Allergy

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1 expression in non-small cell lung cancer (NSCLC) and to explore its additional value in predicting the prognosis of immunotherapy. MATERIALS AND METHODS: A retrospective analysis was conducted of 328 NSCLC patients with available PD-L1 immunohistoch...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), characterised by early progression or short-lived disease stabilisation following treatment, remains a significant challenge. METHODS: We analysed 1296 patients with HCC treated with frontline A+B (AB-real) and validated findings in 645 trial participants...

Mitochondrial Metabolic Biomarkers in Periodontitis: Discovery and Clinical Validation.

INTRODUCTION AND AIMS: Mitochondrial metabolic dysregulation is associated with periodontitis (PD); however, related biomarkers remain unclear. In thi...

A customized AI-based machine learning model for evaluating participants' activities under a workshop setting.

Human action recognition (HAR) in the workshop-style environment poses unique challenges due to class imbalance, fused action boundaries, and context ...

Toward generalizable prediction of cancer signal using a cell-free DNA language model.

Cell-free DNA can be used for early cancer detection, minimal residual disease monitoring, and post-treatment risk stratification. However, current as...

Predicting device-related complications among women undergoing breast implant surgeries.

BACKGROUND: Breast implant surgery is a high-volume procedure, yet predicting device-related complications that require revision surgery remains chall...

A Single-Cell Guided Machine Learning Model Predicts Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Resistance to immune checkpoint inhibitors is a major clinical obstacle in the treatment of gastric cancer. Identifying drug-resistant cell population...

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogeneity limits therapeutic benefit to specific patient...

Enhancing the detection of LTP through lyophilized protein samples and NIR spectroscopy with explainable deep learning.

Lipid transfer proteins (LTPs) are clinically relevant allergens widely present in plant-based foods, and their reliable detection in complex food mat...

Informatics-Based Psychotherapeutic and Psychiatric Interventions in Dermatology: Scoping Review of Impacts on Skin Disease Severity and Mental Health Outcomes.

BACKGROUND: Chronic dermatologic conditions such as psoriasis, atopic dermatitis, and hidradenitis suppurativa are associated with a high burden of ps...

Dynamic urinary proteomics integrates single-cell and spatial transcriptomics to reveal tumour microenvironment and predict immunotherapy response in biliary tract cancer.

BACKGROUND: Most patients with biliary tract cancer (BTC) do not derive durable clinical benefit (DCB) from immune checkpoint inhibitors (ICIs), under...

Targeting colorectal cancer stem-like cells: Overcoming therapeutic resistance and reprogramming the immunosuppressive niche.

Colorectal cancer stem-like cells (CRC-SCs) represent a tumor subpopulation with enhanced tumor-initiating capacity and have been implicated in metast...

A multimodal approach to equine thoracolumbar myofascial pain: integrating clinical examination, AI-based gait analysis, and the ridden horse pain ethogram.

BACKGROUND: Myofascial pain syndrome (MPS) is considered highly prevalent in horses, yet it is frequently underdiagnosed due to limited understanding ...

Artificial intelligence in food allergen detection and prediction: advances, methodologies, and challenges.

Food allergies affect over 220 million individuals worldwide and present increasing challenges due to complex food matrices and processing-induced pro...

Enhance autism spectrum disorder detection using stacking ensemble learning model with explainable AI.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is manifested by sensory abnormalities such as hypersensitivity to sound and touc...

Smart detection of juice adulteration: An approach based on ion mobility spectrometry and machine learning.

BACKGROUND: Fruit juice quality is regulated by European Union legislation; however, adulteration by adding cheaper alternatives, such as white grape ...

Machine learning models predict the immunotherapy response in tumors on the basis of DNA methylation.

BACKGROUND: The epigenetic control of immune responses plays a crucial role in the development and progression of cancer. The need to identify biomark...

Explainable AI reveals squamous histology and U-shaped PD-L1 patterns as primary subgroup predictors of neoadjuvant and perioperative immunotherapy benefit in NSCLC: a machine learning analysis.

BACKGROUND: The introduction of neoadjuvant and perioperative immunotherapy has broadened treatment options for resectable non-small cell lung cancer ...

Understanding resistance in glioblastoma: insights into personalized and targeted therapeutic strategies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despite combined treatments, including surgical removal...

Early Immunological Biomarkers for Personalized Treatment Selection in Severe COVID-19: Post Hoc Machine Learning Analysis of a Randomized Clinical Trial.

BACKGROUND: Severe COVID-19 is a global health concern despite continuous vaccination campaigns because current therapies, such as dexamethasone and r...