Low-Dose Radiation Hypersensitivity Is Associated With p53-Dependent Apoptosis
Bibliographic record
Abstract
Exposure to environmental radiation and the application of new clinical modalities, such as radioimmunotherapy, have heightened the need to understand cellular responses to low dose and low-dose rate ionizing radiation. Many tumor cell lines have been observed to exhibit a hypersensitivity to radiation doses <50 cGy, which manifests as a significant deviation from the clonogenic survival response predicted by a linear-quadratic fit to higher doses. However, the underlying processes for this phenomenon remain unclear. Using a gel microdrop/flow cytometry assay to monitor single cell proliferation at early times postirradiation, we examined the response of human A549 lung carcinoma, T98G glioma, and MCF7 breast carcinoma cell lines exposed to gamma radiation doses from 0 to 200 cGy delivered at 0.18 and 22 cGy/min. The A549 and T98G cells, but not MCF7 cells, showed the marked hypersensitivity at doses <50 cGy. To further characterize the low-dose hypersensitivity, we examined the influence of low-dose radiation on cell cycle status and apoptosis by assays for active caspase-3 and phosphatidylserine translocation (Annexin V binding). We observed that caspase-3 activation and Annexin V binding mirrored the proliferation curves for the cell lines. Furthermore, the low-dose hypersensitivity and Annexin V binding to irradiated A549 and T98G cells were eliminated by treating the cells with pifithrin, an inhibitor of p53. When p53-inactive cell lines (2800T skin fibroblasts and HCT116 colorectal carcinoma cells) were examined for similar patterns, we found that there was no hyperradiosensitivity and apoptosis was not detectable by Annexin V or caspase-3 assays. Our data therefore suggest that low-dose hypersensitivity is associated with p53-dependent apoptosis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".