Endothelial function in large and small arteries is closely correlated in human hypertension
Bibliographic record
Abstract
Structure and function of blood vessels vary along the vascular tree, and alterations in hypertension are also different. The aim of this study is to determine whether noninvasive measurement of endothelial function in conduit arteries reflects that of subcutaneous resistance arteries measured in vitro. Ten male hypertensive patients (age: 49±2 years) were studied. Flow-mediated dilation (FMD) during reactive hyperemia (endothelium-dependent) and sublingual nitroglycerin (NTG)-induced dilation (endothelium-independent) were assessed in brachial arteries by ultrasound. Structure and acetylcholine (10−9 to 10−4 mol/L)-and sodium nitroprusside (SNP; 10−8 to 10−3 mol/L)-induced vasorelaxation were measured in gluteal subcutaneous resistance arteries in vitro, using a pressurized myograph. Dilatory responses in brachial arteries were compared to those in resistance arteries. Brachial artery FMD and NTG-induced dilation were 8.3±1.5 and 18.4±1.2%, respectively. In subcutaneous resistance arteries, the media/lumen ratio was 8.9±0.5%, and the maximal acetylcholine- and SNP-responses were 75±4 and 86±2%, respectively. FMD was strongly correlated with maximal acetylcholine responses (adjusted r2; 0.76, p<0.001, statistic power with alpha=0.05; >0.90), and weakly correlated with media/lumen ratio in resistance arteries (adjusted r2; 0.34, p=0.058). By multivariate analysis, FMD predicted resistance artery endothelial function independently of age, body mass index, and blood lipid status. In conclusion, endothelial dilatory responses are similar in small and large arteries in hypertensive patients. FMD in the brachial artery is a powerful predictor of endothelial function in human resistance arteries, but not of their structure.
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 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 | 0.002 |
| 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) | 0.001 | 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".