Complex Interplay between DNA Damage and Autophagy in Disease and Therapy
Bibliographic record
Abstract
Cancer, a multifactorial disease characterized by uncontrolled cellular proliferation, remains a global health challenge with significant morbidity and mortality. Genomic and molecular aberrations, coupled with environmental factors, contribute to its heterogeneity and complexity. Chemotherapeutic agents like doxorubicin (Dox) have shown efficacy against various cancers but are hindered by dose-dependent cytotoxicity, particularly on vital organs like the heart and brain. Autophagy, a cellular process involved in self-degradation and recycling, emerges as a promising therapeutic target in cancer therapy and neurodegenerative diseases. Dysregulation of autophagy contributes to cancer progression and drug resistance, while its modulation holds the potential to enhance treatment outcomes and mitigate adverse effects. Additionally, emerging evidence suggests a potential link between autophagy, DNA damage, and caretaker breast cancer genes BRCA1/2, highlighting the interplay between DNA repair mechanisms and cellular homeostasis. This review explores the intricate relationship between cancer, Dox-induced cytotoxicity, autophagy modulation, and the potential implications of autophagy in DNA damage repair pathways, particularly in the context of BRCA1/2 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".