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Record W4316041941 · doi:10.1016/j.isci.2023.105984

Angiopoietin-1 derived peptide hydrogel promotes molecular hallmarks of regeneration and wound healing in dermal fibroblasts

2023· article· en· W4316041941 on OpenAlexafffund
Katrina Vizely, Karl T. Wagner, Serena Mandla, Dakota Gustafson, Jason E. Fish, Milica Radisic

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
FundersNational Institutes of HealthCanadian Institutes of Health ResearchNational Heart, Lung, and Blood InstituteKillam TrustsNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWound healingFibroblastDermisRegeneration (biology)Dermal fibroblastCell biologyChemistryBiomaterialSelf-assembling peptidePeptideIn vitroMedicineImmunologyBiochemistryPathologyBiology

Abstract

fetched live from OpenAlex

By providing an ideal environment for healing, biomaterials can be designed to facilitate and encourage wound regeneration. As the wound healing process is complex, there needs to be consideration for the cell types playing major roles, such as fibroblasts. As a major cell type in the dermis, fibroblasts have a large impact on the processes and outcomes of wound healing. Prevopisly, conjugating the angiopoietin-1 derived Q-peptide (QHREDGS) to a collagen-chitosan hydrogel created a biomaterial with in vivo success in accelerating wound healing. This study utilized solvent cast Q-peptide conjugated collagen-chitosan seeded with fibroblast monolayers to investigate the direct impact of the material on this major cell type. After 24 h, fibroblasts had a significant change in release of anti-inflammatory, pro-healing, and ECM deposition cytokines, with demonstrated immunomodulatory effects on macrophages and upregulated expression of critical wound healing genes.

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 score0.234
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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