Objective To identify the risk factors for psychological distress in electroencephalography (EEG) technicians during the coronavirus disease 2019 (COVID-19) pandemic. Method In this national level cross-sectional survey, a questionnaire was administered to 173 technicians engaged in EEG at four clinics specializing in epilepsy care and 20 hospitals accredited as (quasi-) epilepsy centers or epilepsy training facilities in Japan from March 1 to April 30, 2021. We collected data on participants’ profiles, information about work, and psychological distress outcome measurements, such as the K-6 and Tokyo Metropolitan Distress Scale for Pandemic (TMDP). Linear regression analysis was used to identify the risk factors for psychological distress. Factors that were significantly associated with psychological distress in the univariate analysis were subjected to multivariate analysis. Results Among the 142 respondents (response rate: 82%), 128 were included in the final analysis. As many as 35.2% of EEG technicians have been under psychological distress. In multivariate linear regression analysis for K-6, female sex, examination for patients (suspected) with COVID-19, and change in salary or bonus were independent associated factors for psychological distress. Contrastingly, in multivariate linear regression analysis for TMDP, female sex, presence of cohabitants who had to be separated from the respondent due to this pandemic, and change in salary or bonus were independent associated factors for psychological distress. Conclusion We successfully identified the risk factors associated with psychological distress in EEG technicians during the COVID-19 pandemic. Our results may help understanding the psychological stress in EEG technicians during the COVID-19 pandemic and improving the work environment, which is necessary to maintain the mental health of EEG technicians.

Auditory cortex subnormal function was more pronounced in patients with right mTLE compared with that in patients with left mTLE as well as HCs. Monaural AEFs can be used to reveal the pathophysiology of mTLE. Overall, our results indicate that altered neural synchronization may provide useful information about possible functional deterioration in patients with unilateral mTLE.

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Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to “omics” data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis

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Rosette-forming glioneuronal tumors (RGNT) of the fourth ventricle are slow-growing tumors that primarily involve the fourth ventricular region. We here report the first patient, an 8-year-old girl, with an RGNT originating in the hypothalamus and manifesting with precocious puberty. After partial removal, the remaining tumor showed rapid enlargement, and the pathologic diagnosis at the second surgery revealed histopathologic features similar to those found in the initial samples, including biphasic patterns of neurocytic rosettes and GFAP-stained astrocytic components. These tumor cells had mildly atypical nuclei; however, mitotic figures and necrosis were absent. Eosinophilic granular bodies and a glomeruloid vasculature were found, but Rosenthal fibers were absent. The Ki-67 proliferative index was 3.5 % (vs 1.1 % at the initial surgery). No recurrence was recorded during the 3-year period after the proton radiotherapy.

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