Prognostic value of elevated plasma angiotensin-converting enzyme 2 in cardiometabolic diseases: A review.

Angiotensin-converting enzyme 2, as an internal anti regulator of the renin-angiotensin hormone cascade reaction, plays a protective role in vasodilation, inhibition of fibrosis, and initiation of anti-inflammatory and antioxidative stress by degrading angiotensin II and generating angiotensin (1-7). Multiple studies have shown that plasma angiotensin-converting enzyme 2 activity is low in healthy populations without significant cardiometabolic disease, and elevated plasma angiotensin-converting enzyme 2 levels can be used as a novel biomarker of abnormal myocardial structure and/or adverse events in cardiometabolic diseases. This article aims to elaborate the determinants of plasma angiotensin-converting enzyme 2 concentration, the relevance between angiotensin-converting enzyme 2 and cardiometabolic disease risk markers, and its relative importance compared with known cardiovascular disease risk factors. Confronted with the known cardiovascular risk factors, plasma angiotensin-converting enzyme 2 (ACE2) concentration uniformly emerged as a firm predictor of abnormal myocardial structure and/or adverse events in cardiometabolic diseases and may improve the risk prediction of cardiometabolic diseases when combined with other conventional risk factors. Cardiovascular disease is the leading cause of death worldwide, while the renin-angiotensin system is the main hormone cascade system involved in the pathophysiology of cardiovascular disease. A multi-ancestry global cohort study from the general population by Narula et al revealed that plasma ACE2 concentration was strongly associated with cardiometabolic disease and might be an easily measurable indicator of renin-angiotensin system disorder. The association between this atypical hormone disorder marker and cardiometabolic disease is isolated from conventional cardiac risk factors and brain natriuretic peptide, suggesting that a clearer comprehending of the changes in plasma ACE2 concentration and activity may help us to improve the risk prediction of cardiometabolic disease, guide early diagnosis and feasible therapies, and develop and test new therapeutic targets.

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.