Liquid Biopsy-Based Detection and Response Prediction for Depression.

ABSTRACT: Proactively predicting antidepressant treatment response before medication failures is crucial, as it reduces unsuccessful attempts and facilitates the development of personalized therapeutic strategies, ultimately enhancing treatment efficacy. The current decision-making process, which heavily depends on subjective indicators, underscores the need for an objective, indicator-based approach. This study developed a method for detecting depression and predicting treatment response through deep learning-based spectroscopic analysis of extracellular vesicles (EVs) from plasma. EVs were isolated from the plasma of both nondepressed and depressed groups, followed by Raman signal acquisition, which was used for AI algorithm development. The algorithm successfully distinguished depression patients

SUBMITTER: Kim S 

PROVIDER: S-EPMC11604100 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature