Using near infrared spectroscopy and diffuse correlation spectroscopy to determine the microvascular effects of phenylephrine in vivo
Bibliographic record
Abstract
Phenylephrine is commonly used in cardiac surgery is common to increase mean arterial pressure without affecting cardiac output; however, its effects on the microcirculation of the brain and in skeletal muscle are unclear. The objective of this study is to use hyperspectral near infrared spectroscopy (h-NIRS) and diffuse correlation spectroscopy (DCS) to monitor the microcirculation during a phenylephrine bolus would be able to discern the timing and effects of a phenylephrine bolus on brain and skeletal muscle microvasculature. The h-NIRS subsystem uses two spectrometers and a halogen light source, and the DCS subsystem uses a long coherence diode laser (785 nm) and a single photon counting module. Probes were positioned on the left hind limb and the top of the skull, with a source-detector distance of 10mm. Data were collected from Sprague Dawley rats (n = 1, 158 g). Nine microvascular challenges were monitored by recording a two-minute baseline, then injecting 0.1mL of phenylephrine (0.1 ug/mL) intravenously, followed by collecting data for 4 additional minutes. Oxygen saturation increased by 9% in the brain and 2% in the muscle. Blood flow increased by 60% in the brain, and 17% in the muscle. This study is the first report on the use of h-NIRS and DCS to investigate the effects of phenylephrine in the microvasculature. Dissimilitude in flow response may be due to differences in regulation mechanisms. Future work will include acquiring data from both subsystems simultaneously to provide further insight into the relationship between oxygen saturation and blood flow.
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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".