Advancements in Ovarian Cancer Research: Targeting DNA Repair Mechanisms and the Role of DNA Polymerase β Inhibitors
Bibliographic record
Abstract
ABSTRACT: Background: Exposure to mutagens causes DNA damage, which, if not repaired properly, can lead to diseases like cancer. Ovarian cancer is a major concern for women globally, including in India, as it is often diagnosed at an advanced stage, making treatment more challenging. Recent research implicates DNA repair proteins like DNA polymerase β (Pol β) in cancer development, emphasising the need to understand these pathways for targeted therapy. This study uses bibliometric analysis to explore ovarian cancer research and DNA repair pathways, providing insights for future research and treatment. Materials and Methods: Data from 37,539 articles related to cancer, ovarian cancer, DNA polymerase β, DNA repair pathways, and inhibitors were analysed from the Dimensions database. Publication distribution, national cooperation, leading authors, and research trends were examined. Results: Variations in publication distribution were observed across journals, with notable contributions from countries like Germany, Canada, and the Netherlands. Prolific authors and institutions were identified, shedding light on the global academic landscape. Co-occurrence analysis revealed thematic clusters, including pathophysiology, cancer risk associations, therapeutic targets, and genomic research. Conclusion: This bibliometric analysis offers valuable insights into ovarian cancer research and DNA repair pathways. It highlights the importance of targeting DNA repair mechanisms in cancer therapy and suggests opportunities for collaboration and personalised medicine. Identifying key trends and future directions aids in advancing our understanding and treatment of ovarian cancer, aiming to improve patient outcomes.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.075 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".