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Record W2622990956 · doi:10.1089/scd.2017.0062

Suppression of miR-34a Expression in the Myocardium Protects Against Ischemia–Reperfusion Injury Through SIRT1 Protective Pathway

2017· article· en· W2622990956 on OpenAlexaff
Bicheng Fu, Jilu Lang, Dongyang Zhang, Lu Sun, Wei Chen, Wei Liu, Kaiyu Liu, Chongyi Ma, Shulin Jiang, Ren‐Ke Li, Hai Tian

Bibliographic record

VenueStem Cells and Development · 2017
Typearticle
Languageen
FieldMedicine
TopicSirtuins and Resveratrol in Medicine
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNatural Science Foundation of Heilongjiang ProvinceNational Natural Science Foundation of China
KeywordsBiologyReperfusion injuryIschemiamicroRNAPharmacologyCell biologyInternal medicineBiochemistryGeneMedicine

Abstract

fetched live from OpenAlex

MicroRNA-34a (miR-34a) is expressed in the myocardium and expression is altered after myocardial injury. We investigated the effects of miR-34a on heart function after ischemia-reperfusion (IR) injury. Cardiomyocytes were isolated from neonatal rat hearts and simulated IR injury was induced in vitro. Following IR injury in rats, infarct size was measured and left ventricular (LV) function was evaluated using echocardiography. Protein expression of silent information regulator 1 (SIRT1), acetylated p53 (ac-p53), Bcl-2 and Bax, and miR-34a and SIRT1 gene levels were analyzed. miR-34a overexpression exacerbated myocardial injury by increasing apoptosis and infarct size and decreasing LV function. Suppression of miR-34a attenuated myocardial IR injury. SIRT1 was negatively regulated by miR-34a and the expression of downstream genes, such as ac-p53, Bcl-2, and Bax were altered correspondingly. Increased expression of miR-34a aggravates injury after IR; miR-34a suppression therapy may represent a new line of treatment for myocardial IR injury.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations42
Published2017
Admission routes1
Has abstractyes

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