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Record W4220827305 · doi:10.1093/jbcr/irac012.244

616 Application of Antibacterial Thermosensitive Collagen-Based Hydrogel in Wound Healing

2022· article· en· W4220827305 on OpenAlexaff
Nafise Amiri, Sahand Ghaffari, Ida Hassanpour, Taesik Chae, Reza B. Jalili, Ruhangiz T. Kilani, Frank Ko, Dirk Lange

Bibliographic record

VenueJournal of Burn Care & Research · 2022
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsWound healingMedicineIn vivoStaphylococcus aureusPseudomonas aeruginosaScaffoldWound careThermal burnIn vitroBurn woundBiomedical engineeringSurgeryBacteriaChemistry

Abstract

fetched live from OpenAlex

Abstract Introduction Burn wound infections are a serious complication of thermal injury. Among the many factors that may limit effective wound healing in patients with burn, bacterial infection and poor cell recruitment appear as the leading causes for 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. As a nutritional protective barrier for the wound, we developed a thermosensitive collagen-based matrix called MeshFill (MF) that contains all nutrition required for cell growth with the ability to fill up all the cavities and void areas in wounds regardless of their geometry. In a previous study, MF was successfully combined with partial-thickness mesh grafted skin in a porcine model and improved healing and aesthetic outcomes. In the present work, we report on the development, and in vitro and in vivo testing of a new formulation of MF containing silver nanoparticles (AgNPs), which simultaneously prevent bacterial infection and promote skin regeneration. Methods We fabricated MF/Ag formulation by loading different concentrations of AgNPs in MF hydrogel. The antibacterial activity of MF/Ag formulation against Methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PA) was examined in vitro. The wound healing efficacy of the formulation was evaluated in a silicon ring splinted delayed wound healing model in rats. The splinted full-thickness wounds were generated on the back of rats and treated with either MF or MF/Ag with different concentrations of AgNPs or were bandaged with no treatment (NT) as a control. The healing process was monitored for 18 days. Clinical wound measurements and histological assessments were performed to compare different treatment regimens Results The results of in vitro antibacterial study showed MF/Ag released a sufficient concentration of silver which caused a marked reduction in colony forming units (CFU) of MRSA and PA as compared to MF alone. MF/Ag did not show any cytotoxicity to human fibroblast. Moreover, the result of the animal study confirmed the safety and efficacy of applying different concentrations of AgNPs loaded in MF without compromising the healing outcome in our rat model. Conclusions These findings suggest that AgNPs loaded MF would be a safe, nutritional, flowable hydrogel that provides an ideal moisture environment for healing while protecting the wound from bacteria and can potentially be used as a functional scaffold in partial-thickness mesh grafted skin in burn patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.400
Teacher spread0.349 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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