The pathophysiology of vascular calcification in a rodent model of chronic kidney disease
Bibliographic record
Abstract
Vascular calcification (VC) occurs in aging populations, but is markedly accelerated in patients with chronic kidney disease (CKD). Through manipulation of dietary adenine, we produced a Sprague Dawley rat model characterized by graded severity of CKD with VC. Circulatory changes were determined by arterial radiotelemetry (n=6) and by assessment of aortic pulse wave velocity (PWV; n=32). VC was assessed using the calcium‐O‐cresophthalein assay and von Kossa staining. At sacrifice, kidney function (creatinine (μmol/L)) was worst in the CKD group with VC (251±60 μmol/L), compared to CKD alone (200±69 μmol/L) or control (50±16 μmol/L). Longitudinal serum phosphate measures revealed that those CKD animals with the highest serum phosphate mid‐study and prior to the development of circulatory changes (i.e. radiotelemetry) subsequently developed calcification (5.96±1.3 mM in calcifying CKD versus 4.4±1.3 mM in CKD versus 2.8±0.2 mM in control). VC animals also demonstrated elevated PWV (3.2±0.3 log(cm/s) vs control 2.9±0.1 log(cm/s)) and widening pulse pressure ((PP) 38±4.7 mmHg to 58 ±15.2 mmHg). Furthermore, there were positive correlations between the phosphate regulating hormone, fibroblast growth factor 23, and PP with left ventricular hypertrophy (LVH; r 2 =0.32 & r 2 =0.58 respectively, p<0.005). Hyperphosphatemia and VC in CKD are likely mechanistically linked and are key factors in enhanced LVH.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".