Endothelial function and hemorheological parameters modulate coronary blood flow in patients without significant coronary artery disease
Bibliographic record
Abstract
BACKGROUND: Coronary (micro)vascular resistance is regulated by the complex interplay of several factors. Two potentially important determinants include endothelial function and the rheological properties of blood. However, their impact on the control of the coronary resistance vasculature is poorly understood. METHODS: The corrected Thrombolysis In Myocardial Infarction frame count (TIMIfc, an index of coronary flow velocity), conduit artery endothelial function, intima-media thickness of the common carotid artery and complete blood counts were measured in 145 patients undergoing elective coronary angiography. Patients with obstructive coronary artery disease or systemic conditions thought to be associated with microvascular disease were excluded from the analysis. RESULTS: There was a strong correlation between the TIMIfc measured in the three main coronary artery distributions (R values between 0.71 and 0.85, P < 0.00001). The TIMIfc was higher in males (P < 0.05), but there was no association with traditional risk factors for coronary artery disease (all P > 0.1). There was a correlation between TIMIfc and L-FMC, a parameter of resting endothelial function (R = 0.33, P < 0.0005). TIMIfc also correlated with mean platelet volume (a marker of platelet activation, R = 0.33, P < 0.001), and hematocrit (R = 0.33, P = 0.0002). There was no correlation between TIMIfc and carotid intima-media thickness and the degree of coronary atherosclerosis. Logistic regression analysis showed that L-FMC and hemorheological variables may explain as much as 19% of the variability in TIMIfc. CONCLUSIONS: Resting peripheral endothelial function, as well as parameters of platelet function, correlate with coronary TIMIfc. These data emphasize the existence of an association between endothelial function, hemorheological variables and coronary blood flow velocity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".