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Record W2911165731 · doi:10.1115/1.4042479

Myosin Crossbridge, Contractile Unit, and the Mechanism of Contraction in Airway Smooth Muscle: A Mechanical Engineer's Perspective

2019· review· en· W2911165731 on OpenAlexaff
Chun Y. Seow

Bibliographic record

VenueJournal of Engineering and Science in Medical Diagnostics and Therapy · 2019
Typereview
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCrossBridgeMyosinSarcomereMyofilamentActinMuscle contractionContraction (grammar)BiophysicsMyosin headAnatomyMeromyosinChemistryMyocyteBiologyMyosin light-chain kinaseCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Muscle contraction is caused by the action of myosin motors within the structural confines of contractile unit arrays. When the force generated by cyclic interactions between myosin crossbridges and actin filaments is greater than the average load shared by the crossbridges, sliding of the actin filaments occurs and the muscle shortens. The shortening velocity as a function of muscle load can be described mathematically by a hyperbola; this characteristic force-velocity relationship stems from stochastic interactions between the crossbridges and the actin filaments. Beyond the actomyosin interaction, there is not yet a unified theory explaining smooth muscle contraction, mainly because the structure of the contractile unit in smooth muscle (akin to the sarcomere in striated muscle) is still undefined. In this review, functional and structural data from airway smooth muscle are analyzed in an engineering approach of quantification and correlation to support a model of the contractile unit with characteristics revealed by mathematical analyses and behavior matched by experimental observation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.325
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2019
Admission routes1
Has abstractyes

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