Defining optimal electrospun membranes to enhance biological activities of human endometrial MSCs
Bibliographic record
Abstract
Introduction: Human endometrial mesenchymal stem cells (H-EMSCs) can inhibit endometrial fibrosis and repair damaged endometrium. However, direct cell injection into dam-aged endometrium shows limited cell survival. Cell seeding onto biomaterial-based electrospun membranes could improve H-EMSCs' survival and prolong their stay at the damaged endometrium. Polycaprolactone (PCL), silk fibroin (SF) and hyaluronic acid (HA) are synthetic or natural biomaterials used by the biomedicine field, however, their effects on the biological activities of H-EMSCs remain unclear. Methods: H-EMSCs were extracted from human endometrium and H-EMSCs showed enhanced adhesion, proliferation on PCL-HA vs. PCL, PCL-SF, establishing the potential of the composite to address cell survival issues. Results: H-EMSCs cultured on PCL-HA showed decreased IL-6 gene expression and increased IL-10, VEGFA, TGF-β gene expression vs. PCL-SF, establishing the potential to create a favorable micro-environment for generating vascularized endometrial tissues. PCL, PCL-SF, PCL-HA all supported CD90 and Meflin expression of the seeded H-EMSCs, establishing PCL as a platform to form enhanced biomaterial composites for endometrial repair in the future. Discussion: This study provided significant evidence sup-porting the potential of appropriately tailored composites of PCL and HA to moder-ate inflammation and wound-healing, which can be applied for endometrial tissue repair and regeneration.
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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".