The Effect of Seal Oil on Paclitaxel Induced Cytotoxicity and Apoptosis in Breast Carcinoma MCF-7 and MDA-MB-231 Cell Lines
Bibliographic record
Abstract
Some studies have suggested that omega-3 polyunsaturated fatty acids (PUFAs) have an inhibitory effect on the growth of cancer cells and therefore have the potential to increase the efficacy of cancer chemotherapeutic drugs. Considering that omega-3 PUFAs are present abundantly in harp seal oil, we investigated the effect of seal oil on the cytotoxicity and apoptosis induced by paclitaxel in 2 breast cancer cell lines, MCF-7 and MDA-MB-231, respectively. Cytotoxicity evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay revealed that the concentration of paclitaxel that is required for 50% inhibition of cell growth in the presence of seal oil was significantly lower than that of paclitaxel alone. Apoptosis assessment based on morphological changes and DNA fragmentation results indicated that more cells treated with paclitaxel in combination with seal oil underwent apoptosis than with paclitaxel alone. Western blot analysis showed that the expression of B cell lymphoma-2 (Bcl-2) protein, an apoptosis inhibitory protein, in both cell lines was decreased more significant by paclitaxel in combination with seal oil than by paclitaxel alone. In addition, seal oil alone was found to induce apoptosis in both cell lines tested, which appeared to be due to the increased intracellular lipid peroxides produced. It is therefore concluded that paclitaxel in combination with seal oil demonstrated enhanced cytotoxicity and apoptosis in MCF-7 and MDA-MB-231 cells compared to paclitaxel alone, and the use of seal oil may be beneficial in the treatment of breast cancer.
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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) | 0.001 | 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) | 0.001 | 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".