Quantitative Analysis of Arsenic- and Sucrose-Induced Liver Collagen Remodeling Using Machine Learning on...

Area of Science:

• Hepatology
• Toxicology
• Biomedical Engineering

Background:

• Non-alcoholic fatty liver disease (NAFLD) is a progressive liver condition linked to diet, potentially leading to cirrhosis.
• Dietary factors are crucial in NAFLD pathogenesis, necessitating research into specific dietary components and their effects.

Purpose of the Study:

• To investigate the impact of arsenic and sucrose-rich diets on liver fibrosis development in male Wistar rats.
• To analyze collagen fiber remodeling in liver tissue using SHG microscopy and machine learning.

Main Methods:

• Male Wistar rats were fed control, arsenic, sucrose, or combined arsenic-sucrose diets.
• Second Harmonic Generation (SHG) microscopy analyzed liver tissue for collagen fibers.
• A neural network classified SHG images to assess fibrosis risk and collagen fiber orientation.

Main Results:

• Fibrosis risk increased significantly with dietary interventions: 10% (control), 24% (arsenic), 40% (sucrose), and 62% (arsenic-sucrose).
• Collagen fiber angular width distribution narrowed significantly, indicating altered fiber structure, particularly in the arsenic-sucrose group (2.8°).
• Key statistical features for image classification included pixel intensity, Mean/std ratio, mode, and total intensity.

Conclusions:

• Diets high in sucrose, especially when combined with arsenic, are significant risk factors for liver fibrosis.
• Dietary interventions induce substantial changes in liver collagen fiber structure and orientation.
• Machine learning analysis of SHG microscopy provides a quantitative method for assessing fibrosis risk.