Microplastic exposure induces epithelial barrier alterations and increases collagen deposition in a 3D human endometrial model in vitro
Bibliographic record
Abstract
PURPOSE: To investigate the effects of microplastics (MPs) on the human endometrium in vitro. METHODS: A predictive 3D endometrial in vitro model was generated using highly porous scaffolds where human endometrial stromal (hESC) and epithelial (hEEC) cells were co-cultured for 35 days. The newly generated endometrial barrier was then exposed to different MP concentrations (from 0.25 to 50 mg/ml) for 24 h and 48 h, respectively. Histological staining and functional analyses were performed to assess the endometrial barrier integrity. Molecular studies and collagen deposition were evaluated to investigate the possible activation of pro-apoptotic and pro-fibrotic related pathways. RESULTS: MP exposure for 24 h does not affect endometrial barrier integrity nor collagen synthesis and deposition. Similar responses are detected when concentrations between 0.25 and 1 mg/ml are used for 48 h. In contrast, 48-h incubations with higher doses (10-50 mg/ml MPs) induce epithelial barrier alterations, reduce TEER values and decrease ZO1 and CDH1 gene transcription. This is accompanied by the activation of pro-fibrotic signalling pathways resulting in collagen increment, which often accompanies endometriosis-related alterations. CONCLUSION: The data obtained suggest MP ability to exert deleterious effects in vitro on human endometrium, with a possible negative impact on its functionality and receptivity.
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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".