ObjectiveTo summarize clinical electrophysiological features and efficacy of some of Anti-epileptic drugs(AEDs) of Juvenile myoclonic epilepsy (JME).
MethodsClinical electrophysiological information of 101 outpatients with JME observed at Xuanwu Hospital from Jul. 2001 to Sep. 2014 was retrospectively analyzed, including the seizure types, trigger factors, electroencephalogram. We followed some of these patients and compared the efficacy between different AEDs. Result According to different seizure types, there are four subtypes: Myoclonus (MJ) only 11.88%, MJ+generalized tonic-clonic seizure(GTCS) 75.24%, MJ+GTCS+Absence(Abs) 11.88%, MJ+Abs 1.00%. Patients with typical ictal generalized poly-spike and waves (PSW) or spike and waves (SW) or spikes account for 96.80%. And 75.00% of patients have no MJ and 91.80% have no GTCS with valproic acid monotherapy. 65.00% and 88.24% of patients were seizure free of MJ and GTCS recpectively. But the difference of efficacy between these two drugs have no statistically significance. Sleep deprivation was the primary trigger factors, accounting for 16.83%.
ConclusionJME has clinical heterogeinety, clinicians should fully understand the whole condition of JME individual, including their clinical manifestation, EEG features, reaction to AEDs, trigger factors, habitual patterns and so on, in order to help making individualized therapy.
ObjectiveTo investigate the relationship between neuroticism and functional gastrointestinal disorders using Mendelian randomized (MR). MethodsBased on the genome-wide association study data of neuroticism and 2 functional gastrointestinal disorders, i.e., functional dyspepsia (FD), and irritable bowel syndrome (IBS), appropriate single nucleotide polymorphisms (SNP) were extracted as instrumental variables, and inverse variance weighted (IVW) was applied as the main analysis method, and sensitivity analyses were performed by Cochran’s Q test, MR-PRESSO test, MR-Egger intercept, and leave one out analysis. Further two-step MR analyses were performed to examine the mediating effects of coffee intake, alcohol consumption, smoking, depression. ResultsThe univariable MR analysis showed that genetically determined neuroticism was positively causally associated with the risk of developing FD and IBS (FD: OR=1.448, 95%CI 1.057 to 1.983, P=0.021; IBS: OR=1.705, 95%CI 1.210 to 2.403, P=0.002). Cochran's Q-test, MR-Egger intercept, MR-PRESSO did not observe significant heterogeneity or horizontal pleiotropy. Leave-one-out analyses also did not find a large effect of individual SNPs on the overall results. Multivariable MR analyses showed that the association between neurotic personality and elevated risk of FD and IBS prevalence persisted even after adjusting for other confounders. Further two-step MR mediation analyses revealed that depression partially mediated this effect, with mediation proportions of 59.41% (95%CI 5.69% to 113.12%) and 67.53% (95%CI 31.55% to 103.51%), respectively. ConclusionThere is a degree of causal association between neuroticism and FD and IBS, and depression may play an important mediating role in this association.
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.
