The dynamics of a living body enables organs to experience mechanical stimulation at cellular level. The human cardiomyocytes cell line provides a source for simulating heart dynamics; however, a limited understanding of the mechanical stimulation effect on them has restricted potential applications. Here, we investigated the effect of mechanical stimulation on the cardiac function-associated protein expressions in human cardiomyocytes. Human cardiomyocyte cell line AC16 was subjected to different stresses: 5% mild and 25% aggressive, at 1 Hz for 24 h. The stretched cardiomyocytes showed down-regulated Piezo1, phosphorylated-Ak transforming serine473 (P-AKTS473), and phosphorylated-glycogen synthase kinase-3 beta serine9 P-GSK3βS9 compared to no stretch. In addition, the stretched cardiomyocytes showed increased low-density lipoprotein receptor-related protein 6 (LRP6), and phosphorylated-c-Jun N-terminal kinase threonine183/tyrosine185 (P-JNKT183/Y185). When Piezo inhibitor was added to the cells, the LRP6, and P-JNKT183/Y185 were further increased under 25%, but not 5%, suggesting that higher mechanical stress further activated the wingless integrated-(Wnt)-related signaling pathway when Piezo1 was inhibited. Supporting this idea, when Piezo1 was inhibited, the expression of phosphorylated-endothelial nitric oxide synthase serine1177 (P-eNOSS1177) and release of calcium ions were reduced under 25% compared to 5%. These studies demonstrate that cyclic mechanical stimulation affects cardiac function-associated protein expressions, and Piezo1 plays a role in the protein regulation.

In patients with HCM, lower risk of new AF is observed in patients treated with either ACEIs or ARBs compared with those receiving neither of these medications.

(1) Background: The role of using artificial intelligence (AI) with electrocardiograms (ECGs) for the diagnosis of significant coronary artery disease (CAD) is unknown. We first tested the hypothesis that using AI to read ECG could identify significant CAD and determine which vessel was obstructed. (2) Methods: We collected ECG data from a multi-center retrospective cohort with patients of significant CAD documented by invasive coronary angiography in Taiwan from 1 January 2018 to 31 December 2020. (3) Results: We trained convolutional neural networks (CNN) models to identify patients with significant CAD (>70% stenosis), using the 12,954 ECG from 2303 patients with CAD and 2090 ECG from 1053 patients without CAD. The Marco-average area under the ROC curve (AUC) for detecting CAD was 0.869 for image input CNN model. For detecting individual coronary artery obstruction, the AUC was 0.885 for left anterior descending artery, 0.776 for right coronary artery, and 0.816 for left circumflex artery obstruction, and 1.0 for no coronary artery obstruction. Marco-average AUC increased up to 0.973 if ECG had features of myocardial ischemia. (4) Conclusions: We for the first time show that using the AI-enhanced CNN model to read standard 12-lead ECG permits ECG to serve as a powerful screening tool to identify significant CAD and localize the coronary obstruction. It could be easily implemented in health check-ups with asymptomatic patients and identifying high-risk patients for future coronary events.

Lower extremities varicose veins (VV) are among the most easily recognized venous abnormalities. The genetic mechanism of VV is largely unknown. In this study, we sought to explore the global expressional change of VV and identify novel genes that might play a role in VV. We used next-generation ribonucleic acid (RNA) sequence (RNA seq) technology to study the global messenger RNA expressional change in the venous samples of five diseased and five control patients. We identified several differentially expressed genes, which were further confirmed by conventional reverse transcription polymerase chain reaction (RT-PCR). Using these significant genes we performed in silico pathway analyses and found distinct transcriptional networks, such as angiogenesis, cell adhesion, vascular injury, and carbohydrate metabolisms that might be involved in the mechanism of VV. Among these significant genes, we also found hyaluronan synthases 2 gene (HAS2) played a pivotal role and governed all these pathways. We further confirmed that HAS2 expression was decreased in the venous samples of patients with VV. Finally, we used a zebrafish model with fluorescence emitting vasculature and red blood cells to see the morphological changes of the venous system and blood flow. We found that HAS2 knockdown in zebrafish resulted in dilated venous structural with static venous flow. HAS2 may modulate the transcriptional networks of angiogenesis, cell adhesion, vascular injury, and carbohydrate metabolisms in venous tissues and downregulation of HAS2 may underlie the mechanism of VV.