Bibliographic record
Abstract
Stem cells, one of the progenitors of cancer, exist predominately in a quiescent state. Thus, understanding the mechanisms of DNA repair and mutagenesis in such arrested cells may help unravel the complex process of tumorigenesis. Two major nucleotide excision repair (NER) pathways are known to remove bulky physical or chemical lesions from DNA. Transcription-coupled repair (TCR) acts solely on the transcribed strand of expressed genes, while global genomic repair (GGR) is responsible for the ubiquitous repair of the genome. Indirectly, it has been shown that while TCR functions in quiescent cells GGR does not. To explicitly elucidate this phenomenon, we adapted a quantitative PCR (QPCR) assay to study UV-damage repair via TCR and GGR in quiescent and proliferating cells. We present evidence that repair of untranscribed silent regions of the genome and repair of the non-transcribed strand of active genes proceeds by two discrete mechanisms in quiescent cells; rather than by GGR, which was believed to encompass both. Thus, our findings suggest the existence of an alternate NER pathway in quiescent cells. The proposed subcategories of NER are as follows: (i) TCR, responsible for maintenance of transcribed strands; (ii) GGR, responsible for ubiquitous genome repair; and (iii) non-transcribed strand repair (NTSR), predominantly responsible for the repair of the NTS in arrested cells. In quiescent cells, it is evident that TCR and NTSR function and GGR are arrested. As a consequence, mutation accumulation at temporally silent genes and incomplete or imperfect repair of transcribed genes, in quiescent stem cells, may provide a source of cancer causing mutations.
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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 | 0.000 | 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) | 0.002 | 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".