The Effect of Seal Oil on Paclitaxel Induced Cytotoxicity and Apoptosis in Breast Carcinoma MCF-7 and MDA-MB-231 Cell Lines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it