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Record W4391439210 · doi:10.1002/adhm.202302836

Dissolvable Immunomodulatory Microneedles for Treatment of Skin Wounds

2024· article· en· W4391439210 on OpenAlexaff
Pejman Ghelich, Mohamadmahdi Samandari, Alireza Hassani Najafabadi, Adam P. Tanguay, Jacob Quint, Nikhil Menon, Delaram Ghanbariamin, Farnoosh Saeedinejad, Fatemeh Alipanah, Ramaswamy M. Chidambaram, Roman Krawetz, Kristo Nuutila, Steven Toro, Lindsay Barnum, Gregory D. Jay, Tannin A. Schmidt, Ali Tamayol

Bibliographic record

VenueAdvanced Healthcare Materials · 2024
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersNational Institutes of HealthU.S. Department of Defense
KeywordsGelatinWound healingIn vivoMacrophage polarizationAngiogenesisInflammationFibrosisMacrophageFibrinMaterials scienceIn vitroMedicineBiomedical engineeringCancer researchImmunologyPathologyChemistryBiology

Abstract

fetched live from OpenAlex

Sustained inflammation can halt or delay wound healing, and macrophages play a central role in wound healing. Inflammatory macrophages are responsible for the removal of pathogens, debris, and neutrophils, while anti-inflammatory macrophages stimulate various regenerative processes. Recombinant human Proteoglycan 4 (rhPRG4) is shown to modulate macrophage polarization and to prevent fibrosis and scarring in ear wound healing. Here, dissolvable microneedle arrays (MNAs) carrying rhPRG4 are engineered for the treatment of skin wounds. The in vitro experiments suggest that rhPRG4 modulates the inflammatory function of bone marrow-derived macrophages. Degradable and detachable microneedles are developed from gelatin methacryloyl (GelMA) attach to a dissolvable gelatin backing. The developed MNAs are able to deliver a high dose of rhPRG4 through the dissolution of the gelatin backing post-injury, while the GelMA microneedles sustain rhPRG4 bioavailability over the course of treatment. In vivo results in a murine model of full-thickness wounds with impaired healing confirm a decrease in inflammatory biomarkers such as TNF-α and IL-6, and an increase in angiogenesis and collagen deposition. Collectively, these results demonstrate rhPRG4-incorporating MNA is a promising platform in skin wound healing applications.

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 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.030
GPT teacher head0.368
Teacher spread0.339 · 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 teacher head, 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".

Quick stats

Citations43
Published2024
Admission routes1
Has abstractyes

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