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The Melanoma Antigen Gene (MAGE) is a large family of highly conserved proteins that share a common MAGE homology domain. Interestingly, many MAGE family members exhibit restricted expression in reproductive tissues but are abnormally expressed in various human malignancies, including bladder cancer, which is a common urinary malignancy associated with high morbidity and mortality rates. The recent literature suggests a more prominent role for MAGEA family members in driving bladder tumorigenesis. This review highlights the role of MAGEA proteins, the potential for them to serve as diagnostic or prognostic biomarker(s), and as therapeutic targets for bladder cancer.
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Post-traumatic stress disorder (PTSD) is defined as a mental health disease that has a high probability of developing among individuals who have experienced traumatic events [...].
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OBJECTIVE: To evaluate the existing evidence of a machine learning-based classification system that stratifies patients with stroke. METHODS: The authors carried out a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations for a review article. PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text were searched from January 2015 to February 2021. RESULTS: There are twelve studies included in this systematic review. Fifteen algorithms were used in the included studies. The most common forms of machine learning (ML) used to classify stroke patients were the support vector machine (SVM) (n = 8 studies), followed by random forest (RF) (n = 7 studies), decision tree (DT) (n = 4 studies), gradient boosting (GB) (n = 4 studies), neural networks (NNs) (n = 3 studies), deep learning (n = 2 studies), and k-nearest neighbor (k-NN) (n = 2 studies), respectively. Forty-four features of inputs were used in the included studies, and age and gender are the most common features in the ML model. DISCUSSION: There is no single algorithm that performed better or worse than all others at classifying patients with stroke, in part because different input data require different algorithms to achieve optimal outcomes.
