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Record W2984512748 · doi:10.1139/apnm-2019-0416

Capillary facilitation of skeletal muscle function in health and disease

2019· review· en· W2984512748 on OpenAlexaffvenue
Gianni Parise, Coral L. Murrant, Matthew Cocks, Tim Snijders, Oliver Baum, Michael J. Plyley

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsBrock UniversityUniversity of GuelphMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsSkeletal muscleVasodilationNitric oxideNoveltyPerfusionCell biologyNeuroscienceAngiogenesisMicrocirculationFunction (biology)BiologyEndocrinologyInternal medicineChemistryAnatomyMedicinePsychology

Abstract

fetched live from OpenAlex

Skeletal muscle is highly vascularized, with perfusion being tightly regulated to meet wide-ranging metabolic demands. For decades, the capillary supply has been explored mainly in terms of evaluating the capillary numbers and their function in the supply of oxygen and substrates and the removal of metabolic byproducts. This review will focus on recent discoveries concerning the role played by capillaries in facilitating other aspects of cell regulation and maintenance, in health and disease, as well as alterations during the aging process. Novelty Capillaries play a central role in the coordination of the vascular response that controls blood flow during contraction and the cellular responses to which they feed into. Nitric oxide is an important regulatory compound within the cardiovascular system, and a significant contributor to skeletal muscle capillary angiogenesis and vasodilatory response to agonists. The microvascular network between muscle fibres may play a critical role in the distribution of signalling factors necessary for optimal muscle satellite cell function.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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 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

Citations13
Published2019
Admission routes2
Has abstractyes

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