Bibliographic record
Abstract
Major depressive disorder (MDD) is the psychiatric disorder that causes the greatest social and economic damage in the world, being thus a disorder of great interest to the scientific community.A growing body of evidence indicates epigenetic mechanisms as key players in the pathophysiology of depression and the resilience phenotype, in which individuals do not develop depressive symptoms despite exposure to stressors.Circular RNAs (circRNAs) are single-stranded non-coding RNAs molecules with a closed-loop structure formed in a process called backsplicing.These molecules are naturally abundant and stable in the brain and in exosomes, through which they can be detected in various body fluids such as blood and saliva; thus serving as potential biomarkers, both for the development of disorders and the response to pharmacological treatments.Therefore, we sought to use the model of chronic unpredictable mild stress (CUMS) in Wistar rats to investigate the expression profile of circRNAs circSTAG1 and circHIPK2 in hippocampi of animals with depressive-like behavior and resilience phenotype, and the effect of ketamine on their expression.We observed that the ketamine-treated susceptible animals show a significant difference in sucrose consumption compared to the salinetreated susceptible animals, but not with the control, saline-treated control, ketamine-treated control, and resilient groups.Employing RT-qPCR, we observed a significant reduction in the expression level of circSTAG1 between the control groups and the saline-treated susceptible animals, while the ketamine-treated susceptible animals and the resilient animals show no significant difference with any of the other groups.Regarding circHIPK2 expression, differential expression of the molecule was not observed in any of the groups.These results indicate a potential role of circSTAG1 as biomarker for depression; however, further analysis is necessary to evaluate the potential of circRNAs as biomarkers for depression and treatment response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".