Electroencephalogram (EEG) has been intensively used as a diagnosis tool for epilepsy. The traditional diagnostic procedure relies on a recording of EEG from several days up to a few weeks, and the recordings are visually inspected by trained medical professionals. This procedure is time consuming with a high misdiagnosis rate. In recent years, computer-aided techniques have been proposed to automate the epilepsy diagnosis by using machine learning methods to analyze EEG data. Considering the time-varying nature of EEG, the goal of this work is to characterize dynamic changes of EEG patterns for the detection and classification of epilepsy. Four different dynamic Bayesian modeling methods were evaluated using multi-subject epileptic EEG data. Experimental results show that an accuracy of 98.0% can be achieved by one of the four methods. The same method also provides an overall accuracy of 87.7% for the classification of seven different seizure types.
Electroencephalography , Epilepsy , Bayes Theorem , Epilepsy/diagnosis , Humans , Machine Learning , Seizures/diagnosis
The ability of tumors to escape from immune destruction is attributed to the protein-protein interaction between programmed cell death protein 1 (PD1) and programmed cell death ligand 1 (PDL1) proteins expressed by immune T cells and cancer cells, respectively. Therefore, pharmacological inhibition of the PD1-PDL1 interaction presents an important therapeutic target against a variety of tumors expressing PDL1 on their cell surface. Recently, five antibodies have been approved and several are in clinical trials against the PD1-PDL1 protein-protein interaction target. In contrast, there are very few reports of small-molecule inhibitors of PD1-PDL1 interaction, and most of them have relatively modest or weak inhibition activities, emphasizing the difficulty in designing small-molecule inhibitors against this challenging target. Therefore, we focused our attention on macrocycles that are known to exhibit target activity comparable to large macromolecules despite having molecular weights closer to small, drug-like molecules. In this context, our present study led to the identification of several macrocyclic compounds from the ansamycin antibiotics class to be inhibitors of PD1-PDL1 interaction. Importantly, one of these macrocyclic antibiotics, Rifabutin, showed an IC50 value of ca. 25 µM. This is remarkable considering it has a relatively low molecular weight and still is capable of inhibiting PD1-PDL1 protein-protein interaction whose binding interface spans over ca. 1970 Å2. Thus, these macrocycles may serve as guiding points for discovery and optimization of more potent, selective small-molecule inhibitors of PD1-PDL1 interaction, one of the most promising therapeutic targets against cancer.
Anti-Bacterial Agents/chemistry , Antineoplastic Agents/chemistry , B7-H1 Antigen/antagonists & inhibitors , Programmed Cell Death 1 Receptor/antagonists & inhibitors , Rifabutin/analogs & derivatives , Rifabutin/chemistry , B7-H1 Antigen/chemistry , Drug Discovery , Humans , Models, Molecular , Programmed Cell Death 1 Receptor/chemistry , Protein Binding
Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain-computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters learned from a subject may not perform well for data acquired from the same subject at a different time or from other subjects performing the same task. Studies have been performed to improve CSP's performance by adding regularization terms into the training. Most of them require target subjects' training data with known class labels. In this work, an adaptive CSP (ACSP) method is proposed to analyze single trial EEG data from single and multiple subjects. The method does not estimate target data's class labels during the adaptive learning and updates spatial filters for both classes simultaneously. The proposed method was evaluated based on a comparison study with the classic CSP and several CSP-based adaptive methods using motor imagery EEG data from BCI competitions. Experimental results indicate that the proposed method can improve the classification performance as compared to the other methods. For circumstances where true class labels of target data are not instantly available, it was examined if adding classified target data to training data would improve the ACSP learning. Experimental results show that it would be better to exclude them from the training data. The proposed ACSP method can be performed in real-time and is potentially applicable to various EEG-based BCI applications.
Brain-Computer Interfaces , Electroencephalography , Signal Processing, Computer-Assisted , Humans
We examined the hepatitis G virus infections among 227 Koreans who were healthy or were suspected of hepatitis and determined the phylogenetic relationship based on a part of the NS-5 region of 5 positive samples. Viral RNA was extracted from sera and cDNA was synthesized and subsequently amplified by RT-PCR (reverse transcription-polymerase chain reaction) or RT-nested PCR using random hexamer and NS-5 specific primers (470-20-1-77F, 470-20-1-211R, HGVNESTFO, HGVNESTRE). Five positives were found to belong to samples of patients showing symptoms of viral hepatitis. Primers used for PCR or nested PCR were derived from the NS-5 region. On the other hand, no amplification was detected using primers derived from the 5'-NCR (G-146F, G-401R). We performed TA cloning and sequencing of 5 amplified fragments, and their sequences were compared with those of foreign isolates of HGV. The phylogenetic analysis using MegAlign programme of DNAstar has shown that the Korean isolates are clustered on the phylogenetic tree. In summary, we confirmed the hepatitis G virus infection in 5 cases out of 12 patients showing the symptoms of viral hepatitis. The phylogenetic analysis of sequences of 5 amplified fragments showed that their relations to each other were closer than those to the foreign HGV isolates reported.
Humans , Clone Cells , Cloning, Organism , DNA, Complementary , GB virus C , Hand , Hepatitis , Korea , Polymerase Chain Reaction , RNA, Viral
