Developing Human Reproductive Organoids to Combat Infertility: A Literature Review
Bibliographic record
Abstract
Introduction: Infertility affects a significant portion of the population, up to 1 in 5 North American adults. The lack of accurate reproductive models has limited clinical research with 15% of infertility cases remaining untreatable. However, advances in stem cell technology have allowed for the development of organoids, artificial 3D organ systems in culture (also referred to as “organs-in-a-dish”), as accurate, human-specific research models. We propose that organoid systems are valuable tools to advance reproductive health research and aim to assess major progress and limitations of this technology relating to infertility. Methods: A literature review was performed using the PubMed and Google Scholar databases. We identified 10 studies published from 2017 onwards that focused on the application of reproductive organoid systems in infertility treatments or the development of model systems for infertility research. These studies were compared and analyzed in terms of methodology, clinical applications, and potential limitations. Results: Both female and male reproductive tracts (FRT and MRT) are complex systems with many potential causes for infertility. We identified the ovary, fallopian tubes and endometrium in the FRT and the prostate, epididymis and testes in the MRT as the most promising current organoid models. Organoid systems have been used in transplantation techniques to treat the infertility disorders of Asherman’s syndrome and azoospermia. As well, organoids function as disease models for drug screening including chemotherapeutic compounds or as physiologic models to study fundamental mechanisms of fertility considering factors like ageing and environmental gonad toxicity. Discussion: The various novel applications of reproductive organoids emphasize their potential in infertility research and the development of personalized medicine. However, lack of cross-organ communication and minimal microbiome modeling limit organoid-based research. Conversion from animal to human organoid models is also a major obstacle to be addressed for the advancement this technology in reproductive health science. Conclusion: This review highlights the unique benefits of using organoids over traditional research models as well as the most critical research gaps in this field to guide future studies and accelerate the development of clinical techniques for human infertility treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".