Diabetes technology & therapeutics最新文献

{"title":"The Association of Time-in-Range and Time-in-Tight-Range with Retinopathy Progression in the Virtual Diabetes Control and Complications Trial Continuous Glucose Monitoring Dataset.","authors":"Benjamin Lobo, Lauren Kanapka, Boris P Kovatchev, Craig Kollman, Roy W Beck","doi":"10.1089/dia.2025.0033","DOIUrl":"10.1089/dia.2025.0033","url":null,"abstract":"<p><p><b><i>Background:</i></b> In a prior work, a virtual continuous glucose monitoring (CGM) trace was generated for each of the 1441 participants in the landmark Diabetes Control and Complications trial (DCCT). These new data allow us to compare whether time-in-tight-range (TITR) is a better predictor of diabetic microvascular complications (specifically retinopathy development or progression) than time-in-range (TIR). <b><i>Methods:</i></b> Discrete Cox proportional hazard models were used to calculate the hazard ratios (HRs) for the development/progression of retinopathy. <b><i>Results:</i></b> For a 1.0 standard deviation (SD) change, the adjusted HR (95% confidence interval) was 2.67 (2.33-3.06) for TIR, 2.74 (2.36-3.18) for TITR, and 2.37 (2.13-2.65) for HbA1c; a similar pattern of results was obtained for a 0.5 SD change. Computing Harrell's C-statistic showed that a survival model adjusted for TIR, TITR, or HbA1c had similar predictive performance. <b><i>Conclusion:</i></b> The associations of TIR and TITR with retinopathy development or progression were similar to HbA1c in the virtual DCCT CGM dataset.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"558-561"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of a Continuous Glucose Monitoring-Derived Glycation Ratio in the Development of Microvascular Complications.","authors":"Laura B Bovee, Theodore A Gooley, Jordan E Perlman, Irl B Hirsch","doi":"10.1089/dia.2024.0580","DOIUrl":"10.1089/dia.2024.0580","url":null,"abstract":"<p><p><b><i>Background:</i></b> Previous studies have evaluated the associations between HbA1c discordance and diabetes complications using indices of glycation. The ideal index would allow for identification of those at increased risk for microvascular complications. This analysis evaluates the association of a newly published index, the glycation ratio (GR), with diabetic retinopathy (DR) and diabetic kidney disease (DKD). <b><i>Methods:</i></b> This is a retrospective review of 661 patients with diabetes seen at the University of Washington. All patients used continuous glucose monitoring (CGM) before the study. Diabetes duration was greater than 20 years in 59%. Median age was 45 years. GR was defined as the ratio of the glucose management indicator (GMI) to the HbA1c. The associations of GR with microvascular complications were each assessed using logistic regression. <b><i>Results:</i></b> Modeling GR as a continuous linear variable, each increase in GR of 0.20 units was associated with a 38% relative reduction in the odds of DR (adjusted odds ratio [OR] = 0.62; 95% confidence interval [CI]: 0.45-0.86, <i>P</i> = 0.004] and a similar reduction in the odds of DKD (OR = 0.61; 95% CI: 0.41-0.93, <i>P</i> = 0.02). <b><i>Conclusions:</i></b> GR may be a useful marker for risk of diabetic microvascular complications. Longitudinal assessment will be required to see how well GR compares with GMI or HbA1c alone.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"553-557"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"10.1089/dia.2025.18800.ac","DOIUrl":"https://doi.org/10.1089/dia.2025.18800.ac","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":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":"27 S3","pages":"S21-S32"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Meal Insulin Bolus Timing and Bedtime Snacking on Continuous Glucose Monitoring-Derived Glycemic Metrics in Hospitalized Inpatients.","authors":"Sara M Alexanian, Michael C Cheney, Jenny C Bello Ramos, Nicole L Spartano, Howard A Wolpert, Devin W Steenkamp","doi":"10.1089/dia.2025.0027","DOIUrl":"10.1089/dia.2025.0027","url":null,"abstract":"<p><p><b><i>Objective:</i></b> In hospitalized inpatients, timely administration of prandial insulin with meals is challenging. Furthermore, the glycemic impact of snacking after dinner (\"bedtime snacking\") without prandial insulin administration has not been previously explored. We present an analysis of the impact of delayed mealtime insulin administration and bedtime snacking on inpatient glycemic control. <b><i>Research Design and Methods:</i></b> We conducted a post hoc analysis from the In-Fi study: a randomized controlled trial comparing Fiasp versus insulin aspart (Novolog) in inpatients with type 2 diabetes. Glycemic outcomes were assessed using the Dexcom G6 PRO continuous glucose monitoring (CGM). We analyzed CGM and insulin administration data from 122 randomized subjects who completed the primary study protocol, which included wearing a CGM for ≥4 meals. This analysis evaluates the impact of delayed mealtime insulin administration and bedtime snacking on glucose control. <b><i>Results:</i></b> Four-hour postprandial time in range (TIR_70-180) was 48% for insulin boluses administered before meals (<i>n</i> = 149) versus 24% when a meal bolus was delayed for >5 min after a meal (mean delay 58.7 min; <i>n</i> = 112; <i>P</i> < 0.001). Bedtime snacking (9 pm-12 am) was associated with significantly higher fasting glucose the next morning (35.2 mg/dL, standard error [SE] = 15.4, <i>P</i> = 0.026) and with a reduced overnight (9 pm-6 am) TIR_70-180 (31.9%, SE = 8.06, <i>P</i> < 0.001), adjusting for bedtime sensor glucose. Bedtime snacking was associated with higher overnight glucose standard deviation (12.3 mg/dL, SE = 3.46, <i>P</i> < 0.001) and with higher overnight glucose percentage coefficient of variation (3.6%; SE = 1.7, <i>P</i> = 0.044), adjusting for initial bedtime sensor glucose. <b><i>Conclusions:</i></b> Delayed mealtime insulin administration and bedtime snacking without insulin administration are significant causes of postprandial and overnight hyperglycemia in hospitalized inpatients. Adjustments in mealtime insulin protocols, attention to food intake, and the potential inpatient adoption of technology, such as CGM and automated insulin delivery systems, are needed to address this shortcoming in inpatient diabetes care.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"511-516"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Stages of Automated Insulin Delivery.","authors":"Howard Zisser, Roman Hovorka, Ananda Basu, Tadej Battelino, Charlotte K Boughton, Marc D Breton, Bruce A Buckingham, Sue A Brown, Daniel R Cherñavvsky, Eyal Dassau, Mark D DeBoer, Francis J Doyle, Laya Ekhlaspour, Chiara Fabris, Gregory P Forlenza, Ahmad Haidar, David C Klonoff, Boris Kovatchev, Aaron J Kowalski, Carol J Levy, Yogish C Kudva, David M Maahs, Moshe Phillip, Eric Renard, Steven J Russell, Viral N Shah, Garry M Steil, R Paul Wadwa, Stuart A Weinzimer","doi":"10.1089/dia.2025.18802.hz","DOIUrl":"https://doi.org/10.1089/dia.2025.18802.hz","url":null,"abstract":"<p><p>The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":"27 S3","pages":"S33-S47"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical Considerations and Implementation of Automated Insulin Delivery Systems.","authors":"Laurel H Messer, Gregory P Forlenza, Linda Gonder-Frederick, Korey Hood, Osagie Ebekozien, Katharine Barnard-Kelly, Lori M Laffel, Jennifer L Sherr, Rayhan Lal, Stuart A Weinzimer","doi":"10.1089/dia.2025.0132","DOIUrl":"https://doi.org/10.1089/dia.2025.0132","url":null,"abstract":"<p><p>Automated Insulin Delivery (AID) is in a new era of implementation challenges and opportunities for people with diabetes (PWD) and healthcare providers (HCPs). Beyond technologic variation, cost, access, and HCP endorsement/experience lead to uneven uptake of AID technologies, and attenuate universal ease of use. For AID to be broadly implemented, we must prioritize the lived experience for PWD, and consider how to alleviate burden to promote holistic wellbeing. Expectations and education help HCPs and PWD navigate the similarities and differences between AID devices, and help find common ties: users need to give the system time to work, learn to trust it, and not try to \"trick\" the system. Despite these learnings, disparities in uptake exist, both in clinical trials and in routine clinical care. Strategies to proactively address AID disparities are needed at multiple levels, all of which increase in importance as AID becomes more common for people with type 2 diabetes. Further, broader implementation will require comprehensive healthcare system integration efforts, including new data solutions. Overall, the success of AID requires ongoing transformation of clinical paradigms, with lockstep alignment between PWD and their families, healthcare professionals, researchers, funders, policy makers, and industry partners.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":"27 S3","pages":"S72-S78"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.","authors":"Peter G Jacobs, Carol J Levy, Sue A Brown, Michael C Riddell, Ali Cinar, Charlotte K Boughton, Marc D Breton, Eyal Dassau, Greg Forlenza, Robert J Henderson, Roman Hovorka, David M Maahs, Medha Munshi, Helen Murphy, Sarit Polsky, Richard Pratley, Melissa S Putman, Viral N Shah, Leah M Wilson, Howard Zisser, Laya Ekhlaspour","doi":"10.1089/dia.2025.0129","DOIUrl":"https://doi.org/10.1089/dia.2025.0129","url":null,"abstract":"<p><p>Since the discovery of the life-saving hormone insulin in 1921 by Dr. Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longer, healthier lives. The development of insulin pumps, continuous glucose monitoring systems, and automated insulin delivery (AID) systems has enabled people living with T1D to safely manage their glucose, reduce their HbA1c, and improve their overall health and quality of life. Nevertheless, AID systems are not yet designed for all people with T1D, and they perform best during the overnight period when meals and exercise are not occurring. AID systems are not fully automated in that they require the person using the system to announce meals and exercise to the system to avoid dangerous hyper- or hypoglycemia, respectively. In this review, which is one of a collection of articles to commemorate the 75th anniversary of the National Institute for Diabetes and Digestive and Kidney Diseases, we celebrate the commercialization of the AID and discuss the major challenges and research gaps that remain to be solved to enable single- and multihormone AID systems to more fully support glucose management in people living with T1D. More research is required to design and evaluate more intelligent AID systems that do not require accurate carbohydrate estimations or announcements for meals and exercise. Current AID systems are also not designed to be used by older adults or pregnant people. Results are presented on new AID systems that can automatically respond to meals and exercise. Results are also presented on evaluations of AID systems in older adults and pregnant people. Next-generation AID systems will need to support all people, including older adults, people during pregnancy, athletes, and people who may be too busy to announce carbohydrates or exercise to the system. Solutions are now becoming available that will enable AID systems to support a broader range of people living with T1D by leveraging the latest technologies in artificial intelligence and adaptive control.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":"27 S3","pages":"S60-S71"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Insulin Delivery in Elderly with Type 1 Diabetes: A Prespecified Analysis of the Extension Phase.","authors":"Yogish C Kudva, Robert J Henderson, Lauren G Kanapka, Ruth S Weinstock, Michael R Rickels, Richard E Pratley, Naomi Chaytor, Kamille Janess, Donna Desjardins, Vishwanath Pattan, Amy J Peleckis, Anna Casu, Shafaq Raza Rizvi, Suzan Bzdick, Keri J Whitaker, Jorge L Jo Kamimoto, Kellee Miller, Craig Kollman, Roy W Beck","doi":"10.1089/dia.2024.0560","DOIUrl":"10.1089/dia.2024.0560","url":null,"abstract":"<p><p>The Automated Insulin Delivery in Elderly with Type 1 Diabetes (AIDE T1D) trial randomized 82 adults ≥65 years with type 1 diabetes (T1D) to hybrid closed loop (HCL), predictive low glucose suspend (PLGS), and sensor-augmented pump (SAP) therapy in a randomized crossover trial. Seventy-five of the 78 completers joined an extension phase in which they were offered the pump mode of their choice for an additional 3 months. Mean age was 71 ± 4 years (range 65-86 years) and mean duration of T1D was 42 ± 17 years (range 1-68 years). Use of HCL was selected by 91%, PLGS by 8%, and continuous glucose monitoring with injections by 1%. For participants selecting HCL, time-in-range 70-180 mg/dL was similar in the randomized controlled trial and extension phase (mean 75% ± 10%). One severe hypoglycemic event was reported. HCL was preferred over PLGS or SAP and remained effective in older adults with T1D. Clinical Trial Registration: NCT04016662.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"572-575"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation Between A1c and Continuous Glucose Monitor Time in Range in a Cohort of Pediatric Patients with Type 1 Diabetes.","authors":"Marisa Sobczak, Laura Pyle, Alexandra Sawyer, Gregory P Forlenza, G Todd Alonso","doi":"10.1089/dia.2024.0500","DOIUrl":"10.1089/dia.2024.0500","url":null,"abstract":"<p><p>Hemoglobin A1c (A1c) is the standard for glycemic control in type 1 diabetes. With recent increase in continuous glucose monitoring (CGM), other metrics (time in range 70-180 mg/dL [TIR]) are increasingly available. Data are limited for youth. We evaluated the association between A1c and time in range in a large pediatric cohort. We included patients from 2018 through 2020, aged <22 years with ≥70% CGM usage (<i>n</i> = 2393). A linear correlation between A1c and TIR was observed (correlation coefficient -0.73), similar to studies in adults. Each 1% increase in A1c was associated with a 9.1% lower TIR. The <i>R</i>² for A1c versus mean sensor glucose was 0.66 (<i>P</i> < 0.001) and A1c versus TIR was 0.55 (<i>P</i> < 0.001). TIR correlates with A1c in children with type 1 diabetes. TIR should be considered alongside A1c. Further research is warranted to establish long-term outcomes associated with TIR in children.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"567-571"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Glucose Monitoring-Based Titration of Once-Weekly Insulin Icodec in Insulin-Naive Individuals with Type 2 Diabetes (ONWARDS 9): A Phase 3b, Multicenter, Single-Arm, Treat-to-Target Clinical Trial.","authors":"Richard M Bergenstal, Björg Ásbjörnsdóttir, Tanvir Johanning Bari, Satish Hulkund, Yvonne Winhofer, Carol Wysham","doi":"10.1089/dia.2025.0050","DOIUrl":"10.1089/dia.2025.0050","url":null,"abstract":"<p><p><b><i>Background:</i></b> ONWARDS 9 explored, for the first time, the effect of continuous glucose monitoring (CGM)-based titration of once-weekly insulin icodec (icodec) on glycemic control and safety outcomes in individuals with type 2 diabetes (T2D). <b><i>Methods:</i></b> In this 26-week, multicenter, single-arm, treat-to-target, phase 3b trial, insulin-naive adults with T2D (glycated hemoglobin [HbA1c] 7.0%-11.0%) initiated icodec at a starting dose of 70 U/week. Participants were provided with an intermittently scanned CGM device, and icodec doses were titrated weekly based on pre-breakfast CGM values (target: 80-130 mg/dL). The primary endpoint was change in HbA1c from week 0 to week 26. Exploratory endpoints included the percentage of time in range (TIR; 70-180 mg/dL), time above range (TAR; >180 mg/dL), and time below range (TBR; <54 mg/dL) from week 22 to week 26. Safety outcomes, including the number of hypoglycemia episodes, were assessed. <b><i>Results:</i></b> Of 58 participants screened, 51 received icodec treatment. HbA1c decreased from an observed mean of 8.18% at week 0 to an estimated mean of 7.00% at week 26. There was a statistically significant reduction in HbA1c of -1.17%-points (95% confidence interval: -1.36; -0.99, <i>P</i> < 0.0001). From week -2 to 0 to week 22-26, a concomitant clinically meaningful increase in TIR (54.4% to 76.4%) and decrease in TAR (45.2% to 22.9%) was observed; TBR remained low throughout the trial (week -2 to 0: 0.03%; week 22-26: 0.04%). No severe hypoglycemic episodes were reported during the trial, and no new safety concerns for icodec were identified. <b><i>Conclusion:</i></b> After 26 weeks of treatment with icodec titrated based on CGM data, there was a statistically significant reduction in HbA1c from baseline, and the internationally recommended CGM targets for TIR, TAR >180 mg/dL, and TBR <54 mg/dL were achieved. These findings suggest that CGM-based titration of icodec is a feasible method for initiating insulin therapy in T2D. <b><i>Trial registration:</i></b> ClinicalTrials.gov identifiers: NCT05823948.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"527-536"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}