Biostability, Inflammatory Response, and Healing Characteristics of a Fluoropassivated Polyester‐Knit Mesh in the Repair of Experimental Abdominal Hernias
Bibliographic record
Abstract
The present study was undertaken to validate the benefits of a fluoropolymer treatment on the biostability, inflammatory response, and healing characteristics of a polyester mesh used for hernia repair, the Fluoromesh, as compared to a commercial monofilament-knit polypropylene mesh, Marlex, used as the control. Both were implanted for the repair of surgically induced abdominal hernias in piglets for prescheduled durations of implantation of 4, 15, and 60 days. The mesh and surrounding tissue were harvested at the sacrifice for the bursting strength and inflammatory response measurements in terms of alkaline and acid phosphatase secretion in the tissue, and for histological observations of the healing sequence and tissue thickness measurements by histomorphometric techniques. After cleaning to remove adherent tissue, the presence of the fluoropolymer at the surface of the mesh was detected using SEM and ESCA. The results demonstrated greater mechanical reinforcement and tissue development for the Fluoromesh than for the polypropylene mesh. The healing performance of the Fluoromesh was attributed to a more intense chronic inflammatory reaction early after implantation that stimulated significantly greater tissue ingrowth and integration. The concentration of fluoropolymer at the surface of the mesh was masked as a result of biological species adsorption. Textile analysis revealed that the Fluoromesh was dimensionally more stable in vivo than the polypropylene control mesh, which demonstrated stretching in the weft direction and shrinking in the warp direction during implantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".