Aberrant DNA Damage Response and DNA Repair Pathway in High Glucose Conditions
Bibliographic record
Abstract
Background: Higher cancer rates and more aggressive behavior of certain cancers have been reported in populations with diabetes mellitus. This association has been attributed in part to the excessive reactive oxygen species generated in diabetic conditions and to the resulting oxidative DNA damage. It is not known, however, whether oxidative stress is the only contributing factor to genomic instability in patients with diabetes or whether high glucose directly also affects DNA damage and repair pathways. Results: Normal renal epithelial cells and renal cell carcinoma cells are more chemo- and radiation resistant when cultured in high concentrations of glucose. In high glucose conditions, the CHK1-mediated DNA damage response is not activated properly. Cells in high glucose also have slower DNA repair rates and accumulate more mutations than cells grown in normal glucose concentrations. Ultimately, these cells develop a transforming phenotype. Conclusions: In high glucose conditions, defective DNA damage responses most likely contribute to the higher mutation rate in renal epithelial cells, in addition to oxidative DNA damage. The DNA damage and repair are normal enzyme dependent mechanisms requiring euglycemic environments. Aberrant DNA damage response and repair in cells grown in high glucose conditions underscore the importance of maintaining good glycemic control in patients with diabetes mellitus and cancer.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".