Modelling the liver's regenerative capacity across different clinical conditions.

ABSTRACT:

Background & aims

Liver regeneration is essential for recovery following injury, but this process can be impaired by factors such as sex, age, metabolic disorders, fibrosis, and immunosuppressive therapies. We aimed to identify key transcriptomic, proteomic, and serum biomarkers of regeneration in mouse models under these diverse conditions using systems biology and machine learning approaches.

Methods

Six mouse models, each undergoing 75% hepatectomy, were used to study regeneration across distinct clinical contexts: young males and females, aged mice, stage 2 fibrosis, steatosis, and tacrolimus exposure. A novel contrastive deep learning framework with triplet loss was developed to map regenerative trajectories and identify genes associated with regenerative efficiency.

SUBMITTER: Nguyen-Lefebvre AT 

PROVIDER: S-EPMC12284365 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature