Androgen ablation promotes neuroendocrine cell differentiation in dog and human prostate
Bibliographic record
Abstract
BACKGROUND: Mechanisms triggering prostatic NE differentiation are poorly understood. Since dog and man naturally develop prostatic proliferative diseases with age, our objectives were to confirm the presence of NE cells in the dog prostate and test their hormonal regulation in both species. METHODS: Serotonin staining was examined by immunohistochemistry in 37 dog prostates: 17 from intact and 20 from castrated animals. In intact dogs, 9 prostates were normal and 8 hyperplastic. In the castrated group, 6 dogs were left untreated while androgens and estrogens were administered to 7 dogs, each. Human prostates were from 48 prostate cancer patients; half of them were submitted to androgen ablation prior to prostatectomy. The density of serotonin-positive NE cells was expressed relatively to the number of acini. RESULTS: Serotonin-positive NE cells were morphologically similar in dog and human prostates and identified in all groups, independent of the hormonal status. NE cell densities were within the same range in normal and hyperplastic dog prostates but significantly higher after castration. Androgens and estrogens after castration restored NE cell density to normal values and induced luminal differentiation and basal metaplasia, respectively. In human, the density of serotonin-positive NE cells was also significantly higher in benign glands after androgen ablation. CONCLUSIONS: The dog is a suitable animal model and mimics the human, since androgen ablation favored prostatic NE differentiation in both species. The down-regulation elicited by steroids suggests that the process may be reversible and hormonally-repressed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".