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Record W2560672496 · doi:10.1139/cjpp-2016-0542

Selective inhibition of HDAC2 by magnesium valproate attenuates cardiac hypertrophy

2016· article· en· W2560672496 on OpenAlexvenueno aff
Suchi Raghunathan, Ramesh K. Goyal, Bhoomika M. Patel

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsnot available
Fundersnot available
KeywordsMagnesiumInternal medicineMuscle hypertrophyCardiac hypertrophyEndocrinologyMedicinePharmacologyCardiologyChemistry

Abstract

fetched live from OpenAlex

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".

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.245
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2016
Admission routes1
Has abstractyes

Explore more

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