MétaCan
Menu
Back to cohort
Record W4385201660 · doi:10.3390/gels9070591

A Review of Metal Nanoparticles Embedded in Hydrogel Scaffolds for Wound Healing In Vivo

2023· review· en· W4385201660 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGels · 2023
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsInternational Collaboration On Repair DiscoveriesMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsNanomedicineNanotechnologyMaterials scienceNanoparticleWound healingBiocompositeSelf-healing hydrogelsIn vivoNanomaterialsBiomedical engineeringEngineeringMedicineComposite materialComposite numberSurgeryBiotechnology

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.435
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.131
GPT teacher head0.426
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it