Nanomaterials in Wound Healing and Infection Control
Bibliographic record
Abstract
Wounds continue to be a serious medical concern due to their increasing incidence from injuries, surgery, burns and chronic diseases such as diabetes. Delays in the healing process are influenced by infectious microbes, especially when they are in the biofilm form, which leads to a persistent infection. Biofilms are well known for their increased antibiotic resistance. Therefore, the development of novel wound dressing drug formulations and materials with combined antibacterial, antibiofilm and wound healing properties are required. Nanomaterials (NM) have unique properties due to their size and very large surface area that leads to a wide range of applications. Several NMs have antimicrobial activity combined with wound regeneration features thus give them promising applicability to a variety of wound types. The idea of NM-based antibiotics has been around for a decade at least and there are many recent reviews of the use of nanomaterials as antimicrobials. However, far less attention has been given to exploring if these NMs actually improve wound healing outcomes. In this review, we present an overview of different types of nanomaterials explored specifically for wound healing properties combined with infection control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 0.002 |
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".