Chemoprevention of colon cancer by diosgenin, a steroidal saponin constituent of fenugreek
Bibliographic record
Abstract
2473 Trigonella foenum graecum (fenugreek) is traditionally used to treat disorders such as high cholesterol, diabetes, wounds, inflammation, and gastrointestinal ailments. Recently we have shown that diosgenin a major constituent of fenugreek inhibit human colon cancer cell growth and induce apoptosis and suppress the carcinogen-induced colonic aberrant crypt foci in rats (CEBP, 13: 1-7, 2004). However, there are no studies indicating disogenin indeed inhibits carcinoma formation in established animal models of colon cancer. The present double-blind study was designed to assess the potential chemopreventive properties of diosgenin on azoxymethane (AOM)-induced rat colon carcinogenesis. Colonic adenocarcinomas were chosen as chemopreventive efficacy end point. In addition, we assessed the markers of proliferation and apoptosis in colonic tumors and normal appearing crypt to understand the mechanism of tumor growth inhibition of diosgenin. Male F344 rats at 7-weeks of age were fed the control (AIN-76A diet) and one week later, rats received s.c. injections of AOM (15 mg/kg body wt., once weekly for 2 weeks) or equal volume of normal saline (vehicle). One week after the carcinogen treatment groups of rats fed experimental diets containing 0 or 0.1% of diosgenin and continued on the experimental diets for 48 weeks and sacrificed. Colon tumors were evaluated histopathologically and expression levels of markers associated with proliferation and apoptosis were determined in colon tumors and normal appearing colon. Administration of diosgenin significantly suppressed both invasive and non-invasive colon tumor incidence up to 60% (p
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.
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 | 0.001 | 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".