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

2023· article· en· W4378649892 on OpenAlexaffabout
Gaylene Russell McEvoy, Brenda N. Wells, Meghan E. Kiley, Kanika Kaur, Graham Fraser

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCapillary actionBlood flowOxygenationCarbon dioxideOxygenIntravital microscopyChemistryMedicineOxygen saturationSkeletal muscleBiomedical engineeringAnatomyAnesthesiaCardiologyMicrocirculationInternal medicineMaterials science

Abstract

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Objectives: We aimed to quantify the magnitude and time transients of capillary blood flow responses to acute changes in local oxygen concentration ([O 2 ]), and carbon dioxide concentration ([CO 2 ]) in skeletal muscle. Additionally, we sought to quantify the combined response to low [O 2 ] and high [CO 2 ] to mimic muscle microenvironment 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 mounted in the stage of an inverted microscope. Four O 2 challenges (7-12%, 12-7%, 7-2%, 2-7%), four CO 2 challenges (5-0%, 0-5%, 5-10%, 10-5%), and a combined low O 2 (7–2%) and high CO 2 (5–10%) challenges were delivered to the muscle surface with simultaneous visualization of capillary blood flow responses. Recordings were made for each challenge over a 1-minute baseline period followed by a 2-minute step change. The combined challenge employed a 1-minute [O 2 ] challenge followed by a 2-minute change in [CO 2 ]. Analysis of intravital videos was completed offline using custom MATLAB software. Mean data for each sequence were fit using least-squared non-linear exponential models to determine the dynamics of each response. All animal protocols were approved by Memorial University’s Animal Care Committee. Results: Increased [O 2 ] from 7-12% and 2-7% provoked significant increases in red blood cell (RBC) saturation (SO 2 ) within 2 s with time constants of 1.01 and 1.27 s respectively. This increase was coupled with a significant decrease in RBC velocity and supply rate (SR) that occurred within 10 s. 7–2% [O 2 ] challenges decreased capillary RBC SO 2 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 RBC SR showed similar dynamics. 5–10% [CO 2 ] 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, with RBC SR and hematocrit showing similar dynamics. Decreased local [CO 2 ] conditions from 5 to 0% caused a small yet significant decrease in RBC SO 2 within 1 s (τ = 0.84 s) while RBC velocity decreased significantly within 3 s (τ = 18.88 s) with a 77% peak decrease. Combined [O 2 ] and [CO 2 ] challenges resulted in additive responses to all microvascular hemodynamic measures with a 103% peak velocity increase by the end of the collection period. Conclusion: Microvascular level changes in muscle [O 2 ] and [CO 2 ] provoked capillary hemodynamic responses with differing time transients. Simulating exercise via combined [O 2 ] and [CO 2 ] challenges demonstrated the independent and additive nature of local blood flow responses to these agents. This project was supported by a Natural Sciences and Engineering Research Council of Canada Discovery grant awarded to GMF. Student support was provided by Memorial University's School of Graduate Studies and Faculty of Medicine. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.409

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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 designBench or experimental
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
Published2023
Admission routes2
Has abstractyes

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