Pediatric Anti-N-Methyl-d-Aspartate Receptor Encephalitis: A Review with Pooled Analysis and Critical Care Emphasis

PurposeAnti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is being recognized with increasing frequency among children. Given the paucity of evidence to guide the critical care management of these complex patients, we provide a comprehensive review of the literature with pooled analysis of published case reports and case series.MethodsWe performed a comprehensive literature search using PubMed, Scopus, EMBASE, and Web of Science for relevant published studies. The literature search was conducted using the terms NMDA, anti-NMDA, Anti-N-methyl-d-aspartate, pediatric encephalitis, and anti-NMDAR and included articles published between 2005 and May 1, 2016.ResultsForty-eight references met inclusion criteria accounting for 373 cases. For first-line treatments, 335 (89.8%) received high-dose corticosteroids, 296 received intravenous immunoglobulin (79.3%), and 116 (31%) received therapeutic plasma exchange. In these, 187 children (50.1%) had a full recovery with only minor deficits, 174 patients (46.7%) had partial recovery with major deficits, and 12 children died. In addition, 14 patients were reported to require mechanical ventilation.ConclusionAnti-NMDA encephalitis is a formidable disease with great variation in clinical presentation and response to treatment. With early recognition of this second most common cause of pediatric encephalitis, a multidisciplinary approach by physicians may provide earlier access to first- and second-line therapies. Future studies are needed to examine the efficacy of these current therapeutic strategies on long-term morbidity

Module-based prediction approach for robust inter-study predictions in microarray data

Motivation: Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering