Differential Expression of tRNA-Derived Small RNA Markers of Antidepressant Response and Functional Forecast of Duloxetine in MDD Patients

Genes, Journal Year: 2025, Volume and Issue: 16(2), P. 162 - 162

Published: Jan. 27, 2025

Background/Objectives: Duloxetine, despite being a leading treatment option for major depressive disorder (MDD), exhibits relatively low adequate response rate when used as monotherapy, and the fundamental molecular mechanisms remain largely elusive. tRNA-derived small RNA (tsRNA) is particularly interesting new class of molecules that becoming increasingly noticeable investigation. Methods: We integrated sequencing with bioinformatics approaches to dissect expression profiles tsRNAs decipher their functional roles post-duloxetine treatment. Subsequently, docking experiments were carried out validate potential functions. Results: Ten significantly changed in duloxetine group after an 8-week therapy. Correlation analyses revealed these predominantly interacted miRNAs across multiple biological pathways processes, such ECM-receptor interaction B cell activation. Molecular analysis corroborated binding capabilities key proteins associated ECM1 BAFF, respectively. Conclusions: The identified changes can precisely mirror MDD treatment, offering novel insights into underlying action.

Language: Английский

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Machine learned text topics improve drop-out risk prediction but not symptom prediction in online psychotherapies for depression and anxiety

Psychotherapy Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: March 18, 2025

Objective: Internet-delivered cognitive behavior therapies (iCBT) are effective and scalable treatments for depression anxiety. However, treatment adherence remains a major limitation that could be further understood by applying machine learning methods to during-treatment messages. We used learned topics predict drop-out risk symptom change in iCBT. Method: applied topic modeling naturalistic messages from 18,117 patients of nationwide iCBT programs generalized anxiety disorder (GAD). elastic net regression outcome predictions cross-validation aid model selection. left 10% the data as held-out test set assess predictive performance. Results: Compared reference covariates, inclusion variables resulted significant decrease prediction loss, both between-patient within-patient session-by-session models. Quantified partial pseudo-R2, increase variance explained was 2.1–6.8 percentage units. Topics did not improve compared model. Conclusions: Message contents can associated with between-patients drop-out. Our predictors were theoretically interpretable. Analysis have practical implications improved assessment allocation additional supportive interventions.

Language: Английский

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Are global estimates of minimally adequate treatment for major depressive disorder based on minimally adequate data?

The Lancet Psychiatry, Journal Year: 2024, Volume and Issue: 11(12), P. 950 - 951

Published: Nov. 19, 2024

Language: Английский

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