Ionizing Radiation Mediates Dose Dependent Effects Affecting the Healing Kinetics of Wounds Created on Acute and Late Irradiated Skin
Bibliographic record
Abstract
Radiotherapy for cancer treatment is often associated with skin damage that can lead to incapacitating hard-to-heal wounds. No permanent curative treatment has been identified for radiodermatitis. This study provides a detailed characterization of the dose-dependent impact of ionizing radiation on skin cells (45, 60, or 80 grays). We evaluated both early and late effects on murine dorsal skin with a focus on the healing process after two types of surgical challenge. The irradiated skin showed moderate to severe damage increasing with the dose. Four weeks after irradiation, the epidermis featured increased proliferation status while the dermis was hypovascular with abundant α-SMA intracellular expression. Excisional wounds created on these tissues exhibited delayed global wound closure. To assess potential long-lasting side effects of irradiation, radiodermatitis features were followed until macroscopic healing was notable (over 8 to 22 weeks depending on the dose), at which time incisional wounds were made. Severity scores and biomechanical analyses of the scar tissues revealed that seemingly healed irradiated skin still displayed altered functionality. Our detailed investigation of both the acute and chronic repercussions of radiotherapy on skin healing provides a relevant new in vivo model that will instruct future studies evaluating the efficacy of new treatments for radiodermatitis.
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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) | 0.002 | 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".