COVID-19

Non-invasive diagnostic evaluation of urinary exosomal let-7c cluster expression in bladder cancer using machine learning approaches.

BACKGROUND: Bladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological fluids and disease specificity. The let-7c cluster, owing to its tumor-suppressive function and frequent dysregulation in BCa, has emerged...

Targeting progressive multiple sclerosis: Toward mechanism-informed precision medicine.

Multiple sclerosis has undergone a therapeutic revolution over the past three decades. Randomized clinical trials and real-world data demonstrate that modern disease-modifying therapies substantially reduce relapse rates and acute inflammatory activity detected by magnetic resonance imaging (MRI). However, disability accumulation increasingly occurs independent of relapse activity, highlighting pr...

Environmental and management drivers of gastrointestinal nematodes in shelter dogs across mainland Portugal revealed by ecological niche modelling.

Gastrointestinal nematodes (GIN) are a major health concern in dogs, particularly in high-density environments such as shelters. Toxocara canis, Ancyl...

Chaotropic Surface Chemistry for Protein Profiling with Solid-State Micropores.

Proteins derive their biological identity from an amino acid sequence, and even subtle sequence variations can mark disease states, therapeutic respon...

Quantum kernel-based delta (Δ)-learning for correcting a semiempirical method in the prediction of reaction barrier heights.

A quantum machine learning based Δ-learning framework is presented for the prediction of reaction barrier heights of organic molecules. Corrections le...

Integrative Transcriptomic Analysis Identifies a Key Candidate Gene Bridging Inflammation and DNA Repair in Endometriosis.

BACKGROUND: Endometriosis is a chronic, estrogen-dependent inflammatory disorder characterized by the ectopic implantation of endometrial-like tissue....

Advances in predictive factors for pathological complete response after neoadjuvant therapy in locally advanced rectal cancer.

For locally advanced rectal cancer (LARC), neoadjuvant chemoradiotherapy (nCRT) combined with total mesorectal excision has emerged as the standard th...

Quantifying the spatial information loss of Dose-Volume Histograms in Gamma Knife radiosurgery via unsupervised 3D dose geometry embedding.

Dose-Volume Histograms (DVH) discard spatial information by summarizing 3D distributions into 1D curves. This study quantifies this loss in Gamma Knif...

Machine Learning Prediction of Liver Fibrosis in Patients with Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD).

BACKGROUND: Accurate staging of liver fibrosis is crucial for risk stratification in patients with metabolic dysfunction-associated steatotic liver di...

Vehicle lane change prediction with explainable soft mask attention mechanism and heterogeneous information encoder.

Lane-change intention prediction is critical for intelligent vehicles, and driver decisions depend on the perception and processing of driving context...

Predicting breast cancer pathological complete response with clinical and imaging data.

INTRODUCTION: Pathological complete response (pCR) is a key prognostic indicator in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NA...

Investigating the mechanisms of malignant progression in colorectal cancer using weighted gene co-expression network analysis and machine learning.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Understanding the complex molecular networks that underlie this...

Bridging the Vaccine Gap: Scientific and Technological Advances for Diseases Lacking Effective Vaccines.

Despite remarkable achievements in infectious disease control, more than 20 major pathogens responsible for significant global morbidity and mortality...

Integrating single-cell sQTL mapping with deep-learning splicing prediction identifies causal variants under influenza infection.

BACKGROUND: Realizing the full potential of human genetics requires identifying causal variants and genes underlying association signals. Molecular qu...

Predicting severe COVID-19 in elderly patients using routine laboratory indicators: Diagnostic accuracy of machine learning models.

To enable early warning of severe 2019 coronavirus disease in elderly patients, this study collected routine laboratory indicators and clinical parame...

Clinical target volume radiomics from planning CT for pretreatment response prediction  in rectal cancer undergoing chemoradiotherapy.

PURPOSE: Response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer remains heterogeneous. This exploratory study evaluated whether radiomics f...

CRISPR/cas-based biosensors for point-of-care testing: a comprehensive review of signal readout strategies.

The CRISPR/Cas system has emerged as a transformative tool for nucleic acid detection, offering significant potential for point-of-care testing (POCT)...

Application of machine learning techniques for predicting seawater intrusion vulnerability in a coastal alluvial aquifer of eastern India.

Groundwater contamination is a major concern worldwide, especially in coastal regions, due to increasing seawater intrusion into aquifers. In eastern ...

Artificial intelligence powered decoding of Plasmodium falciparum sporozoite motility patterns.

Sporozoites (SPZ), the infective mosquito stage of Plasmodium falciparum malaria, are widely studied as therapeutic targets for antibodies either endo...

Techniques, Performance, and Feasibility of Natural Language Processing for Abstract Screening in Evidence Synthesis: A Systematic Review.

BACKGROUND: Natural language processing (NLP) techniques offer promising solutions for semi-automating the time-consuming process of abstract screenin...