Measurement of Respiratory Muscle Blood Flow in Humans Using Near Infrared Spectroscopy and Indocyanine Green
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".