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Introduction: Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV)., Methods: Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested., Results and Discussion: In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Ono, Warita, Shoji, Nakagawa, Usukura, Yu, Takahashi, Ichiji, Sugita, Kobayashi, Kikuchi, Kimura, Hamaie, Hino, Kunii, Murakami, Ishikuro, Obara, Nakamura, Nagami, Takai, Ogishima, Sugawara, Hoshiai, Saito, Tamiya, Fuse, Fujii, Nakayama, Kuriyama, Yamamoto, Yaegashi, Homma and Tomita.)

Numerous studies have investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health among university students within a year of its onset, but few have examined the impact of a prolonged pandemic on university life. This study aimed to evaluate the impact of the COVID-19 pandemic on the mental health of students in a large university community. Online questionnaire surveys were conducted on students from March 24 to April 14 (first survey, n = 3,357) and December 2-23, 2021 (second survey, n = 2,604). The questionnaires included items on demographic data, living conditions, and mental health status as measured using the Patient Health Questionnaire-9 for depressive symptoms and the Generalized Anxiety Disorder-7 scale for anxiety symptoms. The results showed that, compared with undergraduate students, graduate students, except those in Medicine, Dentistry, and Pharmaceutical Science courses, had more anxiety symptoms. Furthermore, among undergraduate students, depressive and anxiety symptoms were significantly higher in fourth- than in first-year students. Logistic regression analyses of data from both surveys revealed the seven risk factors associated with depressive or anxiety symptoms that affected the mental health of university students throughout the COVID-19 pandemic: 1) female or nonbinary gender, 2) graduate student, 3) quarantine experience due to COVID-19, 4) isolation from friends and acquaintances, 5) disorganized pattern of daily life, 6) worse financial situation, and 7) no availability of consultations regarding health, life, and finances. These findings suggest that mental health measures for university students need to be designed specific to each course.

Objective: This study aims to evaluate the long-term impact of living in postdisaster prefabricated temporary housing on social interaction activities and mental health status., Methods: A total of 917 adult residents in a coastal town, whose residences were destroyed by the tsunami caused by the Great East Japan Earthquake (GEJE), were enrolled for the assessment held 5 y after the disaster. They answered questions about their experience and consequence of living in prefabricated temporary housing after the disaster. Their present scores on 5 types of self-reported measures regarding the psychosocial or psychiatric status and their present and recalled social interaction activities were cross-sectionally collected., Results: A total of 587 (64.0%) participants had a history of living in prefabricated temporary housing, while the other 330 (36.0%) had not. The prevalence of social interaction activities significantly decreased after the GEJE. However, the experience of living in prefabricated temporary housing did not adversely affect the subsequent social interaction activities or mental conditions of the participants 5 y after the disaster., Conclusions: Living in postdisaster prefabricated temporary housing may not negatively impact subsequent psychosocial conditions or social interaction activities 5 y later.

After the Great East Japan Earthquake, Tohoku University began to provide mental health services during the acute phase of the disaster in cooperation with Shichigahama Town, one of the municipalities located in the coastal area of the Miyagi Prefecture that was severely damaged by the earthquake and tsunami; it continued to be providing long-term mental health activities, incorporating annual surveys for affected residents in the town for 10 years. Ten years of combination of surveys and outreach activities first depicted detailed longitudinal alterations in the mental health conditions of communities affected by a catastrophe. While posttraumatic stress reaction had recovered year by year after the year following the Great East Japan Earthquake, recovery from psychological distress retreated between 2014 and 2017, probably due to the relocation from temporal to eternal housing conditions. The annual cycles of assessment and provision of mental health support and promotion activities continued for 10 years can be an initial model for evidence-based, long-term post-disaster mental health and psychosocial support for the affected communities. Data regarding subsequent disasters should be collected in comparable ways, in order to improve the accuracy and usefulness of the accumulated data for planning and providing evidence-based post-disaster mental health and psychosocial support.

Post-disaster mental health and psychosocial support have drawn attention in Japan after the 1995 Great Hanshin-Awaji Earthquake, with mental health care centers for the affected communities being organized. After the catastrophe, a reconstruction budget was allocated to organize mental health care centers to provide psychosocial support for communities affected by the 2007 Chūetsu offshore earthquake, the 2011 Great East Japan Earthquake, and the 2016 Kumamoto Earthquake. There were several major improvements in post-disaster mental health measures after the Great East Japan Earthquake. The Disaster Psychiatric Assistance Team system was organized after the earthquake to orchestrate disaster response related to the psychiatric health system and mental health of the affected communities. Special mental health care efforts were drawn to the communities affected by the nuclear power plant accident through Chemical, Biological, Radiological, Nuclear, and high yield Explosives, being succeeded by measures against the coronavirus pandemic. As another new movement after the Great East Japan Earthquake, the number of surveys involving communities affected by disasters has soared. More than 10 times the number of scientific publications were made in English during the decade following the Great East Japan Earthquake, compared with the previous decades. In this review, we examined the results and issues acquired in the 10 years since the Great East Japan Earthquake, proposing evidence-based disaster psychiatry as the direction of future mental health measures related to emergency preparedness and response., (© 2022 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.)

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including "happy," as a positive emotion and "anxiety," "sad," "frustrated," as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Ono, Warita, Shoji, Nakagawa, Usukura, Yu, Takahashi, Ichiji, Sugita, Kobayashi, Kikuchi, Kunii, Murakami, Ishikuro, Obara, Nakamura, Nagami, Takai, Ogishima, Sugawara, Hoshiai, Saito, Tamiya, Fuse, Kuriyama, Yamamoto, Yaegashi, Homma and Tomita.)

After disasters, people are often forced to reconstruct or move to new residences. This study aimed to reveal the association between the types of reconstructed residences and psychosocial or psychiatric conditions among the population. A total of 1071 adult residents in a coastal town, whose houses were destroyed by the tsunami caused by the Great East Japan Earthquake, enrolled in the study five years after the disaster. The type of reconstructed post-disaster residences (reconstructed on the same site/disaster-recovery public condominium/mass-translocation to higher ground/privately moving to remote areas) and the current psychosocial indicators were investigated. The results revealed that individuals living in public condominiums showed significantly worse scores on the Lubben Social Network Scale-6 (p < 0.0001) and the Center for Epidemiologic Studies Depression Scale (p < 0.0001), and slightly worse scores on the Kessler Psychological Distress Scale (p = 0.035) and the Impact of Event Scale-Revised (p = 0.028). Lower psychosocial indicator scores in the public condominium group were more remarkable in younger adults aged < 65 years. Insomnia evaluated using the Athens Insomnia Scale was not different among the four residential types. In summary, residents moving into disaster-recovery public condominiums are likely to have less social interaction, be more depressed, and may need additional interventions., (© 2021. The Author(s).)

Objective: This study aims to evaluate the long-term impact of living in post-disaster prefabricated temporary housing on social interaction activities and mental health status., Methods: A total of 917 adult residents in a coastal town, whose residences were destroyed by the tsunami caused by the Great East Japan Earthquake (GEJE), were enrolled for the assessment held five years after the disaster. They answered questions about their experience and consequence of living in prefabricated temporary housing after the disaster. Their present scores on five types of self-reported measures regarding the psychosocial or psychiatric status and their present and recalled social interaction activities were cross-sectionally collected., Results: A total of 587 (64.0%) participants had a history of living in prefabricated temporary housing, while the other 330 (36.0%) had not. The prevalence of social interaction activities significantly decreased after the GEJE. However, the experience of living in prefabricated temporary housing did not adversely affect the subsequent social interaction activities or mental conditions of the participants five years after the disaster., Conclusions: Living in post-disaster prefabricated temporary housing may not negatively impact subsequent psychosocial conditions or social interaction activities five years later.

The Great East Japan Earthquake devasted the old community in coastal areas characterized by primary industry. The number of unemployed people increased from 150,000 to 190,000 after the earthquake. All of the adult residents of Shichigahama (18 years old or older), located in the coastal area of the Miyagi prefecture, whose houses were totally or majorly damaged, were recruited for a survey conducted in October 2011. All of the residents who responded with written informed consent were included in this study. Among 904 individuals who had a job before the Great East Japan Earthquake, 19% became unemployed. Concerning gender and age, 9% of young men, 34% of elderly men, 21% of young women, and 49% of elderly women became unemployed. Concerning the type of industry, 38%, 15%, and 16% of people who had belonged to the primary, secondary, and tertiary industries, respectively, before the disaster became unemployed. Those who became unemployed exhibited a significantly higher risk of insomnia compared to those who maintained jobs. The study pointed out the severe impact of the Great East Japan Earthquake on populations who had belonged to the primary industry, especially among elderly women, and its effect on sleep conditions.

As electromotility may arise from a conformational change of the molecules' 'protein motors', which might be distributed along the outer hair cell (OHC) lateral wall, the force generated by the OHC electromotility would be related not only to the conformational change of the protein motors but also to the mechanical properties of the lateral wall. Therefore, a detailed understanding of the mechanical properties of the OHC lateral wall is important. In our previous reports, to understand the difference in the stiffness along the cell axis, the local deformation of the OHC in response to hypotonic stimulation was analyzed by measuring the displacement of microspheres attached randomly to the cell lateral wall, and the distribution of Young's modulus along the cell axis was obtained using the contact mode of an atomic force microscope (AFM). These investigations revealed that the stiffness of the cell in the apical region was greater than that in other regions where the stiffness is constant. In this study, the ultrastructure of the OHC lateral wall was investigated with the oscillation imaging mode of the AFM (Tapping Mode), and the relationship between the stiffness along the cell axis and the ultrastructure that was observed by the AFM imaging was analyzed. From the analysis, it was concluded that the circumferential filaments observed in the tapping mode AFM are actins which are part of the cortical lattice, and that the difference between the intervals of the circumferential filaments in the apical region and those in other regions is one factor that causes the high stiffness in the apical region.