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Record W2343366340 · doi:10.1155/2016/5276271

miR-145a-5p Promotes Myoblast Differentiation

2016· article· en· W2343366340 on OpenAlexaff
Jingjing Du, Qiang Li, Linyuan Shen, Huaigang Lei, Jia Luo, Yihui Liu, Peiwen Zhang, Qiang Pu, Yi Zhang, Surong Shuai, Xuewei Li, Shunhua Zhang, Li Zhu

Bibliographic record

VenueBioMed Research International · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Alberta
FundersNational Development and Reform Commission
KeywordsC2C12MyogenesisMyocyteWnt signaling pathwayBiologyCellular differentiationmicroRNACell biologyGeneGene expressionGenetics

Abstract

fetched live from OpenAlex

MicroRNAs are a class of 18-22-nucleotide noncoding RNAs that posttranscriptionally regulate gene expression and have been shown to play an important role during myoblast differentiation. In this study, we found that the expression of miR-145a-5p was gradually increased during C2C12 myoblast differentiation, and miR-145a-5p inhibitors or mimics significantly suppressed or promoted the relative expression of specific myogenesis related marker genes. Moreover, overexpression or inhibition of miR-145a-5p enhanced or repressed the expression of some special genes involved in the endogenous Wnt signaling pathway during C2C12 myoblast differentiation, including Wnt5a, LRP5, Axin2, and β-catenin. These results indicated that miR-145a-5p might be considered as a new myogenic differentiation-associated microRNA that can promote C2C12 myoblast differentiation by enhancing genes related to myoblasts differentiation.

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 score0.003

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 head0.036
GPT teacher head0.348
Teacher spread0.313 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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