Co-administration of finasteride and the pure anti-oestrogen ICI 182,780 act synergistically in modulating the IGF system in rat prostate
Bibliographic record
Abstract
Prostate cancer is the most diagnosed invasive malignancy in males. Androgens and oestrogens have been implicated in the pathogenesis of prostate cancer. We report herein that the pure anti-oestrogen ICI 182,780 (ICI) reduces Ki-67 labelling index and IGF-I receptor levels in rat prostate. Increase of IGF-I mRNA and IGF-binding protein 3 (IGFBP-3) accumulation occur without any effect on prostate weight. Finasteride significantly decreases prostate weight and inhibits IGF-I gene expression. IGFBP-3 mRNA, Akt and phospho-Akt are not affected by finasteride. Co-administration of ICI plus finasteride reduces prostate weight by approximately 50% and causes acinar dilation with decreased luminal epithelial cell thickness. The acinar epithelial cells became atrophic and inactive with minimal cytoplasm. We also demonstrate a synergistic effect of ICI and finasteride on induction of IGFBP-3 accumulation and inhibition of Akt phosphorylation. Because the IGF and IGFBP-3 system plays an important role in prostate epithelial cell proliferation, apoptosis and tumour progression, the inhibitory effects of finasteride and ICI on IGF system may contribute to their anti-proliferative activity. These observations support a potential use of ICI in conjunction with finasteride in the prevention and/or treatment of prostate cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".