Eating the Dead to Keep Atherosclerosis at Bay

Atherosclerosis is the primary cause of coronary heart disease (CHD), ischemic stroke, and peripheral arterial disease. Despite effective lipid-lowering therapies and prevention programs, atherosclerosis is still the leading cause of mortality in the United States. Moreover, the prevalence of CHD in developing countries worldwide is rapidly increasing at a rate expected to overtake those of cancer and diabetes. Prominent risk factors include the hardening of arteries and high levels of cholesterol, which lead to the initiation and progression of atherosclerosis. However, cell death and efferocytosis are critical components of both atherosclerotic plaque progression and regression, yet, few currently available therapies focus on these processes. Thus, understanding the causes of cell death within the atherosclerotic plaque, the consequences of cell death, and the mechanisms of apoptotic cell clearance may enable the development of new therapies to treat cardiovascular disease. Here, we review how endoplasmic reticulum stress and cholesterol metabolism lead to cell death and inflammation, how dying cells affect plaque progression, and how autophagy and the clearance of dead cells ameliorates the inflammatory environment of the plaque. In addition, we review current research aimed at alleviating these processes and specifically targeting therapeutics to the site of the plaque

Web search engine misinformation notifier extension (SEMiNExt):a machine learning based approach during COVID-19 pandemic

Abstract Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues