학술논문
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纳微快报（英文版） / Nano-Micro Letters . 15(11):435-502
Academic Journal
Academic Journal
Kuch, Aleksandra; Procyk, Grzegorz; Borowiec, Karolina; Gąsecka, Aleksandra; Biernacka, Elżbieta Katarzyna
Academic Journal
Reading Research Quarterly, 2021 May 01. 56, S249-S272.
Academic Journal
Academic Journal
CARRANZA ESTEBAN, RENZO FELIPE; MAMANI-BENITO, OSCAR; TURPO CHAPARRO, JOSUÉ EDISON; RUIZ MAMANI, PERCY G.; HERNÁNDEZ, RONALD M.
Academic Journal
physica status solidi (RRL) - Rapid Research Letters . 18(6)
Report
Spindox Labs; The National Research Council, Italy; The Institute of Electronics and Computer Sciences, Latvia; Euronet-Consulting; Scuola di Robotica; CheckHealth; The Italian Liver Foundation; Medical University of Graz; HK3 Lab S.R.L., Italy
Physics Informed Machine Learning-based Prediction and Reversion of Impaired Fasting Glucose Management
Ezzati M, Riboli E. Can noncommunicable diseases be prevented? Lessons from studies of populations and individuals.Science . 2012 Sep 21;337(6101):1482-7. doi: 10.1126/science .1227001.
Piovani D, Nikolopoulos GK, Bonovas S. Non-Communicable Diseases: The Invisible Epidemic. J Clin Med. 2022 Oct 8;11(19):5939. doi: 10.3390/jcm11195939.
International Diabetes Federation. IDF Diabetes Atlas, 10th Edn. Brussels, Belgium: 2021. Available at: Https://www.Diabetesatlas.Org.
Centers for Disease Control and Prevention. National Diabetes Statistics Report 2020 Website. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
Ley SH, Schulze MB, Hivert MF, Meigs JB, Hu FB. Risk Factors for Type 2 Diabetes. In: Cowie CC, Casagrande SS, Menke A, Cissell MA, Eberhardt MS, Meigs JB, Gregg EW, Knowler WC, Barrett-Connor E, Becker DJ, Brancati FL, Boyko EJ, Herman WH, Howard BV, Narayan KMV, Rewers M, Fradkin JE, editors. Diabetes in America. 3rd edition. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018 Aug. CHAPTER 13. Available from http://www.ncbi.nlm.nih.gov/books/NBK567966/
Yang J, Qian F, Chavarro JE, Ley SH, Tobias DK, Yeung E, Hinkle SN, Bao W, Li M, Liu A, Mills JL, Sun Q, Willett WC, Hu FB, Zhang C. Modifiable risk factors and long term risk of type 2 diabetes among individuals with a history of gestational diabetes mellitus: prospective cohort study. BMJ. 2022 Sep 21;378:e070312. doi: 10.1136/bmj-2022-070312.
American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021 Jan;44(Suppl 1):S15-S33. doi: 10.2337/dc21-S002.
Tabak AG, Herder C, Rathmann W, Brunner EJ, Kivimaki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012 Jun 16;379(9833):2279-90. doi: 10.1016/S0140-6736(12)60283-9. Epub 2012 Jun 9.
Almeda-Valdes P, Cuevas-Ramos D, Aguilar-Salinas CA. Metabolic syndrome and non-alcoholic fatty liver disease. Ann Hepatol. 2009;8 Suppl 1:S18-24.
Hegde H, Shimpi N, Panny A, Glurich I, Christie P, Acharya A. Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment. Inform Med Unlocked. 2019;17:100254. doi: 10.1016/j.imu.2019.100254. Epub 2019 Oct 16.
Bernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes. 2018 Dec;12(6):517-525. doi: 10.1016/j.pcd.2018.07.015. Epub 2018 Aug 18.
Jolle A, Midthjell K, Holmen J, Carlsen SM, Tuomilehto J, Bjorngaard JH, Asvold BO. Validity of the FINDRISC as a prediction tool for diabetes in a contemporary Norwegian population: a 10-year follow-up of the HUNT study. BMJ Open Diabetes Res Care. 2019 Nov 28;7(1):e000769. doi: 10.1136/bmjdrc-2019-000769. eCollection 2019.
Tsalamandris S, Antonopoulos AS, Oikonomou E, Papamikroulis GA, Vogiatzi G, Papaioannou S, Deftereos S, Tousoulis D. The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives. Eur Cardiol. 2019 Apr;14(1):50-59. doi: 10.15420/ecr.2018.33.1.
Castiglione F, Tieri P, De Graaf A, Franceschi C, Lio P, Van Ommen B, Mazza C, Tuchel A, Bernaschi M, Samson C, Colombo T, Castellani GC, Capri M, Garagnani P, Salvioli S, Nguyen VA, Bobeldijk-Pastorova I, Krishnan S, Cappozzo A, Sacchetti M, Morettini M, Ernst M. The onset of type 2 diabetes: proposal for a multi-scale model. JMIR Res Protoc. 2013 Oct 31;2(2):e44. doi: 10.2196/resprot.2854.
Palumbo MC, de Graaf AA, Morettini M, Tieri P, Krishnan S, Castiglione F. A computational model of the effects of macronutrients absorption and physical exercise on hormonal regulation and metabolic homeostasis. Comput Biol Med. 2023 Sep;163:107158. doi: 10.1016/j.compbiomed.2023.107158. Epub 2023 Jun 16.
Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. BMC Bioinformatics. 2020 Dec 14;21(Suppl 17):508. doi: 10.1186/s12859-020-03763-4.
Prana V, Tieri P, Palumbo MC, Mancini E, Castiglione F. Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes. Comput Math Methods Med. 2019 Feb 3;2019:7525834. doi: 10.1155/2019/7525834. eCollection 2019.
Palumbo MC, Morettini M, Tieri P, Diele F, Sacchetti M, Castiglione F. Personalizing physical exercise in a computational model of fuel homeostasis. PLoS Comput Biol. 2018 Apr 26;14(4):e1006073. doi: 10.1371/journal.pcbi.1006073. eCollection 2018 Apr.
Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-440. doi:10.1038/s42254-021-00314-5
Zafar H, Channa A, Jeoti V, Stojanovic GM. Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring. Sensors (Basel). 2022 Jan 14;22(2):638. doi: 10.3390/s22020638.
Yao H, Shum AJ, Cowan M, Lahdesmaki I, Parviz BA. A contact lens with embedded sensor for monitoring tear glucose level. Biosens Bioelectron. 2011 Mar 15;26(7):3290-6. doi: 10.1016/j.bios.2010.12.042. Epub 2010 Dec 31.
Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med. 2020 Aug;133(8):895-900. doi: 10.1016/j.amjmed.2020.03.033. Epub 2020 Apr 20.
Al-Shamsi S, Govender RD, King J. External validation and clinical usefulness of three commonly used cardiovascular risk prediction scores in an Emirati population: a retrospective longitudinal cohort study. BMJ Open. 2020 Oct 28;10(10):e040680. doi: 10.1136/bmjopen-2020-040680.
Rodrigues PM, Madeiro JP, Marques JAL. Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering (Basel). 2023 Jul 2;10(7):792. doi: 10.3390/bioengineering10070792.
Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016 Jun 22;353:i3140. doi: 10.1136/bmj.i3140. Erratum In: BMJ. 2019 Jun 25;365:l4379. doi: 10.1136/bmj.l4379.
Bleeker SE, Moll HA, Steyerberg EW, Donders AR, Derksen-Lubsen G, Grobbee DE, Moons KG. External validation is necessary in predictionresearch : a clinical example. J Clin Epidemiol. 2003 Sep;56(9):826-32. doi: 10.1016/s0895-4356(03)00207-5.
Ezzati M, Riboli E. Can noncommunicable diseases be prevented? Lessons from studies of populations and individuals.
Piovani D, Nikolopoulos GK, Bonovas S. Non-Communicable Diseases: The Invisible Epidemic. J Clin Med. 2022 Oct 8;11(19):5939. doi: 10.3390/jcm11195939.
International Diabetes Federation. IDF Diabetes Atlas, 10th Edn. Brussels, Belgium: 2021. Available at: Https://www.Diabetesatlas.Org.
Centers for Disease Control and Prevention. National Diabetes Statistics Report 2020 Website. https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf
Ley SH, Schulze MB, Hivert MF, Meigs JB, Hu FB. Risk Factors for Type 2 Diabetes. In: Cowie CC, Casagrande SS, Menke A, Cissell MA, Eberhardt MS, Meigs JB, Gregg EW, Knowler WC, Barrett-Connor E, Becker DJ, Brancati FL, Boyko EJ, Herman WH, Howard BV, Narayan KMV, Rewers M, Fradkin JE, editors. Diabetes in America. 3rd edition. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018 Aug. CHAPTER 13. Available from http://www.ncbi.nlm.nih.gov/books/NBK567966/
Yang J, Qian F, Chavarro JE, Ley SH, Tobias DK, Yeung E, Hinkle SN, Bao W, Li M, Liu A, Mills JL, Sun Q, Willett WC, Hu FB, Zhang C. Modifiable risk factors and long term risk of type 2 diabetes among individuals with a history of gestational diabetes mellitus: prospective cohort study. BMJ. 2022 Sep 21;378:e070312. doi: 10.1136/bmj-2022-070312.
American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care. 2021 Jan;44(Suppl 1):S15-S33. doi: 10.2337/dc21-S002.
Tabak AG, Herder C, Rathmann W, Brunner EJ, Kivimaki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012 Jun 16;379(9833):2279-90. doi: 10.1016/S0140-6736(12)60283-9. Epub 2012 Jun 9.
Almeda-Valdes P, Cuevas-Ramos D, Aguilar-Salinas CA. Metabolic syndrome and non-alcoholic fatty liver disease. Ann Hepatol. 2009;8 Suppl 1:S18-24.
Hegde H, Shimpi N, Panny A, Glurich I, Christie P, Acharya A. Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment. Inform Med Unlocked. 2019;17:100254. doi: 10.1016/j.imu.2019.100254. Epub 2019 Oct 16.
Bernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes. 2018 Dec;12(6):517-525. doi: 10.1016/j.pcd.2018.07.015. Epub 2018 Aug 18.
Jolle A, Midthjell K, Holmen J, Carlsen SM, Tuomilehto J, Bjorngaard JH, Asvold BO. Validity of the FINDRISC as a prediction tool for diabetes in a contemporary Norwegian population: a 10-year follow-up of the HUNT study. BMJ Open Diabetes Res Care. 2019 Nov 28;7(1):e000769. doi: 10.1136/bmjdrc-2019-000769. eCollection 2019.
Tsalamandris S, Antonopoulos AS, Oikonomou E, Papamikroulis GA, Vogiatzi G, Papaioannou S, Deftereos S, Tousoulis D. The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives. Eur Cardiol. 2019 Apr;14(1):50-59. doi: 10.15420/ecr.2018.33.1.
Castiglione F, Tieri P, De Graaf A, Franceschi C, Lio P, Van Ommen B, Mazza C, Tuchel A, Bernaschi M, Samson C, Colombo T, Castellani GC, Capri M, Garagnani P, Salvioli S, Nguyen VA, Bobeldijk-Pastorova I, Krishnan S, Cappozzo A, Sacchetti M, Morettini M, Ernst M. The onset of type 2 diabetes: proposal for a multi-scale model. JMIR Res Protoc. 2013 Oct 31;2(2):e44. doi: 10.2196/resprot.2854.
Palumbo MC, de Graaf AA, Morettini M, Tieri P, Krishnan S, Castiglione F. A computational model of the effects of macronutrients absorption and physical exercise on hormonal regulation and metabolic homeostasis. Comput Biol Med. 2023 Sep;163:107158. doi: 10.1016/j.compbiomed.2023.107158. Epub 2023 Jun 16.
Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. BMC Bioinformatics. 2020 Dec 14;21(Suppl 17):508. doi: 10.1186/s12859-020-03763-4.
Prana V, Tieri P, Palumbo MC, Mancini E, Castiglione F. Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes. Comput Math Methods Med. 2019 Feb 3;2019:7525834. doi: 10.1155/2019/7525834. eCollection 2019.
Palumbo MC, Morettini M, Tieri P, Diele F, Sacchetti M, Castiglione F. Personalizing physical exercise in a computational model of fuel homeostasis. PLoS Comput Biol. 2018 Apr 26;14(4):e1006073. doi: 10.1371/journal.pcbi.1006073. eCollection 2018 Apr.
Karniadakis GE, Kevrekidis IG, Lu L, Perdikaris P, Wang S, Yang L. Physics-informed machine learning. Nat Rev Phys. 2021;3(6):422-440. doi:10.1038/s42254-021-00314-5
Zafar H, Channa A, Jeoti V, Stojanovic GM. Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring. Sensors (Basel). 2022 Jan 14;22(2):638. doi: 10.3390/s22020638.
Yao H, Shum AJ, Cowan M, Lahdesmaki I, Parviz BA. A contact lens with embedded sensor for monitoring tear glucose level. Biosens Bioelectron. 2011 Mar 15;26(7):3290-6. doi: 10.1016/j.bios.2010.12.042. Epub 2010 Dec 31.
Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med. 2020 Aug;133(8):895-900. doi: 10.1016/j.amjmed.2020.03.033. Epub 2020 Apr 20.
Al-Shamsi S, Govender RD, King J. External validation and clinical usefulness of three commonly used cardiovascular risk prediction scores in an Emirati population: a retrospective longitudinal cohort study. BMJ Open. 2020 Oct 28;10(10):e040680. doi: 10.1136/bmjopen-2020-040680.
Rodrigues PM, Madeiro JP, Marques JAL. Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering (Basel). 2023 Jul 2;10(7):792. doi: 10.3390/bioengineering10070792.
Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016 Jun 22;353:i3140. doi: 10.1136/bmj.i3140. Erratum In: BMJ. 2019 Jun 25;365:l4379. doi: 10.1136/bmj.l4379.
Bleeker SE, Moll HA, Steyerberg EW, Donders AR, Derksen-Lubsen G, Grobbee DE, Moons KG. External validation is necessary in prediction
Academic Journal
IEEE Electron Device Letters IEEE Electron Device Lett. Electron Device Letters , IEEE. 27(6):486-488 Jun, 2006
Academic Journal
Helmers, Henning; Lopez, Esther; Hohn, Oliver; Lackner, David; Schon, Jonas; Schauerte, Meike; Schachtner, Michael; Dimroth, Frank; Bett, Andreas W.; Cheng, Yudong; Wahl, Sophia; Wuttig, Matthias; Shang, Chen; Selvidge, Jennifer; Hughes, Eamonn; Norman, Justin C.; Taylor, Aidan A.; Gossard, Arthur C.; Mukherjee, Kunal; Bowers, John E.; Reshchikov, Michael A.; Hassa, Anna; Storm, Philipp; Kneiss, Max; Splith, Daniel; von Wenckstern, Holger; Lorenz, Michael; Grundmann, Marius; Trifunovic, Luka; Brouwer, Piet W.
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Report
Enhancing Cancer Prevention and Control Pathways-Native Health Initiative
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Learning and Living With Wildfire Smoke: Creating Air Clean Environments in Schools Through Youth Participatory Action Research
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