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Objectives Electroencephalography (EEG) is a central part of the medical evaluation for patients with neurological disorders. Training an algorithm to label the EEG normal vs abnormal seems challenging, because of EEG heterogeneity and dependence of contextual factors, including age and sleep stage. Our objectives were to validate prior work on an independent data set suggesting that deep learning methods can discriminate between normal vs abnormal EEGs, to understand whether age and sleep stage information can improve discrimination, and to understand what factors lead to errors. Methods We train a deep convolutional neural network on a heterogeneous set of 8522 routine EEGs from the Massachusetts General Hospital. We explore several strategies for optimizing model performance, including accounting for age and sleep stage. Results The area under the receiver operating characteristic curve (AUC) on an independent test set (n = 851) is 0.917 marginally improved by including age (AUC = 0.924), and both age and sleep stages (AUC = 0.925), though not statistically significant. Conclusions The model architecture generalizes well to an independent dataset. Adding age and sleep stage to the model does not significantly improve performance. Significance Insights learned from misclassified examples, and minimal improvement by adding sleep stage and age suggest fruitful directions for further research.

Aims Although approving new anticonvulsants was a major breakthrough in the field of epilepsy control, so far we have met limited success in almost one third of patients suffering from epilepsy and a definite and reliable method is yet to be found. Levosimendan demonstrated neuroprotective effects and reduced mortality in conditions in which seizure can be an etiology of death; however, the underlying neuroprotective mechanisms of levosimendan still eludes us. In the light of evidence suggesting levosimendan can be a K ATP channel opener and nitrergic pathway activator, levosimendan may exert antiseizure effects through K ATP channels and nitrergic pathway. Main methods In this study, the effects of levosimendan on seizure susceptibility was studied by PTZ-induced seizures model in mice. Key findings Administration of a single effective dose of levosimendan significantly increased seizures threshold and the nitrite level in the hippocampus and temporal cortex. Pretreatment with noneffective doses of glibenclamide (a K ATP channel blocker) and L-NAME (a non-selective NOS inhibitor) neutralize the anticonvulsant and nitrite elevating effects of levosimendan. While 7-NI (a neural NOS inhibitor) blocked the anticonvulsant effect of levosimendan, Aminoguanidine (an inducible NOS inhibitor) failed to affect the anticonvulsant effects of levosimendan. Cromakalim (a K ATP channel opener) or l -arginine (an NO precursor) augmented the anticonvulsant effects of a subeffective dose of levosimendan. Moreover, co-administration of noneffective doses of Glibenclamide and L-NAME demonstrated a synergistic effect in blocking the anticonvulsant effects of levosimendan. Significance Levosimendan has anticonvulsant effects possibly via K ATP /nNOS/NO pathway activation in the hippocampus and temporal cortex.

Background Posterior reversible encephalopathy syndrome (PRES) is a well-recognized complication of hypertensive encephalopathy. Recently, pre-eclampsia, connective tissue disorders, and immunosuppressive drugs have been reported to be the etiologies of this rare syndrome. Methods We evaluated 9 cases of PRES whose diagnosis were confirmed based on clinical and radiologic evidence between July 2011 and December 2013 in a tertiary center, Imam Khomeini Hospital, Tehran, Iran. Results Immunosuppressive drugs, especially cyclosporine, and hypertension were the main precipitating factors. In this study, seizure was the most common clinical presentation (100%), whereas other common clinical presentations were confusion (78%), visual loss (67%), and headaches (67%). With conservative management and elimination of predisposing factor, the patients improved gradually except for 2 cases who experienced prolonged recovery period because of delayed diagnosis. Conclusions With timely diagnosis, PRES generally has a good prognosis with complete recovery. However, in missed conditions, it could be associated with catastrophic burden especially in organ transplantation after a prolonged time spending to find matched donors or in chronic immunosuppressive conditions. Thereupon, physicians should be aware of clinical and radiologic manifestations of this preventable but potentially disabling syndrome.