Blockade of the acute activation of mTOR complex 1 decreases hypertrophy development in rats with severe aortic valve regurgitation
Bibliographic record
Abstract
BACKGROUND: Hypertrophy (H) is an adaptive response of the heart to a hemodynamic overload. Severe left ventricular (LV) volume overload (VO) from valve regurgitations (aortic (AR) or mitral regurgitation) leads to eccentric LVH. Increased protein turnover is a major event during development of LVH and the mechanistic target of rapamycin (mTOR) is a key molecule for its control. The role of mTOR inhibition in the development of LVH using rapamycin for relatively short periods of time (days to a few weeks) has been studied in the past in pressure overload models but not in VO models. We investigated if mTOR pathway was activated during LVH development in a model of severe VO (AR) in rats and if a rapamycin treatment can slow heart remodeling in this situation. METHODS AND RESULTS: Male rats with severe AR were studied acutely at 2 days, at 8 weeks (compensated phase) and 6 months (late phase) after VO induction. mTOR complex (mTORC) 1 (ribosomal S6 protein phosphorylation) was activated early after AR induction but not later in the disease whereas mTORC2 activity levels (Akt phosphorylation at Ser473) remained stable. We observed that a moderate dose of rapamycin (2 mg/kg/day; orally) for 8 weeks prevented severe LVH caused by AR (-46 %: p < 0.001). Rapamycin treatment specifically inhibited LV mTORC1 without altering mTORC2 activity at 8 weeks. Rapamycin also prevented cardiac myocyte hypertrophy caused by AR. CONCLUSION: Rapamycin slows hypertrophy in LV VO by inhibiting early activation of mTORC1 without modulating mTORC2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".