Mineralocorticoid receptor blockade improves arginine transport and nitric oxide generation through modulation of cationic amino acid transporter-1 in endothelial cells.

Blockade of the mineralocorticoid receptor (MCR) has been shown to improve endothelial function far beyond blood pressure control. In the current studies we have looked at the effect of MCR antagonists on cationic amino acid transporter-1 (CAT-1), a major modulator of endothelial nitric oxide (NO) generation. Using radio-labeled arginine, {[3H] l-arginine} uptake was determined in human umbilical vein endothelial cells (HUVEC) following incubation with either spironolactone or eplerenone with or without silencing of MCR. Western blotting for CAT-1, PKCα and their phosphorylated forms were performed. NO generation was measured by using Griess reaction assay. Both Spironolactone and eplerenone significantly increased endothelial arginine transport, an effect which was further augmented by co-incubation with aldosterone, and blunted by either silencing of MCR or co-administration of amiloride. Following MCR blockade, we identified two bands for CAT-1. The addition of tunicamycin (an inhibitor of protein glycosylation) or MCR silencing resulted in disappearance of the extra band and prevented the increase in arginine transport. Only spironolactone decreased CAT-1 phosphorylation through inhibition of PKCα (CAT-1 inhibitor). Subsequently, incubation with either MCR antagonists significantly augmented NO2/NO3 levels (stable NO metabolites) and this was attenuated by silencing of MCR or tunicamycin. GO 6076 (PKCα inhibitor) intensified the increase of NO metabolites only in eplerenone treated cells. In conclusion spironolactone and eplerenone augment arginine transport and NO generation through modulation of CAT-1 in endothelial cells. Both MCR antagonists activate CAT-1 by inducing its glycosylation while only spironolactone inhibits PKCα.

Electrocardiographic Predictors of Long-Term Cardiac Pacing Dependency Following Transcatheter Aortic Valve Implantation.

BACKGROUND: Conduction disorders requiring permanent pacemaker (PPM) implantation are a known complication of transcatheter aortic valve implantation (TAVI). Indications for permanent pacing in this setting are still controversial. The study aim was to characterize the natural history of conduction disorders related to TAVI, and to identify predictors for long-term pacing dependency. METHODS: Consecutive patients who underwent TAVI were included in this prospective observational study. The conduction system was investigated by reviewing 12-lead ECGs during hospitalization and up to 1-year follow-up and by analyzing pacemaker interrogation data. Multivariate analysis was performed in order to identify independent predictors for pacemaker dependency. RESULTS: Of 110 patients included in the analysis, 38 (34.5%) underwent PPM implantation. Of those, 26 (68.4%) had a long-term pacing dependency (required PPM), while 12 (31.6%) did not (not-required PPM). Logistic regression revealed that baseline RBBB (P = 0.01, OR = 18.0), baseline PR interval (P = 0.019, OR = 1.14), post-TAVI PR interval and the change in PR interval from baseline (P < 0.001 for both, OR = 1.17 for each 10 milliseconds increment) were independent predictors for long-term pacing dependency. A PR interval increment of greater than 28 milliseconds had the best accuracy in predicting pacemaker dependency. CONCLUSIONS: Increased pre- and postprocedural PR intervals and pre-existing RBBB are reliable predictors for long-term PPM dependency, while left bundle branch block or QRS width are misleading factors. Our study suggests that the decision for implanting PPM after TAVI should be based mostly on the prolongation of the PR interval.

Artificial Intelligence-Based Stethoscope for the Diagnosis of Aortic Stenosis.

BACKGROUND: The diagnostic accuracy of the stethoscope is limited and highly dependent on clinical expertise. Our purpose was to develop an electronic stethoscope, based on artificial intelligence (AI) and infrasound, for the diagnosis of aortic stenosis (AS). METHODS: We used an electronic stethoscope (VoqX; Sanolla, Nesher, Israel) with subsonic capabilities and acoustic range of 3-2000 Hz. The study had 2 stages. In the first stage, using the VoqX, we recorded heart sounds from 100 patients referred for echocardiography (derivation group), 50 with moderate or severe AS and 50 without valvular disease. An AI-based supervised learning model was applied to the auscultation data from the first 100 patients used for training, to construct a diagnostic algorithm that was then tested on a validation group (50 other patients, 25 with AS and 25 without AS). In the second stage, conducted at a different medical center, we tested the device on 106 additional patients referred for echocardiography, which included patients with other valvular diseases. RESULTS: Using data collected at the aortic and pulmonic auscultation points from the derivation group, the AI-based algorithm identified moderate or severe AS with 86% sensitivity and 100% specificity. When applied to the validation group, the sensitivity was 84% and specificity 92%; and in the additional testing group, 90% and 84%, respectively. The sensitivity was 55% for mild, 76% for moderate, and 93% for severe AS. CONCLUSION: Our initial findings show that an AI-based stethoscope with infrasound capabilities can accurately diagnose AS. AI-based electronic auscultation is a promising new tool for automatic screening and diagnosis of valvular heart disease.