Hepatitis

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination on people's immunity vary across several factors. After the pandemic, real-life post-vaccination anomalies significantly impact women's health, access to medical treatments and medications, mental well-being, and daily physical activities. However, th...

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we concei...

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.

2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, s...

Predicting drug-Protein interaction with deep learning framework for molecular graphs and sequences: Potential candidates against SAR-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 disease, which represents a new life-threatening disaster. Regard...

Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches.

To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing the microfluidic conditions and lipid mix ratios of messenger RNA...

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays ...

Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach.

BACKGROUND: SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, wheth...

A potential new way to facilitate HCV elimination: The prediction of viremia in anti-HCV seropositive patients using machine learning algorithms.

BACKGROUND AND STUDY AIMS: The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in he...

Exploring Antiviral Drugs on Monolayer Black Phosphorene: Atomistic Theory and Explainable Machine Learning-Assisted Platform.

Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infectio...

Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA.

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much rese...

Weighted Gene Co-expression Network Analysis and Machine Learning Validation for Identifying Major Genes Related to Sjogren's Syndrome.

Sjogren's syndrome (SS) is an autoimmune disorder characterized by dry mouth and dry eyes. Its pathogenic mechanism is currently unclear. This study a...

Ontological representation, modeling, and analysis of parasite vaccines.

BACKGROUND: Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pa...

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk...

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.

Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accel...

Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction.

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health...

Machine-learning-based plasma metabolomic profiles for predicting long-term complications of cirrhosis.

BACKGROUND AND AIMS: The complications of liver cirrhosis occur after long asymptomatic stages of progressive fibrosis and are generally diagnosed lat...

Multicolor-Assay-on-a-Chip Processed by Robotic Operation (MACpro) with Improved Diagnostic Accuracy for Field-Deployable Detection.

The ability to deploy decentralized laboratories with autonomous and reliable disease diagnosis holds the potential to deliver accessible healthcare s...

An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study.

BACKGROUND AND AIMS: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mo...

A natural language processing algorithm accurately classifies steatotic liver disease pathology to estimate the risk of cirrhosis.

BACKGROUND: Histopathology remains the gold standard for diagnosing and staging metabolic dysfunction-associated steatotic liver disease (MASLD). The ...

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma.

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has no...