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Record W2057819889 · doi:10.1258/ebm.2011.011101

MicroRNA regulation in mammalian adipogenesis

2011· review· en· W2057819889 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExperimental Biology and Medicine · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdipogenesismicroRNAAdipocyteBiologyTranscription factorAdipose tissueCell biologyPhenotypeTranscription (linguistics)Cellular differentiationRegulation of gene expressionGeneMesenchymal stem cellGeneticsEndocrinology

Abstract

fetched live from OpenAlex

Adipogenesis, the complex development from preadipocytes or mesenchymal stem cells to mature adipocytes, is essential for fat formation and metabolism of adipose tissues in mammals. It has been reported to be regulated by hormones and various adipogenic transcription factors which are expressed as a transcriptional cascade promoting adipocyte differentiation, leading to the mature adipocyte phenotype. Recent findings indicate that microRNAs (miRNAs), a family of small RNA molecules of approximately 22 nucleotides in length, are involved in the regulatory network of many biological processes, including cell differentiation, through post-transcriptional regulation of transcription factors and/or other genes. In this review, we focus on the recent understanding of the roles of miRNAs in adipogenesis, including the most recent and relevant findings that support the role of several miRNAs as pro- or antiadipogenic factors regulating adipogenesis in mice, human and cattle to propose the future role of miRNA in adipogenesis of farm animal models.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.031
GPT teacher head0.344
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