Vangsgaardlarson8583

ous system alterations related to central neuropathic pain in MS patients that lead to the disturbances in cortical communication. Copyright © 2020 Krupina, Churyukanov, Kukushkin and Yakhno.Glucocorticoids are used intra-operatively in cochlear implant surgeries to reduce the inflammatory reaction caused by insertion trauma and the foreign body response against the electrode carrier after cochlear implantation. To prevent higher systemic concentrations of glucocorticoids that might cause undesirable systemic side effects, the drug should be applied locally. Since rapid clearance of glucocorticoids occurs in the inner ear fluid spaces, sustained application is supposedly more effective in suppressing foreign body and tissue reactions and in preserving neuronal structures. Embedding of the glucocorticoid dexamethasone into the cochlear implant electrode carrier and its continuous release may solve this problem. The aim of the present study was to examine how dexamethasone concentrations in the electrode carrier influence drug levels in the perilymph at different time points. Silicone rods were implanted through a cochleostomy into the basal turn of the scala tympani of guinea pigs. The silicone roilicone electrode carrier dummies in a controlled and sustained way over a period of several weeks, leading to constant drug concentrations in the scala tympani perilymph. No accumulation of dexamethasone was observed in the cochlear tissue. In consideration of experimental studies using similar drug depots and investigating physiological effects, an effective dose range between 50 and 100 ng/ml after burst release is suggested for the CI insertion trauma model. Copyright © 2020 Liebau, Schilp, Mugridge, Schön, Kather, Kammerer, Tillein, Braun and Plontke.[This corrects the article DOI 10.3389/fneur.2018.00395.]. Copyright © 2020 Teggi, Colombo, Albera, Asprella Libonati, Balzanelli, Batuecas Caletrio, Casani, Espinosa-Sanchez, Gamba, Lopez-Escamez, Lucisano, Mandalà, Neri, Nuti, Pecci, Russo, Martin-Sanz, Sanz, Tedeschi, Torelli, Vannucchi, Comi and Bussi.Objective Elevated levels of anti-EBNA-1 antibodies and infectious mononucleosis (IM) history have consistently been associated with multiple sclerosis (MS) risk. We aimed to study whether these aspects of Epstein-Barr virus (EBV) infection represent separate risk factors for MS and whether they both interact with MS-associated HLA genes in disease development. Methods Two Swedish-population-based case-control studies were used, comprising 5,316 cases and 5,431 matched controls. Subjects with different HLA alleles, EBNA-1, and IM status were compared regarding MS risk by calculating odds ratios (OR) with 95% confidence intervals (CI) employing logistic regression. Causal mediation analysis was used to assess to what extent the relationship between IM history and MS risk was mediated by high anti-EBNA-1 antibody levels and vice versa. Results The causal mediation analysis revealed that both aspects of EBV infection mainly act directly on MS risk. The direct effect of elevated anti-EBNA-1 antibody levels on MS risk, expressed on the OR scale, was 2.8 (95% CI 2.5-3.1), and the direct effect of IM history on MS risk was 1.7 (95% CI 1.5-2.0). A significant interaction between the two aspects of EBV infection was observed (RERI 1.2, 95% CI 0.3-2.0), accounting for about 50% of the total effect. Further, both aspects of EBV infection interacted with DRB1*1501 and absence of A*0201. selleck kinase inhibitor Interpretation Elevated anti-EBNA-1 antibody levels and IM history are different risk factors for MS. The two aspects of EBV infection act synergistically to increase MS risk, indicating that they partly are involved in the same biological pathways. Copyright © 2020 Hedström, Huang, Michel, Butt, Brenner, Hillert, Waterboer, Kockum, Olsson and Alfredsson.Background In view of the recent literature, the negative impact of traumatic brain injury (TBI) on social cognition remains a debated issue. On one hand, a considerable number of studies reported significant impairments in emotion recognition, empathy, moral reasoning, social problem solving, and mentalizing or theory of mind (ToM) abilities in patients with TBI. On the other hand, the ecological validity of social cognition tasks is still a matter of concern and debate for clinicians and researchers. Objectives The objectives of the present study were 2-fold (1) to assess social cognition in TBI with an ecological performance-based test which focuses on ToM ability, and (2) to study the relationship between performances on this task and behavioral disorders. To this end, 47 patients with moderate to severe TBI in the chronic stage were assessed with a ToM task, the Movie for the Assessment of Social Cognition (MASC), a film displaying social interactions in natural settings and asking for an evaluation of t Allain, Hamon, Saoût, Verny, Dinomais and Besnard.Background In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members. Level of evidence This observational study provides a level IV evidence on prognosis after TBI. Copyright © 2020 Amorim, Oliveira, Malbouisson, Nagumo, Simoes, Miranda, Bor-Seng-Shu, Beer-Furlan, De Andrade, Rubiano, Teixeira, Kolias and Paiva.