Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90–2.50). Conclusion An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

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GPR4, a pH-sensing G protein-coupled receptor, is highly expressed in endothelial cells and may be activated in myocardial infarction due the decreased tissue pH. We are interested in GPR4 antagonists as potential effective pharmacologic tools and/or drug leads for the treatment of myocardial infarction. We investigated the structure−activity relationship of a known GPR4 antagonist 1 as a lead compound to identify 3b as the first potent and selective GPR4 antagonist, whose effectiveness was demonstrated in a mouse myocardial infarction model.

G protein-coupled receptor 4 (GPR4), previously proposed as the receptor for sphingosylphosphorylcholine, has recently been identified as the proton-sensing G protein-coupled receptor (GPCR) coupling to multiple intracellular signaling pathways, including the Gs protein/cAMP and G13 protein/Rho. In the present study, we characterized some imidazopyridine compounds as GPR4 modulators that modify GPR4 receptor function. In the cells that express proton-sensing GPCRs, including GPR4, OGR1, TDAG8, and G2A, extracellular acidification stimulates serum responsive element (SRE)-driven transcriptional activity, which has been shown to reflect Rho activity, with different proton sensitivities. Imidazopyridine compounds inhibited the moderately acidic pH-induced SRE activity only in GPR4-expressing cells. Acidic pH-stimulated cAMP accumulation, mRNA expression of inflammatory genes, and GPR4 internalization within GPR4-expressing cells were all inhibited by the GPR4 modulator. We further compared the inhibition property of the imidazopyridine compound with psychosine, which has been shown to selectively inhibit actions induced by proton-sensing GPCRs, including GPR4. In the GPR4 mutant, in which certain histidine residues were mutated to phenylalanine, proton sensitivity was significantly shifted to the right, and psychosine failed to further inhibit acidic pH-induced SRE activation. On the other hand, the imidazopyridine compound almost completely inhibited acidic pH-induced action in mutant GPR4. We conclude that some imidazopyridine compounds show specificity to GPR4 as negative allosteric modulators with a different action mode from psychosine, an antagonist susceptible to histidine residues, and are useful for characterizing GPR4-mediated acidic pH-induced biological actions.

Background: Left ventricular global longitudinal strain (GLS) is associated with long-term outcomes of patients with severe aortic stenosis. However, its prognostic value in patients with moderate aortic stenosis remains unknown. Methods: Patients diagnosed with moderate aortic stenosis (1.0< aortic valve area ≤1.5 cm 2 ) and left ventricular ejection fraction ≥50% were identified. GLS was assessed by 2-dimensional strain imaging using speckle-tracking method. All-cause mortality was assessed according to the median GLS value. Results: Two hundred eighty-seven patients were included (median age 76 years; 47% male). Mean aortic valve area was 1.25 cm 2 , left ventricular ejection fraction 62%, and median GLS −15.2%. During a median follow-up of 3.9 years, there were 103 deaths (36%). Mortality was higher in patients with GLS>−15.2% (hazard ratio 2.62 [95% CI 1.69–4.06]) compared with patients with GLS ≤−15.2% even after adjusting for confounders. Mortality rates at 1, 3, 5 years were 21%, 35%, 48%, respectively, in patients with GLS >−15.2%, and 6%, 15%, 19% in those with GLS ≤−15.2%. Even among those with left ventricular ejection fraction ≥60%, GLS discriminated higher-risk patients ( P =0.0003). During follow-up, 106 (37%) patients underwent aortic valve replacement with median waiting-time of 2.4 years, and their survival was better than patients without aortic valve replacement. Among those patients undergoing aortic valve replacement, prognosis was still worse in patients with GLS >−15.2% ( P =0.04). Mortality rates at 1, 3, 5 years were 2%, 10%, 20%, respectively, in patients with GLS >-15.2% and 2%, 5%, 6% in those with GLS ≤−15.2%. Conclusions: Impaired GLS in moderate aortic stenosis patients is associated with higher mortality rates even among those undergoing aortic valve replacement.