Non‐invasive point‐of‐care optical technique for continuous in vivo assessment of microcirculatory function: Application to a preclinical model of early sepsis
Bibliographic record
Abstract
Abstract Increased amplitude of peripheral vasomotion is a potential early marker of sepsis‐related microcirculatory impairment; however, previous reports relied on clinically unsuitable invasive techniques. Hyperspectral near‐infrared spectroscopy (hsNIRS) and diffuse correlation spectroscopy (DCS) are non‐invasive, bedside techniques that can be paired to continuously monitor tissue hemoglobin content (HbT), oxygenation (StO 2 ), and perfusion (rBF) to detect vasomotion as low‐frequency microhemodynamic oscillations. While previous studies have primarily focused on the peripheral microcirculation, cerebral injury is also a common occurrence in sepsis and hsNIRS‐DCS could be used to assess cerebral microcirculatory function. This work aimed to use a hybrid hsNIRS‐DCS system to continuously monitor changes in the peripheral and cerebral microcirculation in a rat model of early sepsis. It was hypothesized that the skeletal muscle would be a more sensitive early indicator of sepsis‐related changes in microhemodynamics than the brain. Control animals received saline while the experimental group received fecal slurry to induce sepsis. Subsequently, hsNIRS‐DCS measurements were acquired from the skeletal muscle and brain for 6 h. Peripheral rBF rapidly decreased in septic animals, but there were no significant changes in peripheral HbT or StO 2 , nor cerebral HbT, rBF, or StO 2 . The power of low‐frequency peripheral oscillations in all parameters (i.e., HbT, StO 2 , and rBF) as well as cerebral HbT oscillations were elevated in septic animals during the final 4 h. These findings suggest that in the early stages of sepsis, while vital organs like the brain are partly protected, changes in peripheral perfusion and vasomotor activity can be detected using hsNIRS‐DCS. Future work will apply the technique to ICU patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".