Universal precautions

Machine Learning Model for Predicting Hepatocellular Carcinoma Development in Patients With Hepatitis B Virus-Related Cirrhosis Receiving Antiviral Therapy.

Few prediction models have been specifically designed to evaluate the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B and cirrhosis. This study aimed to develop a machine learning-based prediction model to assess the risk of developing HCC in patients with hepatitis B virus (HBV)-related cirrhosis undergoing nucleos(t)ide analogue (NA) therapy. We included 1592 patients...

Machine learning-enabled wastewater-based surveillance for emerging pathogen detection and monitoring: current applications, challenges, and future prospects.

AbstractWastewater-based surveillance (WBS) has become an important public-health tool for tracking community-level circulation of emerging and re-emerging pathogens, but wastewater measurements are not directly interpretable public-health indicators. Signals recovered from sewer systems are shaped by sampling variability, environmental and laboratory noise, population-dependent bias, and high-dim...

Large Language Models for Heterogeneous Data Mining in Liver Disease: Framework Development and Retrospective Validation Study.

BACKGROUND: Differentiating among liver disease entities such as autoimmune liver disease (AILD), drug-induced liver injury (DILI), and chronic hepati...

Programmable design of synthetic plant immune receptors for pathogen protein recognition.

The limited diversity and recognition scope of natural plant immune receptors impede resistance breeding against rapidly evolving pathogens. Here, we ...

A Reliability-Guided Fusion and Dynamic Dual-Domain Mask-Modulated Shearlet Network for Medical Image Segmentation.

Deep learning methods are becoming increasingly important for medical image segmentation. However, previous approaches often performed poorly in spati...

From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions.

Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial intelligence methods, though, average per...

MicroNucML enables machine learning-based micronuclei segmentation and micronuclei-nuclei association.

Micronuclei (MN) are structures containing small DNA fragments that arise from mitotic errors or failed DNA repair and serve as markers of genome inst...

Integrating Microfluidics Chips into Vector-Borne Disease Surveillance: Technological Breakthroughs and Persistent Hurdles.

BACKGROUND: Vector-borne diseases (VBDs), such as malaria, dengue, and Zika virus infections, remain a critical global health burden, particularly in ...

A data-driven approach to hepatitis C forecasting using machine learning and epidemiological models.

The hepatitis C virus is a significant global health concern and a major cause of chronic liver disease. Therefore, developing a mathematical model is...

Artificial intelligence in Latin American hepatology: current evidence, implementation barriers, and strategic priorities.

Chronic liver diseases are an increasing cause of morbidity and mortality in Latin America, driven by the convergence of alcohol and metabolic-associa...

Microphysiological immune-on-chip systems: engineering human immunity for precision immunotherapy, vaccine development, and autoimmune disease modelling.

Microphysiological immune-on-chip systems have emerged as transformative biomedical microdevices capable of recreating the structural, biochemical, an...

Comparable Predictive Performance of Non-VCTE-Based Machine Learning Model for HBV-Infected Hepatocellular Carcinoma Risk.

BACKGROUND: Accurate risk stratification for hepatocellular carcinoma (HCC) among chronic hepatitis B (CHB) patients remains challenging. Vibration-co...

Leveraging synthetic and genetic data to improve epidemic forecasting.

Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper,...

PAC-Net: A physics-guided multimodal hybrid network for antibody-antigen interaction prediction.

The precise prediction of Antibody-Antigen Interaction (AAI) is a pivotal task for accelerating antibody drug discovery and virtual screening. To addr...

Clinical phenotyping of bloodstream infections: a review of current evidence.

BACKGROUND: Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effe...

Ultrafast and reference-free sequence discovery in single-cell data.

Knowledge of RNA sequences, expression, splicing, isoforms, structure and modifications is central for understanding and targeting cellular processes....

Microbiome-metabolome multi-omics biomarkers for infectious disease prognosis: Current evidence, AI-driven integration, and translational challenges.

Microbiome-metabolome interactions are emerging as promising predictors of infectious disease, beyond conventional pathogen detection. Growing evidenc...

[Rational design and translational applications of hepatitis B virus-like particles].

Hepatitis B virus-like particles (HBV-VLPs), characterized by precise biomimetic topological architecture and favorable biocompatibility, have become ...

Ensuring pragmatic and equitable paths forward for hepatitis B virus infection eradication in Latin America.

INTRODUCTION: Latin America occupies a paradoxical position in hepatitis B virus (HBV) elimination: aggregate prevalence is low, yet the region remain...

OT-SAN: One Timestep Spiking Actor Network with Mask-Adaptive Global Connection and Dynamic Thresholds.

Spiking neural networks (SNNs) have garnered significant attention in reinforcement learning tasks for their low power consumption. However, tradition...