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Record W4396894604 · doi:10.1021/acs.biomac.4c00180

Directed Assembly of Elastic Fibers via Coacervate Droplet Deposition on Electrospun Templates

2024· article· en· W4396894604 on OpenAlexafffund
Kirklann Lau, Sean E. Reichheld, Mingqian Xian, Simon Sharpe, Marta Cerruti

Bibliographic record

VenueBiomacromolecules · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenCanada Research ChairsMcGill University
FundersCanadian Institutes of Health ResearchFonds Québécois de la Recherche sur la Nature et les TechnologiesRéseau de Recherche en Santé Buccodentaire et OsseuseCanada Research ChairsMcGill University
KeywordsCoacervateTemplateElectrospinningChemistryDeposition (geology)Chemical engineeringNanotechnologyMaterials scienceChromatographyPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Elastic fibers provide critical elasticity to the arteries, lungs, and other organs. Elastic fiber assembly is a process where soluble tropoelastin is coacervated into liquid droplets, cross-linked, and deposited onto and into microfibrils. While much progress has been made in understanding the biology of this process, questions remain regarding the timing of interactions during assembly. Furthermore, it is unclear to what extent fibrous templates are needed to guide coacervate droplets into the correct architecture. The organization and shaping of coacervate droplets onto a fiber template have never been previously modeled or employed as a strategy for shaping elastin fiber materials. Using an in vitro system consisting of elastin-like polypeptides (ELPs), genipin cross-linker, electrospun polylactic- co -glycolic acid (PLGA) fibers, and tannic acid surface coatings for fibers, we explored ELP coacervation, cross-linking, and deposition onto fiber templates. We demonstrate that integration of coacervate droplets into a fibrous template is primarily influenced by two factors: (1) the balance of coacervation and cross-linking and (2) the surface energy of the fiber templates. The success of this integration affects the mechanical properties of the final fiber network. Our resulting membrane materials exhibit highly tunable morphologies and a range of elastic moduli (0.8–1.6 MPa) comparable to native elastic fibers.

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 score0.069
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.251
Teacher spread0.244 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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