DNA Repair Protein: Endo-Exonuclease as a new Frontier in Cancer Therapy
Bibliographic record
Abstract
DNA repair mechanisms are essential for cellular survival in mammals. A rapid repair of DNA breaks ensures faster growth of normal cells as well as cancer cells, making DNA repair machinery, a potential therapeutic target. Although efficiency of these repair processes substantially decrease the efficacy of cancer chemotherapies that target DNA, compromised DNA repair contributes to mutagenesis and genomic instability leading to carcinogenesis. Thus, an ideal target in DNA repair mechanisms would be one that specifically kills the rapidly dividing cancer cells without further mutagenesis and does not affect normal cells. Endo-exonucleases play a pivotal role in nucleolytic processing of DNA ends in different DNA repair mechanisms especially in homologous recombination repair (HRR) which mainly repairs damaged DNA in S and G2 phases of the cell cycle in rapidly dividing cells. HRR machinery has also been implicated in cell signaling and regulatory functions in response to DNA damage that is essential for cell viability in mammalian cells where as the predominant nonhomologous end-joining pathway is constitutive. Although HRR is thought to be involved at other stages of the cell cycle, it is predominant in growing phases (S and G2) of the cell cycle. The faster growing cells are believed to carryout more HRR in replicative stages of the cell cycle where homologous DNA is available for HRR. Targeting endo-exonucleases specifically involved in HRR will make the normal cells less prone to mutagenesis, rendering the fast growing tumor cells more susceptible to DNA-damaging agents, used in cancer chemotherapy.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".