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.

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.

Introduction: The pathogenesis of Parkinson’s disease (PD) is multifactorial in which oxidative stress, neuroinflammation, and mitochondrial dysfunction are the leading factors. Currently, the antioxidant and anti-inflammatory agents of natural sources as neuroprotectants have raised much attention. The current study aimed to explore the neuroprotective effect of methanolic extract of Sargassum wightii in male Wistar albino rats against rotenone-induced PD. Methods: The rats were administered with rotenone (10 mg/kg orally) daily for 28 days to induce PD. S. wightii (200 mg/kg and 400 mg/kg) and levodopa+carbidopa combination (10 mg/kg) were administered to different groups of rats one hour prior to rotenone for 28 days. Behavioral parameters (akinesia, tremor, motor coordination, and locomotor activities) and body weight were recorded on days 14th and 28th of drug treatment. On the 28th day, the animals were sacrificed for the neurobiochemical analyses of brain tissue. Results: Rotenone treatment caused a significant reduction in behavioural parameters (P < 0.001), neurochemical deficits (P < 0.001), and elevation of oxidative stress markers (P < 0.001) in the brain. Pre-treatment with S. wightii at 200 mg/kg and 400 mg/kg doses significantly attenuated the rotenone-induced behavioral alterations and restored the mitochondrial NADH dehydrogenase activity and dopamine level in the striatum (P < 0.001). Moreover, 400 mg/kg of S. wightii restored the rotenone-induced increased oxidative stress markers like malondialdehyde (MDA), superoxide dismutase (SOD), and reduced glutathione (GSH) in the striatum (P < 0.01). Conclusion: S. wightii has provided a neuroprotective effect, probably by virtue of its antioxidant and dopamine restoring potential. Hence, it may offer a promising and new therapeutic lead for the treatment of PD but needs further research.