Allergy

Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients responding poorly to T2-targeted biologic therapies. We developed a contrastive machine learning method for patient stratification based on whole-blood DNA methylation (DNAm), applying it to pediatric asthma cohorts of Latino (discovery; n=1,016) and...

Revealing Shared Tumor Microenvironment Dynamics Related to Microsatellite Instability Across Different Cancers Using Cellular Social Network Analysis

Microsatellite instability (MSI) is a key biomarker for immunotherapy response and prognosis across multiple cancers, yet its identification from routine Hematoxylin and Eosin (H&E) slides remains challenging. Current deep learning predictors often operate as black-box, weakly supervised models trained on individual slides, limiting interpretability, biological insight, and generalization; particu...

Multiregional CT Features Improve Prediction of Immunotherapy Response in Advanced Melanoma

Immunotherapy has improved outcomes for advanced-stage melanoma, however, predictive biomarkers remain limited. We evaluated whether computed tomograp...

Understanding the Relationship Between Germ Layer Origin and Cancer Therapy Response: A Systematic Review

Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malig...

Generalizable AI predicts immunotherapy outcomes across cancers and treatments

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging ...

Comparative Benchmarking of Five Contemporary Language Models on Clinical Reasoning

The rapid integration of Large Language Models (LLMs) into healthcare raises critical questions regarding their safety and reliability. While models o...

Operational Survival Deficit of Neoadjuvant Chemotherapy in Early-Stage Breast Cancer: A Target Trial Emulation and Causal Machine Learning Study

Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized tria...

Demographics, Overlap, and Latency of Severe Cutaneous Adverse Reactions in an FDA Database

Severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS-TEN), drug reaction with eosinophilia a...

A medically grounded LLM agent–based tool to detect patient safety events in medical records

Large language models (LLMs) have shown incredible promise in medicine. While LLMs may be particularly useful in areas requiring extensive review of c...

Integrative AI Model Combining Radiomics and Phenomics to Predict Survival in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy Containing Regimen

Immunotherapy has improved outcomes in non-small cell lung cancer (NSCLC), but only a subset of patients achieves durable survival benefit. Convention...

Deciphering mitochondrial dysfunction in keratoconus: Insights into ACSL4 from machine learning-based bulk and single-cell transcriptome analyses and experimental validation.

Keratoconus (KC) is a prevalent ectatic corneal disease and the leading cause of corneal transplantation globally. Despite evidence of mitochondrial a...

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effect...

Multi-omics analysis and experiments uncover the link between cancer intrinsic drivers, stemness, and immunotherapy in ovarian cancer with validation in a pan-cancer census.

BACKGROUND: Although immune checkpoint inhibitors (ICIs) represent a substantial breakthrough in cancer treatment, it is crucial to acknowledge that t...

Autoencoder techniques for survival analysis on renal cell carcinoma.

Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of...

Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The ...

Automated Evaluation of Antibiotic Prescribing Guideline Concordance in Pediatric Sinusitis Clinical Notes.

BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer imm...

Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.

Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response to treatment, making it one of the...

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as...

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer im...