Effects of recipient mouse strain, sex and gonadal status on the outcome of testis tissue xenografting1
Bibliographic record
Abstract
The aim of the present study was to examine factors that may affect the outcome of testis tissue xenografting. Recipient factors were examined by grafting small fragments of testis tissue from newborn piglets under the back skin of immunodeficient mice of different strains (severe combined immunodeficiency (SCID) v. nude), sex (male v. female) and gonadal status (intact v. gonadectomised) using a factorial design (eight groups; n = 7 mice per group). Recipient mice were killed after 8 months to compare the gross and histological attributes of the recovered grafts. Overall, approximately 94% of grafts were recovered. Gonadectomy of male or female recipients did not affect any of the measured outcomes of testis tissue xenografting, therefore data were pooled. Overall, in terms of sex, male mice and, in terms of strain, SCID mice tended to show higher gross and histological development of grafts. The group of female nude mice had the lowest graft recovery rate (75%) compared with the other groups (95-100%; P < 0.05). The grafts from male SCID mice were, on average the largest and had the highest percentage of spermatozoa-containing seminiferous tubules among all the groups (P < 0.05). These results suggest that male SCID mice provide a suitable recipient model for testis tissue xenografting and that the mice do not need to be castrated for optimal results.
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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 | 0.001 |
| 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 | 0.000 |
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".