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Record W2316391459 · doi:10.1021/bm5002813

Microfluidic Generation of Composite Biopolymer Microgels with Tunable Compositions and Mechanical Properties

2014· article· en· W2316391459 on OpenAlexafffund
Mokit Chau, Milad Abolhasani, Héloïse Thérien‐Aubin, Yang Li, Yihe Wang, Diego Velasco, Ethan Tumarkin, Arun Ramachandran, Eugenia Kumacheva

Bibliographic record

VenueBiomacromolecules · 2014
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of New BrunswickUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGelatinBiopolymerSelf-healing hydrogelsMicrofluidicsAgaroseDispersityPolymerChemical engineeringCell encapsulationNanotechnologyChemistryExtracellular matrixTissue engineeringComposite numberBiocompatibilityMaterials sciencePolymer chemistryBiomedical engineeringOrganic chemistryChromatographyComposite materialBiochemistry

Abstract

fetched live from OpenAlex

To develop an understanding of the nature of complex, spatiotemporal interactions between cells and the extracellular matrix (ECM), artificial ECMs formed from hydrogels with a particular spectrum of properties are being developed at a rapid pace. We report the microfluidic generation of small, monodisperse composite agarose-gelatin hydrogel modules (microgel particles) that can be used for cell encapsulation and can serve as instructive cellular microenvironments. The agarose component of the microgels gelled under reduced temperature, while gelatin modified with phenolic hydroxyl groups underwent peroxidase-catalyzed gelation. Microgel composition, structure, morphology, and rigidity were tuned in a high-throughput manner. The results of this work are important for the generation of libraries of cell-laden polymer microgels for single-cell analysis, tissue engineering, and fundamental studies of the role of local microenvironments in cell fate.

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)0.000
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 score0.014
Threshold uncertainty score0.402

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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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

Citations37
Published2014
Admission routes2
Has abstractyes

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