Acute myocardial infarction (AMI) is one of the most severe manifestations of coronary artery disease, caused by complete or partial occlusion of a coronary artery. In 2015, the global AMI cases were estimated to be 7.3 million, and South Asia had the highest age-standardized myocardial infarction incidence. 1 Although the mortality rate of AMI in high-income countries has been declining over the past decades because of evidence-based AMI care and therapy, 2 AMI remains a global health priority, especially in

Objective Long non-coding RNAs (lncRNAs) are involved in carcinogenesis and could be used as diagnostic biomarkers. Our study aimed to elucidate the clinical role of serum exosomal lncRNA H19 in gastric cancer (GC). Methods In this prospective clinical study, we determined serum exosomal lncRNA H19 levels in 81 patients with GC and analysed the correlations between serum lncRNA H19 levels and clinical characteristics. Receiver operating characteristics (ROC) curves were constructed to determine the diagnostic performance of exosomal lncRNA H19 in GC. Results Serum exosomal lncRNA H19 levels were significantly upregulated in patients with GC both before and after surgery compared with healthy controls. Furthermore, serum exosomal lncRNA H19 levels were significantly decreased after compared with before surgery in patients with GC. Preoperative lncRNA H19 levels were significantly correlated with TNM stage. The area under the ROC curve (AUC) value for exosomal lncRNA H19 was 0.849, which was significantly higher than the AUC values for cancer antigens 19-9 and 72-4 and carcinoembryonic antigen, either alone or combined. Conclusions These results suggest that circulating exosomal lncRNA H19 may be a potential biomarker with diagnostic and prognostic value in GC.

In the present study, a hypoxia/reoxygenation (H/R) model of cardiomyocytes was established to investigate the effects of long non-coding RNA (LncRNA) Nuclear Enriched Abundant Transcript 1 (NEAT1) and microRNA (miR)-520a on H/R-induced cardiomyocyte apoptosis. Flow cytometry and terminal deoxynucleotidyl transferase dUTP nick end labeling staining were used to evaluate cell apoptosis. Luciferase activity assay was used to investigate whether miR-520a targets NEAT1. Results revealed that NEAT1 was significantly upregulated and miR-520a was downregulated in the ischemia/reperfusion myocardium and the cardiomyocytes that received H/R treatment. Further study demonstrated that knockdown of NEAT1 and overexpression of miR-520a serves a protective role against H/R-induced cardiomyocyte apoptosis. miR-520a directly targets NEAT1 and its expression level is negatively correlated with that of NEAT1. The findings suggested that NEAT1 and miR-520a may protect cardiomyocytes from apoptosis through regulating apoptotic proteins B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X protein, and altering cleaved caspase3 expression levels.

Big data has been reported widely to facilitate epidemic prevention and control in health care during the coronavirus disease 2019 (COVID-19) pandemic. However, there is still a lack of practical experience in applying it to hospital prevention and control. This study is devoted to the practical experience of design and implementation as well as the preliminary results of an innovative big data-driven COVID-19 risk personnel screening management system in a hospital. Our screening system integrates data sources in four dimensions, which includes Health Quick Response (QR) code, abroad travelling history, transportation close contact personnel and key surveillance personnel. Its screening targets cover all patients, care partner and staff who come to the hospital. As of November 2021, nearly 690 000 people and 5.79 million person-time had used automated COVID-19 risk screening and monitoring. A total of 10 376 person-time (0.18%) with abnormal QR code were identified, 242 person-time with abroad travelling history were identified, 925 person-time were marked based on the data of key surveillance personnel, no transportation history personnel been reported and no COVID-19 nosocomial infection occurred in the hospital. Through the application of this system, the hospital's expenditure on manpower and material resources for epidemic prevention and control has also been significantly reduced. Collectively, this study has proved to be an effective and efficient model for the use of digital health technology in response to the COVID-19 pandemic. Based on the data from multiple sources, this system has an irreplaceable role in identifying close contacts or suspicious person, and can significantly reduce the social burden caused by COVID-19, especially the human resources and economic costs of hospital prevention and control. It may provide guidance for clinical epidemic prevention and control in hospitals, as well as for future public health emergencies.