The Psychological Impact of ‘Mild Lockdown’ in Japan during the COVID-19 Pandemic : A Nationwide Survey under a Declared State of Emergency

This study examined the psychological distress caused by non-coercive lockdown (mild lockdown) in Japan. An online survey was conducted with 11,333 people (52.4% females; mean age = 46.3 ± 14.6 years, range = 18–89 years) during the mild lockdown in the seven prefectures most affected by COVID-19 infection. Over one-third (36.6%) of participants experienced mild-to-moderate psychological distress (Kessler Psychological Distress Scale [K6] score 5–12), while 11.5% reported serious psychological distress (K6 score ≥ 13). The estimated prevalence of depression (Patient Health Questionnaire-9 score ≥ 10) was 17.9%. Regarding the distribution of K6 scores, the proportion of those with psychological distress in this study was significantly higher when compared with the previous national survey data from 2010, 2013, 2016, and 2019. Healthcare workers, those with a history of treatment for mental illness, and younger participants (aged 18–19 or 20–39 years) showed particularly high levels of psychological distress. Psychological distress severity was influenced by specific interactional structures of risk factors: high loneliness, poor interpersonal relationships, COVID-19-related sleeplessness and anxiety, deterioration of household economy, and work and academic difficulties. Even when non-coercive lockdowns are implemented, people’s mental health should be considered, and policies to prevent mental health deterioration are needed. Cross-disciplinary public–private sector efforts tailored to each individual’s problem structure are important to address the mental health issues arising from lockdown

Empirical Bayesian significance measure of neuronal spike response

Background: Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information. Results: In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method's performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors. Conclusions: The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a neuron pair is small because of growing size of observed network

Regulation of Antitumor Immune Responses by the IL-12 Family Cytokines, IL-12, IL-23, and IL-27

The interleukin (IL)-12 family, which is composed of heterodimeric cytokines including IL-12, IL-23, and IL-27, is produced by antigen-presenting cells such as macrophages and dendritic cells and plays critical roles in the regulation of helper T (Th) cell differentiation. IL-12 induces IFN-γ production by NK and T cells and differentiation to Th1 cells. IL-23 induces IL-17 production by memory T cells and expands and maintains inflammatory Th17 cells. IL-27 induces the early Th1 differentiation and generation of IL-10-producing regulatory T cells. In addition, these cytokines induce distinct immune responses to tumors. IL-12 activates signal transducers and activator of transcription (STAT)4 and enhances antitumor cellular immunity through interferon (IFN)-γ production. IL-27 activates STAT1, as does IFN-γ and STAT3 as well, and enhances antitumor immunity by augmenting cellular and humoral immunities. In contrast, although exogenously overexpressed IL-23 enhances antitumor immunity via memory T cells, endogenous IL-23 promotes protumor immunity through STAT3 activation by inducing inflammatory responses including IL-17 production

A Pivotal Role for Interleukin-27 in CD8+ T Cell Functions and Generation of Cytotoxic T Lymphocytes

Cytotoxic T lymphocytes (CTLs) play a critical role in the control of various cancers and infections, and therefore the molecular mechanisms of CTL generation are a critical issue in designing antitumor immunotherapy and vaccines which augment the development of functional and long-lasting memory CTLs. Interleukin (IL)-27, a member of the IL-6/IL-12 heterodimeric cytokine family, acts on naive CD4+ T cells and plays pivotal roles as a proinflammatory cytokine to promote the early initiation of type-1 helper differentiation and also as an antiinflammatory cytokine to limit the T cell hyperactivity and production of pro-inflammatory cytokines. Recent studies revealed that IL-27 plays an important role in CD8+ T cells as well. Therefore, this article reviews current understanding of the role of IL-27 in CD8+ T cell functions and generation of CTLs

Data-driven categorization of postoperative delirium symptoms using unsupervised machine learning

BackgroundPhenotyping analysis that includes time course is useful for understanding the mechanisms and clinical management of postoperative delirium. However, postoperative delirium has not been fully phenotyped. Hypothesis-free categorization of heterogeneous symptoms may be useful for understanding the mechanisms underlying delirium, although evidence is currently lacking. Therefore, we aimed to explore the phenotypes of postoperative delirium following invasive cancer surgery using a data-driven approach with minimal prior knowledge.MethodsWe recruited patients who underwent elective invasive cancer resection. After surgery, participants completed 5 consecutive days of delirium assessments using the Delirium Rating Scale-Revised-98 (DRS-R-98) severity scale. We categorized 65 (13 questionnaire items/day × 5 days) dimensional DRS-R-98 scores using unsupervised machine learning (K-means clustering) to derive a small set of grouped features representing distinct symptoms across all participants. We then reapplied K-means clustering to this set of grouped features to delineate multiple clusters of delirium symptoms.ResultsParticipants were 286 patients, of whom 91 developed delirium defined according to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria. Following the first K-means clustering, we derived four grouped symptom features: (1) mixed motor, (2) cognitive and higher-order thinking domain with perceptual disturbance and thought content abnormalities, (3) acute and temporal response, and (4) sleep–wake cycle disturbance. Subsequent K-means clustering permitted classification of participants into seven subgroups: (i) cognitive and higher-order thinking domain dominant delirium, (ii) prolonged delirium, (iii) acute and brief delirium, (iv) subsyndromal delirium-enriched, (v) subsyndromal delirium-enriched with insomnia, (vi) insomnia, and (vii) fit.ConclusionWe found that patients who have undergone invasive cancer resection can be delineated using unsupervised machine learning into three delirium clusters, two subsyndromal delirium clusters, and an insomnia cluster. Validation of clusters and research into the pathophysiology underlying each cluster will help to elucidate the mechanisms of postoperative delirium after invasive cancer surgery