Regeneration of testis tissue after ectopic implantation of porcine testis cell aggregates in mice: improved consistency of outcomes and in situ monitoring
Bibliographic record
Abstract
Ectopic implantation of donor testis cell aggregates in recipient mice results in de novo formation or regeneration of testis tissue and, as such, provides a unique invivo model for the study of testis development. However, currently the results are inconsistent and the efficiency of the model remains low. This study was designed to: (1) examine several factors that can potentially improve the consistency and efficiency of this model and (2) explore the use of ultrasound biomicroscopy (UBM) for the non-invasive invivo evaluation of implants. Testis cell aggregates, containing ~40% gonocytes, from 1-week-old donor piglets were implanted under the back skin of immunodeficient mice through skin incisions using gel matrices or through subcutaneous injection without using gel matrices. The addition of gel matrices led to inconsistent tissue development; gelatin had the greatest development, followed by collagen, whereas agarose resulted in poor development. The results also depended on the implanted cell numbers since implants with 100×106 cells were larger than those with 50×106 cells. The injection approach for cell implantation was less invasive and resulted in more consistent and efficient testis tissue development. UBM provided promising results as a means of non-invasive monitoring of implants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".