In Vitro Analysis of a Nanocrystalline Silver-Coated Surgical Mesh
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: One million nosocomial infections occur each year in patients with prosthetic devices. We analyzed a polypropylene mesh coated with nanocrystalline silver particles (NCSP) as a means of preventing such infections. METHODS: Nanocrystalline silver was applied to polypropylene mesh using physical vapor deposition in three doses: low (0.31 mg/cm(2)), medium (0.64 mg/cm(2)), and high (1.13 mg/cm(2)). The zone of inhibition (ZOI) test was conducted by incubating either uncoated polypropylene mesh (UM) or silver-coated mesh (CM) with and without various amounts of bovine serum albumin (BSA) and then on agar plates with Staphylococcus aureus and calculating the ZOI. The bactericidal effects of the NCSP-coated meshes were assessed by incubating either UM or CM in medium with S. aureus and performing serial dilutions at 4 and 8 h. Scanning electron microscopy (SEM) was used to examine the surface of UM and CM with and without bacterial incubation. RESULTS: There was an increasing ZOI for low-, medium-, and high-dose CM and no ZOI for UM (p < 0.001 for all CM compared with UM). Incubating the mesh with various amounts of BSA produced persistent ZOIs with the medium- and high-dose CM; however, the low-dose CM became attenuated by such treatment, with no ZOI being seen with meshes incubated in 10% BSA. All concentrations of CM were bactericidal, as no bacteria grew at 8 h of incubation. The SEM images showed clusters of S. aureus on the surface of UM and no clusters on CM. CONCLUSIONS: The CM demonstrated significant bactericidal activity compared with UM. Nanocrystalline silver particles may decrease the incidence of postoperative prosthetic mesh infections and be useful as a coating for other prosthetic materials.
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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".