Using Near Infrared Spectroscopy, Diffuse Correlation Spectroscopy, and Intravital Video Microscopy to Monitor the Skeletomuscular and Cerebral Microcirculation
Bibliographic record
Abstract
Introduction The microcirculation is the primary facilitator of oxygen delivery in tissue, and dysfunction in the microvasculature is often the first indication of a disease state. The microcirculation can be studied using a variety of methods, both invasive and non‐invasive. Two non‐invasive optical methods are near infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS). NIRS is sensitive to changes in tissue hemoglobin concentration and oxygen, while DCS is sensitive to changes in blood flow. However, neither method can directly visualize changes on a capillary by capillary basis. Intravital Video Microscopy (IVVM) allows us to do so, but is an invasive technique that requires surgery to expose the microcirculation. By using IVVM we can compare a direct visualization of the microvasculature to the signal received by NIRS/DCS in an equivalent microvascular bed. In addition, we can compare the NIRS/DCS signal from the skeletal muscle to the signal from the brain, allowing us to simultaneously monitor change in these two organs in response to disease progression or in response to stimulus. Methods Data is collected from Sprague Dawley rats (n=4) using both a dual wavelength Olympus inverted microscope (with 2 Rolera XR cameras and a beam splitter) and an in‐house NIRS/DCS device. Rats are anaesthetized and have their right extensor digitorum longus (EDL) muscle, found in the hind limb, exposed and reflected over the objective. Probes for the NIRS/DCS device are located on the left hind limb and the top of the skull. IVVM is used to collect microvascular velocity, hematocrit, and oxygen saturation from the right EDL, and NIRS/DCS will be used to collect microvascular hematocrit, oxygen saturation, and blood flow data from the left hind limb and the brain. Microvascular blood flow and oxygen saturation data from the left and right hind limbs will be compared, then the NIRS/DCS‐gathered data from the left hind limb will be compared to NIRS/DCS‐gathered data from the brain. Discussion This study will be the first to make a direct comparison between NIRS/DCS and IVVM measurements of the microcirculation, and will provide insight into the sensitivity of NIRS/DCS and its ability to accurately monitor the microvasculature. In addition, this study will be the first to make a direct comparison between skeletomuscular and cerebral NIRS/DCS measurements. Future work includes using this approach to determine the differences in effect of a phenylephrine bolus or an inspired oxygen challenge on these two microvascular beds. Further studies will determine whether disease progression occurs on the same timeline in the brain as it does in skeletal muscle. Support or Funding Information NSERC Discovery Grant to C.G. Ellis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".