Bibliographic record
Abstract
A large body of evidence supports the role of dietary factors in prostate cancer development and progression. We are interested in investigating the chemopreventive potential of capsaicin, the active compound in chilli peppers that is traditionally used topically to treat various pain-related syndromes. Recently capsaicin has been reported to have anti-carcinogenic properties. In our study, we aim to study the chemopreventive properties of capsaicin using the transgenic adenocarcinoma of the mouse prostate (TRAMP) model, a mouse model that closely resembles the progression of human disease. Methods: Thirty-five 6-week old TRAMP mice were randomized into two groups. Mice received either capsaicin (5 mg/kg body weight) or vehicle 3-times a week by oral gavage. Body weight (BW) was measured thrice weekly. All mice were sacrificed at 30 weeks. BW, genito-urinary (GU) weight, tumour burden were assessed. Serum, prostate, seminal vesicles, lung, liver, esophagus, lymph nodes and pancreas were obtained for analysis. All tumours were scored by an on-site pathologist according to the histopathic grading scale and analyzed using proliferative and mechanistic markers. Results: Interim results revealed that higher percentage of high-grade cancer in control group (n=18). The presence of PIN-like pre-cancerous lesions in only the treatment group and not the control group (n=18), and capsaicin treated mice also had a reduced proportion of metastatic cancers compared to the control group. There were no significant changes in the GU wet weight between groups. Immunohistochemical analysis of the prostate tumour is ongoing. There were no pathological liver or esophagus or gastrointestinal toxicities or changes in BW between grouped. Conclusions: Interim results suggest that oral administration of capsaicin is well tolerated and may reduce the metastatic burden in the TRAMP model. Ongoing studies to delineate the mechanism of action are underway.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.496 | 0.256 |
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".