Human organoid systems in modeling reproductive tissue development, function, and disease
Bibliographic record
Abstract
Research focused on human reproductive biology has primarily relied upon clinical samples affording mainly descriptive studies with limited implementation of functional or mechanistic understanding. More importantly, restricted access to human embryonic material has necessitated the use of animals, primarily rats and mice, and short-term primary cell cultures derived from human patient material. While reproductive developmental processes are generally conserved across mammals, specific features unique to human reproduction have resulted in the development of human-based in vitro systems designed to retain or recapitulate key molecular and cellular processes important in humans. Of note, major advances in 3D epithelial stem cell-based systems modeling human reproductive organ development have been made. These cultures, broadly referred to as organoids, enable research aimed at understanding cellular hierarchies and processes controlling cellular differentiation and function. Moreover, organoids allow the pre-clinical testing of pharmacological substances, both from safety and efficacy standpoints, and hold large potential in driving aspects of personalized medicine that were previously not possible with traditional models. In this mini-review, we focus on summarizing the current state of regenerative organoid culture systems of the female and male reproductive tracts that model organ development, maintenance, and function. Specifically, we will introduce stem cell-based organoid models of the ovary/fallopian tube, endometrium, cervix, prostate gland, and testes. We will also describe organoid systems of the pre-implanting blastocyst and trophoblast, as the blastocyst and its extraembryonic trophectoderm are central to fetal, maternal, and overall pregnancy health. We describe the foundational studies leading to their development and outline the utility as well as specific limitations that are unique and common to many of these in vitro platforms.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".