Machine learning-enhanced confocal Raman imaging enables label-free diagnosis and spatial metabolic profiling of isoniazid-induced hepatotoxicity.

ABSTRACT: Rationale: Isoniazid-induced liver injury (INH-ILI) poses a significant clinical challenge due to the lack of reliable, non-invasive, and real-time diagnostic tools. Here, we present an integrated platform that combines label-free confocal Raman spectroscopy imaging, machine learning (ML), and targeted metabolomics to identify and classify INH-ILI in a murine model. Methods: An INH-ILI mouse model was established, and Raman imaging and subsequent data analysis were performed on the control and INH-ILI at 7, 14, 21, and 28-day groups. Alterations in hepatic metabolites following INH-ILI were elucidated. Furthermore, ML techniques were employed to identify subtle differences between the control and INH-ILI groups. Results: Distinct Raman spectral shifts, notably the emer

SUBMITTER: Wang S 

PROVIDER: S-EPMC12486251 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature