Application of Gleason analogous grading system and flow cytometry DNA analysis in a novel knock-in mouse prostate cancer model
Bibliographic record
Abstract
OBJECTIVE: A new knock-in mouse adenocarcinoma prostate model (KIMAP) was established, which showed a close to human kinetics of tumour development. This study used a new mouse histological grading system similar to the human Gleason grading system and flow cytometry DNA analysis to measure and compare the new KIMAP model with human CaP and transgenic mouse adenocarcinoma prostate (TGMAP) model. METHODS: According to heterogeneity of the clinical standard for prostate cancer diagnosis, a close to human mouse standard for histological grading and scoring system, Gleason analogous grading system, was established in this study. Sixty KIMAP and 48 TGMAP prostate cancer samples were measured and compared with human CaP. Flow cytometry DNA analysis was performed on malignant prostate tissues obtained from both TGMAP and KIMAP models. RESULTS: Mice with CaP from KIMAP (n = 60) and TGMAP (n = 48) models showed a different distribution of histological scores (p = 0.000). KIMAP mice showed higher percentage (53.3%) of compound histological score rate than TGMAP (25%), but closer to the human clinical average (50%), which showed significant correlation with age (p = 0.001), while TGMAP mice showed unbalanced and random score distribution in all age groups. Flow cytometry analyses showed that most tumour tissues in KIMAP were diploid, analogous to the human condition, while all the TGMAP mice showed aneuploid tumours. CONCLUSIONS: Results of this study further show that KIMAP, a new generation of murine prostate cancer model, could be used as a supplementary model in addition to the currently widely used transgenic models.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".