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In contrast, PQ interval ≥240 ms, QRS duration ≥120 ms, nutrition, or respiratory failure were not associated with the incidence of sudden death. The multivariable analysis revealed that a PQ interval ≥240 ms (HR, 2.79; 95% CI, 1.9-7.19, P less then 0.05) or QRS duration ≥120 ms (HR, 9.41; 95% CI, 2.62-33.77, P less then 0.01) were independent factors associated with a higher occurrence of cardiac events than those observed with a PQ interval less then 240 ms or QRS duration less then 120 ms; these cardiac conduction parameters were not related to sudden death. Conclusions Cardiac conduction disorders are independent markers associated with cardiac events. Further investigation on the prediction of occurrence of sudden death is warranted.The decision to continue or to stop antiepileptic drug (AED) treatment in patients with prolonged seizure remission is a critical issue. https://www.selleckchem.com/products/Romidepsin-FK228.html Previous studies have used certain risk factors or electroencephalogram (EEG) findings to predict seizure recurrence after the withdrawal of AEDs. However, validated biomarkers to guide the withdrawal of AEDs are lacking. In this study, we used quantitative EEG analysis to establish a method for predicting seizure recurrence after the withdrawal of AEDs. A total of 34 patients with epilepsy were divided into two groups, 17 patients in the recurrence group and the other 17 patients in the nonrecurrence group. All patients were seizure free for at least two years. Before AED withdrawal, an EEG was performed for each patient that showed no epileptiform discharges. These EEG recordings were classified using Hjorth parameter-based EEG features. We found that the Hjorth complexity values were higher in patients in the recurrence group than in the nonrecurrence group. The extreme gradient boosting classification method achieved the highest performance in terms of accuracy, area under the curve, sensitivity, and specificity (84.76%, 88.77%, 89.67%, and 80.47%, respectively). Our proposed method is a promising tool to help physicians determine AED withdrawal for seizure-free patients.Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a 'top-down' pattern that occurs across four brain network states that arise at different time instants (0-170[Formula see text]ms, 170-370[Formula see text]ms, 380-620[Formula see text]ms, and 620-1000[Formula see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision-recall curve (AUPRC) of 0.838[Formula see text]±[Formula see text]0.040 and false detection rate of 0.2[Formula see text]±[Formula see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.Cardiac tamponade is a rare complication that occurs during hemihepatectomy. This particular complication has a high degree of mortality and morbidity. A 51-year-old woman was admitted to our hospital for surgical treatment of a malignant liver tumor. During surgery, she developed sudden hemodynamic instability and signs suggesting cardiac tamponade, which was confirmed via transthoracic echocardiogram. Cardiac compression and creation of a pericardial window resulted in immediate hemodynamic improvement. At completion of surgery, a repeated transthoracic echocardiogram showed no pericardial effusion. Early ultrasound-assisted diagnosis and treatment of cardiac tamponade are crucial. Although cardiac tamponade rarely occurs during hemihepatectomy, medics should be aware of this possibility to ensure prompt diagnosis. Our findings strongly support the use of early cardiac compression in cardiac arrest during surgery with echocardiography for prompt and accurate diagnosis of cardiac tamponade. Additionally, our findings will hopefully make anesthesiologists aware of the need to maintain a high index of suspicion for cardiac tamponade with sudden hypotension and a large reduction in differential pressure, and encourage early use of echocardiography and timely cardiac compression.