Citations

ABSTRACT Objective This study assessed the impact of sex and age on weight loss outcomes in patients receiving tirzepatide in real‐world clinical practice. Methods We conducted a retrospective cohort study of adults with overweight or obesity who initiated tirzepatide at Mayo Clinic between June 2022 and May 2024 and completed ≥ 12 months of continuous therapy. Primary outcome was total body weight loss percentage (TBWL%) at 15 months, stratified by sex and age groups (≤ 45, 46–59, ≥ 60 years). Results Among 1039 patients (57% women; mean age 56 ± 11 years), women achieved significantly greater TBWL% than men after 15 months of treatment (15.1% vs. 10.7%, p < 0.001). Patients aged ≤ 45 years had greater weight loss than those ≥ 60 years (15.1% vs. 12.3%, p = 0.024). In multivariable analyses, greater weight loss was independently associated with female sex, absence of type 2 diabetes, no prior obesity medication use, no concomitant weight gain‐promoting medications, and higher tirzepatide dosage. Age was not an independent predictor. Conclusions Sex, but not age, predicted weight loss with tirzepatide among individuals using the medication continuously for ≥ 12 months. These findings underscore the importance of incorporating sex‐specific considerations into personalized obesity treatment strategies.

Introduction To assess and compare the accuracy, readability, and overall performance of large language models (LLMs) in answering questions about functional hypothalamic amenorrhea (FHA) for patients and healthcare professionals. Methods A total of 11 patient-level and 15 clinician-level FHA-related questions were entered separately into four LLMs: ChatGPT 3.5 (free version), ChatGPT 4.0 (updated, paid subscription), Gemini, and OpenEvidence. OpenEvidence was used only for clinician-based questions. Responses were evaluated by three expert reviewers blinded to the LLM used who rated them as accurate and complete, accurate but incomplete, or inaccurate. A fourth reviewer resolved discordant scores. Readability for patient-level questions was assessed using the Flesch Reading Ease Score (FRES) and word count. Lower FRES scores indicate more difficult reading. Accuracy and completeness were compared using odds ratios (95% CI) with ChatGPT 3.5 as the reference model, and differences in readability were analyzed using Friedman’s test. Results LLM performance varied across question types. For patient-level questions, ChatGPT 4.0 achieved the highest accuracy (9 of 11; 82%), followed by ChatGPT 3.5 and Gemini (each 8 of 11; 73%), with no statistically significant differences. Among clinician-level questions, OpenEvidence demonstrated perfect accuracy (15 of 15; 100%), compared with 93% for and 80% for ChatGPT 4.0 and Gemini. Completeness followed similar patterns, with OpenEvidence providing the most complete clinician responses (93%) and ChatGPT 4.0 the most complete patient-level responses (89%). Readability differed significantly among models ( p = 0.012), with Gemini producing the most readable patient-level content (median FRES 43.5 [IQR 36.8–53.4]) compared with ChatGPT 3.5 (30.6 [16.8–48.4]) and ChatGPT 4.0 (28.8 [22.1–37.6]). Word counts did not differ significantly ( p = 0.39). Discussion LLMs demonstrated good overall performance in answering FHA-related questions but often provided incorrect or incomplete information. Fine tuning field-specific data, engineered prompts, and obtaining human-in-the-loop feedback may help improve the accuracy of these models.

Objective To evaluate the effects of mepolizumab therapy on the blood transcriptome in patients with severe eosinophilic asthma (SEA) and to determine whether changes in gene expression predict responsiveness to mepolizumab treatment. Methods Fourteen consecutive patients diagnosed with SEA were prospectively enrolled. Each patient had sequential blood samples obtained pre-initiation and at 4, 8, and 12 weeks post-initiation of mepolizumab. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples for bulk RNA sequencing. Paired differential expression (DE) gene analysis was performed comparing baseline gene expression versus all timepoints. Changes in gene expression were also compared between responders versus nonresponders to mepolizumab treatment. Finally, gene set enrichment analysis was evaluated across timepoints and its interaction with the response to mepolizumab treatment. Results Principal component analysis of all genes demonstrated a homogeneous distribution, which did not separate into distinct groups based on either timepoint or therapeutic response. At week 12, 149 genes had significant DE (FDR &lt; 0.05) compared to baseline. When performing all-way timepoint comparisons, 74 genes (63 protein-coding) were found to be downregulated at weeks 4, 8 and 12 compared to baseline, but no differences were observed between post baseline timepoints. Twenty-eight genes (16 protein-coding) did change with progression of time and were associated with response to therapy, and 56 protein-coding genes distinguished responders and nonresponders. At week 12, nonresponders had a high enrichment of genes involved with signaling pathways, including cytokines, growth factors, cell differentiation markers, and protein kinases. Conclusion In this exploratory study, mepolizumab use was associated with limited global transcriptomic separation with some preliminary differential-expression and enrichment findings. No definitive mechanistic or predictive response signatures were identified due to the study limitations. Future studies, including larger cohorts followed for longer durations with interrogation of specific immune subsets, may offer additional insight into the pathophysiology of severe eosinophilic asthma and into predicting response to mepolizumab treatment.

ABSTRACT Reactive infectious mucocutaneous eruption (RIME) is a post‐infectious mucositis which primarily affects pediatric patients. We report a 14‐year‐old girl with recent intensive sun exposure who developed RIME in a photodistributed pattern. Treatment with etanercept yielded clinical improvement. Potential mechanisms underlying photodistributed RIME syndrome are discussed, with comparison to photodistributed instances of Stevens‐Johnson syndrome.

ABSTRACT HLA‐C*01:155 is characterised by a single nucleotide substitution in codon 225 in exon 4.

Coronary artery disease (CAD) remains a leading cause of global morbidity and mortality. Accurate diagnosis of ischemia-causing coronary stenoses is essential for guiding revascularization and improving outcomes. Although invasive fractional flow reserve (FFR) remains the gold standard for functional lesion assessment, its use is limited by procedural invasiveness, cost, and complexity. CT-derived FFR (FFRct), based on computational fluid dynamics (CFD), was the first major advance in noninvasive physiological assessment, but its adoption has been hindered by intensive off-site computation and dependence on high-quality imaging. This review summarizes the evolution from invasive FFR to AI-driven functional assessment of coronary lesions. We examine the principles and validation of CFD-based FFRct and then focus on the shift toward artificial intelligence, including both machine learning (ML) and deep learning (DL) approaches. These methods range from models using engineered geometric and plaque features trained on large synthetic datasets to end-to-end systems that learn directly from imaging data. We discuss key validation studies evaluating diagnostic accuracy, prognostic value, and clinical utility, with attention to performance in challenging settings such as intermediate stenoses, heavy calcification, and patients with comorbidities. We also highlight major barriers to widespread adoption, including dependence on input data quality, limited explainability, regulatory hurdles, and integration into clinical workflows. Finally, we outline future directions, including AI-enabled virtual PCI planning, multimodal risk stratification, and broader access to functional cardiac assessment. AI has the potential to transform noninvasive coronary imaging by enabling a single CCTA scan to provide rapid, integrated evaluation of anatomy, plaque characteristics, and physiological significance, supporting more personalized care and better clinical outcomes.

Abstract Hematoxylin and eosin (H&E) slides are routinely available in cancer care, but molecular profiling often requires additional tissue processing and turnaround time. We introduce HistoMethyl, a DNA methylation-aware pathology foundation model that aligns whole-slide histopathology with matched genome-scale methylation beta-value profiles during pre-training while requiring only an H&E slide at inference. We evaluated HistoMethyl across four task groups: gene mutation prediction, morphology-associated classification, overall survival prediction, and direct DNA methylation beta-value recovery. Evaluation spanned TCGA cross-validation, a disease-held-out lower-grade glioma cohort, and external cohort validation in CPTAC glioblastoma and SurGen rectal adenocarcinoma, totaling 14 cancer cohorts and 81 gene mutation tasks. The best-performing configuration improved mean mutation AUROC by 5.03 percentage points. By converting DNA methylation supervision into an H&E-only representation, His-toMethyl could support an early molecular triage layer that helps prioritize cases for confirmatory sequencing, methylation profiling, immunohistochemistry, and molecular tumor board review. These image-only predictions are intended to accelerate downstream molecular testing and tissue allocation while leaving final diagnosis and treatment selection anchored in validated molecular assays.

As a widely distributed network of cells, tissues, and organs, the human immune system is profoundly vulnerable to the effects of aging. Intrinsic and extrinsic stressors progressively erode its structural integrity and functional resilience, weakening core protective responses and increasing susceptibility to infection, malignancy, and tissue degeneration. At the same time, aging heightens the risk of chronic inflammation and autoimmune disease. Hematopoietic stem cells become uniquely compromised as aging intensifies metabolic and replicative stress. Their continuous high-volume turnover results in diminished self-renewal capacity, skewed lineage output, and dominance of expanded clones. These changes undermine innate immune competence and amplify inflammatory activity. Adaptive immune function declines with age through coordinated cellular and molecular programs. T and B lymphocytes exhibit a decline in naive cells, progressive loss of stemness, shortened lifespan, and constrained clonal diversity. Aging lymphocytes reconfigure transcriptional networks, undergo widespread organelle dysfunction, develop maladaptive stress responses, and redistribute into noncanonical tissue niches. Collectively, these alterations reduce antigen specificity and precision, promote innate-like immune behavior, and confer resistance to tolerance. These mechanisms result in concurrent immunodeficiency and autoimmunity, exemplified by two autoimmune diseases disproportionately affecting older adults: rheumatoid arthritis and giant cell arteritis.