BASIC AND TRANSLATIONAL AT cell expansion reduced chronic intestinal inflammation in mice. Strategies to support tuft cells might be developed for treatment of CD.

We demonstrate that a number of lipid-, amino acid-, and tricarboxylic acid (TCA) cycle- related metabolites were significantly altered in IBD patients, more specifically in CD. Therefore, alterations in lipid and amino acid metabolism and energy homeostasis may play a key role in the pathogenesis of CD.

Background: Natural language processing models such as ChatGPT can generate text-based content and are poised to become a major information source in medicine and beyond. The accuracy and completeness of ChatGPT for medical queries is not known. Methods: Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes/no) or descriptive answers. The physicians then graded ChatGPT-generated answers to these questions for accuracy (6-point Likert scale; range 1 – completely incorrect to 6 – completely correct) and completeness (3-point Likert scale; range 1 – incomplete to 3 - complete plus additional context). Scores were summarized with descriptive statistics and compared using Mann-Whitney U or Kruskal-Wallis testing. Results: Across all questions (n=284), median accuracy score was 5.5 (between almost completely and completely correct) with mean score of 4.8 (between mostly and almost completely correct). Median completeness score was 3 (complete and comprehensive) with mean score of 2.5. For questions rated easy, medium, and hard, median accuracy scores were 6, 5.5, and 5 (mean 5.0, 4.7, and 4.6; p=0.05). Accuracy scores for binary and descriptive questions were similar (median 6 vs. 5; mean 4.9 vs. 4.7; p=0.07). Of 36 questions with scores of 1-2, 34 were re-queried/re-graded 8-17 days later with substantial improvement (median 2 vs. 4; p<0.01). Conclusions: ChatGPT generated largely accurate information to diverse medical queries as judged by academic physician specialists although with important limitations. Further research and model development are needed to correct inaccuracies and for validation.

OBJECTIVEPatients with diabetes may experience high burden of treatment (BOT), including treatment-related effects and self-care demands. We examined whether patients with type 2 diabetes and their clinicians discuss BOT, the characteristics of their discussions, and their attempts to address BOT during visits.RESEARCH DESIGN AND METHODSTwo coders independently reviewed videos of 46 primary care visits obtained during a practice-based trial and identified utterances concerning BOT, classifying them by topic and by whether BOT was addressed (i.e., whether statements emerged aimed at alleviating BOT).RESULTSOf the 46 visits, 43 (93.5%) contained BOT discussions. Both coders identified 83 discussions: 12 involving monitoring, 28 treatment administration, 19 access, and 24 treatment effects. BOT was unambiguously addressed only 30% of the time.CONCLUSIONSBOT discussions usually arise during visits but rarely beget problem-solving efforts. These discussions represent missed opportunities for reducing treatment-related disruptions in the lives of patients with diabetes, which may affect adherence and well-being.