Biocompatibility of PLGA/sP(EO-stat-PO)-Coated Mesh Surfaces under Constant Shearing Stress
Bibliographic record
Abstract
BACKGROUND: In order to allow inflammatory response modification and ultimately improvement in tissue remodeling, we developed a new surface modification for meshes that will serve as a carrier for other substances. Biocompatibility is tested in an animal model. METHODS: The animal model for diaphragmatic hernia repair was established in prior studies. Meshes were surface modified with star-configured PEO (polyethylene oxide)-based molecules [sP(EO-stat-PO)]. An electrospun nanoweb of short-term absorbable PLGA (polylactide-co-glycolide) with integrated sP(EO-stat-PO) molecules was applied onto the modified meshes. This coating also served as aerial sealing of the diaphragm. A final layer of hydrogel was applied to the product. Adhesive properties, defect size and mesh shrinkage were determined, and histological and immunohistochemical investigations performed after 4 months. RESULTS: The mean defect size decreased markedly in both modified mesh groups. Histologically and with regard to apoptosis and proliferation rate, smooth muscle cells, collagen I/III ratio and macrophage count, no statistically significant difference was seen between the 3 mesh groups. CONCLUSIONS: In this proof-of-principle investigation, we demonstrate good biocompatibility for this surface-modified mesh compared to a standard polypropylene-based mesh. This new coating represents a promising tool as a carrier for bioactive substances in the near future.
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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".