The application of transcriptional benchmark dose modeling for deriving thresholds of effects associated with solar‐simulated ultraviolet radiation exposure
Bibliographic record
Abstract
Considerable data has been generated to elucidate the transcriptional response of cells to ultraviolet radiation (UVR) exposure providing a mechanistic understanding of UVR‐induced cellular responses. However, using these data to support standards development has been challenging. In this study, we apply benchmark dose (BMD) modeling of transcriptional data to derive thresholds of gene responsiveness following exposure to solar‐simulated UVR. Human epidermal keratinocytes were exposed to three doses (10, 20, 150 kJ/m 2 ) of solar simulated UVR and assessed for gene expression changes 6 and 24 hr postexposure. The dose‐response curves for genes with p‐fit values (≥ 0.1) were used to derive BMD values for genes and pathways. Gene BMDs were bi‐modally distributed, with a peak at ∼16 kJ/m 2 and ∼108 kJ/m 2 UVR exposure. Genes/pathways within Mode 1 were involved in cell signaling and DNA damage response, while genes/pathways in the higher Mode 2 were associated with immune response and cancer development. The median value of each Mode coincides with the current human exposure limits for UVR and for the minimal erythemal dose, respectively. Such concordance implies that the use of transcriptional BMD data may represent a promising new approach for deriving thresholds of actinic effects. Environ. Mol. Mutagen. 59:502–515, 2018. © 2018 The Authors Environmental and Molecular Mutagenesis published by Wiley Periodicals, Inc. on behalf of Environmental Mutagen Society
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".