Refining the orthotopic dog prostate cancer (DPC)‐1 model to better bridge the gap between rodents and men
Bibliographic record
Abstract
BACKGROUND: Rodent models are often suboptimal for translational research on human prostate cancer (PCa). To better fill the gap with human, we refined the previously described orthotopic dog prostate cancer (DPC)-1 model. METHODS: Cyclosporine (Cy) A was used for immune suppression at varying doses and time-periods prior and after orthotopic DPC-1 cell implantation in the dog prostate (n = 12). Follow up included digital rectal examination, ultrasound prostate imaging and biopsies of hypoechoic areas. At necropsy, the prostate, iliosacral lymph nodes (LN), lung nodules, and suspicious bone segments were collected for histopathology. RESULTS: 15 mg CyA/kg daily for 10 days was optimal for tumor take. Maintaining these conditions post-implantation resulted in a rapid tumor development within and beyond the prostate and in iliosacral LNs. To minimize tumor burden, 10 times less DPC-1 cells were implanted. A series of dogs was next followed for 3-4 months, under continuous immune suppression (n = 3) or with CyA interruption at 8.5 weeks (n = 2). In all instances, multifocal tumors were found within the prostate. Predominant patterns were micropapillary and cribriform. Metastases were present in iliosacral LNs and lungs. Moreover, pelvic bone metastases producing a mixed osteoblastic/osteolytic reaction were confirmed in two dogs, one per group. Lastly, the release of CyA 1-2 weeks post-implantation (n = 3) did not prevent tumor growth and spreading to LNs. CONCLUSIONS: The continuing growth of DPC-1 tumors despite the release of CyA and, for the first time, spreading to bones renders this refined model closer to the spontaneous canine and hormone-refractory phase of human PCa.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".