Abstract 19029: The Sirt1 Activator, SRT1720, Attenuates Cardiac Hypertrophy and Fibrosis in a Rodent Pressure Overload Model
Bibliographic record
Abstract
Background: Transforming growth factor β1 ( TGFβ1) is a prosclerotic cytokine involved in cardiac remodeling leading to heart failure (CHF). In order to precisely regulate TGFβ1 signaling, acetylation/de-acetylation of lysine 19 within the MH1 domain of Smad2 has been shown to alter DNA binding and transcriptional activity. Recently the lysine de-acetylase Sirt1 has been shown to have a cardioprotective effect, however both Sirt1 expression and activity are reduced in CHF. We hypothesized that pharmacological activation of Sirt1 using SRT1720 would induce cardioprotection in a transverse aortic constriction (TAC) model through modulation of TGF-s/Smad signaling. Methods: Wildtype C57BL/6 mice were randomized to receive TAC or sham surgery at 8 weeks of age; each group was subsequently randomized to receive SRT1720 (100mg/kg/day) or placebo, administered once daily via oral gavage. Six weeks following TAC, cardiac function was assessed followed by left ventricular (LV) tissue collection for analysis. Results: Animals randomized to TAC demonstrated reduced systolic function, LV hypertrophy and increased fibrosis (all p Conclusion: Treatment of mice with SRT1720 improves cardiac function and reduces cardiac fibrosis and hypertrophy following TAC. Given that Sirt1 expression and activity are reduced in CHF, enhancing Sirt1 activity may represent a novel intervention to reduce TGF-s mediated fibrosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".