Transcriptional benchmark dose modeling of ultraviolet radiation‐induced genomic activation in mouse skin
Bibliographic record
Abstract
Abstract The in vivo transcriptional response of mouse skin to ultraviolet radiation (UV‐R) exposure reveals key genomic alterations associated with UV‐R‐induced damage but it does not provide precise dose thresholds for these effects. These initial findings provided the impetus to advance dose–response characterization by integrating benchmark dose (BMD) modeling with transcriptomic data, aiming to identify biologically relevant points of departure for gene and pathway activation. To accomplish this, mice were exposed to five erythemally weighted UV‐R doses (0–40 mJ/cm 2 ) emitted from a UV‐emitting tanning device, across six post‐exposure timepoints (0–96 h). Four analytical methods were used to estimate BMDs, with the lowest consistent response dose (LCRD) approach yielding the most sensitive estimates (1.21–3.44 mJ/cm 2 ). Transcriptomic responses revealed activation of shared pathways related to DNA damage and cancer, oxidative stress and metabolism, inflammation and immunity, and hormonal disruption. Notably, the majority of LCRD BMD estimates (1.21–3.44 mJ/cm 2 ) were lower than the International Electrotechnical Commission standard actinic exposure limit (3 mJ/cm 2 (erythemally weighted)) for broadband UV‐R (200–400 nm) for unprotected skin and the eye for an 8 h period. These findings suggest that transcriptomic BMD modeling can detect early biological responses to UV‐R at doses lower than current exposure limits.
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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".