Multidrug Resistance Protein 4 (MRP4/ABCC4): A Suspected Efflux Transporter for Human's Platelet Activation.

The main role of platelets is to contribute to hemostasis. However, under pathophysiological conditions, platelet activation may lead to thrombotic events of cardiovascular diseases. Thus, anti-thrombotic treatment is important in patients with cardiovascular disease. This review focuses on a platelet receptor, a transmembrane protein, the Multidrug Resistance Protein 4, MRP4, which contributes to platelet activation, by extruding endogenous molecules responsible for their activation and accumulation. The regulation of the intracellular concentration levels of these molecules by MRP4 turned to make the protein suspicious and at the same time an interesting regulatory factor of platelet normal function. Especially, the possible role of MRP4 in the excretion of xenobiotic and antiplatelet drugs such as aspirin is discussed, thus imparting platelet aspirin tolerance and correlating the protein with the ineffectiveness of aspirin antiplatelet therapy. Based on the above, this review finally underlines that the development of a highly selective and targeted strategy for platelet MRP4 inhibition will also lead to inhibition of platelet activation and accumulation.

Hepatocellular Carcinoma Detection Using Machine Learning Techniques.

Hepatocellular carcinoma (HCC) is a form of primary cancer appearing in the liver. In this work used the hepatocellular carcinoma dataset from the UCI machine learning repository and tested different techniques for feature selection and classification. The following algorithms were used: decision trees, random forests, SVMs, k-NN classifiers, AdaBoost, and gradient boost. The best results were obtained using gradient boost with 84% accuracy and 93% precision. Finally, we deployed the model to a web application as a decision support system for clinicians.

Mobile Application for Monitoring and Preventing Cognitive Decline Through Lifestyle Intervention.

Diagnosing and preventing Alzheimer's disease is a complex task, partly due to being characterized by a lengthy asymptomatic stage. In order to tackle this, most preclinical studies are multidimensional in nature and largely focus on prevention through lifestyle interventions, such as improving nutrition and introducing physical as well as cognitive exercise. With the widespread use of mobile smart devices today, mobile health applications can help inform high-risk individuals at a low cost, while also aiding in the prevention of cognitive decline through constant virtual coaching services that contribute to lifestyle interventions. Under this light, a mobile application is developed in the context of this paper that provides risk assessment of individuals, daily monitoring of factors that have been found to help prevent cognitive impairment, and individually tailored guidance based on the individual's progress. The developed application is also capable of reassessing users' risk to track their progress, while also providing these services in an intuitive and user-friendly manner, which could enable the future development of more accurate models through the collected data.