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Record W1943235395 · doi:10.1002/adhm.201500192

Cell Interactions with Vascular Regenerative MAA‐Based Materials in the Context of Wound Healing

2015· review· en· W1943235395 on OpenAlexafffund
Alexandra Lisovsky, M. Dean Chamberlain, Laura A. Wells, Michael V. Sefton

Bibliographic record

VenueAdvanced Healthcare Materials · 2015
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsQueen's UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchOntario Research Foundation
KeywordsWound healingRegenerative medicineContext (archaeology)Regeneration (biology)Materials scienceBiomedical engineeringNanotechnologyMedicineCell biologySurgeryStem cellBiology

Abstract

fetched live from OpenAlex

In diabetic patients the development of chronic non-healing wounds is a common complication. A methacrylic acid-based biomaterial is a vascular regenerative material that enhances diabetic healing without the use of cells or growth factors. The bioactive nature of this material is thought to be associated with its anionic charge or surface chemistry. Contact between the methacrylic acid-based biomaterial and tissue begins with protein (including complement) adsorption and is followed by interaction of the biomaterial with resident and infiltrating cells in the wound bed (e.g., macrophages and endothelial cells). This results in changes to their surface receptors to activate phosphorylation cascades that lead to differential activation of signalling pathways such as those involving osteopontin and sonic hedgehog. These changes modulate the phenotype of the cells in the wound bed, eventually improving vessel formation and wound healing. Understanding the molecular and cellular mechanisms will have broad implications for biomaterials, not just the methacrylic acid-based material, and will facilitate the advancement of regenerative biomaterials for diverse 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.668
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.050
GPT teacher head0.375
Teacher spread0.325 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations31
Published2015
Admission routes2
Has abstractyes

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