Machine Learning as a Tool in Investigating the Possible Role of Microbiome in Development and Treatment of Cancer

In recent times, cancer has become a leading cause of death worldwide, and a need for new therapeutic methods to save lives has become an inevitable necessity. Microbiome and its composition have been a key area of interest among the scientific community. Microbiota appears to hold the key to the therapeutic outcome of cancer by modulating the anti-tumor activity of drugs. Furthermore, the genetic composition of the microbiota and its matching gene sequences in the oncogene has added a new dimension to cancer research. However, it requires adaptive learning techniques and high computational power to bring this research to light empirically. This paper explores the role of machine learning (ML), a subset of artificial intelligence (AI), as a tool to investigate the possible role of the microbiome in the detection and treatment of cancer.

Pathophysiological Mechanisms to Review Association of Atrial Fibrillation in Heart Failure With Obstructive Sleep Apnea

Sleep-disordered breathing (SDB) comprising obstructive sleep apnea (OSA) is found in more than half of patients with heart failure (HF) and may have negative impacts on cardiovascular function. Increased atherosclerotic cardiovascular disease and the development of coronary events and congestive heart failure are associated with OSA. It is associated with a substandard quality of life, increased hospitalizations, and a poor prognosis. Despite its association with severe cardiovascular morbidity and mortality, the condition is frequently underdiagnosed. The substantial clinical evidence has established OSA as an independent risk factor for bradyarrhythmias and tachyarrhythmias in the last decade. The mechanisms which lead to such arrhythmias are uncertain. In short, OSA patients have a significantly elevated risk of HF and atrial fibrillation (AF). The direct correlation between HF, SDB, and cardiac arrhythmias has been poorly understood. The purpose of this study is to get a better understanding of the relation between AF, OSA, and HF by focusing on the pathophysiological mechanisms underlying these conditions. Therefore, we searched for articles to support our association in PubMed and Google Scholar databases.