Hypertension is a global public health challenge and a major cause of morbidity and mortality. Alcohol is one of the most frequently abused substances around the world. The financial implications of treating hypertension are also significant. Developing successful strategies to prevent hypertension may be as important, if not more important, than managing the disease once it arises. In this review we assess the relationship between alcohol use and hypertension development. We have searched the available literature using the PubMed database and identified studies that discussed the relationship between alcohol use and either primary or any of the common causes of secondary hypertension. We found that heavy alcohol use was almost invariably associated with increased risk of developing primary hypertension, regardless of the age or gender of the participants. The relationship between low or moderate alcohol use and hypertension is less clear and some evidence even points towards possible protective effects. The pathophysiology behind the association is incompletely understood and a number of mechanisms have been proposed. Heavy alcohol use also increases the risk of insulin resistance and obstructive sleep apnea, known causes of secondary hypertension. It has also been linked to a state of hypercortisolism, sometimes called pseudo-Cushing state. Moderate alcohol consumption may be protective against diabetes and hyperthyroidism associated with Graves' disease. Overall, public health efforts should address the issue of heavy alcohol use. There does not appear to be enough evidence to recommend abstinence to those consuming low amounts of alcohol with the aim of protecting against hypertension. We believe that the current understanding of the issue is insufficient and that more both basic science and clinical research needs to be done.

Artificial Intelligence (AI) has taken radiology by storm, in particular, mammogram interpretation, and we have seen a recent surge in the number of publications on potential uses of AI in breast radiology. Breast cancer exerts a lot of burden on the National Health Service (NHS) and is the second most common cancer in the UK as of 2018. New cases of breast cancer have been on the rise in the past decade, while the survival rate has been improving. The NHS breast cancer screening program led to an improvement in survival rate. The expansion of the screening program led to more mammograms, thereby putting more work on the hands of radiologists, and the issue of double reading further worsens the workload. The introduction of computeraided detection (CAD) systems to help radiologists was found not to have the expected outcome of improving the performance of readers. Unreliability of CAD systems has led to the explosion of studies and development of applications with the potential use in breast imaging. The purported success recorded with the use of machine learning in breast radiology has led to people postulating ideas that AI will replace breast radiologists. Of course, AI has many applications and potential uses in radiology, but will it replace radiologists? We reviewed many articles on the use of AI in breast radiology to give future radiologists and radiologists full information on this topic. This article focuses on explaining the basic principles and terminology of AI in radiology, potential uses, and limitations of AI in radiology. We have also analysed articles and answered the question of whether AI will replace radiologists.

A whole new pathogen, to which humans have virtually no pre-existing immunity, has caused fear all over the world. Severe acute respiratory syndrome coronavirus (SARS CoV-2) is one of the types of human novel-coronavirus of the family coronavirus. The nature of transmission of the virus makes it one of the most infectious pathogenic diseases that has ever existed. Though the human coronaviruses have existed since the discovery of the human coronavirus 229E (HCoV-229E) and human coronavirus OC43 (HCoV-OC43) in 1960, it has been a challenge to develop an effective cure as well as vaccine for the diseases associated with coronaviruses. Commonly, human coronaviruses cause illnesses such as intestinal and respiratory tract illnesses. Nevertheless, the symptoms reflected after infection from the coronaviruses take some time before being identified. Thus, viruses can replicate and cause more harm to the human body before being detected. Moreover, research continues to explain why some gene variations in some individuals increase the risk of some infectious diseases, while others are not affected. Looking at gene variations in people infected with Coronavirus Disease 2019 (COVID-19) and studying how genes influence people's response to infection will help to develop a vaccine that will help strengthen the immune system. Knowing how the human genes respond to the virus COVID-19 will help to cure people more effectively.