Alzheimer's diseases in America, Europe, and Asian regions: a global genetic variation.

Background: Alzheimer's disease (AD) is one of the multifaceted neurodegenerative diseases influenced by many genetic and epigenetic factors. Genetic factors are merely not responsible for developing AD in the whole population. The studies of genetic variants can provide significant insights into the molecular basis of Alzheimer's disease. Our research aimed to show how genetic variants interact with environmental influences in different parts of the world. Methodology: We searched PubMed and Google Scholar for articles exploring the relationship between genetic variations and global regions such as America, Europe, and Asia. We aimed to identify common genetic variations susceptible to AD and have no significant heterogeneity. To achieve this, we analyzed 35 single-nucleotide polymorphisms (SNPs) from 17 genes (ABCA7, APOE, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, TOMM40, MS4A6A, ARID5B, SORL1, APOC1, MTHFD1L, BDNF, TFAM, and PICALM) from different regions based on previous genomic studies of AD. It has been reported that rs3865444, CD33, is the most common polymorphism in the American and European populations. From TOMM40 and APOE rs2075650, rs429358, and rs6656401, CR1 is the common investigational polymorphism in the Asian population. Conclusion: The results of all the research conducted on AD have consistently shown a correlation between genetic variations and the incidence of AD in the populations of each region. This review is expected to be of immense value in future genetic research and precision medicine on AD, as it provides a comprehensive understanding of the genetic factors contributing to the development of this debilitating disease.

A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.

In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known complementary techniques, called fuzzy entropy clustering method and region-based active contour method, using multi-objective particle swarm optimization (MOPSO) approach. In order to obtain accurate segmentation results, firstly, two fitness functions with independent characteristics, compactness and separation, are derived from kernelized fuzzy entropy clustering with local spatial information and bias correction (KFECSB) and a novel adaptive energy weight combined with global and local fitting energy active contour (AWGLAC) model. Then, they are simultaneously optimized to finally produce a set of non-dominated solutions, from which L2-metric method is used to select the best trade-off solution. Our algorithm is both verified and compared with other state-of-the-art methods using simulated MR images and real MR images from the McConnell Brain Imaging Center (BrainWeb) and the Internet Brain Segmentation Repository (IBSR), respectively. The experimental results demonstrate that the proposed technique achieves superior segmentation performance in terms of accuracy and robustness.

A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation.

Appropriate initialization and stable evolution are desirable criteria to satisfy in level set methods. In this study, a novel region-based level set method utilizing both global and local image information complementarily is proposed. The global image information is extracted from mean shift clustering without any prior knowledge. Appropriate initial contours are obtained by regulating the clustering results. The local image information, as extracted by a data fitting energy, is employed to maintain a stable evolution of the zero level set curves. The advantages of the proposed method are as follows. First, the controlling parameters of the evolution can be easily estimated by the clustering results. Second, the automaticity of the model increases because of a reduction in computational cost and manual intervention. Experimental results confirm the efficiency and accuracy of the proposed method for medical image segmentation.