The intestinal microbiota is a critical determinant of mucosal homeostasis, yet the functional contributions of Archaea—which constitute up to 10% ...

Two intestinal cell types share developmental traits and combine microbial sensing with antigen delivery to shape antibody responses.

The Endoscopic Mucosal Resection (EMR) market represents a high-precision segment of gastrointestinal endoscopy focused on minimally invasive lesion resection within the mucosal layer. EMR techniques,...

The Endoscopic Mucosal Resection (EMR) market represents a high-precision segment of gastrointestinal endoscopy focused on minimally invasive lesion resection within the mucosal layer. EMR techniques,...

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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, a...

Read the full paper: Methanogenic archaea Methanobrevibacter smithii and Methanosphaera stadtmanae promote mucosal homeostasis and protect from colitis

Background: Machine learning has been extensively applied in #medical prediction; however, existing machine-assisted pharmacotherapy review systems predominantly rely on structured, codifiable data while neglecting unstructured natural language clinical narratives, thereby allowing latent medication risks to remain undetected. Objective: To address the limitations of structured data–based review, this study proposes and validates a machine learning framework grounded in text classification for the automated identification of “prophylactic” vs “nonprophylactic” medication indications within clinical narratives. As a result, it aims to assist in the automated detection of prophylactic indication mentions for stress-related mucosal disease in clinical narratives as a preliminary step toward comprehensive medication-use review. Methods: A total of 3695 progress notes were independently annotated by 2 clinical pharmacists to construct a binary dataset for classifying prophylactic medication indication for stress-related mucosal disease, yielding an interannotator agreement κ of 0.8934. Comparative experiments were conducted using traditional machine learning models, including logistic regression, support vector machine, random forest, CatBoost, and LightGBM, alongside TextCNN and a Chinese pretrained BERT model. The dataset was split into training, validation, and test sets on a #patient-wise basis, and model evaluation was conducted on the independent test set. SHAP (Shapley additive explanations) analysis was used to achieve model interpretability and elucidate decision-making processes, ultimately enabling automated indication detection of prophylactic proton pump inhibitor administration. Results: All models and strategies demonstrated efficacy in #medical text classification tasks. In a single-center cohort of 66 #patients (3695 notes, 11.0% positive), the CatBoost model with balanced class weights achieved a note-level -score of 0.727 (precision=0.693; recall=0.765) and a #patient-level -score of 0.889 (precision=0.800; recall=1.000) on the independent test set. At the note level, CatBoost achieved a higher -score than TextCNN (-score=0.718) and BERT (-score=0.628). Conclusions: In summary, the text classifier constructed through machine learning and natural language processing effectively extracts prophylactic medication indications for stress-related mucosal disease from unstructured progress notes. Combined with #patient-level data partitioning, standardized annotation protocols, and an ethics-compliant framework, this approach provides an interpretable preliminary screening tool for identifying potential prophylactic indications to support subsequent pharmacist adjudication. Trial Registration: Chinese Clinical Trial Registry ChiCTR2600126317; https://tinyurl.com/yjzjd443

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Asymptomatic bacteriuria (ASB) is a common finding in many populations, including healthy women and persons with underlying urologic abnormalities. The 2005 guideline from the Infectious Diseases Soci...

<p>Drs. Emily Eshleman, and Theresa Alenghat are in the Author&rsquo;s Corner with Kathryn Knoop to discuss their work: <a href='https://www.mucosalimmunology.org/article/S1933-0219(26)00058-9/fulltex...