Ali Cinar, Ananda Basu, B Wayne Bequette, Marc D Breton, Bruce Buckingham, Eda Cengiz, Claudio Cobelli, Eyal Dassau, Francis J Doyle, Chiara Fabris, Andrea Facchinetti, Irl Hirsch, Roman Hovorka, Peter G Jacobs, Boris P Kovatchev, Chiara Dalla Man, Laurie Quinn, Jay Skyler
{"title":"代谢模型,在硅试验,和算法。","authors":"Ali Cinar, Ananda Basu, B Wayne Bequette, Marc D Breton, Bruce Buckingham, Eda Cengiz, Claudio Cobelli, Eyal Dassau, Francis J Doyle, Chiara Fabris, Andrea Facchinetti, Irl Hirsch, Roman Hovorka, Peter G Jacobs, Boris P Kovatchev, Chiara Dalla Man, Laurie Quinn, Jay Skyler","doi":"10.1177/19322968251338300","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":"19 4","pages":"895-907"},"PeriodicalIF":3.7000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213549/pdf/","citationCount":"0","resultStr":"{\"title\":\"Metabolic Models, in Silico Trials, and Algorithms.\",\"authors\":\"Ali Cinar, Ananda Basu, B Wayne Bequette, Marc D Breton, Bruce Buckingham, Eda Cengiz, Claudio Cobelli, Eyal Dassau, Francis J Doyle, Chiara Fabris, Andrea Facchinetti, Irl Hirsch, Roman Hovorka, Peter G Jacobs, Boris P Kovatchev, Chiara Dalla Man, Laurie Quinn, Jay Skyler\",\"doi\":\"10.1177/19322968251338300\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.</p>\",\"PeriodicalId\":15475,\"journal\":{\"name\":\"Journal of Diabetes Science and Technology\",\"volume\":\"19 4\",\"pages\":\"895-907\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213549/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Diabetes Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/19322968251338300\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/19322968251338300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Metabolic Models, in Silico Trials, and Algorithms.
Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.
期刊介绍:
The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.