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Record W2776128086 · doi:10.1016/j.bpj.2017.10.007

Sarcomere Stiffness during Stretching and Shortening of Rigor Skeletal Myofibrils

2017· article· en· W2776128086 on OpenAlexafffund
Nabil Shalabi, Malin Persson, Alf Månsson, Srikar Vengallatore, Dilson E. Rassier

Bibliographic record

VenueBiophysical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchLinnéuniversitetetVetenskapsrådetNatural Sciences and Engineering Research Council of CanadaWenner-Gren Foundation
KeywordsMyofibrilSarcomereStiffnessMaterials scienceTitinChemistryDisplacement (psychology)ViscoelasticityAnatomyBiophysicsComposite materialMyocyteBiologyMedicineBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

In this study, we measured the stiffness of skeletal muscle myofibrils in rigor. Using a custom-built atomic force microscope, myofibrils were first placed in a rigor state then stretched and shortened at different displacements (0.1-0.3 μm per sarcomere) and nominal speeds (0.4 and 0.8 μm/s). During stretching, the myofibril stiffness was independent of both displacement and speed (average of 987 nN/μm). During shortening, the myofibril stiffness was independent of displacement, but dependent on speed (1234 nN/μm at 0.4 μm/s; 1106 nN/μm at 0.8 μm/s). Furthermore, the myofibril stiffness during shortening was greater than that during stretching and the difference depended on speed (31% at 0.4 μm/s; 8% at 0.8 μm/s). The results suggest that the myofibrils exhibit nonlinear viscoelastic properties that may be derived from myofibril filaments, similar to what has been observed in muscle 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 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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.018
GPT teacher head0.290
Teacher spread0.272 · 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

Citations11
Published2017
Admission routes2
Has abstractno

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