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Record W3161771913 · doi:10.3390/ijms22094995

A Brief Review on the Biology and Effects of Cellular and Circulating microRNAs on Cardiac Remodeling after Infarction

2021· review· en· W3161771913 on OpenAlexafffund
Mihir Parikh, Grant N. Pierce

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsmicroRNABiologyMicrovesiclesVentricular remodelingSignal transductionMyocardial infarctionBioinformaticsBiogenesisCell biologyMedicineGeneInternal medicineGenetics

Abstract

fetched live from OpenAlex

Despite advances in diagnostic, prognostic, and treatment modalities, myocardial infarction (MI) remains a leading cause of morbidity and mortality. Impaired cellular signaling after an MI causes maladaptive changes resulting in cardiac remodeling. MicroRNAs (miRNAs/miR) along with other molecular components have been investigated for their involvement in cellular signaling in the pathogenesis of various cardiac conditions like MI. miRNAs are small non-coding RNAs that negatively regulate gene expression. They bind to complementary mRNAs and regulate the rate of protein synthesis by altering the stability of their targeted mRNAs. A single miRNA can modulate several cellular signaling pathways by targeting hundreds of mRNAs. This review focuses on the biogenesis and beneficial effects of cellular and circulating (exosomal) miRNAs on cardiac remodeling after an MI. Particularly, miR-1, -133, 135, and -29 that play an essential role in cardiac remodeling after an MI are described in detail. The limitations that will need to be addressed in the future for the further development of miRNA-based therapeutics for cardiovascular conditions will also be discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.903
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.329
Teacher spread0.311 · 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 designOther design
Domainnot available
GenreReview

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

Citations20
Published2021
Admission routes2
Has abstractyes

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