INVESTIGATING THE PARAMETERS OF PRE-/POST-CONDITIONING ON HUMAN-DERIVED CANCER CELLS
Bibliographic record
Abstract
There is a large amount of interest and research currently going into studying the effects of low dose radiation on humans, and bridging the gap with the data from the effects of high dose radiation. Much work is to be done to understand low dose exposures such as from medical treatments and those who work with or around radiation. Two popular and widely known examples of low-dose phenomena are the radiation induced bystander effects and the radioadaptive response (RAR). This research involves the study of the impact of a low dose of radiation that is administered several hours after a high – even fatal – dose is given, which contrasts the traditional RAR where a low priming dose is given before a high dose and can lead to increased cell survival. Many different parameters were checked to see if cell survival can be enhanced or diminished depending on the stage of the cell cycle, cell growth conditions, and cell profiling differences in protein function (namely the TP53 gene). Additionally, the post-conditioning response was contrasted to see if it was possible to see any effects from the newly emerging area of bystander signalling, UV BioPhotons, would be present in cell lines that either did or did not exhibit a post-conditioning effect. It was shown that post-conditioning has a protective effect on survival of the cells in certain dose ranges and certain cell lines. The post-conditioning effect also appears to be stronger in magnitude than the classic RAR. No relationship between gamma-induced biophoton signalling and post-conditioning was observed, nor is it certain whether an acute gamma-field can induce significant UV biophoton damage. This thesis is aimed to explore the various parameters by which post-conditioning effects occur on various Human cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 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 teacher head, 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".