Stem Cell Research for the Treatment of Malignant Glioma

Glioblastoma is the most aggressive brain tumor. Gene therapies, such as cytokine-based, suicide gene, and oncolytic virus therapies, are different types of treatments from chemotherapy such as using temozolomide as a standard treatment. However, overall survival was not prolonged in some clinical trials because of the low efficiency of gene transduction and viral infection. Neural stem cells (NSCs) have tumor trophic migratory capacity and can be cellular delivery vehicles of cytokines, suicide genes, and oncolytic virus. NSCs can be differentiated from embryonic stem cells. In addition, mesenchymal stem cells can be another cellular delivery vehicle. Recently, induced pluripotent stem cells (iPSCs) have been established. iPSCs are multipotent; hence, they can efficiently differentiate to NSCs and can possibly overcome ethical and practical issues in clinical application. In this study, current topics about stem cell therapy for malignant glioma are reviewed

Primary Neuroendocrine Tumor in Brain

The incidence of brain metastases for neuroendocrine tumor (NET) is reportedly 1.5~5%, and the origin is usually pulmonary. A 77-year-old man presented to our hospital with headache and disturbance of specific skilled motor activities. Computed tomography (CT) showed a massive neoplastic lesion originating in the left temporal and parietal lobes that caused a mass edematous effect. Grossly, total resection of the tumor was achieved. Histological examination revealed much nuclear atypia and mitotic figures. Staining for CD56, chromogranin A, and synaptophysin was positive, indicating NET. The MIB-1 index was 37%. Histopathologically, the tumor was diagnosed as NET. After surgery, gastroscopy and colonoscopy were performed, but the origin was not seen. After discharge, CT and FDG-PET (fluoro-2-deoxy-d-glucose positron emission tomography) were performed every 3 months. Two years later we have not determined the origin of the tumor. It is possible that the brain is the primary site of this NET. To our knowledge, this is the first reported case of this phenomenon

Study of Role Language Impression: Comparison between Korean Learners of Japanese and Native Japanese Speakers

Yakuwarigo is a Japanese role language defined by Kinsui (2003). Yakuwarigo is a particle, always used in the end of the sentences such as "Taberu-zo", "TO EAT : verb- ZO : particle" It is said that Yakuwarigo has gender difference by using this. For example. wa is used by females and zo and nojya used by males. Recently, learners of Japanese have greater opportunities to expand their ability through manga and anime. However, Tei (2008) has observed that the Yakuwarigo used in manga and anime emphasizes gender differences. In this study, We investigated the impression that Yakuwarigo exerts in terms of gender differences by means of a questionnaire, which was completed by Korean learners of Japanese and native Japanese speakers. I found that Yakuwarigo produces a similar effect in both groups. However, with the particle zo. which previous studies have identified as being used only by males, I found that it was believed to be used by both males and females. Further, I determined that native Japanese speakers have a different image with respect to the particle wa depending on the associated verb. These results indicate that Korean learners of Japanese have a fixed impression with respect to Yakuwarigo

Development of electromagnetic and piezoelectric hybrid actuator system

An ordinal force-feedback device typically uses an electromagnetic motor (EMM), which provides an excellent expression of elasticity. However, it is not easy to express the sense of hardness and roughness because the response of the current is delayed due to the inductance of the armature winding. On the contrary, a piezoelectric actuator, which has a rapid response, is good at expressing the sense of hardness and roughness. Thus, if different types of actuators are used in the same actuator system (AS), the weaknesses of each type can be compensated for. In this study, as an ideal force-feedback device, a hybrid actuator system combining an EMM with an ultrasonic motor (USM) and a piezoelectric clutch/brake (piezo-clutch/brake) is proposed and examined. This AS can expand the range of representable feelings. This paper describes the construction of a hybrid AS and some experimental results of a force-feedback display. In this experiment, the feelings of roughness, friction, and elasticity were represented. The feeling of roughness was represented by the on-off control of the piezo-brake at defined positions. The feeling of friction was represented by the PID control of braking using the piezo-clutch. The feeling of elasticity was represented by two methods: the use of the EMM and brake and the use of a combination of the USM, clutch, and brake. As a result, the hardness feeling was realistically represented by the piezo-brake, and the elastic feeling was represented by either the EMM or the USM

Phenomenology of the chiral dd-wave state in the hexagonal pnictide superconductor SrPtAs

The pairing symmetry of the hexagonal pnictide superconductor SrPtAs is discussed with taking into account its multiband structure. The topological chiral $d$-wave state with time-reversal-symmetry breaking has been anticipated from the spontaneous magnetization observed by the muon-spin-relaxation experiment. We point out in this paper that the recent experimental reports on the nuclear-spin-lattice relaxation rate $T_1^{-1}$ and superfluid density $n_s(T)$, which seemingly support the conventional $s$-wave pairing, are also consistent with the chiral $d$-wave state. The compatibility of the gap and multiband structures is crucial in this argument. We propose that the measurement of the bulk quasiparticle density of states would be useful for the distinction between two pairing states.Comment: 6 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1902.1051

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions

Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target antigens such as viruses and bacteria. However, the publicly available datasets in existing works have notable limitations, such as small sizes and the lack of non-binding samples and exact amino acid sequences. To overcome these limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting antigen-antibody interactions in the variable domain of heavy chain of heavy chain antibodies (VHHs), produced from an alpaca immunized with the human interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure of VHHs, which facilitates identification of full-length amino acid sequences by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs with amino acid sequences. All the antigen-VHH pairs have reliable labels for binding or non-binding, as generated by a novel labeling method. Furthermore, via introduction of artificial mutations, AVIDa-hIL6 contains 30 different mutants in addition to wild-type IL-6 protein. This characteristic provides opportunities to develop machine learning models for predicting changes in antibody binding by antigen mutations. We report experimental benchmark results on AVIDa-hIL6 by using neural network-based baseline models. The results indicate that the existing models have potential, but further research is needed to generalize them to predict effective antibodies against unknown mutants. The dataset is available at https://avida-hil6.cognanous.com