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Record W2103591522 · doi:10.1139/cjpp-2014-0522

Nuclear and extranuclear effects of vitamin A

2015· review· en· W2103591522 on OpenAlexvenueno aff
Madina Iskakova, Mikhail S. Karbyshev, Aleksandr Piskunov, Cécile Rochette‐Egly

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsnot available
Fundersnot available
KeywordsRetinoic acidNuclear receptorBiologyCell biologyTranscription factorReceptorVitaminRetinolCalcitriol receptorSignal transductionGene expressionRegulation of gene expressionTretinoinGeneBiochemistry

Abstract

fetched live from OpenAlex

Vitamin A or retinol is a multifunctional vitamin that is essential at all stages of life from embryogenesis to adulthood. Up to now, it has been accepted that the effects of vitamin A are exerted by active metabolites, the major ones being 11-cis retinal for vision, and all trans-retinoic acid (RA) for cell growth and differentiation. Basically RA binds nuclear receptors, RARs, which regulate the expression of a battery of target genes in a ligand dependent manner. During the last decade, new scenarios have been discovered, providing a rationale for the understanding of other long-noted but not explained functions of retinol. These novel scenarios involve: (i) other nuclear receptors such as PPAR β/δ, which regulate the expression of other target genes with other functions; (ii) extranuclear and nontranscriptional effects, such as the activation of kinases, which phosphorylate RARs and other transcription factors, thus expanding the list of the RA-activated genes; (iii) finally, vitamin A is active per se and can work as a cytokine that regulates gene transcription by activating STRA6. New effects of vitamin A and RA are continuously being discovered in new fields, revealing new targets and new mechanisms thus improving the understanding the pleiotropicity of their effects.

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)0.000
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 score0.963
Threshold uncertainty score0.780

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 studies0.0000.001
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 head0.012
GPT teacher head0.282
Teacher spread0.270 · 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 designNot applicable
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

Citations45
Published2015
Admission routes1
Has abstractyes

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