Role of Phytosterols in Cancer Prevention and Treatment
Bibliographic record
Abstract
Plant sterols or phytosterols have been shown to be effective in improving blood lipid profile and thereby protective against cardiovascular disease. In addition to their cardioprotective effects, phytosterols have gained more insight for their protective effect against various forms of cancer. Phytosterols have been reported to alleviate cancers of breast, prostate, lung, liver, stomach and ovary. Reductions in growth of various cancer cells including liver, prostate and breast by phytosterols treatment have been demonstrated. Although exact mechanisms of phytosterols for their anticancer effects are not very well delineated, there have been several mechanisms proposed such as inhibition of carcinogen production, cancer cell growth and multiplication, invasion and metastasis and induction of cell cycle arrest and apoptosis. Other mechanisms including reduction of angiogenesis, invasion and adhesion of cancer cells and production of reactive oxygen species have also been suggested. However, cancer therapy using phytosterol formulations have yet to be designed, largely due to the gap in the literature with regards to mode of action. Furthermore, most of the studies on anticancer effects of phytosterols were conducted in vitro and animal studies and need to be confirmed in humans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".