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Record W2127294524 · doi:10.1093/icb/icp007

From ultra-soft slime to hard  -keratins: The many lives of intermediate filaments

2009· article· en· W2127294524 on OpenAlexaff
Douglas S. Fudge, Timothy M. Winegard, Randy H. Ewoldt, Daniel R. Beriault, Lawrence Szewciw, Gareth H. McKinley

Bibliographic record

VenueIntegrative and Comparative Biology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIntermediate filamentTreadmillingProtein filamentKeratinCytoskeletonBiophysicsCytoplasmElasticity (physics)BiologyMaterials scienceNanotechnologyMicrofilamentComposite materialCell biologyCell

Abstract

fetched live from OpenAlex

Intermediate filaments are filaments 10 nm in diameter that make up an important component of the cytoskeleton in most metazoan taxa. They are most familiar for their role as the fibrous component of α-keratins such as skin, hair, nail, and horn but are also abundant within living cells. Although they are almost exclusively intracellular in their distribution, in the case of the defensive slime produced by hagfishes, they are secreted. This article surveys the impressive diversity of biomaterials that animals construct from intermediate filaments and will focus on the mechanisms by which the mechanical properties of these materials are achieved. Hagfish slime is a dilute network of hydrated mucus and compliant intermediate filament bundles with ultrasoft material properties. Within the cytoplasm of living cells, networks of intermediate filaments form soft gels whose elasticity arises via entropic mechanisms. Single intermediate filaments or bundles are also elastic, but substantially stiffer, exhibiting modulus values similar to that of rubber. Hard α-keratins like wool are stiffer still, an effect that is likely achieved via dehydration of the intermediate filaments in these epidermal appendages. The diverse mechanisms described here have been employed by animals to generate materials with stiffness values that span an impressive eleven orders of magnitude.

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 score0.049
Threshold uncertainty score0.530

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 head0.037
GPT teacher head0.332
Teacher spread0.296 · 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

Citations44
Published2009
Admission routes1
Has abstractyes

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