Long-Term In Vitro Maintenance of Piglet Testicular Tissue: Effects of Tissue Fragment Size, Preparation Method, and Serum Source
Bibliographic record
Abstract
Long-term culture of testicular tissue has important applications, including the preservation of fertility potential of prepubertal boys undergoing gonadotoxic cancer treatment. This study was designed to define optimal conditions for the long-term culture of neonatal porcine testicular tissue as an animal model for preadolescent individuals. Testes from 1 wk old donor piglets were used to examine the effects of tissue fragment size (~2, 4, 6, or 8 mg), preparation method (intact, semi-digested, or physically dispersed fragments), and serum source in the media (fetal bovine serum—FBS—or knockout serum replacement—KSR). Testicular fragments were examined weekly for 4 weeks for tissue integrity, seminiferous cord density and morphology, and gonocyte counts. Testicular tissue integrity was dependent on fragment size and preparation method, where the smallest size (2 mg, p < 0.05) and intact preparation method were advantageous (p < 0.05). Seminiferous cord density decreased over the culture period (p < 0.05). Although the relative number of gonocytes decreased over time for all sizes and methods (p < 0.01), smaller intact fragments (2 and 4 mg) had greater numbers of gonocytes (p < 0.05). Our findings suggest that intact or physically dispersed testicular fragments of the smallest size (2 mg) cultured in KSR-supplemented media could be effectively maintained in vitro for the duration of 4 weeks.
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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 | 0.000 | 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".