Role of Phytosterols in Cancer Prevention and Treatment
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
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.
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) | 0.001 | 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) | 0.000 | 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