Treatment with enalapril and not diltiazem ameliorated progression of chronic kidney disease in rats, and normalized renal AT1 receptor expression as measured with PET imaging
Bibliographic record
Abstract
ACE inhibitors are considered first line of treatment in patients with many forms of chronic kidney disease (CKD). Other antihypertensives such as calcium channel blockers achieve similar therapeutic effectiveness in attenuating hypertension-related renal damage progression. Our objective was to explore the value of positron emission tomography (PET) imaging of renal AT1 receptor (AT1R) to guide therapy in the 5/6 subtotal-nephrectomy (Nx) rat model of CKD. Ten weeks after Nx, Sprague-Dawley rats were administered 10mg/kg/d enalapril (NxE), 30mg/kg/d diltiazem (NxD) or left untreated (Nx) for an additional 8-10 weeks. Kidney AT1R expression was assessed using in vivo [18F]fluoropyridine-losartan PET and in vitro autoradiography. Compared to shams, Nx rats exhibited higher systolic blood pressure that was reduced by both enalapril and diltiazem. At 18-20 weeks, plasma creatinine and albuminuria were significantly increased in Nx, reduced to sham levels in NxE, but enhanced in NxD rats. Enalapril treatment decreased kidney angiotensin II whereas diltiazem induced significant elevations in plasma and kidney levels. Reduced PET renal AT1R levels in Nx were normalized by enalapril but not diltiazem, and results were supported by autoradiography. Reduction of renal blood flow in Nx was restored by enalapril, while no difference was observed in myocardial blood flow amongst groups. Enhanced left ventricle mass in Nx was not reversed by enalapril but was augmented with diltiazem. Stroke volume was diminished in untreated Nx compared to shams and restored with both therapies. [18F]Fluoropyridine-Losartan PET allowed in vivo quantification of kidney AT1R changes associated with progression of CKD and with various pharmacotherapies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".