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Record W2770277975 · doi:10.22037/rrr.v2i1.18537

L-Ascorbic acid affect the DNA methytransferase expression in Mouse Embryonic Fibroblasts

2017· article· en· W2770277975 on OpenAlexaff
Leila Mohammadi Amirabad, Mohammad Massumi, Mohammad Reza Alivand

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReprogrammingAscorbic acidDNMT1Embryonic stem cellMethyltransferaseDNA methyltransferaseInduced pluripotent stem cellViability assayCell biologyCellBiologyChemistryMolecular biologyGene expressionDNAGeneBiochemistryMethylation

Abstract

fetched live from OpenAlex

Introduction: Induced pluripotent stem cells (iPSCs) can be generated from different source of cells with different efficiencies. Two DNA methyltransferases DNMT1 and DNMT3A have been shown to regulate epigenetically the gene expression involved in cell viability and reprogramming. L-ascorbic acid (L-AA) is a chemical factor that can accelerate reprogramming. Here, we sought to investigate the effect of L-AA on DNMT1 and DNMT3A expressions. Materials and Methods: First, mouse embryonic fibroblasts at passage 3 were cultured in the presence of 10 µg/ml L-AA days for 5 days. Then, DNMT1 and DNMT3A expressions were determined using real-time PCR at days 3 and 5. Results: It was showed that L-AA could enhance DNMT-1 expression which involve in cell viability and decrease the DNMT3A which involve in cell differentiation. Conclusion: The results therefore suggest a new insight into L-AA mechanism impact on reprogramming process.

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 score0.004

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 integrity0.0000.000
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 head0.150
GPT teacher head0.518
Teacher spread0.368 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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