Hepatitis

Development and Validation of an Explainable Machine Learning Model for Warning of Hepatitis E Virus-Related Acute Liver Failure.

BACKGROUND AND AIMS: Early identification of patients with acute hepatitis E (AHE) who are at high risk of progressing to hepatitis E virus-related acute liver failure (HEV-ALF) is crucial for enabling timely monitoring and intervention. This multicentre retrospective cohort study aimed to develop and validate an interpretable machine learning (ML) model for predicting the risk of HEV-ALF in hospi...

An informed deep learning model of the Omicron wave and the impact of vaccination.

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicron in Germany, France, and Italy by employing Physics-Informed Neural Networks to estimate the temporal parameters influencing its spread. We validate...

Identification of novel therapeutic targets in hepatitis-B virus-associated membranous nephropathy using scRNA-seq and machine learning.

Hepatitis B Virus-associated membranous nephropathy (HBV-MN) significantly impacts renal health, particularly in areas with high HBV prevalence. Under...

GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients.

Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced survival. Liver biopsy, the diagnostic gold standard,...

Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.

Cirrhosis represents the end stage of chronic liver disease, significantly reducing life expectancy as it progresses from a compensated to a decompens...

Machine Learning of Molecular Dynamics Simulations Provides Insights into the Modulation of Viral Capsid Assembly.

An effective approach in the development of novel antivirals is to target the assembly of viral capsids by using capsid assembly modulators (CAMs). CA...

Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis.

BACKGROUND: Vaccines are crucial for preventing infectious diseases; however, they may also be associated with adverse events (AEs). Conventional anal...

A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective.

BACKGROUND: Hepatitis B virus (HBV) can be transmitted from mother to child either through transplacental infection or via blood-to-blood contact duri...

Prospective study using artificial neural networks for identification of high-risk COVID-19 patients.

The COVID-19 pandemic caused a major public health crisis, with severe impacts on global health and the economy. Machine learning (ML) has been crucia...

Optimizing influenza vaccine allocation: A predictive analytics approach for informed public health planning.

PURPOSE: Excessive purchasing of influenza vaccine can lead to costly overages and waste of resources. Insufficient quantities, however, can jeopardiz...

HR-VILAGE-3K3M: A Human Respiratory Viral Immunization Longitudinal Gene Expression Dataset for Systems Immunity

Respiratory viral infections pose a global health burden, yet the cellular immune responses driving protection or pathology remain unclear. Natural ...

Classification of disease subtypes in medical spectroscopy for multi-category sample imbalances based on grouping and hierarchical learning.

In the medical domain, the challenges in sample acquisition and collection often result in imbalanced training sets for multi-class models, especially...

Fine-Tuning an Existing Large Language Model with Knowledge from the Medical Expert System Hepaxpert.

The analysis and individual interpretation of hepatitis serology test results is a complex task in laboratory medicine, requiring either experienced p...

Practical Approach for Evaluating Machine Learning Anomaly Detection Algorithms for Epidemic Early Warning Systems.

Anomaly detection methods in time series data can play a pivotal role in epidemic surveillance Early Warning Systems (EWS). Statistical and rules-base...

An oral norovirus vaccine generates mucosal immunity and reduces viral shedding in a phase 2 placebo-controlled challenge study.

There are currently no licensed vaccines for norovirus, a leading cause of epidemic and endemic gastroenteritis worldwide. Clinical advancement of pro...

Allocation of Oral Cholera Vaccines in Africa.

OBJECTIVES: In this study, we examine the allocation of oral cholera vaccines (OCVs) across 25 African countries between 2013 and 2019.

Indirect Detection of Swine Influenza Activity in Porcine Blood Using Raman Spectroscopy and Machine Learning.

Over the past decade, several swine influenza variants, including H1N1 and H1N2, have been periodically detected in swine. Raman spectroscopy (RS) off...

Innovative Strategies in the Diagnosis and Treatment of Liver Cirrhosis and Associated Syndromes.

Liver cirrhosis continues to be a major global health issue, contributing to high morbidity and mortality due to its progressive nature and associated...

Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning.

BACKGROUND: Metabolic associated steatohepatitis (MASH) represents a severe subtype of metabolic associated fatty liver disease (MASLD), with an incre...

Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations.

Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostat...