Establishment of an in vitro placental barrier model cultured under physiologically relevant oxygen levels
Bibliographic record
Abstract
The human placental barrier facilitates many key functions during pregnancy, most notably the exchange of all substances between the mother and fetus. However, preclinical models of the placental barrier often lacked the multiple cell layers, syncytialization of the trophoblast cells and the low oxygen levels that are present within the body. Therefore, we aimed to design and develop an in vitro model of the placental barrier that would reinstate these factors and enable improved investigations of barrier function. BeWo placental trophoblastic cells and human umbilical vein endothelial cells were co-cultured on contralateral sides of an extracellular matrix-coated transwell insert to establish a multilayered barrier. Epidermal growth factor and forskolin led to significantly increased multi-nucleation of the BeWo cell layer and increased biochemical markers of syncytial fusion, for example syncytin-1 and hCGβ. Our in vitro placental barrier possessed size-specific permeability, with 4000-Da molecules experiencing greater transport and a lower apparent permeability coefficient than 70 000-Da molecules. We further demonstrated that the BeWo layer had greater resistance to smaller molecules compared to the endothelial layer. Chronic, physiologically low oxygen exposure (3-8%) increased the expression of hypoxia-inducible factor 1α and syncytin-1, further increased multi-nucleation of the BeWo cell layer and decreased barrier permeability only against smaller molecules (457 Da/4000 Da). In conclusion, we built a novel in vitro co-culture model of the placental barrier that possessed size-specific permeability and could function under physiologically low oxygen levels. Importantly, this will enable future researchers to better study the maternal-fetal transport of nutrients and drugs 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".