A Combination of Micronutrients Is Beneficial in Reducing the Incidence of Prostate Cancer and Increasing Survival in the<i>Lady</i>Transgenic Model
Bibliographic record
Abstract
We have previously shown that administration of a combination of micronutrients (selenium, vitamin E, and lycopene) inhibits prostate cancer (PCa) development in the Lady transgenic model. In the present study, we examine timing of initiation of micronutrients, and the effect of micronutrient combinations, on PCa development in Lady transgenic model. Transgenic males were randomized to either a control diet; control diet supplemented with human equivalent doses of vitamin E, selenium, and lycopene (E+S+L); or control diet supplemented with vitamin E and selenium (E+S). In separate experiments, the combination of E+S+L was initiated at varying time points (4, 8, 20, and 36 weeks of age). A combination of E+S+L resulted in a significant reduction in PCa and liver metastasis when intervention was commenced within 8 weeks of age (P < 0.0001). Immunohistochemical analysis revealed a strong correlation between disease-free state with up-regulation of the prognostic marker p27(Kip1) (P < 0.0001) and decreased expression of proliferating cell nuclear antigen and significantly increased apoptotic index (P < 0.0001). On the contrary, a combination of E+S was not effectual in preventing PCa, with a high proportion (84.6%) of animals developing PCa and a small proportion (11.5%) developing high-grade PIN. Early commencement of micronutrients (E+S+L) is beneficial in reducing PCa. Lycopene is an essential component of the combination and effective (when used with E+S) for PCa prevention. These observations provide support for their chemopreventive effect and some clues about their mechanism of action. These key findings will be complementary to the outcome from the Selenium and Vitamin E Chemoprevention Trial.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".