Advances in human In vitro spermatogenesis: A review
Bibliographic record
Abstract
Recent advances surrounding in vitro spermatogenesis (IVS) have shown potential in creating a new paradigm of regenerative medicine in the future of fertility treatments for males experiencing non-obstructive azoospermia (NOA). Male infertility is a common condition affecting approximately 15% of couples, with azoospermia being present in 15% of infertile males (Cocuzza et al., 2013; Esteves et al., 2011a). Treatment for patients with NOA has primarily been limited to surgical sperm retrieval combined with in vitro fertilization intracytoplasmic sperm injection (IVF-ICSI); however, sperm retrieval is successful in only half of these patients, and live birth rates typically range between 10 and 25% (Aljubran et al., 2022). Therefore, a significant need exists for regenerative therapies in this patient population. IVS has been considered as a model for further understanding the molecular and cellular processes of spermatogenesis and as a potential regenerative therapeutic approach. While 2D cell cultures using human testicular cells have been attempted in previous research, lack of proper spatial arrangement limits germ cell differentiation and maturation, posing challenges for clinical application. Recent research suggests that 3D technology may have advantages for IVS due to mimicry of the native cytoarchitecture of human testicular tissue along with cell-cell communication directly or indirectly. 3D organotypic cultures, scaffolds, organoids, microfluidics, testis-on-a-chip, and bioprinting techniques have all shown potential to contribute to the technology of regenerative treatment strategies, including in vitro fertilization (IVF). Although promising, further work is needed to develop technology for successful, replicable, and safe IVS for humans. The intersection between tissue engineering, molecular biology, and reproductive medicine in IVS development allows for multidisciplinary involvement, where challenges can be overcome to realize regenerative therapies as a viable option.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".