (Krois J.; Schwendicke F., falk.schwendicke@charite.de) Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany M1 - (Uribe S.E.) Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Oral Health & Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia M1 - (Mahmoudinia E.) Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran M1 - (Rokhshad R.) Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany M1 - (Nadimi M.) Cardiovascular Diseases Research Center, Department of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran T1 - Deep learning for caries detection: A systematic review LA - English KW - dental caries KW - diagnostic imaging KW - human KW - reproducibility KW - sensitivity and specificity N2 - OBJECTIVES: Detecting caries lesions is challenging for dentists, and deep learning models may help practitioners to increase accuracy and reliability. We aimed to systematically review deep learning studies on caries detection. DATA: We selected diagnostic accuracy studies that used deep learning models on dental imagery (including radiographs, photographs, optical coherence tomography images, near-infrared light transillumination images). The latest version of the quality assessment tool for diagnostic accuracy studies (QUADAS-2) tool was used for risk of bias assessment. Meta-analysis was not performed due to heterogeneity in the studies methods and their performance measurements. SOURCES: Databases (Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. STUDY SELECTION: From 252 potentially eligible references, 48 studies were assessed full-text and 42 included, using classification (n = 26), object detection (n = 6), or segmentation models (n = 10). A wide range of performance metrics was used; image, object or pixel accuracy ranged between 68%-99%. The minority of studies (n = 11) showed a low risk of biases in all domains, and 13 studies (31.0%) low risk for concerns regarding applicability. The accuracy of caries classification models varied, i.e. 71% to 96% on intra-oral photographs, 82% to 99.2% on peri-apical radiographs, 87.6% to 95.4% on bitewing radiographs, 68.0% to 78.0% on near-infrared transillumination images, 88.7% to 95.2% on optical coherence tomography images, and 86.1% to 96.1% on panoramic radiographs. Pooled diagnostic odds ratios varied from 2.27 to 32,767. For detection and segmentation models, heterogeneity in reporting did not allow useful pooling. CONCLUSION: An increasing number of studies investigated caries detection using deep learning, with a diverse types of architectures being employed. Reported accuracy seems promising, while study and reporting quality are currently low. CLINICAL SIGNIFICANCE: Deep learning models can be considered as an assistant for decisions regarding the presence or absence of carious lesions. ER - TY - JOUR M3 - Review Y1 - 2022 VL - 12 IS - 5 SN - 2075-4418 JF - Diagnostics JO - Diagn. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2016660186&from=export U2 - L2016660186 DB - Embase U3 - 2022-05-12 U4 - 2022-07-14 L2 - http://dx.doi.org/10.3390/diagnostics12051083 DO - 10.3390/diagnostics12051083 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=20754418&id=doi:10.3390%2Fdiagnostics12051083&atitle=Application+and+Performance+of+Artificial+Intelligence+Technology+in+Detection%2C+Diagnosis+and+Prediction+of+Dental+Caries+%28DC%29%E2%80%94A+Systematic+Review&stitle=Diagn.&title=Diagnostics&volume=12&issue=5&spage=&epage=&aulast=Khanagar&aufirst=Sanjeev+B.&auinit=S.B.&aufull=Khanagar+S.B.&coden=&isbn=&pages=-&date=2022&auinit1=S&auinitm=B A1 - Khanagar, S.B. A1 - Alfouzan, K. A1 - Awawdeh, M. A1 - Alkadi, L. A1 - Albalawi, F. A1 - Alfadley, A. M1 - (Khanagar S.B., sanjeev.khanagar76@gmail.com; Awawdeh M., m97a97@gmail.com; Albalawi F., balawif@ksau-hs.edu.sa) Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia M1 - (Khanagar S.B., sanjeev.khanagar76@gmail.com; Alfouzan K., kalfouzan@yahoo.com; Awawdeh M., m97a97@gmail.com; Alkadi L., lubna.alkadi@gmail.com; Albalawi F., balawif@ksau-hs.edu.sa; Alfadley A., fadleya@ksau-hs.edu.sa) King Abdullah International Medical Research Centre, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia M1 - (Alfouzan K., kalfouzan@yahoo.com; Alkadi L., lubna.alkadi@gmail.com; Alfadley A., fadleya@ksau-hs.edu.sa) Restorative and Prosthetic Dental Sciences Department, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia AD - S.B. Khanagar, Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia T1 - Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review LA - English KW - accuracy KW - artificial intelligence KW - clinical practice KW - decision making KW - deep neural network KW - dental caries KW - dental practice KW - dental procedure KW - diagnostic accuracy KW - disease classification KW - head and neck cancer KW - human KW - micro-computed tomography KW - panoramic radiography KW - prediction KW - predictive value KW - review KW - sensitivity and specificity KW - support vector machine KW - systematic review KW - task performance N2 - Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent elec-tronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC. ER - TY - JOUR M3 - Review Y1 - 2022 VL - 34 IS - 4 SP - 270 EP - 281 SN - 1013-9052 JF - Saudi Dental Journal JO - Saudi Dent. J. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2018003362&from=export U2 - L2018003362 DB - Embase U3 - 2022-05-18 U4 - 2022-12-08 L2 - http://dx.doi.org/10.1016/j.sdentj.2022.04.004 DO - 10.1016/j.sdentj.2022.04.004 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=10139052&id=doi:10.1016%2Fj.sdentj.2022.04.004&atitle=The+role+of+neural+artificial+intelligence+for+diagnosis+and+treatment+planning+in+endodontics%3A+A+qualitative+review&stitle=Saudi+Dent.+J.&title=Saudi+Dental+Journal&volume=34&issue=4&spage=270&epage=281&aulast=Asiri&aufirst=Ashwaq+F.&auinit=A.F.&aufull=Asiri+A.F.&coden=&isbn=&pages=270-281&date=2022&auinit1=A&auinitm=F A1 - Asiri, A.F. A1 - Altuwalah, A.S. M1 - (Asiri A.F., af.asiri@mu.edu.sa; Altuwalah A.S.) Department of Restorative Science, College of Dentistry, Majmaah University, Al Zulfi, Saudi Arabia AD - A.F. Asiri, Department of Restorative Science, College of Dentistry, Majmaah University, Al Zulfi, Saudi Arabia T1 - The role of neural artificial intelligence for diagnosis and treatment planning in endodontics: A qualitative review LA - English KW - X ray film KW - artificial intelligence KW - artificial neural network KW - data base KW - dental caries KW - diagnostic accuracy KW - endodontics KW - human KW - learning algorithm KW - panoramic radiography KW - periodontitis KW - reliability KW - review KW - search engine KW - sensitivity and specificity KW - systematic review KW - tooth disease KW - tooth fracture KW - tooth periapical disease KW - treatment planning KW - validity N2 - Introduction: The role of artificial intelligence (AI) is currently increasing in terms of diagnosing diseases and planning treatment in endodontics. However, findings from individual research studies are not systematically reviewed and compiled together. Hence, this study aimed to systematically review, appraise, and evaluate neural AI algorithms employed and their comparative efficacy to conventional methods in endodontic diagnosis and treatment planning. Methods: The present research question focused on the literature search about different AI algorithms and models of AI assisted endodontic diagnosis and treatment planning. The search engine included databases such as Google Scholar, PubMed, and Science Direct with search criteria of primary research paper, published in English, and analyzed data on AI and its role in the field of endodontics. Results: The initial search resulted in 785 articles, exclusion based on abstract relevance, animal studies, grey literature and letter to editors narrowed down the scope of selected articles to 11 accepted for review. The review data supported the findings that AI can play a crucial role in the area of endodontics, such as identification of apical lesions, classifying and numbering teeth, detecting dental caries, periodontitis and periapical disease, diagnosing different dental problems, helping dentists make referrals, and also helping them make plans for treatment of dental disorders in a timely and effective manner with greater accuracy. Conclusion: AI with different models or frameworks and algorithms can help dentists to diagnose and manage endodontic problems with greater accuracy. However, endodontic fraternity needs to provide more emphasis on the utilization of AI, provision of evidence based guidelines and implementation of the AI models. ER - TY - GEN M3 - Preprint Y1 - 2022 SN - 2692-8205 JF - bioRxiv JO - bioRxiv UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2017678913&from=export U2 - L2017678913 U4 - 2022-05-03 L2 - http://dx.doi.org/10.1101/2022.03.18.484474 DO - 10.1101/2022.03.18.484474 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=26928205&id=doi:10.1101%2F2022.03.18.484474&atitle=The+genetic+and+biochemical+determinants+of+mRNA+degradation+rates+in+mammals&stitle=bioRxiv&title=bioRxiv&volume=&issue=&spage=&epage=&aulast=Agarwal&aufirst=Vikram&auinit=V.&aufull=Agarwal+V.&coden=&isbn=&pages=-&date=2022&auinit1=V&auinitm= A1 - Agarwal, V. A1 - Kelley, D. M1 - (Agarwal V., vagar@calicolabs.com; Kelley D., drk@calicolabs.com) Calico Life Sciences LLC, South San Francisco, CA, United States AD - V. Agarwal, Calico Life Sciences LLC, South San Francisco, CA, United States AD - D. Kelley, Calico Life Sciences LLC, South San Francisco, CA, United States T1 - The genetic and biochemical determinants of mRNA degradation rates in mammals LA - English KW - consensus KW - deep neural network KW - gene control KW - gene expression KW - genetic transcription KW - half life time KW - human KW - male KW - mammal cell KW - meta analysis KW - mouse KW - noise KW - nonhuman KW - prevalence KW - RNA degradation KW - RNA sequence KW - messenger RNA KW - transcriptome N2 - Background: Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable a more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. Results: Here, we establish a compendium of 54 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. Conclusions: Our work produces a more robust “ground truth” with regards to transcriptome-wide mRNA half-lives in mammalian cells. Using these consolidated measurements, we trained a model that is over 50% more accurate in predicting half-life from sequence than existing models. Our best model, Saluki, succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome. ER - TY - JOUR M3 - Review Y1 - 2022 VL - 2022 SN - 2040-2309 SN - 2040-2295 JF - Journal of Healthcare Engineering JO - J. Healthc. Eng. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2017687668&from=export U2 - L2017687668 C5 - 35399834 DB - Embase DB - Medline U3 - 2022-05-03 U4 - 2022-05-12 L2 - http://dx.doi.org/10.1155/2022/5032435 DO - 10.1155/2022/5032435 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=20402309&id=doi:10.1155%2F2022%2F5032435&atitle=Uses+of+Different+Machine+Learning+Algorithms+for+Diagnosis+of+Dental+Caries&stitle=J.+Healthc.+Eng.&title=Journal+of+Healthcare+Engineering&volume=2022&issue=&spage=&epage=&aulast=Talpur&aufirst=Sarena&auinit=S.&aufull=Talpur+S.&coden=&isbn=&pages=-&date=2022&auinit1=S&auinitm= A1 - Talpur, S. A1 - Azim, F. A1 - Rashid, M. A1 - Syed, S.A. A1 - Talpur, B.A. A1 - Khan, S.J. M1 - (Talpur S., sarena13487@gmail.com; Syed S.A., sidra.agha@zu.edu.pk; Khan S.J., sj.khan@zu.edu.pk) Department of Biomedical Engineering, Ziauddin University, Karachi, Pakistan M1 - (Azim F., fahad.azim@zu.edu.pk) Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan M1 - (Rashid M., munaf.rashid@zu.edu.pk) Department of Software Engineering, Ziauddin University, Karachi, Pakistan M1 - (Talpur B.A., alishatalpur27@gmail.com) Liaquat University of Medical and Health Sciences, Jamshoro, Pakistan AD - S.J. Khan, Department of Biomedical Engineering, Ziauddin University, Karachi, Pakistan T1 - Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries LA - English KW - algorithm KW - back propagation KW - dental caries KW - disease severity KW - human KW - machine learning KW - measurement accuracy KW - Medline KW - meta analysis KW - Preferred Reporting Items for Systematic Reviews and Meta-Analyses KW - randomized controlled trial (topic) KW - review KW - ScienceDirect KW - search engine KW - systematic review KW - tooth radiography N2 - Background. Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives. The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods. A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result. This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion. In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records. ER - TY - JOUR M3 - Conference Abstract Y1 - 2021 VL - 72 IS - SUPPL 1 SP - 1250 EP - 1251 SN - 1536-4801 JF - Journal of Pediatric Gastroenterology and Nutrition JO - J. Pediatr. Gastroenterol. Nutr. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L635174345&from=export U2 - L635174345 DB - Embase U4 - 2021-06-08 L2 - http://dx.doi.org/10.1097/MPG.0000000000003177 DO - 10.1097/MPG.0000000000003177 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=15364801&id=doi:10.1097%2FMPG.0000000000003177&atitle=Opportunity+to+shape+lifelong+immune+health%3A+A+network-based+strategy+to+predict+and+prioritize+markers+related+to+immune+interventions+in+early+life&stitle=J.+Pediatr.+Gastroenterol.+Nutr.&title=Journal+of+Pediatric+Gastroenterology+and+Nutrition&volume=72&issue=SUPPL+1&spage=1250&epage=1251&aulast=Van+Bilsen&aufirst=Jolanda+H.M.&auinit=J.H.M.&aufull=Van+Bilsen+J.H.M.&coden=&isbn=&pages=1250-1251&date=2021&auinit1=J&auinitm=H.M. A1 - Van Bilsen, J.H.M. A1 - Dulos, R. A1 - Van Stee, M.F. A1 - Meima, M.Y. A1 - Rankouhi, T.R. A1 - Jacobsen, L.N. A1 - Kvistgaard, A.S. A1 - Garthoff, J.A. A1 - Knippels, L.M.J. A1 - Knipping, K. A1 - Houben, G.F. A1 - Verschuren, L. A1 - Meijerink, M. A1 - Krishnan, S. M1 - (Van Bilsen J.H.M., j.vanbilsen@tno.nl; Van Stee M.F.; Meima M.Y.; Rankouhi T.R.; Houben G.F.; Meijerink M.; Krishnan S.) TNO,RAPID, Utrecht, Netherlands M1 - (Dulos R.; Verschuren L.) TNO,MSB, Zeist, Netherlands M1 - (Jacobsen L.N.; Kvistgaard A.S.) Arla Foods Ingredients, Pediatrics, Viby J., Denmark M1 - (Garthoff J.A.) Danone Food Safety Center, Central Secretary, Utrecht, Netherlands M1 - (Knippels L.M.J.; Knipping K.) Danone Nutricia Research, Centre of Excellence Immunology, Utrecht, Netherlands M1 - (Knippels L.M.J.; Knipping K.) Utrecht University, Utrecht Institute of Pharmaceutical Sciences, Utrecht, Netherlands M1 - (Houben G.F.) University Medical Center Utrecht, Laboratory for Translational Immunology, Utrecht, Netherlands AD - J.H.M. Van Bilsen, TNO,RAPID, Utrecht, Netherlands T1 - Opportunity to shape lifelong immune health: A network-based strategy to predict and prioritize markers related to immune interventions in early life LA - English KW - biological marker KW - CD4 antigen KW - CD79a antigen KW - chemokine KW - complement component C4b binding protein KW - cytochrome P450 1A2 KW - decay accelerating factor KW - endogenous compound KW - gamma interferon KW - interleukin 10 KW - interleukin 4 KW - interleukin 5 KW - interleukin 6 KW - transcription factor FOXP3 KW - transcription factor GATA 3 KW - tumor necrosis factor KW - algorithm KW - bioprocess KW - child KW - conference abstract KW - controlled study KW - drug safety KW - female KW - gene ontology KW - genetic marker KW - human KW - immune system KW - immunomodulation KW - machine learning KW - male KW - Medline KW - mining KW - nonhuman KW - prediction KW - preschool child KW - Scopus KW - systematic review KW - third trimester pregnancy N2 - Objectives: A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for immune modulation have been identified, there is no regulatory guidance on how to assess the risk and benefit balance of such interventions. In this study we used a network-based strategy to predict and prioritize markers to assess effects of early life immune intervention. Methods: First an inventory of available literature (till Jan. 2019) regarding 6 immune developmental periods (1st/2nd/3rd trimester of gestation, birth, newborn (0-28 days), infant (1-24 months)) in human and experimental animals was made using Scopus and PubMed. Information was extracted and structured from relevant early life articles using the automated text mining and machine learning tool INDRA. INDRA identified relevant entities (e.g. genes/ proteins/metabolites/ processes/ diseases), extracted causal relationships among these entities, and assembled them into six early life-immune causal networks, each compromising a different time period in early life. These causal early life immune networks were denoised using GeneMania, enriched with data from the gene-disease association database DisGeNET and Gene Ontology resource tools, inferred missing relationships and added expert knowledge to generate information-dense early life immune networks.The resulting complex early life human immune networks were subjected to the PageRank centrality algorithm to identify and prioritize genes of the early life immune system as candidate biomarkers of early life immune intervention. Results: In total 2966 articles were selected using the literature databases of which 829 articles were considered relevant after screening. From these full text articles, INDRA extracted resp. 2101, 3234, 3654, 1568, 2917 and 1487 unique relationships between entities, resulting in 6 causal early-life immune networks, each covering a different time period. Gene enrichment steps further increased the number of gene-bioprocess and gene-disease relationships (range of 5816-16082 unique relationships). In addition the inference steps added 1529 to 3343 relationships to the networks. Analysis of the complex early life networks by PageRank, not only confirmed the central role of the usual suspects (e.g. chemokines, cytokines and other immune activation regulators (e.g. CD55, FOXP3, GATA3, CD79A, C4BPA), but also identified less obvious key marker candidates (e.g. CYP1A2, FOXK2, NELFCD, RENBP). Comparison of the different early life periods, resulted in the prediction of 11 key early life genes overlapping all early life periods (TNF, IL6, IL10, CD4, FOXP3, IL4, NELFCD, CD79A, IL5, RENBP and IFNG), and also genes that were only described in certain early life period(s). Conclusion: Here we describe a promising network-based approach that provides a science-based and systematical way to explore the functional development of the early life immune system in time. This systems approach aids the generation of a testing strategy for assessing the safety and efficacy of early life immune modulation by predicting the key candidate markers during different phases of early life immune development. ER - TY - JOUR M3 - Article Y1 - 2021 VL - 10 SN - 2050-084X JF - eLife JO - eLife UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2013463123&from=export U2 - L2013463123 C5 - 34350827 DB - Embase DB - Medline U3 - 2021-08-23 U4 - 2022-03-25 L2 - http://dx.doi.org/10.7554/ELIFE.64653 DO - 10.7554/ELIFE.64653 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=2050084X&id=doi:10.7554%2FELIFE.64653&atitle=Unsupervised+machine+learning+reveals+key+immune+cell+subsets+in+covid-19%2C+rhinovirus+infection%2C+and+cancer+therapy&stitle=eLife&title=eLife&volume=10&issue=&spage=&epage=&aulast=Barone&aufirst=Sierra+M.&auinit=S.M.&aufull=Barone+S.M.&coden=&isbn=&pages=-&date=2021&auinit1=S&auinitm=M A1 - Barone, S.M. A1 - Paul, A.G.A. A1 - Muehling, L.M. A1 - Lannigan, J.A. A1 - Kwok, W.W. A1 - Turner, R.B. A1 - Woodfolk, J.A. A1 - Irish, J.M. M1 - (Barone S.M.; Irish J.M., jonathan.irish@vanderbilt.edu) Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States M1 - (Barone S.M.; Irish J.M., jonathan.irish@vanderbilt.edu) Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, United States M1 - (Paul A.G.A.; Muehling L.M.; Woodfolk J.A., jaw4m@virginia.edu) Allergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United States M1 - (Muehling L.M.; Lannigan J.A.; Woodfolk J.A., jaw4m@virginia.edu) Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, United States M1 - (Kwok W.W.) Benaroya Research Institute at Virginia Mason, Seattle, United States M1 - (Turner R.B.) Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, United States M1 - (Irish J.M., jonathan.irish@vanderbilt.edu) Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, United States AD - J.A. Woodfolk, Allergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United States AD - J.M. Irish, Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States T1 - Unsupervised machine learning reveals key immune cell subsets in covid-19, rhinovirus infection, and cancer therapy LA - English KW - NCT02796001 KW - flow cytometer KW - ADP ribosyl cyclase/cyclic ADP ribose hydrolase 1 KW - beta1 integrin KW - CD14 antigen KW - CD147 antigen KW - CD27 antigen KW - CD64 antigen KW - chemokine receptor CCR5 KW - chemokine receptor CCR6 KW - chemokine receptor CCR7 KW - chemokine receptor CXCR3 KW - chemokine receptor CXCR5 KW - decay accelerating factor KW - HLA antigen KW - inducible T cell costimulator KW - magnetic bead KW - major histocompatibility antigen class 1 KW - major histocompatibility antigen class 2 KW - protein KW - t box transcription factor 21 KW - transcription factor t cell factor 1 KW - tumor necrosis factor receptor superfamily member 6 KW - unclassified drug KW - acute myeloid leukemia KW - adult KW - algorithm KW - article KW - autofluorescence imaging KW - cancer chemotherapy KW - cancer therapy KW - CD4+ T lymphocyte KW - cell count KW - cell proliferation KW - computer analysis KW - controlled study KW - convalescence KW - coronavirus disease 2019 KW - density gradient centrifugation KW - flow cytometry KW - human KW - immune response KW - immunocompetent cell KW - induced pluripotent stem cell KW - induction chemotherapy KW - influenza KW - machine learning KW - meta analysis (topic) KW - middle aged KW - myelodysplastic syndrome KW - natural killer cell KW - nonhuman KW - phenotype KW - respiratory tract disease KW - Rhinovirus infection KW - T lymphocyte KW - Th17 cell KW - tracking responder expanding C4 - Cytek Biosciences(United States) N2 - For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infec-tions. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mech-anistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes. ER - TY - JOUR M3 - Note Y1 - 2021 VL - 40 IS - Special Issue SP - 221 EP - 227 SN - 0326-2383 JF - Latin American Journal of Pharmacy JO - Lat. Am. J. Pharm. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2015721955&from=export U2 - L2015721955 DB - Embase U3 - 2022-02-28 U4 - 2022-03-07 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=03262383&id=doi:&atitle=In+Silico+Identification+of+Potential+Inhibitors+for+Streptococcus+mutans+Signal+Peptidase+I+Using+Structure-Based+Drug+Design&stitle=Lat.+Am.+J.+Pharm.&title=Latin+American+Journal+of+Pharmacy&volume=40&issue=Special+Issue&spage=221&epage=227&aulast=Al-Khafaji&aufirst=Zahra+M.&auinit=Z.M.&aufull=Al-Khafaji+Z.M.&coden=&isbn=&pages=221-227&date=2021&auinit1=Z&auinitm=M A1 - Al-Khafaji, Z.M. A1 - Mahmood, S.B. A1 - Mahmood, A.B. M1 - (Al-Khafaji Z.M., zahranasserk@gmail.com) Institute of Genetic Engineering and Biotechnology for Postgraduate Studies, University of Baghdad, Iraq M1 - (Mahmood S.B.) Dentistry Dept., Dijla College University, Baghdad, Iraq M1 - (Mahmood A.B.) Ministry of Agriculture, Veterinary Directorate, Baghdad Veterinary Hospital, Al-Dora Hospital, Iraq AD - Z.M. Al-Khafaji, Institute of Genetic Engineering and Biotechnology for Postgraduate Studies, University of Baghdad, Iraq T1 - In Silico Identification of Potential Inhibitors for Streptococcus mutans Signal Peptidase I Using Structure-Based Drug Design LA - English KW - ligand KW - signal peptidase I KW - bacterial growth KW - binding affinity KW - carcinogenicity KW - cell survival KW - cell viability KW - chemical interaction KW - computer model KW - crystallography KW - dental caries KW - drug design KW - drug formulation KW - drug structure KW - molecular docking KW - mouth cavity KW - nonhuman KW - note KW - nuclear magnetic resonance KW - Streptococcus mutans KW - systematic review N2 - Streptococcus mutans is considered the most virulent bacteria in the mouth cavity; it is accused of teeth caries and mouth carcinogenicity. It uses signal peptidase I to export its preproteins to the mouth cavity. This study was carried out to find inhibitors to this enzyme since it is vital for bacterial growth and survival. Structure-based drug design (SBDD) was used to find the inhibitory ligands, two ligands were obtained from the Food database and 10 ligands from a special database (diverse Lib database), these ligands passed different filtration steps, they docked with target protein at considerable binding affinity, and with different chemical interactions. ER - TY - JOUR M3 - Conference Abstract Y1 - 2021 VL - 8 SP - 7 SN - 2345-5829 JF - Frontiers in Biomedical Technologies JO - Front. Biomed. Technol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L642264924&from=export U2 - L642264924 DB - Embase U4 - 2023-09-20 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=23455829&id=doi:&atitle=Application+of+Artificial+Intelligence+in+Dentomaxillofacial+Imaging%3A+A+Science+Mapping+Approach&stitle=Front.+Biomed.+Technol.&title=Frontiers+in+Biomedical+Technologies&volume=8&issue=&spage=7&epage=&aulast=Tofanghchiha&aufirst=Maryam&auinit=M.&aufull=Tofanghchiha+M.&coden=&isbn=&pages=7-&date=2021&auinit1=M&auinitm= A1 - Tofanghchiha, M. A1 - Karagah, A. A1 - Kolahi, J. M1 - (Tofanghchiha M.; Karagah A., aida_karagah@yahoo.com) Department of Oral and Maxillofacial Radiology, Dental Caries Prevention Research Center, Qazvin University of Medical Sciences, Qazvin, Iran M1 - (Karagah A., aida_karagah@yahoo.com) Department of Oral and Maxillofacial Surgery, Dental Caries Prevention Research Center, Qazvin University of Medical Sciences, Qazvin, Iran M1 - (Kolahi J.) Independent Research Scientist, Founder of Dental Hypotheses, Isfahan, Iran AD - A. Karagah, Department of Oral and Maxillofacial Radiology, Dental Caries Prevention Research Center, Qazvin University of Medical Sciences, Qazvin, Iran T1 - Application of Artificial Intelligence in Dentomaxillofacial Imaging: A Science Mapping Approach LA - English KW - endogenous compound KW - protein c jun KW - artificial intelligence KW - bibliometrics KW - clinical practice KW - computer assisted diagnosis KW - conference abstract KW - convolutional neural network KW - deep learning KW - digital imaging KW - human KW - interdisciplinary research KW - machine learning KW - network analysis KW - panoramic radiography KW - preclinical study KW - radiologist KW - radiology KW - reliability KW - Scopus KW - software KW - systematic review KW - workflow KW - writing N2 - Background: Artificial intelligence has recently been applied to radiographic images in the field of dentistry, especially Oral and Maxillofacial (OMF) radiology. Radiological imaging diagnosis plays important roles in clinical patient management. Artificial intelligence is recently gaining wide attention for its high performance in recognizing images. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. The aim of this study is a brief report on the use of artificial intelligence in recent research in various fields of oral and maxillofacial imaging. Materials and Methods: Scopus database was searched in Jun 10, 2021 with the following query TITLE-ABS-KEY (( machine learning OR deep learning ) AND image) AND SUBJAREA(DENT). Bibliometric data of 91 results analyzed via VOSviewer software using author keyword co-occurrence, co-citation and co-authorship network analysis. Results: convolutional neural network, digital imaging/radiology and panoramic radiography were the hottest topics. dentomaxillofac radiol, sci rep and angle orthod had the most influence on the network. Among authors ariji e., ariji y. and fukuda m. had the most influence on the network. Conclusion: Due to the high performance of deep learning in image recognition tasks, the application of this technology to radiological imaging is increasing. With the development of artificial intelligence, it can be predictable to change clinical practice by helping radiologists practice with better performance, greater reliability, and enhanced workflow for more appropriate recommendations. Development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools could be the other outcomes for this technology. OMF radiologists will play a key role in the development of artificial intelligence applications in this field. Therefore, we suggest interdisciplinary research in related sciences in the country to be supported by research centers and research institutes. And thus we can benefit from new technologies and researchers in engineering sciences in the clinical and preclinical fields. ER - TY - JOUR M3 - Review Y1 - 2020 VL - 9 IS - 11 SP - 1 EP - 13 SN - 2077-0383 JF - Journal of Clinical Medicine JO - J. Clin. Med. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2005411396&from=export U2 - L2005411396 DB - Embase U3 - 2020-11-11 U4 - 2020-12-22 L2 - http://dx.doi.org/10.3390/jcm9113579 DO - 10.3390/jcm9113579 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=20770383&id=doi:10.3390%2Fjcm9113579&atitle=Dental+caries+diagnosis+and+detection+using+neural+networks%3A+A+systematic+review&stitle=J.+Clin.+Med.&title=Journal+of+Clinical+Medicine&volume=9&issue=11&spage=1&epage=13&aulast=Prados-Privado&aufirst=Mar%C3%ADa&auinit=M.&aufull=Prados-Privado+M.&coden=&isbn=&pages=1-13&date=2020&auinit1=M&auinitm= A1 - Prados-Privado, M. A1 - Villalón, J.G. A1 - Martínez-Martínez, C.H. A1 - Ivorra, C. A1 - Prados-Frutos, J.C. M1 - (Prados-Privado M., maria.prados@uah.es; Villalón J.G., javier.villalon@asisadental.com; Ivorra C., juancarlos.prados@urjc.es) Asisa Dental, Research Department, C/José Abascal, 32, Madrid, Spain M1 - (Prados-Privado M., maria.prados@uah.es) Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcala de Henares, Ctra, Madrid-Barcelona, Km. 33,600, Alcala de Henares, Spain M1 - (Prados-Privado M., maria.prados@uah.es; Prados-Frutos J.C., carlos.martinez@asisa.es) IDIBO GROUP (Group of High-Performance Research, Development and Innovation in Dental Biomaterials of Rey Juan Carlos University), Avenida de Atenas s/n, Alcorcon, Spain M1 - (Martínez-Martínez C.H., carlos.ivorra@asisadental.com) Faculty of Medicine, Universidad Complutense de Madrid, Plaza de Ramón y Cajal, s/n, Madrid, Spain M1 - (Prados-Frutos J.C., carlos.martinez@asisa.es) Department of Medical Specialties and Public Health, Faculty of Health Sciences, Universidad Rey Juan Carlos, Avenida de Atenas, Alcorcon, Spain AD - M. Prados-Privado, Asisa Dental, Research Department, C/José Abascal, 32, Madrid, Spain AD - M. Prados-Privado, Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcala de Henares, Ctra, Madrid-Barcelona, Km. 33,600, Alcala de Henares, Spain AD - M. Prados-Privado, IDIBO GROUP (Group of High-Performance Research, Development and Innovation in Dental Biomaterials of Rey Juan Carlos University), Avenida de Atenas s/n, Alcorcon, Spain T1 - Dental caries diagnosis and detection using neural networks: A systematic review LA - English KW - intraoral camera KW - X ray film KW - artificial intelligence KW - artificial neural network KW - computer assisted diagnosis KW - data base KW - dental caries KW - diagnostic accuracy KW - human KW - molar tooth KW - panoramic radiography KW - premolar tooth KW - review KW - systematic review KW - tooth radiography KW - transillumination N2 - Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary. ER - TY - JOUR M3 - Review Y1 - 2020 VL - 59 IS - 11 SP - 1320 EP - 1331 SN - 1365-4632 SN - 0011-9059 JF - International Journal of Dermatology JO - Int. J. Dermatol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2005561963&from=export U2 - L2005561963 C5 - 32662887 DB - Embase DB - Medline U3 - 2020-07-22 U4 - 2020-07-22 L2 - http://dx.doi.org/10.1111/ijd.15015 DO - 10.1111/ijd.15015 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=13654632&id=doi:10.1111%2Fijd.15015&atitle=The+efficacy+and+safety+of+treatments+for+infantile+hemangiomas%3A+a+Bayesian+network+meta-analysis&stitle=Int.+J.+Dermatol.&title=International+Journal+of+Dermatology&volume=59&issue=11&spage=1320&epage=1331&aulast=Yang&aufirst=Hao&auinit=H.&aufull=Yang+H.&coden=IJDEB&isbn=&pages=1320-1331&date=2020&auinit1=H&auinitm= A1 - Yang, H. A1 - Hu, D.-L. A1 - Xuan, X.-X. A1 - Chen, J.-J. A1 - Xu, S. A1 - Wu, X.-J. A1 - Zhang, H. A1 - Shu, Q. A1 - Guo, X.-D. M1 - (Yang H.; Xuan X.-X.; Zhang H.) Zhejiang University School of Medicine, Hangzhou, China M1 - (Yang H.; Hu D.-L.; Chen J.-J.; Xu S.; Wu X.-J.; Zhang H.; Guo X.-D., guoxiaodong@zju.edu.cn) Department of Pediatric Surgery, Zhejiang University Jinhua Hospital, Jinhua, China M1 - (Yang H.; Xuan X.-X.; Zhang H.; Shu Q.) Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China M1 - (Xuan X.-X.; Shu Q.) National Clinical Research Center for Child Health, Hangzhou, China AD - X.-D. Guo, Department of Pediatric Surgery, Zhejiang University Jinhua Hospital, Jinhua, China T1 - The efficacy and safety of treatments for infantile hemangiomas: a Bayesian network meta-analysis LA - English KW - captopril KW - prednisolone KW - propranolol KW - timolol KW - triamcinolone KW - Bayesian network KW - bradycardia KW - bronchiolitis KW - capillary hemangioma KW - Cushing syndrome KW - Cushingoid syndrome KW - dental caries KW - depigmentation KW - depressed blood pressure KW - diarrhea KW - somnolence KW - drug efficacy KW - drug safety KW - drug tolerance KW - dyspnea KW - faintness KW - gastrointestinal symptom KW - growth disorder KW - human KW - hyperpigmentation KW - hypertension KW - hypopigmentation KW - hypotension KW - insomnia KW - lethargy KW - loss of appetite KW - meta analysis KW - patient participation KW - quality control KW - randomized controlled trial (topic) KW - review KW - risk assessment KW - skin atrophy KW - skin exfoliation KW - sleep disorder KW - Streptococcus infection KW - systematic review KW - treatment outcome KW - treatment response KW - ulcer KW - viral gastroenteritis KW - viral respiratory tract infection KW - visual disorder KW - vomiting N2 - Whether infantile hemangiomas (IHs) need to be treated and which treatment should be preferred are still controversial. We aimed to compare and rank the treatments and identify the optimal treatment for IHs. We searched PubMed, EMBASE, the Cochrane Library, Web of Science, and other sources for randomized controlled trials up to August 2019. We included trials comparingdifferent treatments and reported response or adverse events rate in IH patients. Two reviewers independently evaluated studies by specific criteria and extracted data. We assessed the risk of bias with the Cochrane risk of bias tool. Random-effects were performed for pair-to-pair and Bayesian framework network meta-analyses. The primary outcomes were efficacy and safety. We deemed 20 studies eligible, including 1149 participants and eight interventions. For efficacy, oral propranolol and topical propranolol/timolol were better than observation/placebo (OR, 95% CrI: 17.05, 4.02–94.94; 9.72, 1.91–59.08). For safety, topical propranolol/timolol was significantly better tolerated than oral propranolol (0.05, 0.001–0.66). Cluster analysis demonstrated oral propranolol was the most effective treatment for IHs, while topical propranolol/timolol showed high efficacy and the highest safety. Laser, intralesional propranolol or glucocorticoid, oral glucocorticoid, or captopril had significantly lower priority than oral propranolol or topical propranolol/timolol considering both efficacy and safety. The quality of evidence was rated as moderate or low in most comparisons. This network meta-analysis found topical beta-blockers had the potential to be the most preferable and beneficial option for IHs in consideration of both efficacy and safety. ER - TY - JOUR M3 - Conference Abstract Y1 - 2020 VL - 41 IS - SUPPL 2 SP - 2897 SN - 1522-9645 JF - European Heart Journal JO - Eur. Heart J. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L634166236&from=export U2 - L634166236 DB - Embase U4 - 2021-02-18 L2 - http://dx.doi.org/10.1093/ehjci/ehaa946.2897 DO - 10.1093/ehjci/ehaa946.2897 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=15229645&id=doi:10.1093%2Fehjci%2Fehaa946.2897&atitle=Artificial+neural+networks+to+compare+the+contribution+of+basic+clinical+factors%2C+ESC+SCORE%2C+and+multidimensional+risk+factors+for+cardiovascular+event+prediction+performance%3A+An+observational+study&stitle=Eur.+Heart+J.&title=European+Heart+Journal&volume=41&issue=SUPPL+2&spage=2897&epage=&aulast=Kim&aufirst=K.&auinit=K.&aufull=Kim+K.&coden=&isbn=&pages=2897-&date=2020&auinit1=K&auinitm= A1 - Kim, K. A1 - Park, S.M. M1 - (Kim K.) Seoul National University, Department of Biomedical Sciences, Seoul, South Korea M1 - (Park S.M.) Seoul National University Hospital, Department of Family Medicine, Seoul, South Korea AD - K. Kim, Seoul National University, Department of Biomedical Sciences, Seoul, South Korea T1 - Artificial neural networks to compare the contribution of basic clinical factors, ESC SCORE, and multidimensional risk factors for cardiovascular event prediction performance: An observational study LA - English KW - corticosteroid KW - neuroleptic agent KW - adult KW - age KW - area under the curve KW - artificial neural network KW - atrial fibrillation KW - body mass KW - cardiology KW - cholesterol blood level KW - chronic kidney failure KW - cigarette smoking KW - conference abstract KW - controlled study KW - coronary risk KW - demography KW - dental caries KW - dental health KW - family history KW - female KW - health data KW - human KW - lifestyle KW - male KW - middle aged KW - national health insurance KW - observational study KW - prediction KW - retina vein occlusion KW - risk assessment KW - risk factor KW - systematic review KW - systolic blood pressure N2 - Background/Introduction: Despite the recent increase in the availability of different data sources that can be used for prediction models for cardiovascular disease (CVD), it remains unclear to what extent such data could contribute to improving performance of the models in data-driven cardiovascular research. Purpose: To compare the contribution of different data types in basic clinical factors, the European Society of Cardiology Systematic Coronary Risk Evaluation (ESC SCORE), and multidimensional risk factors for CVD prediction performance of artificial neural networks (ANN) using the relevant input features derived from a large-scale medical claims database. Methods:We abstracted data through the National Health Insurance Sharing Service and collected information on 258,896 middle-aged individuals free of CVD at baseline (2009-2010) who were followed up for incident CVD until 2013. Multidimensional risk factors identifiable from the database were chosen from a systematic review of published articles. Input features in ANN were classified as follows: basic clinical factors (age, sex, and body mass index), ESC SCORE (age, sex, total cholesterol, systolic blood pressure, and cigarette smoking), and multidimensional risk factors (sociodemographic, lifestyle behavior, underlying medical conditions, dental health, medication use, etc). The data were partitioned into the training and test sets with 7:3 ratio and the performance of each ANN model was evaluated with area under the curve (AUC). Results: The ANN model with multidimensional risk factors had higher prediction performance (AUC: 0.692) compared to the models with basic clinical factors (AUC: 0.671) and ESC SCORE (AUC: 0.684). Within the multidimensional risk factors, atrial fibrillation, family history, chronic kidney disease, retinal vein occlusion, dental caries, antipsychotics, and corticosteroid use were some of the strong predictors. However, adding multidimensional risk factors only showed marginal improvement (increase in 1.17% of AUC) compared with the ESC SCORE model. Conclusions: Adding multidimensional risk factors as input features in the ANN only showed marginal improvement in the CVD prediction performance. When assessing cardiovascular risk from the large-scale healthcare data, variables included in the ESC SCORE should primarily be considered in the model. (Table Presented) . ER - TY - JOUR M3 - Article Y1 - 2019 VL - 98 IS - 21 SN - 1536-5964 SN - 0025-7974 JF - Medicine (United States) JO - Medicine UR - https://www.embase.com/search/results?subaction=viewrecord&id=L2018732786&from=export U2 - L2018732786 C5 - 31124977 DB - Embase DB - Medline U3 - 2022-06-20 L2 - http://dx.doi.org/10.1097/MD.0000000000015796 DO - 10.1097/MD.0000000000015796 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=15365964&id=doi:10.1097%2FMD.0000000000015796&atitle=KEGG-expressed+genes+and+pathways+in+intervertebral+disc+degeneration%3A+Protocol+for+a+systematic+review+and+data+mining&stitle=Medicine&title=Medicine+%28United+States%29&volume=98&issue=21&spage=&epage=&aulast=Mo&aufirst=Sen&auinit=S.&aufull=Mo+S.&coden=MEDIA&isbn=&pages=-&date=2019&auinit1=S&auinitm= A1 - Mo, S. A1 - Liu, C. A1 - Chen, L. A1 - Ma, Y. A1 - Liang, T. A1 - Xue, J. A1 - Zeng, H. A1 - Zhan, X. M1 - (Mo S.; Liu C.; Chen L.; Ma Y.; Liang T.; Xue J.; Zeng H.; Zhan X., 3cstar@163.com) Spine and Osteopathy Ward, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China AD - X. Zhan, Spine and Osteopathy Ward, First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Guangxi, Nanning, China T1 - KEGG-expressed genes and pathways in intervertebral disc degeneration: Protocol for a systematic review and data mining LA - English KW - article KW - bioinformatics KW - bioinformatics software KW - cell adhesion KW - cell organelle KW - data mining KW - DNA microarray KW - endocytosis KW - extracellular matrix KW - focal adhesion KW - gene expression KW - genetic marker KW - genetic transcription KW - hippo signaling KW - human KW - intervertebral disk degeneration KW - KEGG KW - malignant neoplasm KW - metabolism KW - microarray analysis KW - nonsense mediated mRNA decay KW - pathway analysis KW - Pi3K/Akt signaling KW - protein modification KW - protein protein interaction KW - protein targeting KW - signal transduction KW - spliceosome KW - systematic review KW - translation initiation KW - virus transcription KW - Wnt signaling KW - biological marker KW - cell protein KW - endogenous compound KW - heat shock protein 72 KW - heat shock protein 90 alpha KW - messenger RNA KW - microRNA KW - neurotrophin KW - nitrogen derivative KW - protein tyrosine kinase A KW - proteoglycan KW - replication factor C KW - ribosome RNA N2 - miRNAs and genes play significant roles in the etiology and pathogenesis of intervertebral disc degeneration (IDD). This study aimed to identify aberrantly expressed miRNAs, genes, and pathways in IDD through a comprehensive bioinformatics analysis. Data of miRNAs expression microarrays (GSE63492) and genes microarrays (GSE23130) were obtained from GEO database. Similarly, aberrantly expressed miRNAs and genes were obtained using GEO2R. In addition, functional and enrichment analyses of selected miRNAs and genes were performed using the DAVID database. Meanwhile, protein-protein interaction (PPI) network was constructed using STRING, and then visualized in Cytoscape. A total of 98 upregulated miRNAs were identified. They were enriched in biological processes of response to organelle, ion binding, cellular nitrogen compound metabolic process, biosynthetic process, small molecule metabolic process, cellular protein modification process, catabolic process, molecular function, neurotrophin TRK receptor signaling pathway, and protein complex. In addition, 1405 high expression protein genes were detected. It indicated enrichment in biological processes, such as translational initiation, nonsense-mediated decay, viral transcription, cell-cell adhesion, rRNA processing, translation, RP-dependent cotranslational protein targeting to membrane, nuclear-transcribed mRNA catabolic process, regulation of mRNA stability, and mRNA splicing via spliceosome and extracellular matrix organization. In addition, pathway analysis exhibited the common enrichment in focal adhesion, Hippo signaling pathway, ECM-receptor interaction, Wnt signaling pathway, PI3K-Akt signaling pathway, endocytosis, proteoglycans in cancer, and so on. The top 10 central genes of PPI network were POTEE, PPP2CA, RPL17, HSP90AA1, POTEF, RPL13A, ACTB, RPL18, RPS24, and HSPA1A. In conclusion, our research proposed abnormally expressed miRNAs, genes, and pathways in IDD through bioinformatics methods, which may provide new insights into the pathogenesis of IDD. Thus, the Hub gene involving POTEE, PPP2CA, RPL17, HSP90AA1, POTEF, RPL13A, ACTB, RPL18, RPS24, and HSPA1A may be biomarkers for accurate diagnosis and treatment of IDD in the future. ER - TY - JOUR M3 - Article Y1 - 2018 VL - 75 IS - 2 SP - 471 EP - 480 SN - 1878-7452 SN - 1931-7204 JF - Journal of Surgical Education JO - J. Surg. Educ. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L618019330&from=export U2 - L618019330 C5 - 28843958 DB - Embase DB - Medline U3 - 2017-09-01 U4 - 2018-12-21 L2 - http://dx.doi.org/10.1016/j.jsurg.2017.08.002 DO - 10.1016/j.jsurg.2017.08.002 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=18787452&id=doi:10.1016%2Fj.jsurg.2017.08.002&atitle=Avoiding+Surgical+Skill+Decay%3A+A+Systematic+Review+on+the+Spacing+of+Training+Sessions&stitle=J.+Surg.+Educ.&title=Journal+of+Surgical+Education&volume=75&issue=2&spage=471&epage=480&aulast=Cecilio-Fernandes&aufirst=Dario&auinit=D.&aufull=Cecilio-Fernandes+D.&coden=&isbn=&pages=471-480&date=2018&auinit1=D&auinitm= A1 - Cecilio-Fernandes, D. A1 - Cnossen, F. A1 - Jaarsma, D.A.D.C. A1 - Tio, R.A. M1 - (Cecilio-Fernandes D., d.cecilio.fernandes@umcg.nl; Jaarsma D.A.D.C.; Tio R.A.) Center for Education Development and Research in Health Professions (CEDAR), University Medical Center Groningen, University of Groningen, Groningen, Netherlands M1 - (Cnossen F.) Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, Groningen, Netherlands M1 - (Tio R.A.) Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands AD - D. Cecilio-Fernandes, Center for Education Development and Research in Health Professions (CEDAR), University Medical Center Groningen, University of Groningen, Antonius Deusinglaan 1, FC40, 9713 AV, Groningen, Netherlands T1 - Avoiding Surgical Skill Decay: A Systematic Review on the Spacing of Training Sessions LA - English KW - article KW - blood vessel shunt KW - end to side anastomosis KW - human KW - laparoscopic surgery KW - microvascular surgery KW - motor performance KW - overlearning KW - priority journal KW - professional competence KW - professional development KW - professional practice KW - psychological theory KW - simulation training KW - skill retention KW - space KW - surgical training KW - suture technique KW - systematic review N2 - Objective: Spreading training sessions over time instead of training in just 1 session leads to an improvement of long-term retention for factual knowledge. However, it is not clear whether this would also apply to surgical skills. Thus, we performed a systematic review to find out whether spacing training sessions would also improve long-term retention of surgical skills. Design: We searched the Medline, PsycINFO, Embase, Eric, and Web of Science online databases. We only included articles that were randomized trials with a sample of medical trainees acquiring surgical motor skills in which the spacing effect was reported. The quality and bias of the articles were assessed using the Cochrane Collaboration's risk of bias assessment tool. Results: With respect to the spacing effect, 1955 articles were retrieved. After removing duplicates and articles that did not meet the inclusion criteria, 11 articles remained. The overall quality of the experiments was “moderate.” Trainees in the spaced condition scored higher in a retention test than students in the massed condition. Conclusions: Our systematic review showed evidence that spacing training sessions improves long-term surgical skills retention when compared to massed practice. However, the optimal gap between the re-study sessions is unclear. ER - TY - JOUR M3 - Review Y1 - 2017 VL - 91 SP - 23 EP - 30 SN - 1878-5921 SN - 0895-4356 JF - Journal of Clinical Epidemiology JO - J. Clin. Epidemiol. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L618234123&from=export U2 - L618234123 C5 - 28912002 DB - Embase DB - Medline U3 - 2017-09-14 U4 - 2020-10-08 L2 - http://dx.doi.org/10.1016/j.jclinepi.2017.08.010 DO - 10.1016/j.jclinepi.2017.08.010 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=18785921&id=doi:10.1016%2Fj.jclinepi.2017.08.010&atitle=Living+systematic+review%3A+1.+Introduction%E2%80%94the+why%2C+what%2C+when%2C+and+how&stitle=J.+Clin.+Epidemiol.&title=Journal+of+Clinical+Epidemiology&volume=91&issue=&spage=23&epage=30&aulast=Elliott&aufirst=Julian+H.&auinit=J.H.&aufull=Elliott+J.H.&coden=JCEPE&isbn=&pages=23-30&date=2017&auinit1=J&auinitm=H A1 - Elliott, J.H. A1 - Synnot, A. A1 - Turner, T. A1 - Simmonds, M. A1 - Akl, E.A. A1 - McDonald, S. A1 - Salanti, G. A1 - Meerpohl, J. A1 - MacLehose, H. A1 - Hilton, J. A1 - Tovey, D. A1 - Shemilt, I. A1 - Thomas, J. A1 - Agoritsas, T. A1 - Perron, C. A1 - Hodder, R. A1 - Pestridge, C. A1 - Albrecht, L. A1 - Horsley, T. A1 - Platt, J. A1 - Armstrong, R. A1 - Nguyen, P.H. A1 - Plovnick, R. A1 - Arno, A. A1 - Ivers, N. A1 - Quinn, G. A1 - Au, A. A1 - Johnston, R. A1 - Rada, G. A1 - Bagg, M. A1 - Jones, A. A1 - Ravaud, P. A1 - Boden, C. A1 - Kahale, L. A1 - Richter, B. A1 - Boisvert, I. A1 - Keshavarz, H. A1 - Ryan, R. A1 - Brandt, L. A1 - Kolakowsky-Hayner, S.A. A1 - Salama, D. A1 - Brazinova, A. A1 - Nagraj, S.K. A1 - Buchbinder, R. A1 - Lasserson, T. A1 - Santaguida, L. A1 - Champion, C. A1 - Lawrence, R. A1 - Santesso, N. A1 - Chandler, J. A1 - Les, Z. A1 - Schünemann, H.J. A1 - Charidimou, A. A1 - Leucht, S. A1 - Chou, R. A1 - Low, N. A1 - Sherifali, D. A1 - Churchill, R. A1 - Maas, A. A1 - Siemieniuk, R. A1 - Cnossen, M.C. A1 - Cossi, M.-J. A1 - Macleod, M. A1 - Skoetz, N. A1 - Counotte, M. A1 - Marshall, I. A1 - Soares-Weiser, K. A1 - Craigie, S. A1 - Marshall, R. A1 - Srikanth, V. A1 - Dahm, P. A1 - Martin, N. A1 - Sullivan, K. A1 - Danilkewich, A. A1 - García, L.M. A1 - Danko, K. A1 - Mavergames, C. A1 - Taylor, M. A1 - Donoghue, E. A1 - Maxwell, L.J. A1 - Thayer, K. A1 - Dressler, C. A1 - McAuley, J. A1 - Egan, C. A1 - Tritton, R. A1 - McKenzie, J. A1 - Tsafnat, G. A1 - Elliott, S.A. A1 - Tugwell, P. A1 - Etxeandia, I. A1 - Merner, B. A1 - Turgeon, A. A1 - Featherstone, R. A1 - Mondello, S. A1 - Foxlee, R. A1 - Morley, R. A1 - van Valkenhoef, G. A1 - Garner, P. A1 - Munafo, M. A1 - Vandvik, P. A1 - Gerrity, M. A1 - Munn, Z. A1 - Wallace, B. A1 - Glasziou, P. A1 - Murano, M. A1 - Wallace, S.A. A1 - Green, S. A1 - Newman, K. A1 - Watts, C. A1 - Grimshaw, J. A1 - Nieuwlaat, R. A1 - Weeks, L. A1 - Gurusamy, K. A1 - Nikolakopoulou, A. A1 - Weigl, A. A1 - Haddaway, N. A1 - Noel-Storr, A. A1 - Wells, G. A1 - Hartling, L. A1 - O'Connor, A. A1 - Wiercioch, W. A1 - Hayden, J. A1 - Page, M. A1 - Wolfenden, L. A1 - Helfand, M. A1 - Pahwa, M. A1 - Yepes Nuñez, J.J. A1 - Higgins, J. A1 - Pardo, J.P. A1 - Yost, J. A1 - Hill, S. A1 - Pearson, L. M1 - (Elliott J.H., julian.elliott@monash.edu; Synnot A.; Turner T.; McDonald S.) Cochrane Australia, School of Public Health and Preventive Medicine, Monash University, 4th Floor, 553 St Kilda Road, Melbourne, Victoria, Australia M1 - (Elliott J.H., julian.elliott@monash.edu) Department of Infectious Diseases, Monash University and Alfred Hospital, 53 Commercial Road, Melbourne, Victoria, Australia M1 - (Synnot A.) Centre for Health Communication and Participation, School of Psychology and Public Health, Level 4, Health Sciences 2, Science Drive, La Trobe University, Bundoora, Victoria, Australia M1 - (Simmonds M.) Centre for Reviews and Dissemination, A/B Block, Alcuin College, University of York, York YO10 5DD, York, United Kingdom M1 - (Akl E.A.) Department of Internal Medicine, American University of Beirut, Office Gefinor Center Block B, Beirut, Lebanon M1 - (Akl E.A.) Department of Epidemiology and Population Health, American University of Beirut, Office Gefinor Center Block B, Beirut, Lebanon M1 - (Akl E.A.) Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, Canada M1 - (Salanti G.) Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, CH-3012, Bern, Switzerland M1 - (Meerpohl J.) Cochrane Germany, Medical Center—University of Freiburg, Breisacher Str. 153, Freiburg, Germany M1 - (MacLehose H.; Hilton J.; Tovey D.) Cochrane Editorial Unit, Cochrane, St Albans House, 57-59 Haymarket, London, United Kingdom M1 - (Shemilt I.; Thomas J.) EPPI-Centre, Institute of Education, University College London, 18 Woburn Square, London, United Kingdom M1 - (Elliott J.H., julian.elliott@monash.edu; Synnot A.; Turner T.; Simmonds M.; Akl E.A.; McDonald S.; Salanti G.; Meerpohl J.; MacLehose H.; Hilton J.; Shemilt I.; Thomas J.; Agoritsas T.; Perron C.; Hodder R.; Pestridge C.; Albrecht L.; Horsley T.; Platt J.; Armstrong R.; Nguyen P.H.; Plovnick R.; Arno A.; Ivers N.; Quinn G.; Au A.; Johnston R.; Rada G.; Bagg M.; Jones A.; Ravaud P.; Boden C.; Kahale L.; Richter B.; Boisvert I.; Keshavarz H.; Ryan R.; Brandt L.; Kolakowsky-Hayner S.A.; Salama D.; Brazinova A.; Nagraj S.K.; Buchbinder R.; Lasserson T.; Santaguida L.; Champion C.; Lawrence R.; Santesso N.; Chandler J.; Les Z.; Schünemann H.J.; Charidimou A.; Leucht S.; Chou R.; Low N.; Sherifali D.; Churchill R.; Maas A.; Siemieniuk R.; Cnossen M.C.; Cossi M.-J.; Macleod M.; Skoetz N.; Counotte M.; Marshall I.; Soares-Weiser K.; Craigie S.; Marshall R.; Srikanth V.; Dahm P.; Martin N.; Sullivan K.; Danilkewich A.; García L.M.; Danko K.; Mavergames C.; Taylor M.; Donoghue E.; Maxwell L.J.; Thayer K.; Dressler C.; McAuley J.; Egan C.; Tritton R.; McKenzie J.; Tsafnat G.; Elliott S.A.; Tugwell P.; Etxeandia I.; Merner B.; Turgeon A.; Featherstone R.; Mondello S.; Foxlee R.; Morley R.; van Valkenhoef G.; Garner P.; Munafo M.; Vandvik P.; Gerrity M.; Munn Z.; Wallace B.; Glasziou P.; Murano M.; Wallace S.A.; Green S.; Newman K.; Watts C.; Grimshaw J.; Nieuwlaat R.; Weeks L.; Gurusamy K.; Nikolakopoulou A.; Weigl A.; Haddaway N.; Noel-Storr A.; Wells G.; Hartling L.; O'Connor A.; Wiercioch W.; Hayden J.; Page M.; Wolfenden L.; Helfand M.; Pahwa M.; Yepes Nuñez J.J.; Higgins J.; Pardo J.P.; Yost J.; Hill S.; Pearson L.) M1 - () T1 - Living systematic review: 1. Introduction—the why, what, when, and how LA - English KW - decision making KW - human KW - machine learning KW - medical research KW - peer review KW - policy KW - priority journal KW - protocol compliance KW - publication KW - publishing KW - review KW - statistical analysis KW - systematic review (topic) KW - validity N2 - Systematic reviews are difficult to keep up to date, but failure to do so leads to a decay in review currency, accuracy, and utility. We are developing a novel approach to systematic review updating termed “Living systematic review” (LSR): systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions. We hypothesize that a continual approach to updating will achieve greater currency and validity, and increase the benefits to end users, with feasible resource requirements over time. ER - TY - JOUR M3 - Article Y1 - 2016 VL - 104 SP - 262 EP - 271 SN - 1879-2448 SN - 0043-1354 JF - Water Research JO - Water Res. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L611680149&from=export U2 - L611680149 C5 - 27543910 DB - Embase DB - Medline U3 - 2016-08-24 U4 - 2019-05-14 L2 - http://dx.doi.org/10.1016/j.watres.2016.08.005 DO - 10.1016/j.watres.2016.08.005 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=18792448&id=doi:10.1016%2Fj.watres.2016.08.005&atitle=Bayesian+meta-analysis+to+synthesize+decay+rate+constant+estimates+for+common+fecal+indicator+bacteria&stitle=Water+Res.&title=Water+Research&volume=104&issue=&spage=262&epage=271&aulast=Brooks&aufirst=Lauren+E.&auinit=L.E.&aufull=Brooks+L.E.&coden=WATRA&isbn=&pages=262-271&date=2016&auinit1=L&auinitm=E A1 - Brooks, L.E. A1 - Field, K.G. M1 - (Brooks L.E., brooksla@onid.orst.edu; Field K.G., kate.field@oregonstate.edu) Oregon State University, Department of Microbiology, Oregon State University, 226 Nash Hall, Corvallis, OR, United States AD - K.G. Field, Oregon State University, Department of Microbiology, Oregon State University, 226 Nash Hall, Corvallis, OR, United States T1 - Bayesian meta-analysis to synthesize decay rate constant estimates for common fecal indicator bacteria LA - English KW - fresh water KW - salt water KW - article KW - Bacteroides KW - Bayesian learning KW - controlled study KW - decay rate constant KW - Enterococcus KW - Escherichia coli KW - genetic marker KW - light KW - low temperature KW - microbial degradation KW - nonhuman KW - predictor variable KW - priority journal KW - rate constant KW - ruminant KW - salinity KW - statistical model KW - water quality N2 - For decades, fecal indicator bacteria have been used as proxies to quantitatively estimate fecal loading into water bodies. Widely used cultured indicators (e.g. Escherichia coli and Enterococcus spp.) and more recently developed genetic markers are well studied, but their decay in the environment is still poorly understood. We used Hierarchical Bayesian Linear Modeling to conduct a series of meta-analyses using published decay rate constant estimates, to synthesize findings into pooled estimates and identify gaps in the data preventing reliable estimates. In addition to the meta-analysis assuming all estimates come from the same population, meta-regressions including covariates believed to contribute to decay were fit and used to provided synthesized estimates for specific combinations of significant variables. Additionally, statements regarding the significance of variables across studies were made using the 95% confidence interval for meta-regression coefficients. These models were used to construct a mean decay rate constant estimate as well as credible intervals for the mean and the distribution of all likely data points. While synthesized estimates for each targeted indicator bacteria were developed, the amount of data available varied widely for each target, as did the predictive power of the models as determined by testing with additional data not included in the modeling. Temperature was found to be significant for all selected indicators, while light was found to be significant only for culturable indicators. Results from the models must be interpreted with caution, as they are based only on the data available, which may not be representative of decay in other scenarios. ER - TY - JOUR M3 - Article Y1 - 2016 VL - 388 IS - 10053 SP - 1545 EP - 1602 SN - 1474-547X SN - 0140-6736 JF - The Lancet JO - Lancet UR - https://www.embase.com/search/results?subaction=viewrecord&id=L613102406&from=export U2 - L613102406 C5 - 27733282 DB - Embase DB - Medline U3 - 2016-11-10 U4 - 2021-03-12 L2 - http://dx.doi.org/10.1016/S0140-6736(16)31678-6 DO - 10.1016/S0140-6736(16)31678-6 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=1474547X&id=doi:10.1016%2FS0140-6736%2816%2931678-6&atitle=Global%2C+regional%2C+and+national+incidence%2C+prevalence%2C+and+years+lived+with+disability+for+310+diseases+and+injuries%2C+1990%E2%80%932015%3A+a+systematic+analysis+for+the+Global+Burden+of+Disease+Study+2015&stitle=Lancet&title=The+Lancet&volume=388&issue=10053&spage=1545&epage=1602&aulast=Vos&aufirst=T.&auinit=T.&aufull=Vos+T.&coden=LANCA&isbn=&pages=1545-1602&date=2016&auinit1=T&auinitm= A1 - Vos, T. A1 - Allen, C. A1 - Arora, M. A1 - Barber, R.M. A1 - Brown, A. A1 - Carter, A. A1 - Casey, D.C. A1 - Charlson, F.J. A1 - Chen, A.Z. A1 - Coggeshall, M. A1 - Cornaby, L. A1 - Dandona, L. A1 - Dicker, D.J. A1 - Dilegge, T. A1 - Erskine, H.E. A1 - Ferrari, A.J. A1 - Fitzmaurice, C. A1 - Fleming, T. A1 - Forouzanfar, M.H. A1 - Fullman, N. A1 - Goldberg, E.M. A1 - Graetz, N. A1 - Haagsma, J.A. A1 - Hay, S.I. A1 - Johnson, C.O. A1 - Kassebaum, N.J. A1 - Kawashima, T. A1 - Kemmer, L. A1 - Khalil, I.A. A1 - Kyu, H.H. A1 - Leung, J. A1 - Lim, S.S. A1 - Lopez, A.D. A1 - Marczak, L. A1 - Mokdad, A.H. A1 - Naghavi, M. A1 - Nguyen, G. A1 - Nsoesie, E. A1 - Olsen, H. A1 - Pigott, D.M. A1 - Pinho, C. A1 - Rankin, Z. A1 - Reinig, N. A1 - Sandar, L. A1 - Smith, A. A1 - Stanaway, J. A1 - Steiner, C. A1 - Teeple, S. A1 - Thomas, B.A. A1 - Troeger, C. A1 - Wagner, J.A. A1 - Wang, H. A1 - Wanga, V. A1 - Whiteford, H.A. A1 - Zoeckler, L. A1 - Alexander, L.T. A1 - Anderson, G.M. A1 - Bell, B. A1 - Bienhoff, K. A1 - Biryukov, S. A1 - Blore, J. A1 - Brown, J. A1 - Coates, M.M. A1 - Daoud, F. A1 - Estep, K. A1 - Foreman, K. A1 - Fox, J. A1 - Friedman, J. A1 - Frostad, J. A1 - Godwin, W.W. A1 - Hancock, J. A1 - Huynh, C. A1 - Iannarone, M. A1 - Kim, P. A1 - Kutz, M. A1 - Masiye, F. A1 - Millear, A. A1 - Mirarefin, M. A1 - Mooney, M.D. A1 - Moradi-Lakeh, M. A1 - Mullany, E. A1 - Mumford, J.E. A1 - Ng, M. A1 - Rao, P. A1 - Reitsma, M.B. A1 - Reynolds, A. A1 - Roth, G.A. A1 - Shackelford, K.A. A1 - Sivonda, A. A1 - Sligar, A. A1 - Sorensen, R.J.D. A1 - Sur, P. A1 - Vollset, S.E. A1 - Woodbrook, R. A1 - Zhou, M. A1 - Murray, C.J.L. A1 - Ellenbogen, R.G. A1 - Kotsakis, G.A. A1 - Mock, C.N. A1 - Anderson, B.O. A1 - Futran, N.D. A1 - Jensen, P.N. A1 - Watkins, D.A. A1 - Bhutta, Z.A. A1 - Nisar, M.I. A1 - Akseer, N. A1 - Abajobir, A.A. A1 - Knibbs, L.D. A1 - Lalloo, R. A1 - Scott, J.G. A1 - Alam, N.K.M. A1 - Gouda, H.N. A1 - Guo, Y. A1 - McGrath, J.J. A1 - Jeemon, P. A1 - Dandona, R. A1 - Kumar, G.A. A1 - Gething, P.W. A1 - Bisanzio, D. A1 - Deribew, A. A1 - Ali, R. A1 - Bennett, D.A. A1 - Rahimi, K. A1 - Kinfu, Y. A1 - Duan, L. A1 - Li, Y. A1 - Liu, S. A1 - Jin, Y. A1 - Wang, L. A1 - Ye, P. A1 - Liang, X. A1 - Azzopardi, P. A1 - Gibney, K.B. A1 - Meretoja, A. A1 - Alam, K. A1 - Borschmann, R. A1 - Colquhoun, S.M. A1 - Patton, G.C. A1 - Weintraub, R.G. A1 - Szoeke, C.E.I. A1 - Ademi, Z. A1 - Taylor, H.R. A1 - Lozano, R. A1 - Campos-Nonato, I.R. A1 - Campuzano, J.C. A1 - Gomez-Dantes, H. A1 - Heredia-Pi, I.B. A1 - Mejia-Rodriguez, F. A1 - Montañez Hernandez, J.C. A1 - Rios Blancas, M.J. A1 - Servan-Mori, E.E. A1 - Mensah, G.A. A1 - Salomon, J.A. A1 - Thorne-Lyman, A.L. A1 - Ajala, O.N. A1 - Bärnighausen, T. A1 - Ding, E.L. A1 - Farvid, M.S. A1 - Wagner, G.R. A1 - Osman, M. A1 - Shrime, M.G. A1 - Fitchett, J.R.A. A1 - Abate, K.H. A1 - Gebrehiwot, T.T. A1 - Gebremedhin, A.T. A1 - Abbafati, C. A1 - Abbas, K.M. A1 - Abd-Allah, F. A1 - Abraham, B. A1 - Abubakar, I. A1 - Banerjee, A. A1 - Benzian, H. A1 - Abu-Raddad, L.J. A1 - Abu-Rmeileh, N.M. A1 - Ackerman, I.N. A1 - Buchbinder, R. A1 - Gabbe, B. A1 - Thrift, A.G. A1 - Adebiyi, A.O. A1 - Akinyemi, R.O. A1 - Fürst, T. A1 - Adou, A.K. A1 - Afanvi, K.A. A1 - Agardh, E.E. A1 - Badawi, A. A1 - Popova, S. A1 - Agarwal, A. A1 - Ahmad Kiadaliri, A. A1 - Norrving, B. A1 - Ahmadieh, H. A1 - Yaseri, M. A1 - Jahanmehr, N. A1 - Al-Aly, Z. A1 - Driscoll, T.R. A1 - Kemp, A.H. A1 - Leigh, J. A1 - Mekonnen, A.B. A1 - Aldhahri, S.F. A1 - Altirkawi, K.A. A1 - Alegretti, M.A. A1 - Alemu, Z.A. A1 - Alhabib, S. A1 - Alkerwi, A. A1 - Alla, F. A1 - Guillemin, F. A1 - Allebeck, P. A1 - Rabiee, R.H.S. A1 - Carrero, J.J. A1 - Fereshtehnejad, S.M. A1 - Weiderpass, E. A1 - Havmoeller, R. A1 - Al-Raddadi, R. A1 - Alsharif, U. A1 - Alvis-Guzman, N. A1 - Amare, A.T. A1 - Melaku, Y.A. A1 - Ciobanu, L.G. A1 - Amberbir, A. A1 - Amini, H. A1 - Karema, C.K. A1 - Ammar, W. A1 - Harb, H.L. A1 - Amrock, S.M. A1 - Andersen, H.H. A1 - Antonio, C.A.T. A1 - Aregay, A.F. A1 - Betsu, B.D. A1 - Hailu, G.B. A1 - Yebyo, H.G. A1 - Ärnlöv, J. A1 - Larsson, A. A1 - Artaman, A. A1 - Asayesh, H. A1 - Assadi, R. A1 - Atique, S. A1 - Avokpaho, E.F.G.A. A1 - Avokpaho, E.F.G.A. A1 - Awasthi, A. A1 - Ayala Quintanilla, B.P. A1 - Bacha, U. A1 - Balakrishnan, K. A1 - Barac, A. A1 - Barker-Collo, S.L. A1 - Mohammed, S. A1 - Barregard, L. A1 - Petzold, M. A1 - Barrero, L.H. A1 - Basu, A. A1 - Bazargan-Hejazi, S. A1 - Beghi, E. A1 - Sheth, K.N. A1 - Bell, M.L. A1 - Huang, J.J. A1 - Santos, I.S. A1 - Bensenor, I.M. A1 - Lotufo, P.A. A1 - Berhane, A. A1 - Wolfe, C.D. A1 - Bernabé, E. A1 - Hay, R.J. A1 - Roba, H.S. A1 - Beyene, A.S. A1 - Bhala, N. A1 - Fürst, T. A1 - Piel, F.B. A1 - Steiner, T.J. A1 - Bhatt, S. A1 - Greaves, F. A1 - Majeed, A. A1 - Soljak, M. A1 - Biadgilign, S. A1 - Bikbov, B. A1 - Bjertness, E. A1 - Htet, A.S. A1 - Boufous, S. A1 - Degenhardt, L. A1 - Resnikoff, S. A1 - Calabria, B. A1 - Mitchell, P.B. A1 - Brainin, M. A1 - Brazinova, A. A1 - Majdan, M. A1 - Lo, W.D. A1 - Shen, J. A1 - Breitborde, N.J.K. A1 - Buckle, G.C. A1 - Butt, Z.A. A1 - Lal, A. A1 - Carabin, H. A1 - Cárdenas, R. A1 - Carpenter, D.O. A1 - Castañeda-Orjuela, C.A. A1 - Castillo Rivas, J. A1 - Catalá-López, F. A1 - Catalá-López, F. A1 - Chang, J. A1 - Chiang, P.P. A1 - Chibueze, C.E. A1 - Chisumpa, V.H. A1 - Choi, J.J. A1 - Chowdhury, R. A1 - Christensen, H. A1 - Christopher, D.J. A1 - Cirillo, M. A1 - Cooper, C. A1 - Cortinovis Biotech, M.D. A1 - Giussani Biol, G. A1 - Perico, D.N. A1 - Remuzzi, G. A1 - Crump, J.A. A1 - Derrett, S. A1 - Poulton, R.G. A1 - Damtew, S.A. A1 - Deribe, K. A1 - Hailu, A.D. A1 - Giref, A.Z. A1 - Haile, D. A1 - Jibat, T. A1 - Taye, B. A1 - Dargan, P.I. A1 - das Neves, J. A1 - Massano, J. A1 - Santos, J.V. A1 - Davey, G. A1 - Davis, A.C. A1 - Newton, J.N. A1 - Steel, N. A1 - De Leo, D. A1 - Del Gobbo, L.C. A1 - Dellavalle, R.P. A1 - Des Jarlais, D.C. A1 - Dharmaratne, S.D. A1 - Dhillon, P.K. A1 - Ganguly, P. A1 - Zodpey, S. A1 - Diaz-Torné, C. A1 - Dubey, M. A1 - Rahman, M.H.U. A1 - Ram, U. A1 - Singh, A. A1 - Verma, R.K. A1 - Yadav, A.K. A1 - Duncan, B.B. A1 - Kieling, C. A1 - Schmidt, M.I. A1 - Ebrahimi, H. A1 - Pishgar, F. A1 - Farzadfar, F. A1 - Kasaeian, A. A1 - Parsaeian, M. A1 - Heydarpour, P. A1 - Malekzadeh, R. A1 - Roshandel, G. A1 - Sepanlou, S.G. A1 - Rahimi-Movaghar, V. A1 - Elyazar, I. A1 - Endres, M. A1 - Endries, A.Y. A1 - Ermakov, S.P. A1 - Eshrati, B. A1 - Farid, T.A. A1 - Khan, A.R. A1 - Farinha, C.S.E.S. A1 - Faro, A. A1 - Feigin, V.L. A1 - Te Ao, B.J. A1 - Kwan, G.F. A1 - Felson, D.T. A1 - Fernandes, J.G. A1 - Fernandes, J.C. A1 - Fischer, F. A1 - Shiue, I. A1 - Fowkes, F.G.R. A1 - Franklin, R.C. A1 - Fürst, T. A1 - Iyer, V.J. A1 - Gankpé, F.G. A1 - Gebre, T. A1 - Geleijnse, J.M. A1 - Gessner, B.D. A1 - Ginawi, I.A. A1 - Giroud, M. A1 - Gishu, M.D. A1 - Tura, A.K. A1 - Glaser, E. A1 - Halasa, Y.A. A1 - Shepard, D.S. A1 - Undurraga, E.A. A1 - Gona, P. 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A1 - Rai, R.K. A1 - Rajsic, S. A1 - Refaat, A.H. A1 - Ribeiro, A.L. A1 - Rojas-Rueda, D. A1 - Roy, A. A1 - Sagar, R. A1 - Satpathy, M. A1 - Tandon, N. A1 - Sahathevan, R. A1 - Sanabria, J.R. A1 - Sanchez-Niño, M.D. A1 - Sarmiento-Suarez, R. A1 - Sartorius, B. A1 - Sawhney, M. A1 - Schaub, M.P. A1 - Schneider, I.J.C. A1 - Silva, D.A.S. A1 - Schöttker, B. A1 - Schwebel, D.C. A1 - Singh, J.A. A1 - Shaheen, A. A1 - Shaikh, M.A. A1 - Sharma, R. A1 - Sharma, U. A1 - Shin, M. A1 - Yoon, S. A1 - Sigfusdottir, I.D. A1 - Silveira, D.G.A. A1 - Singh, O.P. A1 - Singh, P.K. A1 - Søreide, K. A1 - Sliwa, K. A1 - Stein, D.J. A1 - Soriano, J.B. A1 - Sposato, L.A. A1 - Sreeramareddy, C.T. A1 - Stathopoulou, V. A1 - Stovner, L.J. A1 - Steinke, S. A1 - Stroumpoulis, K. A1 - Sunguya, B.F. A1 - Swaminathan, S. A1 - Sykes, B.L. A1 - Tabarés-Seisdedos, R. A1 - Takala, J.S. A1 - Tanne, D. A1 - Terkawi, A.S. A1 - Tuzcu, E.M. A1 - Thomson, A.J. A1 - Thurston, G.D. A1 - Tobe-Gai, R. A1 - Topor-Madry, R. A1 - Topouzis, F. A1 - Truelsen, T. A1 - Tsala Dimbuene, Z. A1 - Tsilimbaris, M. A1 - Tyrovolas, S. A1 - Ukwaja, K.N. A1 - Uneke, C.J. A1 - Uthman, O.A. A1 - van Gool, C.H. A1 - Vasankari, T. A1 - Venketasubramanian, N. A1 - Violante, F.S. A1 - Vladimirov, S.K. A1 - Vlassov, V.V. A1 - Waller, S.G. A1 - Weichenthal, S. A1 - White, R.A. A1 - Williams, H.C. A1 - Wubshet, M. A1 - Xavier, D. A1 - Xu, G. A1 - Yan, L.L. A1 - Yano, Y. A1 - Yip, P. A1 - Yonemoto, N. A1 - Younis, M.Z. A1 - Yu, C. A1 - Zaidi, Z. A1 - Zaki, M.E. A1 - Zeeb, H. A1 - Zuhlke, L.J. 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Helsinki University Hospital, Comprehensive Cancer Center, Breast Surgery Unit, Helsinki, Finland M1 - (Mhimbira F.A.) Ifakara Health Institute, Bagamoyo, Tanzania M1 - (Miller T.R.) Pacific Institute for Research & Evaluation, Calverton, MD, United States M1 - (Miller T.R.) Centre for Population Health, Curtin University, Perth, WA, Australia M1 - (Mills E.J.) University of Ottawa, Ottawa, ON, Canada M1 - (Mohammadi A.) Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran M1 - (Mohammed S.) Health Systems and Policy Research Unit, Ahmadu Bello University, Zaria, Nigeria M1 - (Monasta L.; Montico M.; Ronfani L.) Institute for Maternal and Child Health, IRCCS “Burlo Garofolo”, Trieste, Italy M1 - (Moradi-Lakeh M.) Department of Community Medicine, Gastrointestinal and Liver Disease Research Center, Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran M1 - (Morawska L.) 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International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh M1 - (Remuzzi G.; Naldi L.) Azienda Ospedaliera Papa Giovanni XXIII, Bergamo, Italy M1 - (Nangia V.) Suraj Eye Institute, Nagpur, India M1 - (Ngalesoni F.N.) Ministry of Health and Social Welfare, Dar es Salaam, Tanzania M1 - (Nguyen Q.L.) Institute for Global Health Innovations, Duy Tan University, Da Nang, Viet Nam M1 - (Nkamedjie Pete P.M.) Institute For Research, Socio-Economic Development and Communication, Yaoundé, Cameroon M1 - (Nolla J.M.) Hospital Universitari de Bellvitge, L'Hospitalet, Spain M1 - (Nunes B.P.) Federal University of Pelotas, Pelotas, Brazil M1 - (Ogbo F.A.) Centre for Health Research, Western Sydney University, Sydney, NSW, Australia M1 - (Oh I.) Department of Preventive Medicine, School of Medicine, Kyung Hee University, Seoul, South Korea M1 - (Ohkubo T.) Teikyo University School of Medicine, Tokyo, Japan M1 - (Olivares P.R.) Universidad Autonoma de Chile, Talca, Chile M1 - (Olusanya B.O.; Olusanya J.O.) Center for Healthy Start Initiative, Lagos, Nigeria M1 - (Ortiz A.) IIS-Fundacion Jimenez Diaz-UAM, Madrid, Spain M1 - (Osman M.) YBank, Cambridge, MA, United States M1 - (Ota E.) St Luke's International University, Tokyo, Japan M1 - (Mahesh P.A.) JSS Medical College, JSS University, Mysore, India M1 - (Park E.) Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, South Korea M1 - (Passos V.M.D.A.) Universidade Federal de Minas Gerais, Belo Horizonte, Brazil M1 - (Paternina Caicedo A.J.) Universidad de Cartagena, Cartagena, Colombia M1 - (Patten S.B.) Department of Community Health Sciences, Canada M1 - (Tonelli M.) University of Calgary, Calgary, AB, Canada M1 - (Pereira D.M.) REQUIMTE/LAQV, Laboratório de Farmacognosia, Departamento de Química, Faculdade de Farmácia, Universidade do Porto, Porto, Portugal M1 - (Perez-Padilla R.) National Institute of Respiratory Diseases, Mexico City, Mexico M1 - (Pesudovs K.) Flinders University, Adelaide, SA, Australia M1 - (Petzold M.) University of the Witwatersrand, Johannesburg, South Africa M1 - (Phillips M.R.) Shanghai Jiao Tong University School of Medicine, Shanghai, China M1 - (Pillay J.D.) Durban University of Technology, Durban, South Africa M1 - (Plass D.) Exposure Assessment and Environmental Health Indicators, German Environment Agency, Berlin, Germany M1 - (Platts-Mills J.A.) University of Virginia, Charlottesville, VA, United States M1 - (Pond C.D.) University of Newcastle, Callaghan, NSW, Australia M1 - (Prasad N.M.) The Fred Hollows Foundation, Sydney, NSW, Australia M1 - (Prasad N.M.) Centre for Eye Research Australia, Melbourne, VIC, Australia M1 - (Qorbani M.) Department of Community Medicine, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran M1 - (Radfar A.) A T Still University, Kirksville, MO, United States M1 - (Rafay A.) Contech School of Public Health, Lahore, Pakistan M1 - (Rahman M.) Research and Evaluation Division, BRAC, Dhaka, Bangladesh M1 - (Rahman S.U.) Hamad Medical Corporation, Doha, Qatar M1 - (Rai R.K.) Society for Health and Demographic Surveillance, Suri, India M1 - (Rajsic S.) ERAWEB Program, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria M1 - (Refaat A.H.) Walden University, Minneapolis, MN, United States M1 - (Refaat A.H.) Suez Canal University, Ismailia, Egypt M1 - (Remuzzi G.) Department of Biomedical and Clinical Sciences L Sacco, University of Milan, Milan, Italy M1 - (Ribeiro A.L.) Hospital das Clinicas da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil M1 - (Rojas-Rueda D.) (ISGlobal) Instituto de Salud Global de Barcelona, Barcelona, Spain M1 - (Roshandel G.) Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran M1 - (Roy A.; Sagar R.; Satpathy M.; Tandon N.) All India Institute of Medical Sciences, New Delhi, India M1 - (Sahathevan R.) Ballarat Health Service, Ballarat, VIC, Australia M1 - (Sahathevan R.) Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia M1 - (Sanabria J.R.) Marshall University J Edwards School of Medicine, Huntington, WV, United States M1 - (Sanabria J.R.) Case Western Reserve University, Cleveland, OH, United States M1 - (Sanchez-Niño M.D.) IIS-Fundacion Jimenez Diaz, Madrid, Spain M1 - (Sarmiento-Suarez R.) Universidad Ciencias Aplicadas y Ambientales, Bogotá, Colombia M1 - (Sartorius B.) University of KwaZulu-Natal, Durban, South Africa M1 - (Sawhney M.) Marshall University, Huntington, WV, United States M1 - (Schaub M.P.) Swiss Research Institute of Public Health and Addiction, Switzerland M1 - (Yebyo H.G.) University of Zurich, Zurich, Switzerland M1 - (Schneider I.J.C.; Silva D.A.S.) Federal University of Santa Catarina, Florianópolis, Brazil M1 - (Schöttker B.) German Cancer Research Center, Heidelberg, Germany M1 - (Schöttker B.) Institute of Health Care and Social Sciences, FOM University, Essen, Germany M1 - (Schwebel D.C.; Singh J.A.) University of Alabama at Birmingham, Birmingham, AL, United States M1 - (Shaheen A.) Department of Public Health, An-Najah University, Nablus, Palestine M1 - (Shaikh M.A.) Independent Consultant, Karachi, Pakistan M1 - (Sharma R.) Indian Institute of Technology Ropar, Rupnagar, India M1 - (Sharma U.) ICMR National Institute of Epidemiology, Chennai, India M1 - (Shen J.) Research Institute at Nationwide Children's Hospital, Columbus, OH, United States M1 - (Shin M.) Department of Public Health Science, Graduate School, South Korea M1 - (Yoon S.) Department of Preventive Medicine, College of Medicine, South Korea M1 - (Shiue I.) Korea University, Seoul, South Korea M1 - (Shiue I.) Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom M1 - (Sigfusdottir I.D.) Reykjavik University, Reykjavik, Iceland M1 - (Silveira D.G.A.) Brasília University, Brasília, Brazil M1 - (Singh O.P.) Department of Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India M1 - (Singh P.K.) Institute for Human Development, New Delhi, India M1 - (Skogen J.C.) Alcohol and Drug Research Western Norway, Norway M1 - (Søreide K.) Stavanger University Hospital, Stavanger, Norway M1 - (Sliwa K.) Faculty of Health Sciences, Hatter Institute for Cardiovascular Research in Africa, South Africa M1 - (Stein D.J.) Department of Psychiatry, South Africa M1 - (Watkins D.A.) University of Cape Town, Cape Town, South Africa M1 - (Soriano J.B.) Instituto de Investigación Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, Cátedra UAM-Linde, Palma de Mallorca, Spain M1 - (Sposato L.A.) Department of Clinical Neurological Sciences, Western University, London, ON, Canada M1 - (Sreeramareddy C.T.) Department of Community Medicine, International Medical University, Kuala Lumpur, Malaysia M1 - (Stathopoulou V.) Attikon University Hospital, Athens, Greece M1 - (Steel N.) University of East Anglia, Norwich, United Kingdom M1 - (Stein D.J.) South African Medical Research Council Unit on Anxiety & Stress Disorders, Cape Town, South Africa M1 - (Steiner T.J.; Stovner L.J.) Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway M1 - (Steinke S.) Department of Dermatology, University Hospital Muenster, Muenster, NRW, Germany M1 - (Stovner L.J.) Norwegian Advisory Unit on Headache, St Olavs Hospital, Trondheim, Norway M1 - (Stroumpoulis K.) Alexandra General Hospital of Athens, Athens, Greece M1 - (Stroumpoulis K.) Centre Hospitalier Public du Cotentin, Cherbourg, France M1 - (Sunguya B.F.) Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania M1 - (Swaminathan S.) Indian Council of Medical Research, New Delhi, India M1 - (Sykes B.L.) Departments of Criminology, Law & Society, Sociology, and Public Health, University of California, Irvine, Irvine, CA, United States M1 - (Tabarés-Seisdedos R.) Department of Medicine, University of Valencia, INCLIVA Health Research Institute and CIBERSAM Valencia, Spain M1 - (Takala J.S.) WSH Institute, Ministry of Manpower, Singapore, Singapore M1 - (Takala J.S.) Tampere University of Technology, Tampere, Finland M1 - (Tanne D.) Chaim Sheba Medical Center, Tel Hashomer, Israel M1 - (Tanne D.) Tel Aviv University, Tel Aviv, Israel M1 - (Tedla B.A.) James Cook University, Cairns, QLD, Australia M1 - (Terkawi A.S.) Outcomes Research Consortium, United States M1 - (Tuzcu E.M.) Cleveland Clinic, Cleveland, OH, United States M1 - (Terkawi A.S.) Department of Anesthesiology, King Fahad Medical City, Riyadh, Saudi Arabia M1 - (Thomson A.J.) Adaptive Knowledge Management, Victoria, BC, Canada M1 - (Thorne-Lyman A.L.) WorldFish, Penang, Malaysia M1 - (Thurston G.D.) Nelson Institute of Environmental Medicine, School of Medicine, United States M1 - (Tobe-Gai R.) New York University, Tuxedo, NY, United States M1 - (Tobe-Gai R.) National Center for Child Health and Development, Tokyo, Japan M1 - (Topor-Madry R.) Institute of Public Health, Faculty of Health Sciences, Jagiellonian University Medical College, Kraków, Poland M1 - (Topor-Madry R.) Faculty of Health Sciences, Wroclaw Medical University, Wroclaw, Poland M1 - (Topouzis F.) Aristotle University of Thessaloniki, Thessaloniki, Greece M1 - (Tran B.X.) Hanoi Medical University, Hanoi, Viet Nam M1 - (Truelsen T.) Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark M1 - (Tsala Dimbuene Z.) Department of Population Sciences and Development, Faculty of Economics and Management, University of Kinshasa, Kinshasa, Democratic Republic Congo M1 - (Tsala Dimbuene Z.) African Population and Health Research Center, Nairobi, Kenya M1 - (Tsilimbaris M.) Department of Medicine, University of Crete, Heraklion, Greece M1 - (Tyrovolas S.) Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Universitat de Barcelona, CIBERSAM Barcelona, Spain M1 - (Ukwaja K.N.) Department of Internal Medicine, Federal Teaching Hospital, Abakaliki, Nigeria M1 - (Uneke C.J.) Ebonyi State University, Abakaliki, Nigeria M1 - (Uthman O.A.) Warwick Medical School, University of Warwick, Coventry, United Kingdom M1 - (van Gool C.H.) National Institute for Public Health and the Environment, Bilthoven, Netherlands M1 - (Vasankari T.) UKK Institute for Health Promotion Research, Tampere, Finland M1 - (Venketasubramanian N.) Raffl es Neuroscience Centre, Raffles Hospital, Singapore, Singapore M1 - (Violante F.S.) University of Bologna, Bologna, Italy M1 - (Vladimirov S.K.) Federal Research Institute for Health Organization and Informatics, Moscow, Russian Federation M1 - (Vlassov V.V.) National Research University Higher School of Economics, Moscow, Russian Federation M1 - (Wagner G.R.) National Institute for Occupational Safety and Health, Washington, DC, United States M1 - (Waller S.G.) Uniformed Services University of Health Sciences, Bethesda, MD, United States M1 - (Weichenthal S.) McGill University, Montreal, QC, Canada M1 - (Weiderpass E.) Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway M1 - (Weiderpass E.) Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway M1 - (Weiderpass E.) Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland M1 - (Westerman R.) German National Cohort Consortium, Heidelberg, Germany M1 - (White R.A.) Department of Infectious Disease Epidemiology and Modelling, United Kingdom M1 - (Williams H.C.) Centre of Evidencebased Dermatology, University of Nottingham, Nottingham, United Kingdom M1 - (Wiysonge C.S.) South African Medical Research Council, Cape Town, South Africa M1 - (Wolfe C.D.) National Institute for Health Research Comprehensive Biomedical Research Centre, Guy's & St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom M1 - (Wubshet M.) St Paul's Hospital, Millennium Medical College, Addis Ababa, Ethiopia M1 - (Xavier D.) St John's Medical College and Research Institute, Bangalore, India M1 - (Xu G.) Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China M1 - (Yan L.L.) Global Health Research Center, Duke Kunshan University, Kunshan, China M1 - (Yano Y.) Department of Preventive Medicine, Northwestern University, Chicago, IL, United States M1 - (Yip P.) Social Work and Social Administration Department and The Hong Kong Jockey Club Centre for Suicide Research and Prevention, University of Hong Kong, Hong Kong, Hong Kong M1 - (Yonemoto N.) Department of Biostatistics, School of Public Health, Kyoto University, Kyoto, Japan M1 - (Younis M.Z.) Jackson State University, Jackson, MS, United States M1 - (Yu C.) Global Health Institute, China M1 - (Zaidi Z.) Wuhan University, Wuhan, China M1 - (Zaidi Z.) University Hospital, Setif, Algeria M1 - (Zaki M.E.) Faculty of Medicine, Mansoura University, Mansoura, Egypt M1 - (Zeeb H.) Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany M1 - (Zuhlke L.J.) Red Cross War Memorial Children's Hospital, Cape Town, South Africa M1 - () T1 - Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015 LA - English KW - accommodation disorder KW - acquired immune deficiency syndrome KW - adolescent KW - adult KW - Africa KW - age KW - anemia KW - article KW - ascariasis KW - Asia KW - automutilation KW - battle injury KW - Bayesian learning KW - cancer epidemiology KW - cancer incidence KW - cardiovascular disease KW - child KW - chronic disease KW - chronic liver disease KW - chronic respiratory tract disease KW - clinical outcome KW - communicable disease KW - comorbidity KW - Democratic Republic Congo KW - demography KW - dental caries KW - diabetes mellitus KW - diarrhea KW - disability KW - drug dependence KW - educational status KW - endocrine disease KW - female KW - funding KW - gastrointestinal disease KW - genital herpes KW - geographic distribution KW - hearing impairment KW - heart failure KW - hematologic disease KW - human KW - immunopathology KW - incidence KW - income KW - infection KW - injury KW - iron deficiency anemia KW - Liberia KW - liver cirrhosis KW - low back pain KW - lower respiratory tract infection KW - major clinical study KW - malaria KW - male KW - malignant neoplasm KW - maternal disease KW - mental disease KW - middle aged KW - Middle East KW - migraine KW - neck pain KW - neglected disease KW - neurologic disease KW - newborn KW - newborn disease KW - non communicable disease KW - North African KW - nutritional deficiency KW - nutritional disorder KW - Pacific islands KW - prevalence KW - priority journal KW - refraction error KW - sex KW - social aspect KW - Somalia KW - South and Central America KW - systematic review KW - tension headache KW - tropical disease KW - tuberculosis KW - Uganda KW - upper respiratory tract infection KW - urogenital tract disease KW - Venezuela KW - visual impairment KW - young adult N2 - Background Non-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015. Methods We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores. Findings We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4–19·2 billion) and diarrhoeal diseases (2·39 billion, 2·30–2·50 billion). Eight causes of chronic disease and injury each affected more than 10% of the world's population in 2015: permanent caries, tension-type headache, iron-deficiency anaemia, age-related and other hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that affected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35–2·37 billion) individuals affected. The second and third leading impairments by number of individuals affected were hearing loss and vision loss, respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause included in our analysis. For low SDI geographies, Group 1 causes typically accounted for 20–30% of total disability, largely attributable to nutritional deficiencies, malaria, neglected tropical diseases, HIV/AIDS, and tuberculosis. Lower back and neck pain was the leading global cause of disability in 2015 in most countries. The leading cause was sense organ disorders in 22 countries in Asia and Africa and one in central Latin America; diabetes in four countries in Oceania; HIV/AIDS in three southern sub-Saharan African countries; collective violence and legal intervention in two north African and Middle Eastern countries; iron-deficiency anaemia in Somalia and Venezuela; depression in Uganda; onchoceriasis in Liberia; and other neglected tropical diseases in the Democratic Republic of the Congo. Interpretation Ageing of the world's population is increasing the number of people living with sequelae of diseases and injuries. Shifts in the epidemiological profile driven by socioeconomic change also contribute to the continued increase in years lived with disability (YLDs) as well as the rate of increase in YLDs. Despite limitations imposed by gaps in data availability and the variable quality of the data available, the standardised and comprehensive approach of the GBD study provides opportunities to examine broad trends, compare those trends between countries or subnational geographies, benchmark against locations at similar stages of development, and gauge the strength or weakness of the estimates available. Funding Bill & Melinda Gates Foundation. ER - TY - JOUR M3 - Review Y1 - 2013 VL - 47 IS - 4 SP - 265 EP - 272 SN - 0008-6568 JF - Caries Research JO - Caries Res. UR - https://www.embase.com/search/results?subaction=viewrecord&id=L52444791&from=export U2 - L52444791 C5 - 23407213 DB - Medline U4 - 2014-03-14 L2 - http://dx.doi.org/10.1159/000346917 DO - 10.1159/000346917 LK - https://search.library.berkeley.edu/openurl/01UCS_BER/01UCS_BER:UCB?sid=EMBASE&sid=EMBASE&issn=00086568&id=doi:10.1159%2F000346917&atitle=Systematic+review+of+publications+on+economic+evaluations+of+caries+prevention+programs&stitle=Caries+Res.&title=Caries+Research&volume=47&issue=4&spage=265&epage=272&aulast=Mari%C3%B1o&aufirst=Rodrigo+J.&auinit=R.J.&aufull=Mari%C3%B1o+R.J.&coden=CAREB&isbn=&pages=265-272&date=2013&auinit1=R&auinitm=J A1 - Mariño, R.J. A1 - Khan, A.R. A1 - Morgan, M. M1 - (Mariño R.J., rmarino@unimelb.edu.au; Morgan M.) Oral Health Cooperative Research Centre, Melbourne Dental School, University of Melbourne, Melbourne, VIC 3010, Australia M1 - (Khan A.R.) Altamash Institute of Dental Medicine, Karachi, Pakistan AD - R.J. Mariño, Oral Health Cooperative Research Centre, Melbourne Dental School, University of Melbourne, Melbourne, VIC 3010, Australia T1 - Systematic review of publications on economic evaluations of caries prevention programs LA - English KW - fissure sealant KW - cost benefit analysis KW - data mining KW - dental caries KW - economics KW - evaluation study KW - fluoridation KW - human KW - preventive dentistry KW - review KW - statistical analysis N2 - The aim of this study was to perform a systematic review of economic evaluations (EEs) of dental caries prevention programs to objectively retrieve, synthesize and describe available information on the field. Several strategies were combined to search for literature published between January 1975 and April 2012. MEDLINE, EconoLit and ISI formed the basis of the literature search. The study selection was done using predefined inclusion and exclusion criteria. Bibliographic listings of all retrieved articles were hand-searched. The search identified 206 references. An evaluative framework was developed based on the Centre for Reviews and Dissemination's 'Guidance for undertaking reviews in health care' (York University, 2009). Background information included publication vehicle, year of publication, geographic focus, type of preventive program and type of economic analysis. 63 studies were included in the review. The most common preventive strategies evaluated were dental sealants (n = 13), water fluoridation (n = 12) and mixed interventions (n = 12). By type of EE undertaken, 30 were cost-effectiveness analyses, 22 were cost-benefit analyses, and 5 presented both cost-effectiveness and cost-benefit analyses. Few studies were cost-utility analyses (n = 5) or cost minimization analyses (n = 2). By year of publication, most were published after 2003. The review revealed that, although the number of publications reporting EEs has increased significantly in recent years, the quality of the reporting needs to be improved. The main methodological problems identified in the review were the limited information provided on adjustments for discounting in addition to inadequate sensitivity analyses. Attention also needs to be given to the analysis and interpretation of the results of the EEs. Copyright © 2013 S. Karger AG, Basel. ER -