Selective inhibition of HDAC2 by magnesium valproate attenuates cardiac hypertrophy
Bibliographic record
Abstract
The regulatory paradigm in cardiac hypertrophy involves alterations in gene expression that is mediated by chromatin remodeling. Various data suggest that class I and class II histone deacetylases (HDACs) play opposing roles in the regulation of hypertrophic pathways. To address this, we tested the effect of magnesium valproate (MgV), an HDAC inhibitor with 5 times more potency on class I HDACs. Cardiac hypertrophy was induced by partial abdominal aortic constriction in Wistar rats, and at the end of 6 weeks, we evaluated hypertrophic, hemodynamic, and oxidative stress parameters, and mitochondrial DNA concentration. Treatment with MgV prevented cardiac hypertrophy, improved hemodynamic functions, prevented oxidative stress, and increased mitochondrial DNA concentration. MgV treatment also increased the survival rate of the animals as depicted by the Kaplan-Meier curve. Improvement in hypertrophy due to HDAC inhibition was further confirmed by HDAC mRNA expression studies, which revealed that MgV decreases expression of pro-hypertrophic HDAC (i.e., HDAC2) without altering the expression of anti-hypertrophic HDAC5. Selective class I HDAC inhibition is required for controlling cardiac hypertrophy. Newer HDAC inhibitors that are class I inhibitors and class II promoters can be designed to obtain "pan" or "dual" natural HDAC "regulators".
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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".