Antibacterial Thermo-Sensitive Silver Hydrogel Nanocomposite Improves Wound Healing
Bibliographic record
Abstract
Introduction: Among the many factors that may limit effective wound healing in patients with chronic ulcers, bacterial infection and poor cell recruitment are primary causes that contribute to prolonged healing. Thus, a novel strategy that aims to prevent bacterial infection within the wound, while at the same time providing structural scaffolding that promotes endogenous tissue repair, would be of great interest. Here, we developed a thermo-sensitive silver nanoparticle hydrogel composite as an antibacterial nutritional scaffold for the wound that contains all nutrients required for cell growth while preventing bacterial infection with the ability to fill up all the cavities and void areas in wounds regardless of their geometry. Methods: Silver nanoparticles (AgNPs) were synthesized by chemical reduction. After characterization, silver hydrogel nanocomposite was developed by reconstitution of collagen-based hydrogel powder in a nanoparticle suspension of varying AgNPs concentrations (200, 400, and 600 ppm). The antibacterial activity of the formulations was examined in vitro and in vivo in subcutaneous implant infected model. The wound healing efficacy of the hydrogel nanocomposite was also evaluated using a splinted wound model in rats through comparison of clinical wound measurements and histological assessments. Cytocompatibility assay and biochemical analysis of blood at the end of in vivo wound healing study were performed to evaluate the safety of formulations. Results: The synthesized nanoparticles were spherical and stable. While hydrogel alone did not show any bacterial reduction in vitro, the inhibition of bacterial growth was significant in all silver hydrogel nanocomposites compared to controls (p <0.05) and was dose-dependent, with maximum reduction observed in the 600 ppm group (4.56±0.26 LOG CFU/mL, P<0.001). All concentrations of AgNPs hydrogel composites showed significant antibacterial activity in vivo as well (P<0.0001). Treatment of splinted wounds with AgNPs hydrogel composite resulted in faster wound closure and accelerated wound re-epithelialization. The formulations were non-cytotoxic and did not differ significantly in hematological and biochemical factors from the control group in in vivo study. Conclusions: By presenting promising antibacterial and wound healing activity, silver hydrogel nanocomposite offers a safe therapeutic option that can be used as a functional scaffold for an acceleration of wound healing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".