Vasodilator Response Assessment in Porcine Myocardium With Magnetic Resonance Relaxometry
Bibliographic record
Abstract
BACKGROUND: This research describes an early preclinical study of the biophysical mechanisms governing changes in myocardial T2 during vasodilation in normal myocardium. METHODS AND RESULTS: Theoretical modeling and experimental studies in an instrumented pig model (n=7) provided measures of changes in myocardial T2, relative blood volume (BV), and microcirculation oxygen levels (%O2) during intracoronary adenosine infusion. Intracoronary adenosine increases perfusion without increasing blood volume or cardiac metabolic rate; thus, T2 elevations should reflect elevated microcirculation oxygen levels. Robust strategies were used for magnetic resonance imaging (MRI) data collection. Measures of myocardial and vascular T1 before and after Clariscan (Amersham Health) injection provided blood volume assessment. Changes in microcirculation oxygen levels were estimated via direct blood sampling from the left anterior descending (LAD) coronary vein. Perfusion changes were monitored using a Doppler flow wire within the left main coronary artery. Myocardial T2 elevations (DeltaT2=17+/-8%) within the LAD arterial perfusion bed were related to elevations in perfusion (coronary velocity reserve=3.2+/-0.4) and coronary venous %O2 [Delta(LAD CV%O2) =56+/-11%], whereas blood volume (DeltaBV=0 +/-2%) and cardiac metabolic rate [Delta(heart rate x blood pressure) = -4+/-11%] remained constant. CONCLUSIONS: Myocardial T2 elevation during intracoronary adenosine infusion was significant and repeatable, caused by increases in microcirculation oxygen levels. Changes in microcirculation oxygen levels of approximately 40%O2 should be detectable by this technique. This sensitivity should suffice for differentiating normal from abnormal myocardium via measurement of myocardial perfusion reserve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".