Late Residual γ-H2AX Foci In Murine Skin are Dose Responsive and Predict Radiosensitivity<i>In Vivo</i>
Bibliographic record
Abstract
Accurate biodosimetry is needed to estimate radiation doses received in vivo from accidental or unwarranted radiation exposures. We investigated the use of DNA repair foci (e.g. gamma-H2AX) at late times after irradiation in vivo as a biodosimeter of initial ionizing radiation dose. Two radiosensitive strains (SCID and BALB/c) and two radioresistant strains (C57BL/6 and C3H/HeJ) were used to quantify gamma-H2AX foci in a skin tissue microarray after doses of 1 to 10 Gy at early and late times after irradiation (1 and 7 days). Using a 3D quantitative immunofluorescence microscopy analysis, we observed a dose response for gamma-H2AX foci for all strains at 30 min, 24 h and 7 days after irradiation. The numbers of residual foci were significantly different between each of the four strains and reflected the relative radiosensitivity in vivo. In comparing gamma-H2AX focus and micronucleus formation after irradiation, we also observed association between the number of micronuclei and number of foci after 1 and 7 days between radiosensitive and radioresistant strains. We conclude that 3D image analysis of gamma-H2AX in skin can be used to detect relative radiosensitivity based on late residual gamma-H2AX foci. This technique may be a useful biodosimeter to determine dose at times up to 1 week after accidental or catastrophic radiation exposure in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".