Bioengineering-tissue strategies to model mammalian implantation in vitro
Bibliographic record
Abstract
During mammalian implantation, complex and well-orchestrated interactions between the trophectoderm of implanting blastocysts and the maternal endometrium lead to a successful pregnancy. On the other hand, alteration in endometrium-blastocyst crosstalk often causes implantation failure, pregnancy loss, and complications that result in overall infertility. In domestic animals, this represents one of the major causes of economic losses and the understanding of the processes taking place during the early phases of implantation, in both healthy and pathological conditions, is of great importance, to enhance livestock system efficiency. Here we develop highly predictive and reproducible functional tridimensional (3D) in vitro models able to mimic the two main actors that play a key role at this developmental stage: the blastocyst and the endometrium. In particular, we generate a 3D endometrial model by co-culturing primary epithelial and stromal cells, isolated from sow uteri, onto highly porous polystyrene scaffolds. In parallel, we chemically reprogram porcine adult dermal fibroblasts and encapsulate them into micro-bioreactors to create trophoblast (TR) spheroids. Finally, we combine the generated artificial endometrium with the TR spheroids to model mammalian implantation in vitro and mimic the embryo-maternal interactions. The protocols here described allow the generation of reproducible and functional 3D models of both the maternal compartment as well as the implanting embryo, able to recreate in vitro the architecture and physiology of the two tissues in vivo . We suggest that these models can find useful applications to further elucidate early implantation mechanisms and to study the complex interactions between the maternal tissue and the developing embryos.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".