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Record W4318323706 · doi:10.1016/j.xcrp.2023.101259

Injectable gelatin-oligo-catechol conjugates for tough thermosensitive bioadhesion

2023· article· en· W4318323706 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

VenueCell Reports Physical Science · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Waterloo
FundersNational Institutes of HealthInstitut National de la Santé et de la Recherche Médicale
KeywordsGelatinPolymer chemistrySelf-healing hydrogelsCatecholAdhesionAdhesiveSurface modificationChemistryViscosupplementationHyaluronic acidMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Extracellular matrix-derived biomaterials, such as gelatin-based hydrogels, are attractive candidates for sealing internal leakages. However, gelatin derivatives are brittle and suffer from poor adhesion. Modifications of gelatin with adhesive catechol moieties have been limited to low degrees of substitution. Here, we propose oxidative oligomerization of catecholic compounds (namely, caffeic acid [CA]) prior to the coupling reaction to augment the number and availability of grafted catechol groups per carbodiimide conjugation reaction, thereby achieving robust bioadhesion. Ex vivo adhesion tests on pig lungs suggests ∼3× improvement in adhesion strength compared with gelatin methacryloyl controls due to their enhanced cohesion (i.e., ∼5.3× and ∼11.5× improvements in stretchability and toughness, respectively). Functionalization of gelatin with CA oligomers enables rapid formation of physical gels upon exposure to room temperature, tunes the viscosity of the gelatin-caffeic acid pre-gel solution for controllable injectability onto multiply curved tissues, and boosts the antioxidant effects of CA.

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)0.001
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 score0.028
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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