Development and characterization of efficient xenograft models for benign and malignant human prostate tissue
Bibliographic record
Abstract
BACKGROUND: Various research groups have attempted to grow fresh, histologically intact human prostate cancer tissues in immunodeficient mice. Unfortunately, grafting of such tissues to the sub-cutaneous compartment was found to be associated with low engraftment rates. Furthermore, xenografts could only be established using high-grade, advanced stage, but not low- or moderate-grade prostate cancer tissues. METHODS: This paper describes methods for xenografting both benign and malignant human prostate tissue to severe combined immunodeficient (SCID) mice. We examine the efficiency and histopathologic consequences of grafting to the sub-cutaneous, sub-renal capsule, and prostatic orthotopic sites. RESULTS: Sub-renal capsule grafting was most efficient in terms of take rate (>90%) for both benign and malignant tissue. Orthotopic grafts consistently exhibited the best histopathologic differentiation, although good differentiation with continued expression of androgen receptors (AR) and PSA was also seen in the sub-renal capsule site. Sub-cutaneous grafting resulted in low take rates and the lowest level of histodifferentiation in surviving grafts. Grafted benign tissues in all sites appropriately expressed AR, PSA, cytokeratins 8, 18, and 14 as well as p63; carcinoma tissues did not express the basal cell markers. Grafting of tissues to castrated hosts did not affect the graft take rates (but was not practical in the case of the orthotopic site). Grafting followed by host castration resulted in epithelial regression with loss PSA and reduced AR expression at all three sites. CONCLUSIONS: These data suggest that sub-renal capsule and orthotopic grafting of human prostate tissue can be used for many basic scientific and translational studies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".