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Record W2068182848 · doi:10.1021/bm401600a

Self-Assembly Enhances the Strength of Fibers Made from Vimentin Intermediate Filament Proteins

2013· article· en· W2068182848 on OpenAlexaff
Nicole Pinto, Fei-Chi Yang, Atsuko Negishi, Maikel C. Rheinstädter, Todd E. Gillis, Douglas S. Fudge

Bibliographic record

VenueBiomacromolecules · 2013
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsHagfishSelf-healing hydrogelsIntermediate filamentProtein filamentThread (computing)VimentinToughnessNanotechnologyMaterials scienceChemistryPolymer scienceCytoskeletonComposite materialPolymer chemistryComputer scienceBiology

Abstract

fetched live from OpenAlex

Hagfish slime threads were recently established as a promising biomimetic model for efforts to produce ecofriendly alternatives to petroleum polymers. Initial attempts to make fibers from solubilized slime thread proteins fell short of achieving the outstanding mechanics of native slime threads. Here we tested the hypothesis that the high strength and toughness of slime threads arise from the ability of constituent intermediate filaments to undergo a stress-induced α-to-β transition. To do this, we made fibers from human vimentin proteins that were first allowed to self-assemble into 10 nm intermediate filaments. Fibers made from assembled vimentin hydrogels underwent an α-to-β transition when strained and exhibited improved mechanical performance. Our data demonstrate that it is possible to make materials from intermediate filament hydrogels and that mimicking the secondary structure of native hagfish slime threads using intermediate filament self-assembly is a promising strategy for improving the mechanical performance of biomimetic protein materials.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations49
Published2013
Admission routes1
Has abstractyes

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