Testicular organoids to study cell–cell interactions in the mammalian testis
Bibliographic record
Abstract
BACKGROUND: Over the last ten years, three-dimensional organoid culture has garnered renewed interest, as organoids generated from primary cells or stem cells with cell associations and functions similar to organs in vivo can be a powerful tool to study tissue-specific cell-cell interactions in vitro. Very recently, a few interesting approaches have been put forth for generating testicular organoids for studying the germ cell niche microenvironment. AIM: To review different model systems that have been employed to study germ cell biology and testicular cell-cell interactions and discuss how the organoid approach can address some of the shortcomings of those systems. RESULTS AND CONCLUSION: Testicular organoids that bear architectural and functional similarities to their in vivo counterparts are a powerful model system to study cell-cell interactions in the germ cell niche. Organoids enable studying samples in humans and other large animals where in vivo experiments are not possible, allow modeling of testicular disease and malignancies and may provide a platform to design more precise therapeutic interventions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".