休斯顿卫理公会医院

Gastric cancer (GC) remains a major global health burden, with poor survival rates. HER2 is a key biomarker for targeted therapy, but discordance between primary tumors and metastases may impact treatment decisions. This meta-analysis evaluated the prevalence and clinical relevance of HER2 status differences between primary gastric cancers and metastatic lesions. A systematic search of PubMed, Embase, and Cochrane was conducted for studies assessing HER2 status in matched primary and metastatic gastric cancer samples. Pooled proportions and 95

Sex disparities in left ventricular assist device (LVAD) therapy remain a significant challenge in advanced heart failure care. This review synthesizes evidence on differences in epidemiology, clinical presentation, anatomical suitability, complications, and long-term recovery to clarify why women remain underrepresented despite comparable survival with contemporary devices. Women constitute nearly half of the heart failure population but only 20–25

Cells can recover from sub-lethal necrosis by repairing plasma-membrane (PM) rupture through the ESCRT-III machinery, but how this process is regulated remains unknown. Here, we identify Toll-interacting protein (Tollip) as a conserved negative regulator for ESCRT-III–mediated PM repair. Quantitative proteomics revealed the enrichment of Tollip at damaged PM. Additionally, microscopy assays in mammalian cells and C. elegans confirmed the recruitment of Tollip to PM injury sites. Tollip deficiency augmented ESCRT-III assembly, improved long-term cell survival after sub-lethal PM damage, and enhanced PM repair, whereas Tollip overexpression suppressed these processes. Tollip translocation occurred independently of Ca2⁺ influx, different from ESCRT-III. Functionally, by limiting PM repair and maintaining sub-lethal PM integrity loss, Tollip ensured optimal chemokine and cytokine production from the plasma-membrane-integrity (PMI) pathway, which is directly triggered by PM ruptures. Thus, Tollip acts as a molecular rheostat that links membrane damage repair and cell recovery to immune signaling.

Patients with obstructive sleep apnea (OSA) frequently seek information online, yet the comparative quality of content delivered by web search engines versus generative AI systems is unclear. This study evaluated how different digital information sources perform in answering common patient questions about OSA. Thirty high-volume, patient-facing OSA questions were identified using Google Trends. Each question was submitted verbatim to four general-purpose large language models (GPT-4, GPT-5, DeepSeek, Mistral), a medically specialized retrieval-augmented model (OpenEvidence), and Google Search. Seven otolaryngologists with clinical experience in OSA independently rated each response for accuracy, clarity, completeness, relevance, and usefulness using a five-point rubric. Composite and domain scores were analyzed using one-way analysis of variance with multiple-comparison correction; inter-rater reliability was assessed with two-way random-effects intraclass correlation coefficients. A total of 180 question–system pairs received 6295 domain-level ratings. OpenEvidence achieved the highest mean composite score (4.33), followed by a tightly clustered group of LLMs (means 4.00–4.04). Google Search scored significantly lower (3.15). Differences among systems were statistically significant across all domains (p < 0.001), with large effect sizes for comparisons of OpenEvidence and general LLMs versus Google. Composite average-rater reliability was good (ICC = 0.70). For common OSA questions, generative AI systems—particularly a retrieval-augmented medical model—produced higher-quality patient-facing information than standard web search. These findings support cautious consideration of GenAI tools to supplement patient education in OSA, while underscoring the need for ongoing evaluation across diseases, disciplines, and patient populations. Patients with obstructive sleep apnea (OSA) frequently rely on online sources such as Google Search to understand symptoms, testing, and treatment, yet the quality of patient-facing information varies widely. As generative artificial intelligence tools are increasingly used for health questions, their comparative performance for OSA education has not been systematically evaluated using blinded expert review. In this blinded comparative study, generative AI systems, particularly a retrieval-augmented medical model, provided more accurate, clear, complete, and useful answers to common OSA questions than standard web search. These findings highlight that the choice of digital information source can meaningfully influence the quality of patient education in sleep medicine and support further evaluation of AI tools within clinical practice.

This article provides a focused update to the clinical practice guideline on the treatment and management of patients with coronavirus disease 2019, developed by the Infectious Diseases Society of America. The guideline panel presents a recommendation on the use of the anti-severe acute respiratory syndrome coronavirus 2 neutralizing antibody pemivibart as pre-exposure prophylaxis. The recommendation is based on evidence derived from a systematic review and adheres to a standardized methodology for rating the certainty of evidence and strength of recommendation according to the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Information on pemivibart is included in the U.S. Food and Drug Administration Emergency Use Authorization for this agent.