Inducible Repair and Intrinsic Radiosensitivity: A Complex but Predictable Relationship?
Bibliographic record
Abstract
Two groups have proposed a simple linear relationship between inducible radioresistance in a variety of mammalian cells and their intrinsic radiosensitivity at 2 Gy (Lambin et al., Int.J. Radiat. Biol. 69, 279-290, 1996; Alsbeih and Raaphorst, unpublished results, 1997). The inducible repair response (IRR) is quantified as a ratio, alpha(S)/alpha(R), i.e. the slope in the hypersensitive low-dose region, alpha(S), relative to the alpha(R) term of the classical linear-quadratic formula. These proposals imply that the intrinsic radiosensitivity at clinically relevant doses is directly linked to the cell's ability to mount an adaptive response as a result of exposure to very low doses of radiation. We have re-examined this correlation and found that the more extensive data set now available in the literature does not support the contention of a simple linear relationship. The two parameters are correlated, but by a much more complex relationship. A more logical fit is obtained with a log-linear equation. A series of log-linear curves are needed to describe the correlation between IRR and SF2, because of the spectrum of alpha/beta ratios among the cell lines and hence the confounding effect of the beta term at a dose of 2 Gy. The degree of repair competence before irradiation starts could also be a major factor in the apparent magnitude of the amount of repair induced. There appears to be a systematic difference in the data sets from different series of cell lines that have been obtained using flow cytometry techniques in the laboratory in Vancouver and using dynamic microscope imaging at the Gray Laboratory. We suggest that the use of a brief exposure to a laser beam in flow cytometry before the cells are irradiated might itself partially induce a stress response and change the DNA repair capacity of the cells. The clinical consequences of the relationship for predicting the benefits of altered fractionation schedules are discussed. [ru5]
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".