Can we grow sperm? A translational perspective on the current animal and human spermatogenesis models
Bibliographic record
Abstract
There have been tremendous advances in both the diagnosis and treatment of male factor infertility; however, the mechanisms responsible to recreate spermatogenesis outside of the testicular environment continue to elude andrologists. Having the ability to 'grow' human sperm would be a tremendous advance in reproductive biology with multiple possible clinical applications, such as a treatment option for men with testicular failure and azoospermia of multiple etiologies. To understand the complexities of human spermatogenesis in a research environment, model systems have been designed with the intent to replicate the testicular microenvironment. Currently, there are both in vivo and in vitro model systems. In vivo model systems involve the transplantation of either spermatogonial stem cells or testicular xenographs. In vitro model systems involve the use of pluripotent stem cells and complex coculturing and/or three-dimensional culturing techniques. This review discusses the basic methodologies, possible clinical applications, benefits and limitations of each model system. Although these model systems have greatly improved our understanding of human spermatogenesis, we unfortunately have not been successful in demonstrating complete human spermatogenesis outside of the testicle.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".