Differential effects of flaxseed oil (Linum usitatissimum) on cell growth between malignant and non‐malignant cell lines
Bibliographic record
Abstract
Many cancer patients take vitamins and supplements as potential treatments and therefore many researchers have begun to systematically evaluate the potential of these “functional foods”. Flax, in the form of seeds or seed‐derived oil, is recognized for its exceptional nutritional value due to its high concentration of fiber‐based lignans and large amounts of omega fatty acids. We have examined the anti‐cancer effects of flaxseed oil by studying its direct effects on cancer cell growth in vitro. A total of seven different oils, including flaxseed oil, were characterized by HPLC and GC/MS analysis and used to treat cells. Treatment with flaxseed oil was associated with a rapid slowing of growth by the aggressive murine melanoma cell line B16‐BL6. Treatment of B16‐BL6 with each of the other characterized oils, showed no significant change in cell growth. Interestingly, non‐malignant cell lines such as HSG cells, showed an increase in cell growth following flaxseed treatment. Treatment with flaxseed oil inhibited growth of all 6 tested malignant cells in a dose‐dependent manner and did not inhibit growth of non‐malignant cell lines. Therefore, in addition to improving the efficacy with which cancer treatments selectively target and destroy neoplastic cells, current efforts in the development of novel therapeutic agents seek to minimize aversive side effects to improve patient‐well being and quality of life.
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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) | 0.001 | 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".