Cranial radiotherapy profoundly affects glia without inducing widespread cellular senescence
Bibliographic record
Abstract
BACKGROUND: More than 70% of cancer patients who underwent cranial radiotherapy have cognitive problems, suggesting that they might undergo accelerated brain aging due to the cancer treatment. Radiotherapy is known to induce cellular senescence in tumors and in various cell types in vitro. Therefore, we hypothesized that cranial radiotherapy induces cellular senescence in the brain. METHODS: We treated male C57BL/6 mice with various dosages of (fractionated) CT-guided cranial radiotherapy. The brains were analyzed for various senescence and glial markers using immunohistochemistry, histochemistry, CosMx SMI and RT-qPCR. To contextualize the findings regarding cranial radiotherapy in young animals and assess whether these changes parallel natural aging, we studied the brains from 77-week-old female mice, which is considered middle-aged to old representing early aging. RESULTS: Surprisingly, we found no increase in markers for cellular senescence in the brain after cranial radiotherapy. However, we did detect profound changes in different types of glia. In early-aged mice we again did not detect an increase in senescence markers, but we observed the same directionality in the effects on glia. These effects on glia were milder compared to those upon cranial radiotherapy. CONCLUSION: Overall, cellular senescence in the healthy brain seems a rather uncommon phenomenon and not induced upon cranial radiotherapy, but profound changes in different types of glia were detected upon cranial radiotherapy.
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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".