Reduction of DNA damage repair efficiency and accumulation of residual damage following chronic UVB-irradiation of HaCaT cells
Bibliographic record
Abstract
Absorption of ultraviolet radiation (UVR) by DNA leads to the predominant formation of cyclobutane pyrimidine dimers (CPD). Since those CPD are responsible for the driver mutations found in skin cancers, their efficient repair is critical. We previously showed that pre-stimulation of fibroblasts with chronic low doses of UVB (CLUV) increases CPD repair efficiency. Since skin cancers are not arising from dermal fibroblasts, this observation is not directly relevant to cutaneous carcinogenesis. We have now exposed HaCaT keratinocytes to a CLUV irradiation protocol to determine whether this pre-stimulation influences CPD removal rate. Similar to fibroblasts, CLUV treatment leads to the accumulation of residual CPD in keratinocytes, which are not repaired but rather tolerated and diluted through DNA replication. In contrast to fibroblasts, in keratinocytes we find that CLUV pre-treatment reduces CPD removal of newly generated damage without inducing a higher sensitivity to UVR-induced cell death. Using our experimental data, we derived a theoretical model to predict CPD induction, dilution and repair that occur in keratinocytes when chronically UVB-irradiated. Altogether, these results suggest that the accumulation of unrepaired CPD and the reduction in repair efficiency caused by chronic UVB exposure might lead to an increase in skin cancer driver mutations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".