Hepatitis

Machine learning and molecular docking prediction of potential inhibitors against dengue virus.

INTRODUCTION: Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, and . While the WHO-approved vaccine, Dengvaxia, and antiviral treatments like Balapiravir and Celgosivir are available, challenges such as drug resistance, reduced efficacy, and high treatment costs persist. This study aims to identify novel potential inhibitors of the Dengue...

Predicting the infecting dengue serotype from antibody titre data using machine learning.

The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correlates of protection. The plaque reduction neutralisation test (PRNT) is the gold standard assay for measuring serotype-specific antibodies, but this tes...

Biosecurity measures reducing spp. and hepatitis E virus prevalence in pig farms-a systematic review and meta-analysis.

spp. and hepatitis E virus (HEV) are significant foodborne zoonotic pathogens that impact the health of livestock, farmers, and the general public. T...

A human monoclonal antibody isolated from Japanese encephalitis virus vaccine-vaccinated volunteer neutralizing various flaviviruses.

INTRODUCTION: Japanese encephalitis virus (JEV) and Zika virus (ZIKV) are prevalent in over 80 countries or territories worldwide, causing hundreds of...

Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis.

OBJECTIVE: To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict ...

Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large coronavirus family with high infectivity and pathogenicity and i...

AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.

BACKGROUND & AIMS: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clini...

Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.

Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are eve...

Geometric deep learning improves generalizability of MHC-bound peptide predictions.

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immun...

Clade Ib: a new emerging threat in the Mpox outbreak.

Monkeypox, a zoonotic virus in the , has drawn global attention for its impact on public health. In the current Mpox outbreak, a novel clade, Ib, has ...

A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B.

BACKGROUND AND AIMS: This study aims to develop and validate a machine learning (ML) model predicting hepatocellular carcinoma (HCC) in chronic hepati...

A meta-analysis of AI and machine learning in project management: Optimizing vaccine development for emerging viral threats in biotechnology.

OBJECTIVES: Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies across various industries, including he...

Rapid Deployment of Antiviral Drugs Using Single-Virus Tracking and Machine Learning.

The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting pheno...

Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications...

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem.

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for de...

Policy brief: Improving national vaccination decision-making through data.

Life course immunisation looks at the broad value of vaccination across multiple generations, calling for more data power, collaboration, and multi-di...

Investigation of the efficiency of different reprocessing methods on disposable laryngeal masks contaminated with HBV DNA.

BACKGROUND: The use of laryngeal masks (LM) has increased widely today, both in anesthesia and in emergency cases. LM are available as reusable and di...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracel...

Evaluating generative artificial intelligence's limitations in health policy identification and interpretation.

Policy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The ...

Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.

INTRODUCTION: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrho...