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Record W1996531718 · doi:10.1002/mabi.201100335

Characterization of a Hierarchical Network of Hyaluronic Acid/Gelatin Composite for use as a Smart Injectable Biomaterial

2011· article· en· W1996531718 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMacromolecular Bioscience · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health Research
KeywordsBiomaterialGelatinComposite numberDynamic mechanical analysisMicrostructureAdhesionComposite materialHyaluronic acidMaterials scienceModulusDrug deliveryTissue engineeringBiomedical engineeringChemistryNanotechnologyPolymer

Abstract

fetched live from OpenAlex

Hybrid HA/Ge hydrogel particles are embedded in a secondary HA network to improve their structural integrity. The internal microstructure of the particles is imaged through TEM. CSLM is used to identify the location of the Ge molecules in the microgels. Through indentation tests, the Young's modulus of the individual particles is found to be 22 ± 2.5 kPa. The overall shear modulus of the composite is 75 ± 15 Pa at 1 Hz. The mechanical properties of the substrate are found to be viable for cell adhesion. The particles' diameter at pH = 8 is twice that at pH = 5. The pH sensitivity is found to be appropriate for smart drug delivery. Based on their mechanical and structural properties, HA-Ge hierarchical materials may be well suited for use as injectable biomaterials for tissue reconstruction.

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)0.000
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 score0.023
Threshold uncertainty score0.633

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 head0.233
Teacher spread0.214 · 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