Impact of Chronic Kidney Disease on Myocardial Blood Flow Regulation in Dogs
Bibliographic record
Abstract
BACKGROUND/AIMS: Chronic kidney disease (CKD) increases cardiovascular risk possibly due to coronary microvessel dysfunction and impaired myocardial flow reserve. This study investigated the effects of CKD on the regulation and transmural distribution of myocardial blood flow along with oxygen demand during intravenous dobutamine-induced increases in cardiac work. METHODS: CKD was produced in dogs by a two-stage subtotal nephrectomy (kidney ablation-infarction model). Serum creatinine and blood urea nitrogen were evaluated during the development of CKD along with systemic blood pressure (tail-cuff plethysmography). After 5 weeks, the CKD dogs were staged according to the International Renal Interest Society staging system; all dogs were anesthetized and surgically prepared for blood flow studies. Data analyses were performed between sham control (CTR) and stage 1 and 2 CKD dogs. RESULTS: At baseline, myocardial blood flow and diastolic aortic pressure were similar for all groups. During intravenous dobutamine, myocardial blood flow was markedly higher than CTR even though hematocrit levels declined with the severity of CKD. In the CTR dogs, myocardial blood flow increased in direct relation to cardiac work. However, in the CKD dogs (stage 1 and 2), maximum blood flow was achieved with low-dose dobutamine, indicating that coronary autoregulation is more readily exhausted with minimal increases in cardiac work during CKD. CONCLUSION: We report that CKD markedly impairs coronary vascular reserve and myocardial blood flow regulation which could contribute to greater cardiovascular risk and poor clinical outcomes in CKD patients.
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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 | 0.000 |
| 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".