Acute antiarrhythmic effects of SGLT2 inhibitors-dapagliflozin lowers the excitability of atrial cardiomyocytes.

In recent years, SGLT2 inhibitors have become an integral part of heart failure therapy, and several mechanisms contributing to cardiorenal protection have been identified. In this study, we place special emphasis on the atria and investigate acute electrophysiological effects of dapagliflozin to assess the antiarrhythmic potential of SGLT2 inhibitors. Direct electrophysiological effects of dapagliflozin were investigated in patch clamp experiments on isolated atrial cardiomyocytes. Acute treatment with elevated-dose dapagliflozin caused a significant reduction of the action potential inducibility, the amplitude and maximum upstroke velocity. The inhibitory effects were reproduced in human induced pluripotent stem cell-derived cardiomyocytes, and were more pronounced in atrial compared to ventricular cells. Hypothesizing that dapagliflozin directly affects the depolarization phase of atrial action potentials, we examined fast inward sodium currents in human atrial cardiomyocytes and found a significant decrease of peak sodium current densities by dapagliflozin, accompanied by a moderate inhibition of the transient outward potassium current. Translating these findings into a porcine large animal model, acute elevated-dose dapagliflozin treatment caused an atrial-dominant reduction of myocardial conduction velocity in vivo. This could be utilized for both, acute cardioversion of paroxysmal atrial fibrillation episodes and rhythm control of persistent atrial fibrillation. In this study, we show that dapagliflozin alters the excitability of atrial cardiomyocytes by direct inhibition of peak sodium currents. In vivo, dapagliflozin exerts antiarrhythmic effects, revealing a potential new additional role of SGLT2 inhibitors in the treatment of atrial arrhythmias.

Deep learning-based atherosclerotic coronary plaque segmentation on coronary CT angiography.

OBJECTIVES: Volumetric evaluation of coronary artery disease (CAD) allows better prediction of cardiac events. However, CAD segmentation is labor intensive. Our objective was to create an open-source deep learning (DL) model to segment coronary plaques on coronary CT angiography (CCTA). METHODS: Three hundred eight individuals' 894 CCTA scans with 3035 manually segmented plaques by an expert reader (considered as ground truth) were used to train (186/308, 60%), validate (tune, 61/308, 20%), and test (61/308, 20%) a 3D U-net model. We also evaluated the model on an external test set of 50 individuals with vulnerable plaques acquired at a different site. Furthermore, we applied transfer learning on 77 individuals' data and re-evaluated the model's performance using intra-class correlation coefficient (ICC). RESULTS: On the test set, DL outperformed the currently used minimum cost approach method to quantify total: ICC: 0.88 [CI: 0.85-0.91] vs. 0.63 [CI: 0.42-0.76], noncalcified: 0.84 [CI: 0.80-0.88] vs. 0.45 [CI: 0.26-0.59], calcified: 0.99 [CI: 0.98-0.99] vs. 0.96 [CI: 0.94-0.97], and low attenuation noncalcified: 0.25 [CI: 0.13-0.37] vs. -0.01 [CI: -0.13 to 0.11] plaque volumes. On the external dataset, substantial improvement was observed in DL model performance after transfer learning, total: 0.62 [CI: 0.01-0.84] vs. 0.94 [CI: 0.87-0.97], noncalcified: 0.54 [CI: -0.04 to 0.80] vs. 0.93 [CI: 0.86-0.96], calcified: 0.91 [CI:0.85-0.95] vs. 0.95 [CI: 0.91-0.97], and low attenuation noncalcified 0.48 [CI: 0.18-0.69] vs. 0.86 [CI: 0.76-0.92]. CONCLUSIONS: Our open-source DL algorithm achieved excellent agreement with expert CAD segmentations. However, transfer learning may be required to achieve accurate segmentations in the case of different plaque characteristics or machinery. KEY POINTS: • Deep learning 3D U-net model for coronary segmentation achieves comparable results with expert readers' volumetric plaque quantification. • Transfer learning may be needed to achieve similar results for other scanner and plaque characteristics. • The developed deep learning algorithm is open-source and may be implemented in any CT analysis software.

MicroRNAs Regulate TASK-1 and Are Linked to Myocardial Dilatation in Atrial Fibrillation.

Background Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. However, underlying molecular mechanisms are insufficiently understood. Previous studies suggested that microRNA (miRNA) dependent gene regulation plays an important role in the initiation and maintenance of AF. The 2-pore-domain potassium channel TASK-1 (tandem of P domains in a weak inward rectifying K+ channel-related acid sensitive K+ channel 1) is an atrial-specific ion channel that is upregulated in AF. Inhibition of TASK-1 current prolongs the atrial action potential duration to similar levels as in patients with sinus rhythm. Here, we hypothesize that miRNAs might be responsible for the regulation of KCNK3 that encodes for TASK-1. Methods and Results We selected miRNAs potentially regulating KCNK3 and studied their expression in atrial tissue samples obtained from patients with sinus rhythm, paroxysmal AF, or permanent/chronic AF. MiRNAs differentially expressed in AF were further investigated for their ability to regulate KCNK3 mRNA and TASK-1 protein expression in human induced pluripotent stem cells, transfected with miRNA mimics or inhibitors. Thereby, we observed that miR-34a increases TASK-1 expression and current and further decreases the resting membrane potential of Xenopus laevis oocytes, heterologously expressing hTASK-1. Finally, we investigated associations between miRNA expression in atrial tissues and clinical parameters of our patient cohort. A cluster containing AF stage, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, left atrial diameter, atrial COL1A2 (collagen alpha-2(I) chain), and TASK-1 protein level was associated with increased expression of miR-25, miR-21, miR-34a, miR-23a, miR-124, miR-1, and miR-29b as well as decreased expression of miR-9 and miR-485. Conclusions These results suggest an important pathophysiological involvement of miRNAs in the regulation of atrial expression of the TASK-1 potassium channel in patients with atrial cardiomyopathy.

Treatment of atrial fibrillation with doxapram: TASK-1 potassium channel inhibition as a novel pharmacological strategy.

AIMS: TASK-1 (K2P3.1) two-pore-domain potassium channels are atrial-specific and significantly up-regulated in atrial fibrillation (AF) patients, contributing to AF-related electrical remodelling. Inhibition of TASK-1 in cardiomyocytes of AF patients was shown to counteract AF-related action potential duration shortening. Doxapram was identified as a potent inhibitor of the TASK-1 channel. In this study, we investigated the antiarrhythmic efficacy of doxapram in a porcine model of AF. METHODS AND RESULTS: Doxapram successfully cardioverted pigs with artificially induced episodes of AF. We established a porcine model of persistent AF in domestic pigs via intermittent atrial burst stimulation using implanted pacemakers. All pigs underwent catheter-based electrophysiological investigations prior to and after 14 days of doxapram treatment. Pigs in the treatment group received intravenous administration of doxapram once per day. In doxapram-treated AF pigs, the AF burden was significantly reduced. After 14 days of treatment with doxapram, TASK-1 currents were still similar to values of sinus rhythm animals. Doxapram significantly suppressed AF episodes and normalized cellular electrophysiology by inhibition of the TASK-1 channel. Patch-clamp experiments on human atrial cardiomyocytes, isolated from patients with and without AF could reproduce the TASK-1 inhibitory effect of doxapram. CONCLUSION: Repurposing doxapram might yield a promising new antiarrhythmic drug to treat AF in patients.

Effect of vessel wall segmentation on volumetric and radiomic parameters of coronary plaques with adverse characteristics.

BACKGROUND: Quantitative coronary plaque parameters are increasingly being utilized as surrogate endpoints of pharmaceutical trials. However, little is known whether differences in segmentation significantly alter parameter values. METHODS: Overall, 100 coronary plaques with adverse imaging characteristics were segmented automatically, by two experts (R1-R2) and three nonexperts (R3-R5). Low attenuation noncalcified (LANCP), noncalcified and calcified plaque volume were calculated and 4310 radiomic features were extracted. Intraclass correlation coefficient (ICC) values were calculated between the segmentations. RESULTS: ICC values between expert readers were 0.84 [CI: 0.77-0.89] for total; 0.83 [CI: 0.76-0.88] for noncalcified; 0.96 [CI: 0.94-0.98] for calcified and 0.65 [CI: 0.51-0.75] for LANCP volumes. Comparing nonexperts' and experts' results, ICC ranged between 0.64 and 0.90 for total; 0.63-0.91 for noncalcified; 0.86-0.96 for calcified and 0.34-0.84 for LANCP volume. All readers (R1-R5) showed poor agreement with automatic segmentation (range: 0.00-0.27), except for calcified plaque volumes (range: 0.73-0.88). Regarding radiomic features, expert readers (R1-R2) achieved good reproducibility (ICC>0.80) in 88.6% (39/44) of first-order, 62.0% (424/684) of gray level co-occurrence matrix (GLCM), 75.8% (50/66) of gray level run length matrix (GLRLM) and 19.8% (696/3516) of geometrical parameters. Between experts and nonexperts, ICC ranged between: 70.5%-86.4% for first-order, 31.0%-58.3% for GLCM, 24.2%-78.8% for GLRLM and 6.2%-21.1% for geometrical features, while between all readers and automatic segmentation ICC ranged between: 25.0%-38.6%; 0.0%-0.0%; 0.0%-3.0% and 1.1%-1.4%, respectively. CONCLUSIONS: Even among experts there is a considerable amount of disagreement in LANCP volumes. Nevertheless, expert readers have the best agreement which currently cannot be replaced with nonexperts' or automatic segmentation.