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Record W2030346583 · doi:10.1002/jbm.a.33305

Enhanced MC3T3 preosteoblast viability and adhesion on polyelectrolyte multilayer films composed of glycol‐modified chitosan and hyaluronic acid

2011· article· en· W2030346583 on OpenAlexafffund
Christina Holmes, Maryam Tabrizian

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsQuartz crystal microbalanceMaterials sciencePolyelectrolyteChitosanAdhesionProtein adsorptionChemical engineeringContact angleHyaluronic acidBiocompatibilityWettingCell adhesionAdsorptionPolymer chemistryPolymerComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Layer-by-layer polyelectrolyte films made of the naturally derived polysaccharides chitosan (CHI) and hyaluronic acid (HA) constitute a well-studied system for the development of cell-responsive biointerfaces. However, many cell lines exhibit decreased adhesion to CHI/HA multilayer films, particularly as the number of bilayers is increased. Here, our group demonstrates that films composed of glycol-modified chitosan exhibit significantly improved MC3T3 preosteoblast adhesion and viability compared to corresponding films consisting of unmodified CHI. These differences in cellular adhesion are likely due to differences in surface topography and roughness, as measured via atomic force microscopy (AFM), as well as in film chemistry and the water solubility of the cation, since both types of films exhibited similar: thickness, as measured via quartz crystal microbalance and AFM; wettability, as measured via contact angle; and serum protein adsorption, as measured via the bicinchoninic acid assay.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.076
GPT teacher head0.354
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2011
Admission routes2
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part ASame topicPolymer Surface Interaction StudiesFrench-language works237,207