Bibliographic record
Abstract
Tumorigenesis can be viewed as an imbalance between the mechanisms of cell-cycle control and mutation rates within the genes. Genomic instability is broadly classified into microsatellite instability (MIN) associated with mutator phenotype, and chromosome instability (CIN) recognized by gross chromosomal abnormalities. Three intracellular mechanisms are involved in DNA damage repair that leads to mutator phenotype. They include the nucleotide excision repair (NER), base excision repair (BER) and mismatch repair (MMR). The CIN pathway is typically associated with the accumulation of mutations in tumor suppressor genes and oncogenes. Defects in DNA MMR and CIN pathways are responsible for a variety of hereditary cancer predisposition syndromes including hereditary non-polyposis colorectal carcinoma (HNPCC), Bloom syndrome, ataxia-telangiectasia, and Fanconi anaemia. While there are many genetic contributors to CIN and MIN, there are also epigenetic factors that have emerged to be equally damaging to cell-cycle control. Hypermethylation of tumor suppressor and DNA MMR gene promoter regions, is an epigenetic mechanism of gene silencing that contributes to tumorigenesis. Telomere shortening has been shown to increase genetic instability and tumor formation in mice, underscoring the importance of telomere length and telomerase activity in maintaining genomic integrity. Mouse models have provided important insights for discovering critical pathways in the progression to cancer, as well as to elucidate cross talk among different pathways. This review examines various molecular mechanisms of genomic instability and their relevance to 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".