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Record W2422887374 · doi:10.1177/120347540000400102

Effect of Retinoic Acid on Apoptosis and DNA Repair in Human Keratinocytes after UVB Irradiation

2000· article· en· W2422887374 on OpenAlexaff
Gang Li, Jason Bush, Vincent Ho

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2000
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsVancouver Hospital and Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsCarcinogenesisApoptosisSkin cancerDNA damageRetinoic acidCancer researchDNA repairMedicineDNAKeratinocyteRetinoidHuman skinMolecular biologyCancerBiologyCell cultureBiochemistryGeneticsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Skin cancer is extremely common. Epidemiological studies indicated that ultraviolet radiation (UV) is the primary cause for skin cancers, and that retinoic acid (RA) is able to inhibit this UV-induced skin carcinogenesis; however, the molecular mechanism of the anti-UV action of RA is unclear. OBJECTIVE: The purpose of this study is to investigate if RA enhances the removal of UV-induced DNA damage. METHODS: The effect of RA on UV-induced apoptosis and DNA repair was investigated by ELISA apoptosis assay and CAT assay. RESULTS: Both all-trans-RA and 9-cis-RA did not promote UV-induced apoptosis nor the repair of UV-damaged DNA in human keratinocytes. Furthermore, RA did not induce the expression of p53. CONCLUSION: The inhibition of RA on skin carcinogenesis is not due to enhanced removal of UV-damaged DNA. Therefore, RA does not inhibit skin cancer development at the initiation stage, but possibly at the promotion and progression stages.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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

Citations14
Published2000
Admission routes1
Has abstractyes

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