ChatGPT-4 Knows Its A B C D E but Cannot Cite Its Source.

Introduction: The artificial intelligence language model Chat Generative Pretrained Transformer (ChatGPT) has shown potential as a reliable and accessible educational resource in orthopaedic surgery. Yet, the accuracy of the references behind the provided information remains elusive, which poses a concern for maintaining the integrity of medical content. This study aims to examine the accuracy of the references provided by ChatGPT-4 concerning the Airway, Breathing, Circulation, Disability, Exposure (ABCDE) approach in trauma surgery. Methods: Two independent reviewers critically assessed 30 ChatGPT-4-generated references supporting the well-established ABCDE approach to trauma protocol, grading them as 0 (nonexistent), 1 (inaccurate), or 2 (accurate). All discrepancies between the ChatGPT-4 and PubMed references were carefully reviewed and bolded. Cohen's Kappa coefficient was used to examine the agreement of the accuracy scores of the ChatGPT-4-generated references between reviewers. Descriptive statistics were used to summarize the mean reference accuracy scores. To compare the variance of the means across the 5 categories, one-way analysis of variance was used. Results: ChatGPT-4 had an average reference accuracy score of 66.7%. Of the 30 references, only 43.3% were accurate and deemed "true" while 56.7% were categorized as "false" (43.3% inaccurate and 13.3% nonexistent). The accuracy was consistent across the 5 trauma protocol categories, with no significant statistical difference (p = 0.437). Discussion: With 57% of references being inaccurate or nonexistent, ChatGPT-4 has fallen short in providing reliable and reproducible references-a concerning finding for the safety of using ChatGPT-4 for professional medical decision making without thorough verification. Only if used cautiously, with cross-referencing, can this language model act as an adjunct learning tool that can enhance comprehensiveness as well as knowledge rehearsal and manipulation.

Standardized In Vitro Models of Human Adipose Tissue Reveal Metabolic Flexibility in Brown Adipocyte Thermogenesis.

Functional human brown and white adipose tissue (BAT and WAT) are vital for thermoregulation and nutritional homeostasis, while obesity and other stressors lead, respectively, to cold intolerance and metabolic disease. Understanding BAT and WAT physiology and dysfunction necessitates clinical trials complemented by mechanistic experiments at the cellular level. These require standardized in vitro models, currently lacking, that establish references for gene expression and function. We generated and characterized a pair of immortalized, clonal human brown (hBA) and white (hWA) preadipocytes derived from the perirenal and subcutaneous depots, respectively, of a 40-year-old male individual. Cells were immortalized with hTERT and confirmed to be of a mesenchymal, nonhematopoietic lineage based on fluorescence-activated cell sorting and DNA barcoding. Functional assessments showed that the hWA and hBA phenocopied primary adipocytes in terms of adrenergic signaling, lipolysis, and thermogenesis. Compared to hWA, hBA were metabolically distinct, with higher rates of glucose uptake and lactate metabolism, and greater basal, maximal, and nonmitochondrial respiration, providing a mechanistic explanation for the association between obesity and BAT dysfunction. The hBA also responded to the stress of maximal respiration by using both endogenous and exogenous fatty acids. In contrast to certain mouse models, hBA adrenergic thermogenesis was mediated by several mechanisms, not principally via uncoupling protein 1 (UCP1). Transcriptomics via RNA-seq were consistent with the functional studies and established a molecular signature for each cell type before and after differentiation. These standardized cells are anticipated to become a common resource for future physiological, pharmacological, and genetic studies of human adipocytes.