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Record W2935811910 · doi:10.1113/jp277580

Studies into the determinants of skeletal muscle oxygen consumption: novel insight from near‐infrared diffuse correlation spectroscopy

2019· article· en· W2935811910 on OpenAlexfundno aff
Wesley J. Tucker, Ryan Rosenberry, Darian Trojacek, Houda H. Chamseddine, Carrie A. Arena‐Marshall, Ye Zhu, Jing Wang, J. Mikhail Kellawan, Mark J. Haykowsky, Fenghua Tian, Michael D. Nelson

Bibliographic record

VenueThe Journal of Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthUniversity of AlbertaUniversity of Texas Southwestern Medical CenterAmerican Heart Association
KeywordsPerfusionMicrocirculationBlood flowSkeletal muscleOxygenForearmOxygen saturationOxygenationCardiologyOxygen deliveryBiomedical engineeringMedicineInternal medicineChemistryAnatomy

Abstract

fetched live from OpenAlex

KEY POINTS: Diffuse correlation spectroscopy (DCS) is emerging as a powerful tool to assess skeletal muscle perfusion. Near-infrared spectroscopy (NIRS) is an established technique for characterizing the transport and utilization of oxygen through the microcirculation. Here we compared a combined NIRS-DCS system with conventional measures of oxygen delivery and utilization during handgrip exercise. The data show good concurrent validity between convective oxygen delivery and DCS-derived blood flow index, as well as between oxygen extraction at the conduit and microvascular level. We then manipulated forearm arterial perfusion pressure by adjusting the position of the exercising arm relative to the position of the heart. The data show that microvascular perfusion can be uncoupled from convective oxygen delivery, and that tissue saturation seemingly compensates to maintain skeletal muscle oxygen consumption. Taken together, these data support a novel role for NIRS-DCS in understanding the determinants of muscle oxygen consumption at the microvascular level. ABSTRACT: Diffuse correlation spectroscopy (DCS) is emerging as a powerful tool to assess skeletal muscle perfusion. Combining DCS with near-infrared spectroscopy (NIRS) introduces exciting possibilities for understanding the determinants of muscle oxygen consumption; however, no investigation has directly compared NIRS-DCS to conventional measures of oxygen delivery and utilization in an exercising limb. To address this knowledge gap, nine healthy males performed rhythmic handgrip exercise with simultaneous measurements by NIRS-DCS, Doppler blood flow and venous oxygen content. The two approaches showed good concurrent validity, with directionally similar responses between: (a) Doppler-derived forearm blood flow and DCS-derived blood flow index (BFI), and (b) venous oxygen saturation and NIRS-derived tissue saturation. To explore the utility of combined NIRS-DCS across the physiological spectrum, we manipulated forearm arterial perfusion pressure by altering the arm position above or below the level of the heart. As expected, Doppler-derived skeletal muscle blood flow increased with exercise in both arm positions, but with markedly different magnitudes (below: +424.3 ± 41.4 ml/min, above: +306 ± 12.0 ml/min, P = 0.002). In contrast, DCS-derived microvascular BFI increased to a similar extent with exercise, regardless of arm position (P = 0.65). Importantly, however, the time to reach BFI steady state was markedly slower with the arm above the heart, supporting the experimental design. Notably, we observed faster tissue desaturation at the onset of exercise with the arm above the heart, resulting in similar muscle oxygen consumption profiles throughout exercise. Taken together, these data support a novel role for NIRS-DCS in understanding the determinants of skeletal muscle oxygen utilization non-invasively and throughout exercise.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.278

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 head0.021
GPT teacher head0.289
Teacher spread0.267 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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