Important role of blood rheology in atherosclerosis of patients with hemodialysis
Bibliographic record
Abstract
Concerning a role of blood rheology for atherosclerosis in patients with hemodialysis (HD), little data are available. It may be due to the fact that the method for evaluating rheologic properties of circulating blood has been limited. We examined blood rheology in 118 HD patients by using microchannel array flow analyzer that makes it possible to directly observe the flow of blood cell elements through the microchannel. Transit time (T(B)) of heparinized whole blood through slit pores (7 x 30 microm) was used as an index of rheology and related with various inflammatory biomarkers such as high-sensitive CRP (hsCRP), monocyte chemotactic protein-1, osteopontin, or fibrinogen (Fg). Moreover, as a surrogate marker of atherosclerosis, carotid intima-media thickness (IMT) and aortic stiffness evaluated by brachial-ankle pulse-wave velocity (baPWV) were studied. In HD patients, T(B) had strong positive correlations with hsCRP (r = 0.427; p < 0.00001), Fg (r = 0.452; p < 0.00001), and osteopontin (r = 0.227; p < 0.0134). Further, T(B) was significantly well correlated with IMT (r = 0.400; p < 0.0001) and PWV (r = 0.470; p < 0.0001). Multivariate regression analysis showed that baPWV, IMT, Fg, hematocrit, white blood cell count, and CRP were chosen as significant explanatory factors for T(B.) These results suggest that blood rheology may play an important role for atherosclerosis in patients with HD.
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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 | 0.003 |
| 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 | 0.000 | 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".