Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. MethodsWe used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model.

Diuretics are widely used in the treatment of hypertension, although the precise mechanisms remain unknown. Epoxyeicosatrienoic acids (EETs), cytochrome P450 (P450) epoxygenase metabolites of arachidonic acid, play critical roles in regulation of blood pressure. The present study was carried out to investigate whether EETs participate in the antihypertensive effect of thiazide diuretics [hydrochlorothiazide (HCTZ)] and thiazide-like diuretics (indapamide). Male spontaneously hypertensive rats (SHRs) were treated with indapamide or HCTZ for 8 weeks. Systolic blood pressure, measured via tail-cuff plethysmography and confirmed via intra-arterial measurements, was significantly decreased in indapamide-and HCTZ-treated SHRs compared with salinetreated SHRs. Indapamide increased kidney CYP2C23 expression, decreased soluble epoxide hydrolase expression, increased urinary and renovascular 11,12-and 14,15-EETs, and decreased production of 11,12-and 14,15-dihydroxyeicosatrienoic acids in SHRs. No effect on expression of CYP4A1 or CYP2J3, or on 20-hydroxyeicosatetraenoic acid production, was observed, suggesting indapamide specifically targets CYP2C23-derived EETs. Treatment of SHRs with HCTZ did not affect the levels of P450s or their metabolites. Increased cAMP activity and protein kinase A expression were observed in the renal microvessels of indapamide-treated SHRs. Indapamide ameliorated oxidative stress and inflammation in renal cortices by down-regulating the expression of p47phox, nuclear factor-kB, transforming growth factor-b1, and phosphorylated mitogen-activated protein kinase. Furthermore, the p47phox-lowering effect of indapamide in angiotensin II-treated rat mesangial cells was partially blocked by the presence of N-(methylsulfonyl)-2-(2-propynyloxy)-benzenehexanamide (MS-PPOH) or CYP2C23 small interfering RNA. Together, these results indicate that the hypotensive effects of indapamide are mediated, at least in part, by the P450 epoxygenase system in SHRs, and provide novel insights into the blood pressure-lowering mechanisms of diuretics.