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Record W2734533472 · doi:10.1002/adhm.201700132

Egg Albumen as a Fast and Strong Medical Adhesive Glue

2017· article· en· W2734533472 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Healthcare Materials · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAdhesiveMaterials scienceGLUEPolydimethylsiloxaneComposite materialFabricationWound closureCovalent bondNanotechnologyWound healingLayer (electronics)SurgeryChemistryPathologyMedicine

Abstract

fetched live from OpenAlex

Sutures penetrate tissues to close wounds. This process leads to inflammatory responses, prolongs healing time, and increases operation complexity. It becomes even worse when sutures are applied to stress-sensitive and fragile tissues. By bonding tissues via forming covalent bonds, some medical adhesives are not convenient to be used by surgeons and have side effects to the tissues. Here egg albumen adhesive (EAA) is reported with ultrahigh adhesive strength to bond various types of materials and can be easily used without any chemical and physical modifications. Compared with several commercial medical glues, EAA exhibits stronger adhesive property on porcine skin, glass, polydimethylsiloxane. The EAA also shows exceptional underwater adhesive strength. Finally, wound closure using EAA on poly(caprolactone) nanofibrous sheet and general sutures is investigated and compared in a rat wound model. EAA also does not show strong long-term inflammatory response, suggesting that EAA has potential as a medical glue, considering its abundant source, simple fabrication process, inherent nontoxicity, and low cost.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.022
GPT teacher head0.370
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