A non-invasive magnetic resonance imaging approach for assessment of real-time microcirculation dynamics
Bibliographic record
Abstract
Abstract We present a novel, non-invasive magnetic resonance imaging (MRI) technique to assess real-time dynamic vasomodulation of the microvascular bed. Unlike existing perfusion imaging techniques, our method is sensitive only to blood volume and not flow velocity. Using graded gas challenges and a long-life, blood-pool T1-reducing agent gadofosveset, we can sensitively assess microvascular volume response in the liver, kidney cortex, and paraspinal muscle to vasoactive stimuli (i.e. hypercapnia, hypoxia, and hypercapnic hypoxia). Healthy adult rats were imaged on a 3 Tesla scanner and cycled through 10-minute gas intervals to elicit vasoconstriction followed by vasodilatation. Quantitative T1 relaxation time mapping was performed dynamically; heart rate and blood oxygen saturation were continuously monitored. Laser Doppler perfusion measurements confirmed MRI findings: dynamic changes in T1 corresponded with perfusion changes to graded gas challenges. Our new technique uncovered differential microvascular response to gas stimuli in different organs: for example, mild hypercapnia vasodilates the kidney cortex but constricts muscle vasculature. Finally, we present a gas challenge protocol that produces a consistent vasoactive response and can be used to assess vasomodulatory capacity. Our imaging approach to monitor real-time vasomodulation may be extended to other imaging modalities and is valuable for investigating diseases where microvascular health is compromised.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".