Mechanobiological regulation of placental trophoblast fusion and function through extracellular matrix rigidity
Bibliographic record
Abstract
The syncytiotrophoblast is a multinucleated layer that plays a critical role in regulating functions of the human placenta during pregnancy. Maintaining the syncytiotrophoblast layer relies on ongoing fusion of mononuclear cytotrophoblasts throughout pregnancy, and errors in this fusion process are associated with complications such as preeclampsia. While biochemical factors are known to drive fusion, the role of disease-specific extracellular biophysical cues remains undefined. Since substrate mechanics play a crucial role in several diseases, and preeclampsia is associated with placental stiffening, we hypothesize that trophoblast fusion is mechanically regulated by substrate stiffness. We developed stiffness-tunable polyacrylamide substrate formulations that match the linear elasticity of placental tissue in normal and disease conditions, and evaluated trophoblast morphology, fusion, and function on these surfaces. Our results demonstrate that morphology, fusion, and hormone release is mechanically-regulated via myosin-II; optimal on substrates that match healthy placental tissue stiffness; and dysregulated on disease-like and supraphysiologically-stiff substrates. We further demonstrate that stiff regions in heterogeneous substrates provide dominant physical cues that inhibit fusion, suggesting that even focal tissue stiffening limits widespread trophoblast fusion and tissue function. These results confirm that mechanical microenvironmental cues influence fusion in the placenta, provide critical information needed to engineer better in vitro models for placental disease, and may ultimately be used to develop novel mechanically-mediated therapeutic strategies to resolve fusion-related disorders during pregnancy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".