The Anti-Inflammatory and Antibacterial Action of Nanocrystalline Silver and Manuka Honey on the Molecular Alternation of Diabetic Foot Ulcer: A Comprehensive Literature Review
Bibliographic record
Abstract
Honey and silver have been used since ancient times for treating wounds. Their widespread clinical application has attracted attention in light of the increasing prevalence of antibiotic-resistant bacteria. While there have been a number of studies exploring the anti-inflammatory and antibacterial effects of manuka honey and nanocrystalline silver, their advantages and limitations with regard to the treatment of chronic wounds remain a subject of debate. The aim of this paper is to examine the evidence on the use of nanocrystalline silver and manuka honey for treating diabetic foot ulcers through a critical and comprehensive review of in vitro studies, animal studies, and in vivo studies. The findings from the in vitro and animal studies suggest that both agents have effective antibacterial actions. Their anti-inflammatory action and related impact on wound healing are unclear. Besides, there is no evidence to suggest that any topical agent is more effective for use in treating diabetic foot ulcer. Overall, high-quality, clinical human studies supported by findings from the molecular science on the use of manuka honey or nanocrystalline silver are lacking. There is a need for rigorously designed human clinical studies on the subject to fill this knowledge gap and guide clinical practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".