Effects of Growth Factors on In Vitro Culture of Neonatal Piglet Testicular Tissue Fragments
Bibliographic record
Abstract
In vitro spermatogenesis (IVS) has important applications including fertility preservation of prepubertal cancer patients; however, thus far, IVS has only been achieved using mouse models. To study the effects of growth factors on the maintenance of testicular tissue integrity, germ cell numbers, and potential induction of IVS using a porcine model, we cultured small testicular fragments (~2 mg) from 1-wk-old piglets under six different media conditions (DMEM + 10%KSR alone or supplemented with GDNF, bFGF, SCF, EGF, or a combination of all) for 8 weeks. Overall, tissues supplemented with GDNF and bFGF had the greatest seminiferous tubule integrity and least number of apoptotic cells. GDNF-supplemented tissues had the greatest number of gonocytes per tubule, followed by bFGF-supplemented tissues. There was evidence of gradual Sertoli cell maturation in all groups. Moreover, histological examination and the expression of c-KIT (a marker of differentiating spermatogonia and spermatocytes) and STRA8 (a marker of the pre/meiotic stage germ cells) confirmed the induction of IVS in all groups. However, GDNF- and bFGF-supplemented tissue cultures had greater numbers of seminiferous tubules with spermatocytes compared to other groups. In conclusion, overall, GDNF and bFGF supplementation better maintained the tissue integrity and gonocyte numbers and induced IVS in cultured testicular tissues.
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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".