Safety and efficacy of composite collagen–silver nanoparticle hydrogels as tissue engineering scaffolds
Bibliographic record
Abstract
The increasing number of multidrug resistant bacteria has revitalized interest in seeking alternative sources for controlling bacterial infection. Silver nanoparticles (AgNPs), are amongst the most promising candidates due to their wide microbial spectrum of action. In this work, we report on the safety and efficacy of the incorporation of collagen coated AgNPs into collagen hydrogels for tissue engineering. The resulting hybrid materials at [AgNPs] < 0.4 μM retained the mechanical properties and biocompatibility for primary human skin fibroblasts and keratinocytes of collagen hydrogels; they also displayed remarkable anti-infective properties against S. aureus, S. epidermidis, E. coli and P. aeruginosa at considerably lower concentrations than silver nitrate. Further, subcutaneous implants of materials containing 0.2 μM AgNPs in mice showed a reduction in the levels of IL-6 and other inflammation markers (CCL24, sTNFR-2, and TIMP1). Finally, an analysis of silver contents in implanted mice showed that silver accumulation primarily occurred within the tissue surrounding the implant.
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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 | 0.001 | 0.001 |
| 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".