Multiple sclerosis (MS) is a disease of the central nervous system characterized by inflammation, demyelination, and neuronal damage. Environmental and genetic factors are associated with the risk of developing MS, but the exact cause still remains unidentified. Epstein-Barr virus (EBV), vitamin D, and smoking are among the most well-established environmental risk factors in MS. Infectious mononucleosis, which is caused by delayed primary EBV infection, increases the risk of developing MS. EBV may also contribute to MS pathogenesis indirectly by activating silent human endogenous retrovirus-W. The emerging B-cell depleting therapies, particularly anti-CD20 agents such as rituximab, ocrelizumab, as well as the fully human ofatumumab, have shown promising clinical and magnetic resonance imaging benefit. One potential effect of these therapies is the depletion of memory B-cells, the primary reservoir site where EBV latency occurs. In addition, EBV potentially interacts with both genetic and other environmental factors to increase susceptibility and disease severity of MS. This review examines the role of EBV in MS pathophysiology and summarizes the recent clinical and radiological findings, with a focus on B-cells and in vivo imaging. Addressing the potential link between EBV and MS allows the better understanding of MS pathogenesis and helps to identify additional disease biomarkers that may be responsive to B-cell depleting intervention.

Background: Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.

No abstract

De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional random fields (CRFs). A preprocessing module, which tokenizes the medical records using regular expressions and an off-the-shelf tokenizer, is introduced, and three groups of features are extracted to train the de-identifier model. The experiment shows that our system is effective in the de-identification of medical records, achieving a micro-F1 of 0.9232 at the i2b2 strict entity evaluation level.

Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method.

No abstract

Multiple sclerosis (MS) is a lifelong inflammatory and neurodegenerative disease influenced by multiple lifestyle-based factors. We provide a narrative review of the effects of modifiable risk factors that are identified as being associated with risk to develop MS and/or influencing the future clinical disease outcomes. The emerging data regarding the beneficial effects of diet modifications and exercise are further reviewed. In contrast, obesity and comorbid cardiovascular diseases are associated with increased MS susceptibility and worse disease progression. In addition, the potential influence of smoking, coffee and alcohol consumption on MS onset and disability development are discussed. Successful management of the modifiable risk factors may lead to better long-term outcomes and improve patients’ quality of life. MS specialists should participate in educating and facilitating lifestyle-based modifications as part of their neurological consults.

BackgroundElectronic Medical Record (EMR) comprises patients’ medical information gathered by medical stuff for providing better health care. Named Entity Recognition (NER) is a sub-field of information extraction aimed at identifying specific entity terms such as disease, test, symptom, genes etc. NER can be a relief for healthcare providers and medical specialists to extract useful information automatically and avoid unnecessary and unrelated information in EMR. However, limited resources of available EMR pose a great challenge for mining entity terms. Therefore, a multitask bi-directional RNN model is proposed here as a potential solution of data augmentation to enhance NER performance with limited data.MethodsA multitask bi-directional RNN model is proposed for extracting entity terms from Chinese EMR. The proposed model can be divided into a shared layer and a task specific layer. Firstly, vector representation of each word is obtained as a concatenation of word embedding and character embedding. Then Bi-directional RNN is used to extract context information from sentence. After that, all these layers are shared by two different task layers, namely the parts-of-speech tagging task layer and the named entity recognition task layer. These two tasks layers are trained alternatively so that the knowledge learned from named entity recognition task can be enhanced by the knowledge gained from parts-of-speech tagging task.ResultsThe performance of our proposed model has been evaluated in terms of micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in all cases. For instance, experimental results conducted on the discharge summaries show that the micro average F-score and the macro average F-score are improved by 2.41% point and 4.16% point, respectively, and the overall accuracy is improved by 5.66% point.ConclusionsIn this paper, a novel multitask bi-directional RNN model is proposed for improving the performance of named entity recognition in EMR. Evaluation results using real datasets demonstrate the effectiveness of the proposed model.