Metabolic network failures in Alzheimer's disease: A biochemical road map

Introduction

The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

Signal-dependent wavelets for electromyogram classification

In the study, an efficient method to perform supervised classification of surface electromyogram (EMG) signals is proposed. The method is based on the choice of a relevant representation space and its optimisation with respect to a training set. As EMG signals are the summation of compact-support waveforms (the motor unit action potentials), a natural tool for their representation is the discrete dyadic wavelet transform. The feature space was thus built from the marginals of a discrete wavelet decomposition. The mother wavelet was designed to minimise the probability of classification error estimated on the learning set (supervised classification). As a representative example, the method was applied to simulate surface EMG signals generated by motor units with different degrees of short-term synchronisation. The proposed approach was able to distinguish surface EMG signals with degrees of synchronisation that differed by 10%, with a misclassification rate of 8%. The performance of a spectral-based classification (error rate approximately 33%) and of the classification with Daubechies wavelet (21%) was significantly poorer than with the proposed wavelet optimisation. The method can be used for a number of different application fields of surface EMG classification, as the feature space is adapted to the characteristics of the signal that discriminate between classes. An erratum to this article is available at .     

Association between epilepsy and oral maxillofacial trauma: A systematic review and meta‐analysis

A systematic literature search was conducted (through April 2017), using Web of Science, PubMed and Virtual Health Library, manual reference list, and grey literature searches. The quality of the studies was evaluated using the Newcastle‐Ottawa Quality Assessment Scale. The meta‐analysis was performed using R software. A total of 30 studies was included in this review. From a total of 25 studies included in the meta‐analysis, the prevalence of oral and maxillofacial injuries among epileptic subjects was 19%. Among the epileptic patients who suffered some type of injury due to epileptic seizures, 52% had facial soft tissue injuries (95%CI: 28‐75%), 18% suffered dental trauma (95%CI: 11‐29%), and 12% (95%CI: 4‐28%) suffered maxillofacial fractures. Epileptic patients were more likely to have oral and maxillofacial injuries than healthy individuals (OR: 5.22, 95%CI: 2.84‐9.36) and subjects with psychogenic nonepileptic seizures (OR: 2.77, 95%CI: 1.28‐5.99), but not than patients with special needs (OR: 2.45,95%CI: 0.95‐6.31).