Serum and tissue endothelin-1 are independent from intima-media thickness of peripheral arteries in patients with chronic kidney disease
Bibliographic record
Abstract
AIM: We aimed to study the relationship of peripheral arteries' atherosclerosis with serum and tissue endothelin-1 in chronic kidney disease patients. METHODS: Ninety patients were enrolled, including 35 patients with chronic kidney disease (case group), 31 patients with coronary artery diseases who were candidates for coronary artery bypass grafting (positive control group), and 24 living kidney donors (negative control group). Intima-media thickness of the common carotid and femoral arteries was determined by ultrasonography. Serum and tissue endothelin-1 were measured by ELISA method. RESULTS: The mean serum and tissue endothelin-1 levels in the donor group were significantly lower than other groups (p < 0.001 for both). The coronary artery bypass grafting group had higher carotid and femoral intima-media thickness than other groups (p < 0.001), and the chronic kidney disease group had higher carotid and femoral intima-media thickness than the donor group (p < 0.001). Regression analysis in all groups did not reveal any correlation between the carotid intima-media thickness/femoral intima-media thickness and the serum/tissue endothelin-1. There was a direct linear correlation between the carotid and femoral intima-media thickness (p < 0.001) in all groups. CONCLUSIONS: Endothelin-1 level and intima-media thickness were higher in the chronic kidney disease patients and coronary artery bypass grafting candidates, without any correlation between endothelin-1 and peripheral arteries' intima-media thickness of both groups. Perhaps endothelin-1 rises and remains high upon endothelial damage and initiation of atherosclerosis.
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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 | 0.001 |
| 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".