Bibliographic record
Abstract
PURPOSE OF REVIEW: Major depressive disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Despite its considerable burden, our understanding of its pathophysiology remains rudimentary, and a validated biomarker has yet to be identified. Antidepressants are the most common treatment for MDD, yet roughly one-third of patients experience an inadequate response. Thus, there is a great need for not only identifying biomarkers of MDD but also those that can predict and monitor or just monitor response to treatment. RECENT FINDINGS: MicroRNAs (miRNAs) act as endogenous fine-tuners and on-off switches of gene expression. Several lines of evidence now suggest that miRNAs are involved in the pathogenesis of neuropsychiatric disorders. As such, miRNAs offer great hope as biomarkers of disease and response to treatment. SUMMARY: In this review, we discuss the growing field, investigating peripheral miRNAs as potential biomarkers of major depression and treatment response. A noninvasive and validated biomarker of MDD or treatment response will help clinicians guide treatment selection. Ultimately, these findings provide important steps in the development of early diagnostic tools, preventive strategies, and effective pharmacological treatment for psychiatric disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".