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Record W2521459783 · doi:10.1073/pnas.1612277113

Diabetic wound regeneration using peptide-modified hydrogels to target re-epithelialization

2016· article· en· W2521459783 on OpenAlexafffund
Yun Xiao, Lewis A. Reis, Nicole Feric, Erica J. Knee, Junhao Gu, Shuwen Cao, Carol Laschinger, Camila Londoño, Julia Antolovich, Alison P. McGuigan, Milica Radisic

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsWound healingAngiogenesisGlutamineArgininePharmacologyPeptideSelf-healing hydrogelsRegeneration (biology)MedicineCarnosineChemistryBiochemistryAmino acidCell biologyCancer researchSurgeryBiology

Abstract

fetched live from OpenAlex

The QHREDGS peptide, both as a soluble supplement and when immobilized in a substrate, protected keratinocytes against hydrogen peroxide stress in a dose-dependent manner. Collective migration of both normal and diabetic human keratinocytes was promoted on chitosan-collagen films with the immobilized QHREDGS peptide. The clinical relevance was demonstrated further by assessing the chitosan-collagen hydrogel with immobilized QHREDGS in full-thickness excisional wounds in a db/db diabetic mouse model; QHREDGS showed significantly accelerated and enhanced wound closure compared with a clinically approved collagen wound dressing, peptide-free hydrogel, or blank wound controls. The accelerated wound closure resulted primarily from faster re-epithelialization and increased formation of granulation tissue. There were no observable differences in blood vessel density or size within the wound; however, the total number of blood vessels was greater in the peptide-hydrogel-treated wounds. Together, these findings indicate that QHREDGS is a promising candidate for wound-healing interventions that enhance re-epithelialization and the formation of granulation tissue.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.046
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.077
GPT teacher head0.351
Teacher spread0.274 · 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

Citations161
Published2016
Admission routes2
Has abstractyes

Explore more

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