High-flow nasal cannula (HFNC) is an open oxygen delivery system, which provides heated and humidified oxygen at a high flow (up to 60 L/min). This effect can improve mucociliary function, airway clearance, and level of comfort to the patient. It can provide controlled and adequate fraction of inspired oxygen (FiO 2 ) between 21% and 100%. Generation of end-expiratory pressure helps in carbon dioxide washout, reduction of anatomical dead space, and recruitment of collapsed alveoli, ultimately improving tissue oxygenation. The use of HFNC in acute hypoxemic respiratory failure, post-extubation period, pre-intubation period, respiratory infection, and obstructive airway disease has been extensively studied, but there are very few studies regarding its use in cardiogenic pulmonary edema. This review provides the current understanding of the physiological effect of HFNC and its application in acute cardiogenic pulmonary edema (ACPE). We conducted a literature search on PubMed using appropriate terms and reviewed relevant articles published within the last 10 years. We found that initial therapy with HFNC in ACPE patients can improve oxygenation and respiratory rate. HFNC can potentially be an alternative to non-invasive positive-pressure ventilation in terms of initial oxygen therapy in patients with ACPE. There is a need for larger prospective studies to evaluate and develop guidelines to consider the use of HFNC in patients with ACPE. We also highlight the fact that if there is no improvement in arterial blood gas parameters after HFNC therapy, initiation of invasive ventilation should not be delayed.

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Coronavirus disease 2019 (COVID-19) is predominantly a respiratory disease that often presents with fever, cough, dyspnea, and myalgia or fatigue. Digestive symptoms such as nausea, vomiting, diarrhea, and abdominal pain may accompany respiratory symptoms. However, gastrointestinal (GI) bleeding among COVID-19 patients is a rare and unusual presentation, since these patients are frequently hypercoagulable and are less likely to bleed and more likely to clot. In this report, we present a case of an 80-year-old male with a history of type 2 diabetes mellitus, hypertension, and obesity who presented with GI bleed and was subsequently found to have COVID-19.

Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, “black box” nature of algorithms and concerns about “alarm fatigue” should be addressed for more mainstream adoption of these models.

Coronary artery disease (CAD) is a multifactorial disease that involves genetic and environmental interaction. In addition to the well-known CAD risk factors, such as diabetes mellitus, hypertension, hyperlipidemia, and atherosclerosis, it has a genetic component that predisposes to its occurrence even in young people. One of the most commonly studied genes that increase the susceptibility to CAD is reninangiotensin system (RAS) genes polymorphisms mainly angiotensin-converting enzyme gene (ACE) polymorphisms, angiotensinogen polymorphisms, angiotensin-II type 1 receptor gene polymorphisms, and many other genes. These genetic polymorphisms have a direct association with CAD development or indirect association through causing atherosclerosis and hypertension which, in turn, are complicated by CAD later on. The difference between genetic mutations and polymorphisms lies in the frequency of the abnormal genotype. If the frequency is 1% and more in the general population, it is called polymorphism and if it is less than 1%, then it is called a mutation.According to our findings, after thorough searching, which support the association of RAS genes polymorphisms with premature CAD, hypertension, hypertrophic cardiomyopathy, and atherosclerosis, we recommend additional studies in the form of clinical trials and meta-analyses aiming to create a specific diagnostic tool for CAD risk assessment and discovering the high-risk people as early as possible. Targeted gene therapy, being the future of medicine, needs to be taken into researchers' consideration. It can have promising results in these cases.Categories: Cardiology, Genetics Keywords: ace polymorphism and myocardial infarction, ras polymorphism and coronary artery disease, ras polymorphism and hypertension, enos polymorphism and coronary artery disease 1 1, 2 3 3 3 1 4 Open Access Review Article