ASSESSMENT OF STIFFNESS OF LARGE TO SMALL ARTERIES IN MULTI-STAGE RENAL DISEASE: A NUMERICAL STUDY
Bibliographic record
Abstract
Objective: Arterial stiffness, as assessed via pulse wave velocity (PWV), is a major biomarker for risk assessment in patients with chronic kidney disease. However, the mechanisms responsible for the changes in PWV in the presence of kidney disease are not yet fully elucidated. In the present study, we aimed to investigate the direct effects attributable to biomechanical alterations to the arterial tree caused by renal disease progression on arterial stiffness, independent of any biochemical or compensatory effects. Design and method: Particularly, we used a previously validated one-dimensional (1-D) model of the cardiovascular system to simulate arterial pressure and flow at control with two kidneys (2KDN) and in arterial tree configurations representative of different stages of kidney disease, namely subject with a single functional kidney (1KDN), without any functional kidney (0KDN) and a transplant recipient (TX) with a single functional kidney re-attached to the external iliac artery. We evaluated the respective variations in blood pressure (BP), as well as arterial stiffness of large, medium, and small-sized vessels via carotid-femoral PWV (cfPWV), carotid-radial PWV (crPWV), and radial-digital PWV (rdPWV), respectively. Results: Our results showed that BP was increased in 1KDN and 0KDN, and that systolic BP values were restored in the TX configuration. Furthermore, a rise was reported in all PWVs for all tested configurations. The relative difference in stiffness from control to 0KDN was higher in the case of crPWV (+20 %); approximately twice the difference observed for cfPWV and rdPWV (+11 %). In TX, we observed a restoration of the PWVs to values close to 1KDN. Globally, it was demonstrated that alterations of the outflow boundaries to the renal arteries as occurring in different stages of renal disease lead to changes in blood pressure and central and peripheral PWV in line with previously reported clinical data. Conclusions: Our findings suggest that the PWV variations observed in clinical practice with renal disease progression may be partially attributed to biomechanical alterations of the arterial tree, and their effect on blood pressure.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".