Gene expression signatures of radiation exposure in peripheral white blood cells of smokers and non-smokers
Bibliographic record
Abstract
PURPOSE: The issue of potential confounding factors is critical to the development of any approach to radiation biodosimetry, and has not been fully addressed for gene expression-based approaches. MATERIALS AND METHODS: As a step in this direction, we have investigated the effect of smoking on the global radiation gene expression response in ex vivo-irradiated peripheral blood cells using microarray analysis. We also evaluated the ability of gene expression signatures to predict the radiation exposure level of ex vivo-exposed samples from smokers and non-smokers of both genders. RESULTS: We identified eight genes with a radiation response that was significantly affected by smoking status, and confirmed an effect of smoking on the radiation response of the four and a half LIM domains 2 (FHL2) gene using quantitative real-time polymerase chain reaction. The performance of our previously defined 74-gene signature in predicting the radiation dose to samples in this study was unaffected by differences in gender or smoking status, however, giving 98% correct prediction of dose category. This is the same accuracy as that found in the original study from which the signature was derived, using different donors. CONCLUSION: The results support the development of peripheral blood gene expression as a viable strategy for radiation biodosimetry.
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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".