AIMC Topic: Non-alcoholic Fatty Liver Disease

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Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models.

Journal of Korean medical science
BACKGROUND: Fatty liver disease is a common condition linked to metabolic syndrome, cardiovascular diseases, and liver cirrhosis, and timely, accurate diagnosis is crucial. In clinical studies, incorporating deep learning models often faces the chall...

Ultrasound and SWE-based transfer learning for predicting fibrotic NASH.

Scientific reports
The aim of this study was to develop a combined deep-learning model utilizing liver ultrasound, liver elastography images, and clinical features to predict and diagnose fibrotic non-alcoholic steatohepatitis (NASH). A rat model of liver steatosis and...

Identification of lipid metabolism-related signature in nonalcoholic fatty liver: evidences from transcriptomics and single cell RNA-sequencing analysis.

European journal of medical research
BACKGROUND: Considering the complex and close-knit relationship between non-alcoholic fatty liver disease (NAFLD) and the metabolic status, this study aimed to identify lipid metabolism-related genes (LMGs), construct an effective diagnostic model, a...

Integrating spatial and chemical information enhances differentiation of non-alcoholic steatohepatitis states in Raman imaging.

Scientific reports
Machine learning studies for Raman imaging have addressed the differentiability of normal and diseased states in biomedical applications by grouping a set of Raman spectra in terms of spectral similarity over the sample. However, Raman imaging provid...

Immune metabolic changes identify causal candidate genes and enable diagnostic frameworks in MAFLD.

Scientific reports
Metabolic dysfunction-associated fatty liver disease (MAFLD), a global epidemic affecting 25% of adults, is driven by immune-metabolic dysregulation, yet the causal mechanisms linking immune cell-specific gene perturbations to disease progression rem...

Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.

Clinical and experimental medicine
This study employed machine learning models to quantitatively analyze liver fat content from MRI images for the evaluation of liver fibrosis and disease severity in patients with metabolic dysfunction-associated fatty liver disease (MAFLD). A total o...

NAFLD progression in metabolic syndrome: a Raman spectroscopy and machine learning approach in an animal model.

The Analyst
Nonalcoholic fatty liver disease (NAFLD) is emerging as the leading cause of chronic liver disease in many regions, particularly in association with the rising prevalence of Metabolic syndrome (MetS), affecting more than 30% of the population worldwi...

Development of a novel deep learning method that transforms tabular input variables into images for the prediction of SLD.

Scientific reports
Steatotic liver disease (SLD), formerly named fatty liver disease, has a prevalence estimated at 30-38% in adults. Detection of SLD is important, since prompt initiation of treatment can stop disease progression, lead to a reduction in adverse outcom...

Integrating transcriptomics, network analysis, and single-cell RNA sequencing to identify and validate key target genes of gynostemma in the treatment of non-alcoholic fatty liver disease.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
This study explores the therapeutic targets and mechanisms of Gynostemma pentaphyllum in non-alcoholic fatty liver disease (NAFLD). Using network analysis and bioinformatics, we identified target genes of Gynostemma's active metabolites in NAFLD thro...