Polydopamine-Inspired Surface Modification of Polypropylene Hernia Mesh Devices via Cold Oxygen Plasma: Antibacterial and Drug Release Properties
Bibliographic record
Abstract
Mesh infection is a major complication of hernia surgery after polypropylene (PP) mesh implantation. Modifying the PP mesh with antibacterial drugs is an effective way to reduce the chance of infection, but the hydrophobic characteristic of PP fibers has obstructed the drug adhesion. Therefore, to prepare antimicrobial PP mesh with a stable drug coating layer and to slow the drug release property during the hernia repair process has a great practical meaning. In this work, PP meshes were coated by bio-inspired polydopamine (PDA), which can load and release levofloxacin. PP meshes were activated with cold oxygen plasma and then plasma activated PP fibers were coated with PDA. The PDA coated meshes were further soaked in levofloxacin. The levofloxacin loaded PP meshes demonstrate excellent antimicrobial properties for 6 days and the drug release has lasted for at least 24 h. Moreover, a control PP mesh sample without plasma treatment was also prepared, after coating with PDA and loading levofloxacin. The antimicrobial property was sustained only for two days. The maximum inhibition zone of PDA coated meshes with and without plasma treatment was 12.5 and 9 mm, respectively. On all accounts, the modification strategy can facilely lead to long-term property of infection prevention.
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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) | 0.000 | 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".