Cardiac development is a complex and intricate process involving numerous molecular signals and pathways. Researchers have explored cardiac development through a long journey, starting with early studies observing morphological changes and progressing to the exploration of molecular mechanisms using various molecular biology methods. Currently, advancements in stem cell technology and sequencing technology, such as the generation of human pluripotent stem cells and cardiac organoids, multi-omics sequencing, and artificial intelligence (AI) technology, have enabled researchers to understand the molecular mechanisms of cardiac development better. Many molecular signals regulate cardiac development, including various growth and transcription factors and signaling pathways, such as WNT signaling, retinoic acid signaling, and Notch signaling pathways. In addition, cilia, the extracellular matrix, epigenetic modifications, and hypoxia conditions also play important roles in cardiac development. These factors play crucial roles at one or even multiple stages of cardiac development. Recent studies have also identified roles for autophagy, metabolic transition, and macrophages in cardiac development. Deficiencies or abnormal expression of these factors can lead to various types of cardiac development abnormalities. Nowadays, congenital heart disease (CHD) management requires lifelong care, primarily involving surgical and pharmacological treatments. Advances in surgical techniques and the development of clinical genetic testing have enabled earlier diagnosis and treatment of CHD. However, these technologies still have significant limitations. The development of new technologies, such as sequencing and AI technologies, will help us better understand the molecular mechanisms of cardiac development and promote earlier prevention and treatment of CHD in the future.

Background: Hypertension is 1 of the major global public health challenges, which means that patients with hypertension need more measures to control their blood pressure. Currently, smart phones and applications are developing rapidly, and mobile health applications are used to manage hypertension, but evidences related to effectiveness are limited. Objective: The purpose was to assess the impact of m-Health apps on blood pressure control, medication adherence. Methods: 480 participants were randomly assigned to the intervention and control groups. The intervention group used the “Yan Fu” app to manage their blood pressure, and the control group did not use any m-Health apps. The outcomes were changes in blood pressure, the percentage of participants with their blood pressure under control and medication adherence. Results: At the end of the study, the baseline characteristics between the 2 groups had no statistically differences (P > .05). Participants in the 2 groups all had lower systolic blood pressure and diastolic blood pressure than they did at baseline, and the intervention group demonstrated a significantly greater systolic blood pressure and diastolic blood pressure reduction than the control group (P < .05). Additionally, the percentage of participants with controlled blood pressure was higher in the intervention group (P < .05). The medication adherence of the intervention group was much higher than that of the control group (P < .05). Conclusion: M-Health apps are effective for hypertension management, it can favor the medication adherence and blood pressure control. Perhaps m-Health apps can be promoted in the blood pressure control. Trial Registration: This study was registered in the Chinese Clinical Trial Registry under the number ChiCTR-IOR-17012069.

Tbx18, Wt1, and Tcf21 have been identified as epicardial markers during the early embryonic stage. However, the gene markers of mature epicardial cells remain unclear. Single-cell transcriptomic analysis was performed with the Seurat, Monocle, and CellphoneDB packages in R software with standard procedures. Spatial transcriptomics was performed on chilled Visium Tissue Optimization Slides (10x Genomics) and Visium Spatial Gene Expression Slides (10x Genomics). Spatial transcriptomics analysis was performed with Space Ranger software and R software. Immunofluorescence, whole-mount RNA in situ hybridization and X-gal staining were performed to validate the analysis results. Spatial transcriptomics analysis revealed distinct transcriptional profiles and functions between epicardial tissue and non-epicardial tissue. Several gene markers specific to postnatal epicardial tissue were identified, including Msln, C3, Efemp1, and Upk3b. Single-cell transcriptomic analysis revealed that cardiac cells from wildtype mouse hearts (from embryonic day 9.5 to postnatal day 9) could be categorized into six major cell types, which included epicardial cells. Throughout epicardial development, Wt1, Tbx18, and Upk3b were consistently expressed, whereas genes including Msln, C3, and Efemp1 exhibited increased expression during the mature stages of development. Pseudotime analysis further revealed two epicardial cell fates during maturation. Moreover, Upk3b, Msln, Efemp1, and C3 positive epicardial cells were enriched in extracellular matrix signaling. Our results suggested Upk3b, Efemp1, Msln, C3, and other genes were mature epicardium markers. Extracellular matrix signaling was found to play a critical role in the mature epicardium, thus suggesting potential therapeutic targets for heart regeneration in future clinical practice.

Aims: This study aimed to determine the effects of sodium-glucose cotransporter-2 inhibitor (SGLT2i) in heart failure with reduced ejection fraction (HFrEF), compare the effect of SGLT2i with angiotensin receptor neprilysin inhibitor (ARNI), and find whether combination of SGLT2i and ARNI is better than monotherapy. Methods and results: Embase, Medline, and Cochrane Central Registry of Controlled Trials were searched for randomized controlled trials evaluating SGLT2i or ARNI in HFrEF. And a total of six trials were included. SGLT2i was found to significantly reduce the risk of cardiovascular death or hospitalization for heart failure by 27% [hazard ratio (HR) 0.73, 95% confidence interval (CI) 0.67–0.80], hospitalization for heart failure by 31% (HR 0.69, 95% CI 0.62–0.77), cardiovascular death by 16% (HR 0.84, 95% CI 0.74–0.95), and all-cause death by 16% (HR 0.84, 95% CI 0.75–0.94) in HFrEF only with a statistically higher risk of genital infection (risk ratio (RR) 2.78, 95% CI 1.46–5.29). The reduction in cardiovascular death or hospitalization for heart failure was of similar magnitude in patients with or without diabetes mellitus (HR 0.71, 95% CI 0.64–0.80 vs. HR 0.75, 95% CI 0.65–0.87) using SGLT2i. Indirect treatment comparison showed that SGLT2i and ARNI had similar effects on primary outcome (HR 0.93, 95% CI 0.82–1.06). And combination of SGLT2i and ARNI achieved a better prognosis performance (HR 0.68, 95% CI 0.53–0.89) compared with ARNI monotherapy. Conclusions: SGLT2i could safely reduce cardiovascular death or hospitalization for heart failure in HFrEF regardless of diabetes mellitus status. SGLT2i and ARNI demonstrate similar effects, while combination of SGLT2i and ARNI results in a better cardiovascular protective effect.

Background: COVID-19 is a global pandemic. The prevention of SARS-CoV-2 infection and the rehabilitation of survivors are currently the most urgent tasks. However, after patients with COVID-19 are discharged from the hospital, how long the antibodies persist, whether the lung lesions can be completely absorbed, and whether cardiopulmonary abnormalities exist remain unclear.Methods: A total of 56 COVID-19 survivors were followed up for 12 months, with examinations including serum virus-specific antibodies, chest CT, and cardiopulmonary exercise testing.Results: The IgG titer of the COVID-19 survivors decreased gradually, especially in the first 6 months after discharge. At 6 and 12 months after discharge, the IgG titer decreased by 68.9 and 86.0%, respectively. The IgG titer in patients with severe disease was higher than that in patients with non-severe disease at each time point, but the difference did not reach statistical significance. Among the patients, 11.8% were IgG negative up to 12 months after discharge. Chest CT scans showed that at 3 and 10 months after discharge, the lung opacity had decreased by 91.9 and 95.5%, respectively, as compared with that at admission. 10 months after discharge, 12.5% of the patients had an opacity percentage >1%, and 18.8% of patients had pulmonary fibrosis (38.5% in the severe group and 5.3% in the non-severe group, P < 0.001). Cardiopulmonary exercise testing showed that 22.9% of patients had FEV1/FVC%Pred <92%, 17.1% of patients had FEV1%Pred <80%, 20.0% of patients had a VO₂ AT 30%.Conclusions: IgG antibodies in most patients with COVID-19 can last for at least 12 months after discharge. The IgG titers decreased significantly in the first 6 months and remained stable in the following 6 months. The lung lesions of most patients with COVID-19 can be absorbed without sequelae, and a few patients in severe condition are more likely to develop pulmonary fibrosis. Approximately one-fifth of the patients had cardiopulmonary dysfunction 6 months after discharge.