Strategies for the analysis of in vitro radiation sensitivity and prediction of interaction with potential radiation modifying agents
Bibliographic record
Abstract
PURPOSE: To better predict radiation-drug interactions in in vitro model systems, thorough assessment of the effects of in vitro exposure is required. The aim of this article is to show that both clonogenic capacity and cellular proliferation, which represent important different elements of tumour conduct, can be considered when assessing in vitro radio sensitisation. METHODS: A model was designed that can predict radiation-drug interactions based on changes in clonogenic capacity and cell proliferation by radiation modifying agents. RESULTS: Using this mechanistical model, the effect of combined exposure to radiation and potential drugs can be tested on both established cell lines and primary cells. In addition, we could obtain more information about the mechanisms underlying the radiation-drug interaction by assessing the results of in vitro exposure on tumour cell proliferation and clonogenic capacity according to our model. CONCLUSIONS: The significance of our model is not to replace the clonogenic gold standard but to give additional information about the radiation-drug combination by determining cell proliferation. Moreover, the advantage is that the interaction can also be predicted in cases where a clonogenic assay is not possible. Additional research into the biological effect of potential radio-sensitisers is warranted for future (pre)clinical studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".