Cardiovascular aging and the microcirculation of skeletal muscle: using contrast-enhanced ultrasound
Bibliographic record
Abstract
Skeletal muscle is the largest and most important site of capillary-tissue exchange, especially during high-energy demand tasks such as exercise; however, information regarding the role of the microcirculation in maintaining skeletal muscle health is limited. Changes in microcirculatory function, as observed with aging, chronic and cardiovascular diseases, and exercise, likely precede any alterations that arise in larger vessels, although further investigation into these changes is required. One of the main barriers to addressing this knowledge gap is the lack of methodologies for quantifying microvascular function in vivo; the utilization of valid and noninvasive quantification methods would allow the dynamic evaluation of microvascular flow during periods of clinical relevance such as during increased demand for flow (exercise) or decreased demand for flow (disuse). Contrast-enhanced ultrasound (CEUS) is a promising noninvasive technique that has been used for diagnostic medicine and more recently as a complementary research modality to investigate the response of the microcirculation in insulin resistance, diabetes, and aging. To improve the reproducibility of these measurements, our laboratory has optimized the quantification protocol associated with a bolus injection of the contrast agent for research purposes. This brief report outlines the assessment of microvascular flow using the raw time-intensity curve incorporated into gamma variate response modeling. CEUS could be used to compliment any macrovascular assessments to capture a more complete picture of the aging vasculature, and the modified methods presented here provide a template for the general analysis of CEUS within a research setting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".