A novel MRI analysis for assessment of microvascular vasomodulation in low-perfusion skeletal muscle
Bibliographic record
Abstract
Abstract Compromised microvascular reactivity underlies many conditions and injuries, but its assessment remains difficult, particularly in low perfusion tissues. In this paper, we develop a new mathematical model for the assessment of vasomodulation in low perfusion settings. A first-order model was developed to approximate changes in T1 relaxation times as a result of vasomodulation. Healthy adult rats (N = 6) were imaged on a 3-Tesla clinical MRI scanner, and vasoactive response was probed on gadofosveset using hypercapnic gases at 20% and 5% CO2 to induce vasoconstriction and vasodilation, respectively. MRI included dynamic 3D T1 mapping and T1-weighted images during gas challenge; heart rate was continuously monitored. Laser Doppler perfusion measurements were performed to corroborate MRI findings. The model was able to identify hypercapnia-mediated vasoconstriction and vasodilation through the partial derivative $$\frac{\partial {T}_{1}}{\partial t}$$ ∂T1∂t . MRI on animals revealed gradual vasoconstriction in the skeletal muscle bed in response to 20% CO2 followed by gradual vasodilation on transitioning to 5% CO2. These trends were confirmed on laser Doppler perfusion measurements. Our new mathematical model has the potential for detecting microvascular dysfunction that manifests in the early stages across multiple metabolic and ischemic pathologies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".