The nitric oxide donor molsidomine rescues cardiac function in rats with chronic kidney disease and cardiac dysfunction
Bibliographic record
Abstract
We recently developed a rat model of cardiorenal failure that is characterized by severe left ventricular systolic dysfunction (LVSD) and low nitric oxide (NO) production that persisted after temporary low-dose NO synthase inhibition. We hypothesized that LVSD was due to continued low NO availability and might be reversed by supplementing NO. Rats underwent a subtotal nephrectomy and were treated with low-dose NO synthase inhibition with N(ω)-nitro-l-arginine up to week 8. After 3 wk of washout, rats were treated orally with either the long-acting, tolerance-free NO donor molsidomine (Mols) or vehicle (Veh). Cardiac and renal function were measured on weeks 11, 13, and 15. On week 16, LV hemodynamics and pressure-volume relationships were measured invasively, and rats were killed to quantify histological damage. On week 15, blood pressure was mildly reduced and creatinine clearance was increased by Mols (both P < 0.05). Mols treatment improved ejection fraction (53 ± 3% vs. 37 ± 2% in Veh-treated rats, P < 0.001) and stroke volume (324 ± 33 vs. 255 ± 15 μl in Veh-treated rats, P < 0.05). Rats with Mols treatment had lower end-diastolic pressures (8.5 ± 1.1 mmHg) than Veh-treated rats (16.3 ± 3.5 mmHg, P < 0.05) and reduced time constants of relaxation (21.9 ± 1.8 vs. 30.9 ± 3.3 ms, respectively, P < 0.05). The LV end-systolic pressure-volume relationship was shifted to the left in Mols compared with Veh treatment. In summary, in a model of cardiorenal failure with low NO availability, supplementing NO significantly improves cardiac systolic and diastolic function without a major effect on afterload.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".