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Record W2131789687 · doi:10.7150/ijbs.4439

Human Circulating MicroRNA-1 and MicroRNA-126 as Potential Novel Indicators for Acute Myocardial Infarction

2012· article· en· W2131789687 on OpenAlexfundno aff
Guangwen Long, Feng Wang, Quanlu Duan, Fuqiong Chen, Shenglan Yang, Wei Gong, Yan Wang, Chen Chen, Dao Wen Wang

Bibliographic record

VenueInternational Journal of Biological Sciences · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaUniversité Laval
KeywordsMyocardial infarctionmicroRNATroponin IInternal medicineMedicineBiomarkerCardiologyBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Circulating miRNAs have been shown as promising biomarkers for various pathologic conditions. The aim of this study was to clarify that circulating miR-1 and miR-126 in human plasma might be useful as biomarkers in acute myocardial infarction (AMI). In our study, after pre-test, two candidate miRNAs were detected by using real-time RT-PCR. Cardiac troponin I (cTnI) concentrations were measured by ELISA assay in plasma from patients with AMI (n=17) and healthy subjects (n=25), simultaneously. Increased miR-1 and decreased miR-126 in plasma from patients with AMI after the onset of symptoms compared with healthy subjects were found. A remarkable finding in this study is that miR-1, miR-126 and cTnI expression levels exhibited the same trend. Our results suggest that the plasma concentrations of miR-1 and miR-126 may be useful indicators for AMI.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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 designObservational
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

Citations177
Published2012
Admission routes1
Has abstractyes

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