Suppression of NLRP3 Inflammasome Activation Ameliorates Chronic Kidney Disease-Induced Cardiac Fibrosis and Diastolic Dysfunction
Bibliographic record
Abstract
Cardiac fibrosis is a common finding in patients with chronic kidney disease. Here, we investigate the cardio-renal effects of theracurmin, a novel formulation of the polyphenolic compound curcumin, in a rat model of chronic kidney disease. Briefly, Sprague-Dawley rats were randomized to undergo sham or subtotal nephrectomy (SNx) surgery. At 3 weeks post surgery, SNx animals were further randomized to received theracurmin via once daily oral gavage or vehicle for 5 consecutive weeks. At 8 weeks post surgery, cardiac function was assessed via echocardiography and pressure volume loop analysis, followed by LV and renal tissue collection for analysis. SNx animals developed key hallmarks of renal injury including hypertension, proteinuria, elevated blood urea nitrogen, and glomerulosclerosis. Renal injury in SNx animals was also associated with significant diastolic dysfunction, macrophage infiltration, and cardiac NLRP3 inflammasome activation. Treatment of SNx animals with theracurmin improved structural and functional manifestations of cardiac injury associated with renal failure and also attenuated cardiac NLRP3 inflammasome activation and mature IL-1β release. Taken together, our findings suggest a significant role for the NLRP3 inflammasome in renal injury-induced cardiac dysfunction and presents inflammasome attenuation as a unique strategy to prevent adverse cardiac remodeling in the setting of chronic kidney disease.
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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".