Ursolic Acid against Prostate and Urogenital Cancers: A Review of In Vitro and In Vivo Studies
Bibliographic record
Abstract
Prostate cancer is the second most diagnosed form of cancer in men worldwide and accounted for roughly 1.3 million cases and 359,000 deaths globally in 2018, despite all the available treatment strategies including surgery, radiotherapy, and chemotherapy. Finding novel approaches to prevent and treat prostate and other urogenital cancers effectively is of major importance. Chemicals derived from plants, such as docetaxel and paclitaxel, have been used in cancer treatment, and in recent years, research interest has focused on finding other plant-derived chemicals that can be used in the fight against cancer. Ursolic acid, found in high concentrations in cranberries, is a pentacyclic triterpenoid compound demonstrated to have anti-inflammatory, antioxidant, and anticancer properties. In the present review, we summarize the research studies examining the effects of ursolic acid and its derivatives against prostate and other urogenital cancers. Collectively, the existing data indicate that ursolic acid inhibits human prostate, renal, bladder, and testicular cancer cell proliferation and induces apoptosis. A limited number of studies have shown significant reduction in tumor volume in animals xenografted with human prostate cancer cells and treated with ursolic acid. More animal studies and human clinical studies are required to examine the potential of ursolic acid to inhibit prostate and other urogenital cancers in vivo.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".