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Record W2561160027 · doi:10.1111/micc.12348

Capillary endothelial cells as coordinators of skeletal muscle blood flow during active hyperemia

2016· review· en· W2561160027 on OpenAlexaff
Coral L. Murrant, Iain Lamb, Nicole Novielli

Bibliographic record

VenueMicrocirculation · 2016
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSkeletal muscleMicrocirculationBlood flowAnatomyContraction (grammar)PerfusionSkeletal Muscle FibersCell biologyBiologyChemistryMedicineNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

In this invited review, we explore the burgeoning possibility of capillary endothelial cells as coordinators of skeletal muscle blood flow in response to muscle contraction. The idea that the capillary is an active vascular unit in skeletal muscle microcirculation starkly diverges from the traditional dogma that seats arterioles as the central controllers of blood flow during exercise. This review aims to incite discussion as we revisit and rethink the role of capillary endothelial cells in skeletal muscle. We discuss the potential for a mismatch in the architectural relationships between the arteriolar microvasculature and contracting motor units that would negate consistent communication between them. We review the data from the past two decades demonstrating that capillaries are ideally located architecturally to communicate with skeletal muscle fibers and are mechanistically capable of signaling upstream arterioles that control their own perfusion. We show that the orchestration of a coordinated vascular response necessary to support active skeletal muscle fibers cannot be achieved by the arterioles, but rather it is the capillaries that drive the blood flow response to muscle contraction. Thus, capillaries need to be seriously considered as critical in the coordination of skeletal muscle blood flow during active hyperemia.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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