Docosahexaenoic acid increases doxorubicin's cytotoxicity against breast cancer cell lines
Bibliographic record
Abstract
It is well established that long chain n‐3 polyunsaturated fatty acids have cytotoxic effects on human breast cancer cells. To determine synergy between n‐3 fatty acids and chemotherapy drugs, we examined the effects of docosahexaenoic acid (DHA) alone and in combination with doxorubicin (DOX) on growth of MDA‐MB‐231 and MCF‐7 human breast cancer cell lines, using the WST‐1 assay. The dose/concentration associated with 50% inhibition (IC 50 ) was determined for DHA and DOX (individually) on both cell lines. After treatment (48h) with DHA at its IC 50 concentration (320 μM for MCF‐7, 87 μM for MDA‐MB‐231), cells were treated with DOX at its IC 50 (5.1 μM for MCF‐7, 0.3 μM for MDA‐MB‐231); resulting in increased inhibition (compared to individual treatments). Incubation with DHA significantly increased DHA content in phospholipids of both cell lines. Isobologram analysis (using the Calcusyn program) indicated a synergistic relationship for DHA in MCF‐7 cells, and an additive relationship for DHA in MDA‐MB‐231 cells. Our results suggest that incorporation of DHA into two different breast cancer cell lines can improve the cytotoxic effects DOX which is a cornerstone of breast cancer chemotherapy. Canadian Breast Cancer Foundation: Prairies/NWT
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".