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Record W4385657324 · doi:10.1016/j.ecoenv.2023.115328

Vitamin A regulates dermal papilla cell proliferation and apoptosis under heat stress via IGF1 and Wnt10b signaling

2023· article· en· W4385657324 on OpenAlexaff
Zhengkai Yue, Mengqi Liu, Bin Zhang, Fan Li, Chenyang Li, Xiaoyang Chen, Fuchang Li, Lei Liu

Bibliographic record

VenueEcotoxicology and Environmental Safety · 2023
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsApoptosisCell growthSignal transductionHair follicleCell biologyChemistryCancer researchBiologyBiochemistry

Abstract

fetched live from OpenAlex

Heat stress (HS) negatively affects the development of hair follicles. The present study investigated the effect of vitamin A (VA) on the development of rabbit dermal papilla cells (DPCs) under HS and the underlying regulatory mechanisms. Addition of 0.4 mg/L VA to the culture medium significantly enhanced cell proliferation (P < 0.001) and inhibited the apoptosis of DPCs (P < 0.01). VA decreased the proportion of DPCs in G0/G1 stage of the cell cycle under HS along with the expression of caspase 3, heat shock protein 70 (HSP70), and microRNA 195 (miR-195) (P < 0.05). VA also activated the insulin-like growth factor 1 (IGF1) and Wnt10b/β-catenin signaling pathways. The results of the dual luciferase reporter assay showed that IGF1 expression was modulated by miR-195-5p. Over-expression of miR-195-5p in DPCs with HS+VA treatment significantly reduced cell viability and IGF1 signaling (P < 0.01) and increased apoptosis (P < 0.01) compared with the HS+VA group. The positive effects of VA on proliferation and apoptosis of DPCs under HS were significantly attenu-ated by blocking Wnt10b and β-catenin signaling with IWP-2 and XAV-939, respectively. These results demonstrate that VA can promote hair follicle development following HS via modulation of miR-195/IGF1 and Wnt10b/β-catenin signaling pathways.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

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