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Application of a Second‐Order Control‐Systems Model to Investigate Effects of Impaired Capillary Signalling in Skeletal Muscle on Regulation of Capillary Blood Flow Velocity and Tissue Oxygenation

2022· article· en· W4225387453 on OpenAlexaff
Keith C. Afas, Daniel Goldman

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicNitric Oxide and Endothelin Effects
Canadian institutionsWestern University
Fundersnot available
KeywordsMicrocirculationSkeletal muscleCapillary actionBlood flowOxygen transportChemistryBiophysicsAnatomyMechanicsBiomedical engineeringBiologyOxygenInternal medicinePhysicsMedicineThermodynamics

Abstract

fetched live from OpenAlex

Microcirculation is the main region of the vasculature where oxygen (O 2 ) is transferred directly from blood to metabolically active tissue. Tissue O 2 partial pressure (PO 2 ) in the microcirculation has been historically infeasible to measure, but critical as an indicator of capillary and surrounding tissue health. Thus, theoretical modelling of capillary‐tissue O 2 transport is crucial as a complement to in vivo experiments in exploring structure‐function relationships in microvascular networks (Ellis et al, Microcirc 2012 , 19:5). To properly complement experiments which stimulate O 2 flow‐dependent regulation systems, modelling of microcirculatory O 2 transport requires incorporation of knowledge on regulatory mechanisms. Capillary networks modulate their blood velocity to recruit sufficient O 2 delivery in many local O 2 conditions; this has led to the conclusion that endothelium signals upstream to arterioles in an O 2 ‐dependent manner (Ghonaim et al, Microcirc 2021 , e12699). Recently, a second‐order control systems model was developed to investigate the interaction between capillary‐tissue O 2 transport and O 2 ‐dependent blood flow regulation under an externally applied O 2 stimulus. This utilized elements from previous arteriolar diameter alteration models in response to skeletal muscle O 2 gradients, as well as elements from a recently developed continuous‐capillary O 2 transport model (Afas et al, Math Biosci 2021 , 333:108535). The control systems model aimed to predict modulations in blood velocity through endothelial signalling which homogenized outlet capillary PO 2 in skeletal muscle. The model demonstrated that variations in O 2 transport parameters such as capillary network density and tissue O 2 consumption rate had varying effects on the averaged blood velocity in a small skeletal muscle segment perfused by a capillary network. In addition, the externally supplied PO 2 had an effect on both blood velocity and the tissue‐capillary O 2 balance. While the tissue‐capillary PO 2 balance and velocity adaptation were investigated, the endothelial signalling parameters were not reported (Afas & Goldman, Vasc. Bio. 2021 , conference). The present study investigates implications of capillary network metric variations on the endothelial signal communicated upstream to modulate capillary blood flow in response to various muscle surface PO 2 levels. Constraints imposed by pathogenic impairment of endothelial signalling will be investigated and interpreted in the context of capillary flow regulation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

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