A Review of Metal Nanoparticles Embedded in Hydrogel Scaffolds for Wound Healing In Vivo
Bibliographic record
Abstract
An evolving field, nanotechnology has made its mark in the fields of nanoscience, nanoparticles, nanomaterials, and nanomedicine. Specifically, metal nanoparticles have garnered attention for their diverse use and applicability to dressings for wound healing due to their antimicrobial properties. Given their convenient integration into wound dressings, there has been increasing focus dedicated to investigating the physical, mechanical, and biological characteristics of these nanoparticles as well as their incorporation into biocomposite materials, such as hydrogel scaffolds for use in lieu of antibiotics as well as to accelerate and ameliorate healing. Though rigorously tested and applied in both medical and non-medical applications, further investigations have not been carried out to bring metal nanoparticle-hydrogel composites into clinical practice. In this review, we provide an up-to-date, comprehensive review of advancements in the field, with emphasis on implications on wound healing in in vivo experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".