The angiotensin-converting enzyme 2 (ACE2) is a type I integral membrane that exists in two forms: the first is a transmembrane protein; the second is a soluble catalytic ectodomain of ACE2. The catalytic ectodomain of ACE2 undergoes shedding by a disintegrin and metalloproteinase domain-containing protein 17 (ADAM17), in which calmodulin mediates the calcium signaling pathway that is involved in ACE2 release, resulting in a soluble catalytic ectodomain of ACE2 that can be measured as soluble ACE2 plasma activity. The shedding of the ACE2 catalytic ectodomain plays a role in cardiac remodeling and endothelial dysfunction and is a predictor of all-cause mortality, including cardiovascular mortality. Moreover, considerable evidence supports that the ACE2 catalytic ectodomain is an essential entry receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Additionally, endotoxins and the pro-inflammatory cytokines interleukin (IL)-1β and tumor necrosis factor-alpha (TNFα) all enhanced soluble catalytic ectodomain ACE2 shedding from the airway epithelia, suggesting that the shedding of ACE2 may represent a mechanism by which viral entry and infection may be controlled such as some types of betacoronavirus. In this regard, ACE2 plays an important role in inflammation and thrombotic response, and its down-regulation may aggravate COVID-19 via the renin-angiotensin system, including by promoting pathological changes in lung injury. Soluble forms of ACE2 have recently been shown to inhibit SARS-CoV-2 infection. Furthermore, given that vitamin D enhanced the shedding of ACE2, some studies reported that vitamin D treatment is associated with prognosis improvement in COVID-19. This is an updated review on the evidence, clinical, and therapeutic applications of ACE2 for COVID-19.

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Background: Since the last century, the electrocardiogram (ECG) remains the non‐invasive test, that is, most easily accessible, feasible, and inexpensive for cardiology assessment. In past years, many novel ECG indexes and patterns have been published that allow for a more advanced evaluation of what is currently being done, especially based on subtle QRS changes and patterns.Objective: The objective of the study was to provide an update on the evidence and clinical applications of these ECG subtle QRS changes and patterns associated with heart disease.Methods: Through the literature review, we will highlight the subtle QRS changes and patterns associated with heart disease, mainly focusing on QRS duration, voltage, morphology, axis, and QT interval.Results: Small increases in QRS duration are associated with a reduction in left ventricular ejection fraction (EF), increased cardiac chamber dimensions, and risk for incident heart failure (HF). Moreover, fragmentation of the QRS complex is associated with myocardial fibrosis and is a substrate for developing arrhythmic events. Besides, low amplitude QRS voltage is associated with congestive HF, and an increase in the voltage of the QRS complexes is associated with the effectiveness of diuresis treatment. Furthermore, small increases in QT interval are associated with diastolic dysfunction due to impaired sarcoplasmic reticulum calcium handling as occurs in myocardial ischemia, hypertension, or diabetes. On the other hand, in patients with left ventricular dysfunction, the QRS area is associated with clinical and echocardiographic response to cardiac resynchronization therapy regardless of the type of bundle branch block. In addition, subtle ECG changes and patterns in the left bundle branch block are associated with concomitant right ventricular dilation, mostly based on the QRS axis and voltage. Notwithstanding, to identify these subtle changes in QRS require exact manual measurements that can take time. In this regard, applying artificial intelligence (AI) to the ECG can make a quicker and more complete assessment, as well as provide a low cost when applied to large populations.Conclusion: We provided an update on the evidence and clinical applications of these subtle QRS changes and patterns associated with diastolic dysfunction, reduced EF, and HF development and therapy responsiveness, as well as their applications for AI to ECG.