Left ventricular ejection fraction (LVEF) plays as an essential role in the assessment of cardiac function, providing quantitative data support for the medical diagnosis of heart disease. Robust evaluation of the ejection fraction relies on accurate left ventricular (LV) segmentation of echocardiograms. Because human bias and expensive labor cost exist in manual echocardiographic analysis, computer algorithms of deep-learning have been developed to help human experts in segmentation tasks. Most of the previous work is based on the convolutional neural networks (CNN) structure and has achieved good results. However, the region occupied by the left ventricle is large for echocardiography. Therefore, the limited receptive field of CNN leaves much room for improvement in the effectiveness of LV segmentation. In recent years, Vision Transformer models have demonstrated their effectiveness and universality in traditional semantic segmentation tasks. Inspired by this, we propose two models that use two different pure Transformers as the basic framework for LV segmentation in echocardiography: one combines Swin Transformer and K-Net, and the other uses Segformer. We evaluate these two models on the EchoNet-Dynamic dataset of LV segmentation and compare the quantitative metrics with other models for LV segmentation. The experimental results show that the mean Dice similarity of the two models scores are 92.92% and 92.79%, respectively, which outperform most of the previous mainstream CNN models. In addition, we found that for some samples that were not easily segmented, whereas both our models successfully recognized the valve region and separated left ventricle and left atrium, the CNN model segmented them together as a single part. Therefore, it becomes possible for us to obtain accurate segmentation results through simple post-processing, by filtering out the parts with the largest circumference or pixel square. These promising results prove the effectiveness of the two models and reveal the potential of Transformer structure in echocardiographic segmentation.

Background. There are no proven effective treatments that can reduce the mortality in heart failure with preserved ejection fraction (HFpEF), probably due to its heterogeneous nature which will weaken the effect of therapy in clinical studies. We evaluated the effect of beta-blocker treatment in HFpEF patients associated with atrial fibrillation (AF), which is a homogeneous syndrome and has seldom been discussed. Methods. This retrospective cohort study screened 955 patients diagnosed with AF and HFpEF. Patients with a range of underlying heart diseases or severe comorbidities were excluded; 191 patients were included and classified as with or without beta-blocker treatment at baseline. The primary outcome was all-cause mortality and rehospitalization due to heart failure. Kaplan-Meier curves and multivariable Cox proportional-hazards models were used to evaluate the differences in outcomes. Results. The mean follow-up was 49 months. After adjustment for multiple clinical risk factors and biomarkers for prognosis in heart failure, patients with beta-blocker treatment were associated with significantly lower all-cause mortality (hazard ratio (HR) = 0.405, 95% confidence interval (CI) = 0.233–0.701, p=0.001) compared with those without beta-blocker treatment. However, the risk of rehospitalization due to heart failure was increased in the beta-blocker treatment group (HR = 1.740, 95% CI = 1.085–2.789, p=0.022). There was no significant difference in all-cause rehospitalization between the two groups (HR = 1.137, 95% CI = 0.803–1.610, p=0.470). Conclusions. In HFpEF patients associated with AF, beta-blocker treatment is associated with significantly lower all-cause mortality, but it increased the risk of rehospitalization due to heart failure.

Objective. To investigate the mechanism of Connexin 37 (Cx37) and Kv1.3 pathways in atherosclerosis (AS). Methods. ApoE^-/- mice were given a high-fat diet to establish atherosclerosis (AS) model, and macrophages in mice were isolated and extracted to transfect Cx37 vectors with silencing or overexpressing, and Kv1.3 pathway blockers were used to inhibit the pathway activity. The indexes of body weight, blood glucose, and blood lipid of mice were collected. The protein and mRNA expression levels of Cx37 and Kv1.3 were detected by reverse transcription-PCR (RT-PCR), Western blot, and immunofluorescence technique. Oil red O staining was used to observe plaque area. Masson staining was used to detect collagen content. The concentrations of chemokine CCL7 were quantified using the ELISA kits. CCK8 was used to detect cell proliferation. Results. Cx37 and Kv1.3 were highly expressed in macrophages of AS mice, and the expression of Kv1.3 and CCL7 decreased after Cx37 was silenced, and the proliferation of macrophages was also decreased. Wild-type mice and AS model mice were treated with Cx37 overexpression vectors and Kv1.3 pathway blocking, and it was found that Cx37 overexpression could improve the blood lipid and blood glucose levels and increase the area of AS in AS mice. However, blocking the activity of Kv1.3 pathway can reduce the levels of blood lipid and blood glucose, increase the body weight of mice, and reduce the area of AS mice. Blocking the activity of Kv1.3 pathway can slow down the plaque development of AS mice and make its indexes close to wild-type mice. And the use of Kv1.3 pathway blockers on the basis of overexpression of Cx37 indicated that inhibition of Kv1.3 pathway activity did not affect the expression of Cx37, but could inhibit the collagen content in the plaque area of AS mice, inhibit the expression of chemokine CCL7, and reverse the effect of Cx37 overexpression. Conclusion. Cx37 can improve the activity of macrophages by regulating the expression of chemokines and the activity of Kv1.3 pathway in AS mice, and enrich macrophages in inflammatory tissues and expand the area of plaque formation.