Can we grow sperm? A translational perspective on the current animal and human spermatogenesis models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it