Docosahexaenoic acid alone and in combination with carboplatin significantly reduces tumor cell growth in preclinical models of ovarian cancer
Bibliographic record
Abstract
ABSTRACT Despite recent advances in diagnosis and treatment, ovarian cancer (OC) is the most lethal gynecological malignancy and improving the efficacy of chemotherapy is of great interest. This study increases our understanding of how dietary intervention with docosahexaenoic acid (DHA)-a supplement proven safe for human consumption – enhances the anti-cancer effects of conventional chemotherapy. Our results demonstrated synergistic cell killing by DHA and carboplatin in OC cell lines. Furthermore, DHA supplementation alone and in combination with carboplatin significantly reduced OC growth in a high-grade serous OC patient-derived xenograft mouse model. Carboplatin administered intraperitoneally significantly reduced tumor growth in DHA-fed mice compared to mice on the control diet. Intravenous carboplatin administration in combination with DHA reduced tumor growth similarly to carboplatin or DHA monotherapies. The DHA-induced reduction in tumor growth in this model was associated with increased tumor necrosis and improved survival. As such, our findings provide a strong rationale to move to clinical trials that will determine whether DHA supplementation enhances the efficacy and tolerance of cytotoxic chemotherapy in patients with OC.
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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 | 0.000 |
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".