Liver Transplantation

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, is invasive and risky. We developed a hybrid multi-class neural network (NN) model, 'GraftIQ,' integrating clinician expertise for non-invasive graft pathology diagnosis. Biopsies from LTRs (1992-2020) were classified into six categories using demog...

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 decompensated state, leading to serious complications. Recent improvements in medical treatment have created a shift in cirrhosis management. Various causes, including hepatitis viruses, alcohol consumption, and fatty liver disease, contribute to cirrhosis an...

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...

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...

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...

Severe community-acquired pneumonia (sCAP): advances in management and future directions.

Severe community-acquired pneumonia (sCAP) is a major global health challenge, with high morbidity and mortality, especially among patients requiring ...

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...

Fusogenic Lipid Nanovesicles as Multifunctional Immunomodulatory Platforms for Precision Solid Tumor Therapy.

Although immunotherapy demonstrates considerable prospect in overcoming solid tumors, its clinical efficacy is limited by several factors, such as poo...

Dendritic cell-based microrobots for enhanced systemic antigen-specific immune tolerance.

Current immunotherapeutic approaches for autoimmune disorders primarily rely on the use of generalized immunosuppressive medications. However, most im...

ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis

Synthesizing medical images remains challenging due to limited annotated pathological data, modality domain gaps, and the complexity of representing...

Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma.

The study by Huang , published in the , advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict te...

Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma.

The study by Huang , published in the , advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict te...

The Association Between Hepatocellular Carcinoma and Gastrointestinal Adenocarcinoma: Is This a New Syndrome in Patients With Cirrhosis? A Case Series.

AIM: This case series aimed to explore the occurrence of synchronous hepatocellular carcinoma (HCC) and gastrointestinal adenocarcinoma in cirrhotic p...

Hybrid Approach Combining Ultrasound and Blood Test Analysis with a Voting Classifier for Accurate Liver Fibrosis and Cirrhosis Assessment

Liver cirrhosis is an insidious condition involving the substitution of normal liver tissue with fibrous scar tissue and causing major health compli...

Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.

BACKGROUND: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of ea...

Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI

The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biom...

Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology.

BACKGROUND: Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening ...