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Record W4312129052 · doi:10.3389/fphys.2022.1052449

Dynamics of capillary blood flow responses to acute local changes in oxygen and carbon dioxide concentrations

2022· article· en· W4312129052 on OpenAlexafffund
Gaylene M. Russell McEvoy, Brenda N. Wells, Meghan E. Kiley, Kanika K. Kaur, Graham Fraser

Bibliographic record

VenueFrontiers in Physiology · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsHematocritCapillary actionCarbon dioxideOxygenationBlood flowChemistrySkeletal muscleOxygen saturationHemoglobinOxygenMedicineBiomedical engineeringAnesthesiaAnatomyInternal medicineMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Objectives:We aimed to quantify the magnitude and time transients of capillary blood flow responses to acute changes in local oxygen concentration ([O2]), and carbon dioxide concentration ([CO2]) in skeletal muscle. Additionally, we sought to quantify the combined response to both low [O2] and high [CO2] to mimic muscle microenvironment changes at the onset of exercise. Methods:13 Sprague Dawley rats were anaesthetized, mechanically ventilated, and instrumented with indwelling catheters for systemic monitoring. The extensor digitorum longus muscle was blunt dissected, and reflected over a microfluidic gas exchange chamber in the stage of an inverted microscope. Four O2challenges, four CO2challenges, and a combined low O2(7–2%) and high CO2(5–10%) challenges were delivered to the surface with simultaneous visualization of capillary blood flow responses. Recordings were made for each challenge over a 1-min baseline period followed by a 2-min step change. The combined challenge employed a 1-min [O2] challenge followed by a 2-min change in [CO2]. Mean data for each sequence were fit using least-squared non-linear exponential models to determine the dynamics of each response. Results:7–2% [O2] challenges decreased capillary RBC saturation within 2 s following the step change (46.53 ± 19.56% vs. 48.51 ± 19.02%,p< 0.0001,τ= 1.44 s), increased RBC velocity within 3 s (228.53 ± 190.39 μm/s vs. 235.74 ± 193.52 μm/s,p< 0.0003,τ= 35.54 s) with a 52% peak increase by the end of the challenge, hematocrit and supply rate show similar dynamics. 5–10% [CO2] challenges increased RBC velocity within 2 s following the step change (273.40 ± 218.06 μm/s vs. 276.75 ± 215.94 μm/s,p= 0.007,τ= 79.34s), with a 58% peak increase by the end of the challenge, supply rate and hematocrit show similar dynamics. Combined [O2] and [CO2] challenges resulted in additive responses to all microvascular hemodynamic measures with a 103% peak velocity increase by the end of the collection period. Data for mean responses and exponential fitting parameters are reported for all challenges. Conclusion:Microvascular level changes in muscle [O2] and [CO2] provoked capillary hemodynamic responses with differing time transients. Simulating exercise via combined [O2] and [CO2] challenges demonstrated the independent and additive nature of local blood flow responses to these agents.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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 designObservational
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

Citations7
Published2022
Admission routes2
Has abstractyes

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