Diabetes technology & therapeutics最新文献

{"title":"Pre-Surgical Diabetes Education Program Increased Preparedness for Diabetes Management after Total Pancreatectomy with Islet Autotransplantation.","authors":"Siobhan Tellez, Catherine Fox, Allyson Essell, Li Lin, Maisam Abu-El-Haija, Deborah Elder","doi":"10.1177/15209156261497788","DOIUrl":"https://doi.org/10.1177/15209156261497788","url":null,"abstract":"<p><strong>Background: </strong>Total pancreatectomy with islet autotransplantation (TPIAT) requires families to manage diabetes with insulin pumps and continuous glucose monitors postoperatively at our institution. Education occurs while the child is hospitalized after TPIAT, and one parent or caregiver receives all education. Diabetes self-management education and support (DSMES) must be effective, efficient, and completed with enough time for families to practice before discharge. We created a pre-TPIAT diabetes education program aimed at reducing subsequent inpatient education time and increasing the primary learner's preparedness for inpatient education.</p><p><strong>Methods: </strong>Thirty-three pediatric TPIAT recipients at Cincinnati Children's received presurgery education across three cycles of improvement. Data were extracted from clinical records and survey responses collected. Cohorts were well-balanced demographically (all <i>P</i> > 0.05, small effect sizes). Survey data, including paired knowledge assessments, were collected from Cohorts 2 and 3.</p><p><strong>Results: </strong>TPIAT recipients were predominantly White (87.9%), English speaking, non-Hispanic (97.0%), with balanced sex distribution (48.5% female). Mean age at surgery was 12.2 years. Parents were primary learners in 87.9% of cases. Presurgery education time showed highly statistically significant increases in Cohorts 2 and 3 compared with Cohort 1 (<i>P</i> = 0.0291 and <i>P</i> = 0.0007, respectively). Inpatient diabetes education showed a marginally significant decrease of 133 min from Cohort 1 to Cohort 2 (<i>P</i> = 0.0717) and directionally maintained in Cohort 3 (0.3285). Assessment results documented substantial knowledge acquisition from the intervention that was retained from pre- to postsurgery education. Learner confidence increased postintervention (<i>P</i> = 0.063), and overall satisfaction scores remained high except for stress/anxiety scores.</p><p><strong>Conclusion: </strong>Changes in TPIAT diabetes education delivery effectively transferred some education to presurgery without compromising content, demonstrated by a statistically significant increase in presurgery education, knowledge scores that indicated retention of information, alongside numerically lower (nonsignificant) total inpatient education time after interventions.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261497788"},"PeriodicalIF":7.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223513","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":"Rethinking the Pre in Prediabetes.","authors":"Roy W Beck","doi":"10.1177/15209156261486550","DOIUrl":"https://doi.org/10.1177/15209156261486550","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261486550"},"PeriodicalIF":7.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220086","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":"Expanding Patient Choice in Diabetes Care: Reducing Access Barriers to Inhaled Insulin.","authors":"Thomas C Blevins, Christopher G Parkin, Roy W Beck, Grazia Aleppo, Eda Cengiz, Daniel R Cherñavvsky, Kelly Close, Steve V Edelman, George Grunberger, Lutz Heinemann, George Huntley, Davida F Kruger, Carol J Levy, Janet B McGill, Henry Rodriguez, Desmond A Schatz, Viral N Shah, Irl B Hirsch","doi":"10.1177/15209156261500243","DOIUrl":"https://doi.org/10.1177/15209156261500243","url":null,"abstract":"<p><p>Inhaled insulin provides a unique prandial insulin option, offering an alternative, with a more rapid onset of effect, to subcutaneous rapid-acting analog insulin. Clinical studies with inhaled insulin have demonstrated its efficacy and safety, with glycemic outcomes comparable to or in some cases better than those of contemporary insulin delivery approaches. However, the use of inhaled insulin for diabetes therapy in daily life remains limited due to payer restrictions, prior authorization requirements, formulary exclusions, clinician knowledge gaps, clinician time constraints, persistent misconceptions regarding safety, and inadequate patient awareness. A panel of clinical experts in diabetes management met in June 2026 to review the safety and efficacy of Technosphere^® inhaled (TI) insulin, discuss the current obstacles that limit access for people with diabetes (PwD) to TI, identify practical strategies to overcome these obstacles, and issue a call-to-action for payers, clinicians, policymakers, professional organizations, and patient advocacy groups. This article summarizes those discussions and proposes strategies to increase access and choice for PwD.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261500243"},"PeriodicalIF":7.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223508","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 Is Not Ready for the Diagnosis of Prediabetes.","authors":"J Hans DeVries","doi":"10.1177/15209156261485015","DOIUrl":"https://doi.org/10.1177/15209156261485015","url":null,"abstract":"<p><p>With the growing availability of over-the-counter continuous glucose monitors, the question arises whether Continuous Glucose Monitoring (CGM) can be used to diagnose prediabetes. Traditionally, prediabetes is diagnosed using the oral Glucose Tolerance Test, a test with poor reproducibility. In a study from Singapore, there was large overlap between Ambulatory Glucose Profiles of people with prediabetes and normoglycemia. The optimal threshold of 5.64% Time above 140 mg/dL, the best discriminator, yielded 88% specificity and 48% sensitivity, indicating insufficient discriminatory value in many cases. In a study from Stanford, administration of a 75-gram oral glucose load while wearing CGM showed poor agreement with venous sampling between the category outcomes normoglycemia, prediabetes and diabetes. Future large cohort studies and AI may improve this situation. But for the moment, when a question of prediabetes arises, the most practical answer is to measure HbA1c, which allows for an elegant classification of normoglycemia, prediabetes and diabetes.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261485015"},"PeriodicalIF":7.4,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220095","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":"Efficacy and Safety of the MiniMed™ Vivera™ Algorithm: Results from a First-in-Human Feasibility Trial.","authors":"Amir Tirosh, Benjamin J Wheeler, Maya Laron Hirsh, Ella Idan, Yael Cohen, Alisa Boucsein, Wipa Warunyuwong, Natalie Kurtz, Magaly Juarez, Anirban Roy, Benyamin Grosman, Ohad Cohen","doi":"10.1177/15209156261496632","DOIUrl":"https://doi.org/10.1177/15209156261496632","url":null,"abstract":"<p><p>MiniMed™ Vivera™ is an automated insulin delivery algorithm designed to make meal announcements optional. This first-in-human feasibility trial evaluated the efficacy and safety of the algorithm under different meal-announcement strategies. Twenty-four adults with type 1 diabetes participated in this prospective study comprising three consecutive 3-week phases: full meal announcement (FA) phase, no meal announcement (NA) phase, and announcement-at-will phase where participants could decide whether to announce each meal (AW). Continuous glucose monitoring data and safety data were assessed for each phase. The international glycemic targets regarding time in, above, and below range were achieved on average across all three meal-announcement phases. Average time in range (70-180 mg/dL) was 82.3 ± 6.60%, 73.8 ± 7.67%, and 76.7 ± 8.98% during FA, NA, and AW phase, respectively. No device-related serious adverse events were reported. In conclusion, this feasibility trial demonstrated that the Vivera algorithm was safe and efficacious in adults with type 1 diabetes, supporting further evaluation in a pivotal trial.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261496632"},"PeriodicalIF":7.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179853","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":"Real-World Clinical Outcomes Following Initiation of the iLet® Bionic Pancreas in Youth with Type 1 Diabetes.","authors":"Elizabeth R Sullivan-Ingram, Ortal Resnick, Mary Margaret Barr, Ambika P Ashraf","doi":"10.1177/15209156261496625","DOIUrl":"https://doi.org/10.1177/15209156261496625","url":null,"abstract":"<p><strong>Background: </strong>The iLet^® Bionic Pancreas replaces carbohydrate counting and manual bolus calculations with simplified qualitative meal announcements. Its performance among diverse patients with high baseline glycated hemoglobin (HbA1c) is not well characterized.</p><p><strong>Methods: </strong>Retrospective, single-center cohort study of 71 patients aged 6-21 years with type 1 diabetes (T1D) who initiated the iLet Bionic Pancreas (2023-2025). Primary outcomes were changes in HbA1c and time in range (TIR; 70-180 mg/dL [3.9-10.0 mmol/L]) from baseline to 3 months. Secondary outcomes included glycemic metrics through 12 months, hypoglycemia, and meal-announcement behavior.</p><p><strong>Results: </strong>Mean age was 13.2 ± 3.5 years; 40.8% Black and 38.0% Medicaid-insured. Baseline HbA1c was 9.4 ± 2.1% (79 ± 23 mmol/mol) and TIR was 30.1 ± 19.0%. At 3 months, HbA1c decreased by 1.8 ± 1.7% (-20 ± 19 mmol/mol) and TIR increased by 22.4 ± 22.5 percentage points (both <i>P</i> < 0.0001) without increased hypoglycemia. Improvements were greatest among participants with baseline HbA1c ≥ 9.5% (≥80 mmol/mol) (ΔHbA1c = -3.0% [-32 mmol/mol]; ΔTIR = +31.2 percentage points). Among participants with paired 12-month data, HbA1c decreased from 9.6%-7.6% and TIR increased from 27.7 to 55.8% (both <i>P</i> < 0.0001). Mean meal announcements were 2.9/d at 3 months and 2.1/d at 12 months (<i>P</i> = 0.722) and were not associated with HbA1c or TIR at 3 months.</p><p><strong>Conclusions: </strong>In this real-world pediatric cohort, iLet initiation was associated with substantial glycemic improvement without increased hypoglycemia, particularly among participants with high baseline HbA1c. Favorable outcomes were observed among participants contributing paired 12-month data.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261496625"},"PeriodicalIF":7.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149179987","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":"Pediatric Inpatient Continuous Glucose Monitoring: Development and Implementation of a Hospital Policy for Accuracy-Validated Insulin Dosing.","authors":"Ming Yeh Lee, Hector I Ortega, Christopher Chow-Parmer, Karen Belanger, Sheridan Jones, Victor Ritter, Whitney Chadwick, Julie J Lee, Kailee Kingston, Cristal Suarez, Mark McGovern, David M Maahs, Priya Prahalad, Sejal Shah, Rayhan Lal, Michael S Hughes","doi":"10.1177/15209156261489912","DOIUrl":"10.1177/15209156261489912","url":null,"abstract":"<p><strong>Objective: </strong>We assessed the safety, clinical accuracy, glycemic outcomes, user outcomes, and implementation of a pediatric hospital-wide continuous glucose monitor (CGM) policy.</p><p><strong>Research design and methods: </strong>We used the Model for Improvement to develop a pediatric hospital policy supporting CGM-based insulin dosing with accuracy validation using capillary finger-stick blood glucose (FSBG) and then retrospectively evaluated the program's clinical effectiveness and implementation outcomes with the Simplified Implementation Logic Model to connect contextual determinants, support strategies, and outcomes.</p><p><strong>Results: </strong>From September 2024 to October 2025, 60 people with diabetes used the CGM policy in 93 hospital encounters, including 46.7% (28/60) people with type 1 diabetes and 38.7% (36/93) encounters with automated insulin delivery use. The most used CGMs were Dexcom G7 (49.5%) and Abbott Libre 3 or 3 Plus (31.2%). Accuracy validation using the %20/20 criteria was successful in 81.5% of attempts, and the mean absolute relative difference was 12.6%, with 1207 paired CGM and reference FSBG values. Nurses responded appropriately after 97.8% (218/223) of failed CGM validation attempts. CGM time in range (70-180 mg/dL) was 54.8%, and time in level 2 hypoglycemia (<54 mg/dL) was 0.8% for the 58 out of 93 encounters with complete CGM data downloads. Nurses preferred using CGM with policy support instead of FSBG for insulin dosing (adjusted <i>P</i> = 0.046). Factors contributing to nursing preference included ease of use, system integration, and the ability to learn the system quickly.</p><p><strong>Conclusions: </strong>We translated an inpatient CGM policy from an adult to pediatric hospital setting, supporting high levels of protocol adherence, clinical accuracy, safety, and improved experiences for clinicians. Program evaluation informs connections between contextual determinants, support strategies, and outcomes of implementing a hospital CGM program. These data offer a roadmap for implementing pediatric hospital CGM at other centers.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261489912"},"PeriodicalIF":7.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164724","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":"<i>Letter:</i> \"Natural History of Mild Non-Proliferative Diabetic Retinopathy in Adults with Type 1 Diabetes Under Contemporary Diabetes Care\".","authors":"Ervic R Cabading, Mary Jane S Alejo","doi":"10.1177/15209156261497212","DOIUrl":"https://doi.org/10.1177/15209156261497212","url":null,"abstract":"","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261497212"},"PeriodicalIF":7.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164158","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":"Association of HbA1c with Time in Range, Time in Tight Range, Empirically Derived Continuous Glucose Monitoring Glucose Ranges, and Mean Glucose.","authors":"Halis Kaan Akturk, Kagan E Karakus, Emma Mason, Zeynep Y Yilmaz, Satish K Garg, Dicle Cengiz","doi":"10.1177/15209156261496613","DOIUrl":"https://doi.org/10.1177/15209156261496613","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate how continuous glucose monitoring (CGM) glucose thresholds, sampling duration, and statistical modeling influence associations between CGM-derived range metrics and HbA1c, and to compare empirically derived glucose ranges with time in range (TIR) and time in tight range (TITR).</p><p><strong>Research design and methods: </strong>Out of 8160 HbA1c-matched CGM datasets from 1584 adults, CGM data from the 14, 30, and 90 days preceding each HbA1c measurement were evaluated. Associations between HbA1c and 7790 CGM glucose ranges were assessed across varying lower and upper glucose thresholds. Linear and log-linear mixed-effects models adjusted for age and race with participant-level random intercepts were compared using root mean square error and marginal <i>R</i>². Participant-level derivation (80%) and validation (20%) cohorts were used for cross-validated threshold selection and discrimination analyses for HbA1c ≤7.0%.</p><p><strong>Results: </strong>Ninety-day CGM sampling showed the strongest associations with HbA1c. Log-linear models frequently provided comparable or improved fit, particularly for glucose ranges with upper thresholds below 180 mg/dL. Stronger HbA1c associations clustered around upper glucose thresholds of 155-175 mg/dL. In validation analyses, empirically derived ranges demonstrated strong discrimination of HbA1c ≤7.0% (area under the receiver operating characteristic curve [AUC] 0.909-0.915), compared with 0.903 for TIR and 0.905 for TITR, although absolute differences were small. Mean glucose demonstrated the strongest explanatory performance for continuous HbA1c (marginal <i>R</i>² = 0.74) and similarly strong discrimination (AUC 0.913).</p><p><strong>Conclusions: </strong>CGM-HbA1c relationships are influenced by glucose range, sampling duration, and statistical modeling. Our findings reinforce TIR and TITR as robust CGM metrics with strong associations with HbA1c, while also showing that selected empirically derived glucose ranges can provide modestly stronger statistical alignment. These results support the continued use of TIR and TITR while highlighting how glucose range definition may refine interpretation of CGM-based endpoints in future research.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261496613"},"PeriodicalIF":7.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164396","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":"Roadmap to Fully Closing the Loop to Optimize Health in Type 1 Diabetes.","authors":"Brynn E Marks, Marisa E Hilliard, Boris Kovatchev, Othmar Moser, Molly L Tanenbaum, Courtney Ackeifi, David T Ahn, Ramzi A Ajjan, Anastasia Albanese-O'Neill, Abdulmohsen Bakhsh, Pratik Choudhary, Kelly L Close, Elizabeth A Davis, Klemen Dovc, Sanjoy Dutta, Satish Garg, Gregory P Forlenza, Julie Heverly, Aaron J Kowalski, Catarina Limbert, David M Maahs, Julia K Mader, Marjana Marinac, Chantal Mathieu, Revital Nimri, Rimei Nishimura, David N O'Neal, Monica Oxenreiter, Chengyuan Press, Michael R Rickels, Michael C Riddell, Jonathan Rosen, Banshi Saboo, Viral N Shah, Jennifer L Sherr, Benjamin J Wheeler, Moshe Phillip, Tadej Battelino, Thomas Danne","doi":"10.1177/15209156261496643","DOIUrl":"10.1177/15209156261496643","url":null,"abstract":"<p><strong>Objective: </strong>Current commercially available automated insulin delivery systems require meal and exercise announcements for optimal function, placing them in the hybrid closed loop category. Ongoing advancements aim to establish fully closed loop (FCL) systems as the next-generation standard for type 1 diabetes (T1D) management, eliminating the need for such inputs. We sought to develop a shared definition of FCL so that emerging technologies, including those using artificial intelligence, multi-hormone delivery, multi-analyte sensing, and/or adjunctive therapies, are held to uniform performance standards.</p><p><strong>Methods: </strong>A group of invited experts, including clinicians, researchers, and people with lived experience convened to propose an outcome-driven definition of FCL systems.</p><p><strong>Results: </strong>FCL systems are defined by three core domains centered on meeting intensive glycemic targets while reducing self-management burden and improving lived experience: Time in Tight Range (TITR) 70-140 mg/dL (3.9-7.8 mmol/L), Time Below Range (TBR) <54 mg/dL (<3.0 mmol/L), and person-reported outcomes (PROs) measured with validated T1D-specific assessments. This definition rests on a three-tiered classification in which all FCL systems improve lived experience and attain >50% TITR and <1% TBR without mandatory meal or activity announcements. Class 3 systems must attain >50% TITR; Class 2, >70%; and Class 1, >90%-while simultaneously demonstrating improved quality of life and reduced treatment burden per validated PROs. Given the elevated hypoglycemia risk from insulin-on-board during exercise, Class 1a requires >90% TITR with demonstrable hypoglycemia prevention during standardized exercise testing (0% TBR).</p><p><strong>Conclusion: </strong>This framework provides a uniform benchmark for evaluating future FCL technologies. Taken together, this person-centered, outcome-driven framework holds that FCL systems should (1) autonomously attain near-normoglycemia, (2) meaningfully improve lived experience across the lifespan and clinical contexts, and (3) prevent exercise-related hypoglycemia.</p>","PeriodicalId":11159,"journal":{"name":"Diabetes technology & therapeutics","volume":" ","pages":"15209156261496643"},"PeriodicalIF":7.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147908","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}