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Record W4409534752 · doi:10.1016/j.isci.2025.112471

A bio-instructive, bioactive in situ polymerizable wound matrix promotes scar-free burn wound repair

2025· article· en· W4409534752 on OpenAlexafffund
Ayesha Aijaz, Margarita Elloso, Yufei Chen, Faraz Chogan, Bhavishya Challagundla, Graham Rix, Supriya Hota, Anna Matveev, Marc G. Jeschke

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsSunnybrook HospitalUniversity of TorontoMcMaster UniversityHamilton Health Sciences
FundersNational Institutes of HealthSunnybrook Research Institute
KeywordsWound healingBurn woundIn situMatrix (chemical analysis)Wound dressingChemistryBiomedical engineeringMaterials scienceSurgeryMedicineComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Early initiation of wound regeneration and healing is the primary determinant of survival for burn patients. While superficial burns do not require secondary interventions and undergo spontaneous healing, full-thickness burns exceed the intrinsic capacity of body to induce skin regeneration. Herein, we developed a wound matrix system, referred to as PEGScarX that provides critical bioactive and bio-instructive cues to modulate cell fate decisions for burn wound healing. Our results indicate that PEGScarX promotes faster wound healing kinetics and reduces scar formation in burn wounds. PEGScarX induces fibroblast and epidermal stem cell repopulation, inhibits transdifferentiation of fibroblasts to myofibroblasts, and induces a pro-regenerative immune cell niche. These findings unravel an opportunity to reduce morbidity and mortality in burn patients due to inadequate wound regeneration and reduce the occurrence of pathological scaring.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.309
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

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 designObservational
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

Citations4
Published2025
Admission routes2
Has abstractyes

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