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Record W3095097676 · doi:10.1002/jbm.a.37125

An in vitro assessment of the response of THP‐1 macrophages to varying stiffness of a glycol‐chitosan hydrogel for vocal fold tissue engineering applications

2020· article· en· W3095097676 on OpenAlexaff
Patrick Thomas Coburn, Alexandre C. Herbay, Mattia Berrini, Nicole Y. K. Li‐Jessen

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsMaterials scienceChitosanIn vitroBiomedical engineeringTissue engineeringSelf-healing hydrogelsStiffnessTHP1 cell lineComposite materialChemical engineeringCell cultureMedicineBiologyBiochemistryPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

The physical properties of a biomaterial play an essential role in regulating immune and reparative activities within the host tissue. This study aimed to evaluate the immunological impact of material stiffness of a glycol-chitosan hydrogel designed for vocal fold tissue engineering. Hydrogel stiffness was varied via the concentration of glyoxal cross-linker applied. Hydrogel mechanical properties were characterized through atomic force microscopy and shear plate rheometry. Using a transwell setup, macrophages were co-cultured with human vocal fold fibroblasts that were embedded within the hydrogel. Macrophage viability and cytokine secretion were evaluated at 3, 24, and 72 hr of culture. Flow cytometry was applied to evaluate macrophage cell surface markers after 72 hr of cell culture. Results indicated that increasing hydrogel stiffness was associated with increased anti-inflammatory activity compared to relevant controls. In addition, increased anti-inflammatory activity was observed in hydrogel co-cultures. This study highlighted the importance of hydrogel stiffness from an immunological viewpoint when designing novel vocal fold hydrogels.

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)0.002
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 score0.007
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.389
Teacher spread0.355 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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