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Record W1975449067 · doi:10.1002/jbm.a.32528

Poly(methacrylic acid‐<i>co</i>‐methyl methacrylate) beads promote vascularization and wound repair in diabetic mice

2009· article· en· W1975449067 on OpenAlexaff
Dan C. Martin, John L. Semple, Michael V. Sefton

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2009
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsVascularityMethacrylic acidBeadMethyl methacrylateMaterials scienceMethacrylateWound healingBiomedical engineeringSurgeryPolymer chemistryCopolymerMedicinePolymerComposite material

Abstract

fetched live from OpenAlex

Topical application of beads made from poly(methacrylic acid-co-methyl methacrylate) (45 mol % methacrylic acid, MAA) increased the number of blood vessels and improved 1.5 x 1.5 cm full thickness wound closure in a diabetic mouse (db/db) model. Three groups were compared: MAA beads, control poly(methyl methacrylate) beads (PMMA), and no bead blanks. MAA bead treatment significantly increased percent wound closure at all timepoints (7, 14, and 21 days) with MAA bead-treated wounds almost closed at day 21 (91 +/- 5.4% MAA vs. 79 +/- 3.2% PMMA or 76 +/- 4.8% no beads; p < 0.05). This was consistent with the expected significant increase in vascularity in the MAA group at days 7 and 14. For example at day 14, MAA bead-treated wounds had a vascular density of 22.7 +/- 2.6 vessels/hpf compared with 17.0 +/- 2.0 vessels/hpf in the PMMA bead group (p < 0.05). Epithelial gap and migration measurements suggested that the increased vascularity leads to enhanced epithelial cell migration as a principal means of wound closure. Although studies are underway to elucidate the mechanism of this angiogenic response, the results presented here support the notion that such materials, perhaps in other forms, may be useful in wound care or in other situations where vascularity is to be enhanced without the use of exogenous growth factors.

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)0.007
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 score0.222
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.399
Teacher spread0.348 · 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

Citations36
Published2009
Admission routes1
Has abstractyes

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