Effects of escitalopram and paroxetine on mTORC1 signaling in the rat hippocampus under chronic restraint stress
Bibliographic record
Abstract
Recent studies have suggested that the activation of mammalian target of rapamycin (mTOR) signaling may be related to antidepressant action. Therefore, the present study evaluated whether antidepressant drugs would exert differential effects on mTOR signaling in the rat hippocampus under conditions of chronic restraint stress. Male Sprague–Dawley rats were subjected to restraint stress for 6 h/days for 21 days with either escitalopram (10 mg/kg) or paroxetine (10 mg/kg) administered after the chronic stress procedure. Western blot analyses were used to assess changes in the levels of phospho-Ser 2448 -mTOR, phospho-Thr 37/46 -4E-BP-1, phospho-Thr 389 -p70S6 K, phospho-Ser 422 -eIF4B, phospho-Ser 240/244 -S6, phospho-Ser 473 -Akt, and phospho-Thr 202 /Tyr 204 -ERK in the hippocampus. Chronic restraint stress significantly decreased the levels of phospho-mTOR complex 1 (mTORC1), phospho-4E-BP-1, phospho-p70S6 K, phospho-eIF4B, phospho-S6, phospho-Akt, and phospho-ERK ( p < 0.05); the administration of escitalopram and paroxetine increased the levels of all these proteins ( p < 0.05 or 0.01). Additionally, chronic restraint stress reduced phospho-mTORC1 signaling activities in general, while escitalopram and paroxetine prevented these changes in phospho-mTORC1 signaling activities. These findings provide further data that contribute to understanding the possible relationships among mTOR activity, stress, and antidepressant drugs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".