[Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform].

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.

Remdesivir Strongly Binds to RNA-Dependent RNA Polymerase, Membrane Protein, and Main Protease of SARS-CoV-2: Indication From Molecular Modeling and Simulations.

Development of new drugs is a time-taking and expensive process. Comprehensive efforts are being made globally toward the search of therapeutics against SARS-CoV-2. Several drugs such as remdesivir, favipiravir, ritonavir, and lopinavir have been included in the treatment regimen and shown effective results in several cases. Among the existing broad-spectrum antiviral drugs, remdesivir is found to be more effective against SARS-CoV-2. Remdesivir has broad-spectrum antiviral action against many single-stranded RNA viruses including pathogenic SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). In this study, we proposed that remdesivir strongly binds to membrane protein (Mprotein), RNA-dependent RNA polymerase (RDRP), and main protease (Mprotease) of SARS-CoV-2. It might show antiviral activity by inhibiting more than one target. It has been found that remdesivir binds to Mprotease, Mprotein, and RDRP with -7.8, -7.4, and -7.1 kcal/mol, respectively. The structure dynamics study suggested that binding of remdesivir leads to unfolding of RDRP. It has been found that strong binding of remdesivir to Mprotein leads to decrease in structural deviations and gyrations. Additionally, the average solvent-accessible surface area of Mprotein decreases from 127.17 to 112.12 nm2, respectively. Furthermore, the eigenvalues and the trace of the covariance matrix were found to be low in case of Mprotease-remdesivir, Mprotein-remdesivir, and RDRP-remdesivir. Binding of remdesivir to Mprotease, Mprotein, and RDRP reduces the average motions in protein due to its strong binding. The MMPBSA calculations also suggested that remdesivir has strong binding affinity with Mprotein, Mprotease, and RDRP. The detailed analysis suggested that remdesivir has more than one target of SARS-CoV-2.