The present study was performed to identify biomarkers for exposure of benzene in blood cells and hematopoietic tissues. Peripheral mononuclear cells, hematopoietic stem cells, and leukemia cell lines were cultured in RPMI 1640 media with the addition of 0, 1, and 10 mM of benzene. Hydrogen peroxide was measured using an enzyme immunoassay. Mitochondrial mass, membrane potential, and mitochondrial DNA (mtDNA) copy number were measured using MitoTracker Green/Red probes, and real-time polymerase chain reaction. In addition, two-dimensional gel electrophoresis and mass spectrometry matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology were performed to identify protein markers. The mitochondrial contents and membrane potentials were dramatically increased after three weeks of direct benzene exposure. The hydrogen peroxide level increased significantly after two weeks of treatment with benzene (4.4 ± 1.9 µM/mg protein) compared to the non-benzene treatment group (1.2 ± 1.0; p = 0.001). The mtDNA copy number gradually increased after exposure to benzene. Numerous protein markers showed significant aberrant expression after exposure to benzene. Among them, the heterogeneous nuclear ribonucleoprotein (hnRNP) A2/B1 was markedly decreased after exposure to benzene. Thus, increased mitochondrial mass, mtDNA copy number, and the hnRNP A2/B1 protein were biomarkers for benzene-related toxicity and hematotoxicity.

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Even if the solitary lesions of skull and spine show a favorable clinical course, some patients could show aggressive behavior.

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The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the braincomputer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to acquire subject/sessionspecific data to tune the model every time the system is used. This problem is recognized as a major obstacle to BCI, and to overcome it, an approach based on domain generalization (DG) has recently emerged. The main purpose of this paper is to reconsider how the zero-calibration problem of BCI for a realistic situation can be overcome from the perspective of DG tasks. In terms of the realistic situation, we have focused on creating an EEG classification framework that can be applied directly in unseen sessions, using only multi-subject/-session data acquired previously. Therefore, in this paper, we tested four deep learning models and four DG algorithms through leaveone-session-out validation. Our experiment showed that deeper and larger models were effective in cross-session generalization performance. Furthermore, we found that none of the explicit DG algorithms outperformed empirical risk minimization. Finally, by comparing the results of fine-tuning using subject-specific data, we found that subject-specific data may deteriorate unseen session classification performance due to inter-session variability.