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Measurement of Respiratory Muscle Blood Flow in Humans Using Near Infrared Spectroscopy and Indocyanine Green

2008· article· en· W2085545107 on OpenAlexaff
Jordan A. Guenette, Ioannis Vogiatzis, Spyros Zakythinos, Dimitrios Athanasopoulos, Spyretta Golemati, Maria Koskolou, Maroula Vassilopolou, Harrieth Wagner, Charis Roussos, Peter D. Wagner, Robert Boushel

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsConcordia UniversityUniversity of British Columbia
Fundersnot available
KeywordsBlood flowMedicineAnesthesiaDiaphragm (acoustics)HyperventilationIndocyanine greenDiaphragmatic breathingVentilation (architecture)Blood volumeTidal volumeBlood samplingRespiratory systemBreathingRectus femoris muscleChemistryAnatomyCardiologyInternal medicineElectromyographySurgeryPathology

Abstract

fetched live from OpenAlex

It has been difficult to measure respiratory muscle blood flow (RMBF) in humans because of the highly invasive measurement techniques and the need to anesthetize patients. To our knowledge, there are no methods available to accurately measure RMBF in conscious humans. PURPOSE: To develop an indicator-dilution approach to measure RMBF using near-infrared spectroscopy (NIRS) to detect indocyanine green dye (ICG) over the respiratory muscles in spontaneously breathing humans. METHODS: NIRS optodes were placed on the left 7th intercostal space at the apposition of the costal diaphragm and on an inactive control muscle (vastus lateralis) in 5 healthy male cyclists. Intravenous injection of ICG allowed cardiac output (by the conventional dye-dilution method with arterial sampling) and RMBF to be measured simultaneously. Esophageal and gastric pressures were also measured to calculate the mechanical work of breathing and trans-diaphragmatic pressure. Measurements were obtained during both resting breathing and three separate 5 minute bouts of constant isocapnic hyperventilation at 27.1±3.2, 56.0±6.1 and 75.9±5.7% of maximum minute ventilation as determined on a previous maximal exercise test. RESULTS: RMBF progressively increased (9.9±0.6, 14.8±2.7, 29.9±5.8 and 50.1±12.5 ml·100ml−1·min) with increasing levels of ventilation while blood flow to the inactive control muscle remained constant (10.39±1.4, 8.7±0.7, 12.9±1.7 and 12.2±1.8 ml·100ml−1·min−1). As ventilation rose, RMBF was closely and significantly correlated with cardiac output (r = 0.994, P = 0.006), the work of breathing (r = 0.995, P= 0.005) and trans-diaphragmatic pressure (r = 0.998, P= 0.002). CONCLUSION: To our knowledge, this is the first study to quantify RMBF in conscious humans during spontaneous hyperventilation. These data suggest that the NIRS-ICG method provides a feasible and sensitive index of respiratory muscle blood flow in humans that should be applicable to rest, light, moderate and heavy 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 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 score0.001
Threshold uncertainty score0.003

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 head0.032
GPT teacher head0.278
Teacher spread0.246 · 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".

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Citations0
Published2008
Admission routes1
Has abstractyes

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