MicroRNAs circulate around Alzheimer's disease

Exosomal MicroRNAs in Military Personnel with Mild Traumatic Brain Injury: Preliminary Results from the Chronic Effects of Neurotrauma Consortium Biomarker Discovery Project

Chronic symptoms after mild traumatic brain injury (mTBI) are common among veterans and service members, and represent a significant source of morbidity, with those who sustain multiple mTBIs at greatest risk. Exosomal micro-RNAs (miRNAs), mediators of intercellular communication, may be involved in chronic TBI symptom persistence. Exosomal miRNA (exomiR) was extracted from 153 participants enrolled in the Chronic Effect of Neurotrauma Consortium (CENC) longitudinal study (no TBI, n = 35; ≥ 3 mTBIs (rTBI), n = 45; 1-2 mTBIs, n = 73). Analyses were performed with nCounter® Human miRNA Expression Panels and Ingenuity Pathway Analysis (IPA) for identification of gene networks associated with TBI. Generalized linear models were used to analyze the predictive value of exomiR dysregulation and remote neurobehavioral symptoms. Compared with controls, there were 17 dysregulated exomiRs in the entire mTBI group and 32 dysregulated exomiRs in the rTBI group. Two miRNAs, hsa-miR-139-5p and hsa-miR-18a-5p, were significantly differentially expressed in the rTBI and 1-2 mTBI groups. IPA analyses showed that these dysregulated exomiRs correlated with pathways of inflammatory regulation, neurological disease, and cell development. Within the rTBI group, exomiRs correlated with gene activity for hub-genes of tumor protein TP53, insulin-like growth factor 1 receptor, and transforming growth factor beta. TBI history and neurobehavioral symptom survey scores negatively and significantly correlated with hsa-miR-103a-3p expression. Participants with remote mTBI have distinct exomiR profiles, which are significantly linked to inflammatory and neuronal repair pathways. These profiles suggest that analysis of exosomal miRNA expression may provide novel insights into the underlying pathobiology of chronic TBI symptom persistence.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Imaging and machine learning techniques for diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.