Anxiety and its risk factors among non-Japanese residents living in Japan undergoing COVID-19 situation: A cross-sectional survey.

INTRODUCTION: In the context of collective efforts taken in Japan to control the spread of COVID-19, the state of emergency and social distancing have caused a negative impact on the mental health of all residents, including foreign communities in Japan. This study aimed to evaluate the level of anxiety and its associated factors among non-Japanese residents residing in Japan during the COVID-19 pandemic. METHODS: A web-based survey in 13 languages was conducted among non-Japanese residents living in Japan during the COVID-19 situation. The State-Trait Anxiety Inventory assessed the level of anxiety-State (STAI-S) scores prorated from its six-item version. The multivariable logistic regression using the Akaike Information Criterion (AIC) method was performed to identify the associated factors of anxiety among participants. RESULTS: From January to March 2021, we collected 392 responses. A total of 357 valid responses were analyzed. 54.6% of participants suffered from clinically significant anxiety (CSA). In multivariable logistic model analysis, the CSA status or the high level of anxiety was associated with three factors, including having troubles/difficulties in learning or working, decreased sleep duration, and decreased overall physical health (p<0.05). CONCLUSION: Our study suggests several possible risk factors of anxiety among non-Japanese residents living in Japan undergoing the COVID-19 pandemic, including the troubles or difficulties in learning or working, the decrease in sleep duration, and the decrease in overall physical health.

Cooperative combination of LIBS-based elemental analysis and near-infrared molecular fingerprinting for enhanced discrimination of geographical origin of soybean paste.

Laser-induced breakdown spectroscopy (LIBS) and near-infrared (NIR) spectroscopy were combined to enhance discrimination of soybean paste samples according to geographical origin. Since element and organic component compositions of soybean pastes depend on soybean cultivation areas and fermentation conditions, utilization of two complementary spectroscopic signatures would be synergetic for the discrimination. When the areas of C (AC) and Ca (ACa) peaks in the LIBS spectra were used as the inputs for linear discriminant analysis, the accuracy was 95.4%. The accuracy became 92.1%, when the principal component (PC) scores obtained by principal component analysis of the NIR spectra were employed. To enhance NIR discrimination, two-trace two-dimensional (2T2D) correlation analysis was adopted to recognize minute spectral differences. With using the 1st/2nd PC scores of 2T2D slice spectra, accuracy increased to 95.0%. When the ratios of ACa/AC and the 2nd PC scores of the samples were combined together, the accuracy improved to 99.6%.

Discrimination between Korean and Chinese Kimchi using inductively coupled plasma-optical emission spectroscopy and mass spectrometry: A multivariate analysis of Kimchi.

Kimchi has been designated as one of the world's five healthiest foods, and it is a traditional Korean fermented food. The purpose of this study was to investigate whether the origin of Kimchi can be discriminated against by using inorganic elements to develop a more accurate method. The OPLS-DA showed that the R2 and Q2 values were 0.908 and 0.81, a high-quality model. We selected 24 elements (133Cs, 238U, 88Sr, K, 157Gd, Na, Mg, 139La, P, 141Pr, 72Ge, 146Nd, 147Sm, 153Eu, 55Mn, 165Ho, 163Dy, 166Er, Fe, 172Yb, 169Tm, 185Re, 175Lu, and 118Sn) with VIP 1 or higher in the OPLS-DA model. In ROC, the selected elements had an accuracy of 100%. The heatmap confirmed the contents of rare earth elements (REEs) in Korean and Chinese Kimchi, the accuracy of distinguishing classification in CDA is 100%. Such results will help to distinguish the origin of agricultural products through inorganic analysis.

Separation of malignant human breast cancer epithelial cells from healthy epithelial cells using an advanced dielectrophoresis-activated cell sorter (DACS).

In this paper, we successfully separated malignant human breast cancer epithelial cells (MCF 7) from healthy breast cells (MCF 10A) and analyzed the main parameters that influence the separation efficiency with an advanced dielectrophoresis (DEP)-activated cell sorter (DACS). Using the efficient DACS, the malignant cancer cells (MCF 7) were isolated successfully by noninvasive methods from normal cells with similar cell size distributions (MCF 10A), depending on differences between their material properties such as conductivity and permittivity, because our system was able to discern the subtle differences in the properties by generating continuously changed electrical field gradients. In order to evaluate the separation performance without considering size variations, the cells collected from each outlet were divided into size-dependent groups and counted statistically. Following that, the quantitative relative ratio of numbers between MCF 7 and MCF 10A cells in each size-dependent group separated by the DEP were compared according to applied frequencies in the range 48, 51, and 53 MHz with an applied amplitude of 8 V(pp). Finally, under the applied voltage of 48 MHz-8 V(pp) and a flow rate of 290 microm/s, MCF 7 and MCF 10A cells were separated with a maximum efficiency of 86.67% and 98.73% respectively. Therefore, our suggested system shows it can be used for detection and separation of cancerous epithelial cells from noncancerous cells in clinical applications.