Fabrication of polycaprolactone electrospun nanofibers doped with silver nanoparticles formed by air plasma treatment
Bibliographic record
Abstract
Implanted devices are prone to bacterial infections, which can result in implant loosening and device failure. Mitigating these infections is important to both implant stability and patient health. The development of antibacterial implant coatings can decrease the presence of bacterial colonies, reducing the risk for bacterial-dependent implant failure. Here, we show that electrospun polycaprolactone (PCL) fibers doped with silver nanoparticles (NPs) from a silver nitrate precursor have the potential to decrease the prevalence of Streptococcus pneumoniae while supporting osteoblast attachment and proliferation. An air plasma reduction method of PCL electrospun fibers was used to prepare fibers doped with silver NPs. Fibers were characterized using scanning electron microscopy and transmission electron microscopy for qualitative evaluation of NP distribution and quantitative analysis of fiber diameters. Antibacterial testing against S. pneumoniae was performed with successful inhibition observed after 24 h of exposure. In vitro testing was completed using Saos-2 cells and suggests that the negative surface charge has the potential to increase mammalian cell viability even in the presence of fibers containing NPs. In conclusion, this study describes a novel method to produce bioresorbable implant coatings with the ability to reduce bacterial infections surrounding the implant surface while remaining biocompatible to the host.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".