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Record W2529760863 · doi:10.1002/jbm.b.33797

Validation and application of a nondestructive and contactless method for rheological evaluation of biomaterials

2016· article· en· W2529760863 on OpenAlexaff
Caroline Ceccaldi, Satu Strandman, Ève Hui, Emmanuel Montagnon, Cédric Schmitt, Anis Hadj Henni, Sophie Lerouge

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsInro Consultants (Canada)École de Technologie Supérieure
Fundersnot available
KeywordsViscoelasticityMaterials scienceRheometrySelf-healing hydrogelsBiomedical engineeringComposite materialRheologyTissue engineeringNondestructive testingPolymer chemistry

Abstract

fetched live from OpenAlex

Hydrogels are extensively used for tissue engineering, cell therapy or controlled release of bioactive factors. Nondestructive techniques that can follow their viscoelastic properties during polymerization, remodeling, and degradation are needed, since these properties are determinant for their in vivo efficiency. In this work, we proposed the viscoelastic testing of bilayered materials (VeTBiM) as a new method for nondestructive and contact-less mechanical characterization of soft materials. The VeTBiM method measures the dynamic displacement response of a material, to a low amplitude vibration in order to characterize its viscoelastic properties. We validated VeTBiM by comparing data obtained on various agar and chitosan hydrogels with data from rotational rheometry, and compression tests. We then investigated its potential to follow the mechanical properties of chitosan hydrogels during gelation and in the presence of papain and lysozyme that induce fast or slow enzymatic degradation. Due to this nondestructive and contactless approach, samples can be removed from the instrument and stored in different conditions between measurements. VeTBiM is well adapted to follow biomaterials alone or with cells, over long periods of time. This new method will help in the fine tuning of the mechanical properties of biomaterials used for cell therapy and tissue engineering. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 2565-2573, 2017.

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)0.009
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 score0.026
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
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 head0.059
GPT teacher head0.413
Teacher spread0.354 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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