Targeting the vivid facets of apolipoproteins as a cardiovascular risk factor in rheumatoid arthritis.

Mostly, cardiovascular diseases are blamed for casualties in rheumatoid arthritis (RA) patients. Customarily, dyslipidemia is probably the most prevalent underlying cause of untimely demise in people suffering from RA as it hastens the expansion of atherosclerosis. The engagement of inflammatory cytokines like tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), interleukin-6 (IL-6), etc., is crucial in the progression and proliferation of both RA and abnormal lipid parameters. Thus, lipid abnormalities should be monitored frequently in patients with both primary and advanced RA stages. An advanced lipid profile examination, i.e., direct role of apolipoproteins associated with various lipid molecules is a more dependable approach for better understanding of the disease and selecting suitable therapeutic targets. Therefore, studying their apolipoproteins is more relevant than assessing RA patients' altered lipid profile levels. Among the various apolipoprotein classes, Apo A1 and Apo B are primarily being focused. In addition, it also addresses how calculating Apo B:Apo A1 ratio can aid in analyzing the disease's risk. The marketed therapies available to control lipid abnormalities are associated with many other risk factors. Hence, directly targeting Apo A1 and Apo B would provide a better and safer option.

Stack Ensemble Oriented Parkinson Disease Prediction Using Machine Learning Approaches Utilizing GridSearchCV-Based Hyper Parameter Tuning.

Since the coronavirus came into existence and brought the entire world to a standstill, there have been drastic changes in people's lives that continue to affect them even as the pandemic recedes. The isolation reduced physical activity and hindered access to non-COVID related healthcare during lockdown and the ensuing months brought increased attention to mental health and the neurological disorders that might have been exacerbated. One nervous system disorder that affects the elderly and needs better awareness is Parkinson's disease. We have machine learning and a growing number of deep learning models to predict, and detect its onset; their scope is not completely exhaustive and can still be optimized. In this research, the authors highlight techniques that have been implemented in recent years for prediction of the disease. Models based on the less redundantly used classifiers-naive Bayes, logistic regression, linear-support vector machine, kernelizing support vector machine, and multilayer perceptron-are initially implemented and compared. Based on limitations of the results, an ensemble stack model of hyper-tuned versions using GridSearchCV out of the top performing supervised classifiers along-with extreme gradient boosting classifier is implemented to further improve overall results. In addition, a convolutional neural network-based model is also implemented, and the results are analyzed using two epoch values to compare the performance of deep learning models. The benchmark datasets-UCI Parkinson's data and the spiral and wave datasets-have been used for machine and deep learning respectively. Performance metrics like accuracy, precision, recall, support, and F1 score are utilized, and confusion matrices and graphs are plotted for visualization. 94.87% accuracy was achieved using the stacking approach.

Lung Cancer Detection Using Machine Learning Techniques.

Cancer has been the deadliest of diseases since decades constituting a large number of deaths annually. Lung cancer remains one of the most significant public health issues, accounting for a substantial proportion of cancer-related deaths globally. Despite ongoing efforts to curb the instances of lung cancer, India continues to see a high number of new diagnoses each year, estimated to be 70,000. Early detection of lung cancer can be difficult due to its asymptomatic nature in its initial stages. However, advancements in technology have given rise to computer-aided diagnostic systems to help overcome this challenge. These systems employ a variety of techniques, such as machine learning, deep learning, image analysis, and text mining, to accurately determine the presence of lung cancer. In an effort to create a more advanced model for lung cancer diagnosis, this study proposes the integration of machine learning algorithms, ensemble learning techniques, and particle swarm optimization to assess the outcomes. The results of the study suggest that the ensemble learning approach outperforms traditional machine learning techniques in terms of accuracy.