Hepatitis

Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial.

BACKGROUND: Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early viral upper respiratory tract infections (VRTIs) compared with symptom-based detection. We aimed to use multimodal wearable sensors, host-response biomarkers, and machine learning to predict systemic inflammation following controlled exposure to a l...

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease worldwide. Driven by obesity and type 2 diabetes, MASH significantly increases the risk of cirrhosis, hepatocellular carcinoma, and liver failure. While public health interventions remain essential, therapeutic strategies targeting metabolic dysfuncti...

Language models learn to represent antigenic properties of human influenza A(H3) virus.

Given that influenza vaccine effectiveness depends on a good antigenic match between the vaccine and circulating viruses, it is important to assess th...

Brain-wide input-output analysis of tuberal nucleus somatostatin neurons reveals hierarchical circuits for orchestrating feeding behavior.

Feeding is an innate behavior critical for survival but is also influenced by many non-nutritional factors such as emotion, social context and environ...

Brainstem noradrenergic modulation of the kisspeptin neuron GnRH pulse generator in mice.

Brainstem noradrenaline (NA) neurons modulate the activity of many neural networks including those responsible for the control of fertility. Using bra...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 a...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction mod...

Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC.

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignant tumor in the field of urology. The effect of cell heterogeneity on the prognosis and ...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely det...

Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning.

OBJECTIVE: To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chr...

MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients.

Traditional methods for measuring body composition in CT scans rely on labor-intensive manual delineation, which is time-consuming and imprecise. This...

Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.

The papain-like protease (PLpro), a cysteine protease found in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), plays a crucial role in v...

HEalth Record Optimization for Identifying Candidates for HIV PRe-Exposure Prophylaxis: A Community-Informed Approach to Model Development.

Electronic health record (EHR)-based models to identify individuals who may benefit from pre-exposure prophylaxis (PrEP) outperform traditional risk s...

Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.

PURPOSE: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to devel...

Multivariate Screening and Automated Clustering of Macrophage Immunoreactome to Nanoparticles and Photothermal Therapy.

Immunotherapy aims to control the immune system against diseases such as cancer or infections. Nanotechnology is part of the armamentarium to reprogra...

Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection.

Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a p...

A Novel nor@DHB Matrix for Direct Microbial Analysis in Lung Cancer Tissues.

Dynamic changes occurring in the lung microbiota can impact the initiation, progression, and prognosis of lung cancer (LC). Consequently, the developm...

In-silico study of approved drugs as potential inhibitors against 3CLpro and other viral proteins of CoVID-19.

The global pandemic, due to the emergence of COVID-19, has created a public health crisis. It has a huge morbidity rate that was never comprehended in...

iAVP-RFVOT: Identify Antiviral Peptides by Random Forest Voting Machine Learning with Unified Manifold Learning Embedded Features.

Viruses are transmitted through multiple routes and can cause a wide range of diseases. Antiviral peptides (AVPs) have emerged as a cost-effective and...

Utilizing Machine Learning to Improve Neutralization Potency of an HIV-1 Antibody Targeting the gp41 N-Heptad Repeat.

The N-heptad repeat (NHR) of the HIV-1 gp41 prehairpin intermediate (PHI) is an attractive potential vaccine target with high sequence conservation ac...