"KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals.

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

SIK3-HDAC4 in the suprachiasmatic nucleus regulates the timing of arousal at the dark onset and circadian period in mice.

Mammals exhibit circadian cycles of sleep and wakefulness under the control of the suprachiasmatic nucleus (SCN), such as the strong arousal phase-locked to the beginning of the dark phase in laboratory mice. Here, we demonstrate that salt-inducible kinase 3 (SIK3) deficiency in gamma-aminobutyric acid (GABA)-ergic neurons or neuromedin S (NMS)-producing neurons delayed the arousal peak phase and lengthened the behavioral circadian cycle under both 12-h light:12-h dark condition (LD) and constant dark condition (DD) without changing daily sleep amounts. In contrast, the induction of a gain-of-function mutant allele of Sik3 in GABAergic neurons exhibited advanced activity onset and a shorter circadian period. Loss of SIK3 in arginine vasopressin (AVP)-producing neurons lengthened the circadian cycle, but the arousal peak phase was similar to that in control mice. Heterozygous deficiency of histone deacetylase (HDAC) 4, a SIK3 substrate, shortened the circadian cycle, whereas mice with HDAC4 S245A, which is resistant to phosphorylation by SIK3, delayed the arousal peak phase. Phase-delayed core clock gene expressions were detected in the liver of mice lacking SIK3 in GABAergic neurons. These results suggest that the SIK3-HDAC4 pathway regulates the circadian period length and the timing of arousal through NMS-positive neurons in the SCN.

Impact of airline network on the global importation risk of mpox, 2022.

From 1 January 2022 to 4 September 2022, a total of 53 996 mpox cases were confirmed globally. Cases are predominantly concentrated in Europe and the Americas, while other regions are also continuously observing imported cases. This study aimed to estimate the potential global risk of mpox importation and consider hypothetical scenarios of travel restrictions by varying passenger volumes (PVs) via airline travel network. PV data for the airline network, and the time of first confirmed mpox case for a total of 1680 airports in 176 countries (and territories) were extracted from publicly available data sources. A survival analysis technique in which the hazard function was a function of effective distance was utilised to estimate the importation risk. The arrival time ranged from 9 to 48 days since the first case was identified in the UK on 6 May 2022. The estimated risk of importation showed that regardless of the geographic region, most locations will have an intensified importation risk by 31 December 2022. Travel restrictions scenarios had a minor impact on the global airline importation risk against mpox, highlighting the importance to enhance local capacities for the identification of mpox and to be prepared to carry out contact tracing and isolation.

Distinct phosphorylation states of mammalian CaMKIIß control the induction and maintenance of sleep.

The reduced sleep duration previously observed in Camk2b knockout mice revealed a role for Ca2+/calmodulin-dependent protein kinase II (CaMKII)ß as a sleep-promoting kinase. However, the underlying mechanism by which CaMKIIß supports sleep regulation is largely unknown. Here, we demonstrate that activation or inhibition of CaMKIIß can increase or decrease sleep duration in mice by almost 2-fold, supporting the role of CaMKIIß as a core sleep regulator in mammals. Importantly, we show that this sleep regulation depends on the kinase activity of CaMKIIß. A CaMKIIß mutant mimicking the constitutive-active (auto)phosphorylation state promotes the transition from awake state to sleep state, while mutants mimicking subsequent multisite (auto)phosphorylation states suppress the transition from sleep state to awake state. These results suggest that the phosphorylation states of CaMKIIß differently control sleep induction and maintenance processes, leading us to propose a "phosphorylation hypothesis of sleep" for the molecular control of sleep in mammals.

The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes.

SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.

A design principle of spindle oscillations in mammalian sleep.

Neural oscillations are mainly regulated by molecular mechanisms and network connectivity of neurons. Large-scale simulations of neuronal networks have driven the population-level understanding of neural oscillations. However, cell-intrinsic mechanisms, especially a design principle, of neural oscillations remain largely elusive. Herein, we developed a minimal, Hodgkin-Huxley-type model of groups of neurons to investigate molecular mechanisms underlying spindle oscillation, which is synchronized oscillatory activity predominantly observed during mammalian sleep. We discovered that slowly inactivating potassium channels played an essential role in characterizing the firing pattern. The detailed analysis of the minimal model revealed that leak sodium and potassium channels, which controlled passive properties of the fast variable (i.e., membrane potential), competitively regulated the base value and time constant of the slow variable (i.e., cytosolic calcium concentration). Consequently, we propose a theoretical design principle of spindle oscillations that may explain intracellular mechanisms behind the flexible control over oscillation density and calcium setpoint.

A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.

Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep-wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.

Identification of optimum combinations of media channels for approaching COVID-19 vaccine unsure and unwilling groups in Japan.

BACKGROUND: Optimizing media campaigns for those who were unsure or unwilling to take coronavirus disease (COVID-19) vaccines is required urgently to effectively present public health messages aimed at increasing vaccination coverage. We propose a novel framework for selecting tailor-made media channels and their combinations for this task. METHODS: An online survey was conducted in Japan during February to March, 2021, with 30,053 participants. In addition to their sociodemographic characteristics, it asked the attitude toward vaccination and information sources (i.e., media channels) for COVID-19 issues. Multinomial logic regression was fitted to estimate the combinations of the media channels and their odds ratio (OR) associated with vaccination attitudes. FINDINGS: The proportion of respondents who were unsure or unwilling to take the vaccination was skewed toward younger generation: 58.1% were aged under 35, while 28.1% were 65 years or older. Media channels such as "Non-medical and Non-TV" and "Non-medical and Non-government" were associated with the unsure group: OR (95% Confidence intervals, (CI)) = 1.75 (1.62, 1.89) and 1.53 (1.44, 1.62), respectively. In addition, media channels such as "Newspapers or the Novel Coronavirus Expert Meeting", "Medical or Local government", and "Non-TV" were associated with the unwilling group: OR (95% CI) were 2.00 (1.47, 2.75), 3.13 (2.58, 3.81), and 2.25 (1.84, 2.77), respectively. INTERPRETATION: To effectively approach COVID-19 vaccine unsure and unwilling groups, generation-specific online and offline media campaigns should be optimized to the type of vaccine attitude. FUNDING: Funded by the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009) and the Japan Agency for Medical Research and Development (AMED) (JP20fk0108535).

Reduced mortality during the COVID-19 outbreak in Japan, 2020: a two-stage interrupted time-series design.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global health burden. This study aims to estimate the all-cause excess mortality occurring in the COVID-19 outbreak in Japan, 2020, by sex and age group. METHODS: Daily time series of mortality for the period January 2015-December 2020 in all 47 prefectures of Japan were obtained from the Ministry of Health, Labour and Welfare, Japan. A two-stage interrupted time-series design was used to calculate excess mortality. In the first stage, we estimated excess mortality by prefecture using quasi-Poisson regression models in combination with distributed lag non-linear models, adjusting for seasonal and long-term variations, weather conditions and influenza activity. In the second stage, we used a random-effects multivariate meta-analysis to synthesize prefecture-specific estimates at the nationwide level. RESULTS: In 2020, we estimated an all-cause excess mortality of -20 982 deaths [95% empirical confidence intervals (eCI): -38 367 to -5472] in Japan, which corresponded to a percentage excess of -1.7% (95% eCI: -3.1 to -0.5) relative to the expected value. Reduced deaths were observed for both sexes and in all age groups except those aged <60 and 70-79 years. CONCLUSIONS: All-cause mortality during the COVID-19 outbreak in Japan in 2020 was decreased compared with a historical baseline. Further evaluation of cause-specific excess mortality is warranted.

Reasons for being unsure or unwilling regarding intention to take COVID-19 vaccine among Japanese people: A large cross-sectional national survey.

BACKGROUND: Identifying and understanding reasons for being unsure or unwilling regarding intention to be vaccinated against coronavirus disease (COVID-19) may help to inform future public health messages aimed at increasing vaccination coverage. We analyzed a broad array of individual's psychological dispositions with regard to decision-making about COVID-19 vaccination in Japan. METHODS: A nationally representative cross-sectional web survey was conducted with 30053 Japanese adults aged 20 years or older at the end of February 2021. In addition to the question on the individual's intention to be vaccinated against COVID-19, respondents were asked about their sociodemographic, health-related, and psychological characteristics as well as information sources about COVID-19 and their levels of trust. Also, those who responded 'not sure' or 'no' regarding intention to take COVID-19 vaccine were asked why. Multinomial logistic regression with sparse group Lasso (Least Absolute Shrinkage and Selection Operator) penalty was used to compute adjusted odds ratios for factors associated with the intention (not sure/no versus yes). FINDINGS: The percentages of respondents who answered 'not sure' or 'no' regarding intention to be vaccinated against COVID-19 vaccine were 32.9% and 11.0%, respectively. After adjusting for covariates, the perceived risks of COVID-19, perceived risk of a COVID-19 vaccine, perceived benefits of a COVID-19 vaccine, trust in scientists and public authorities, and the belief that healthcare workers should be vaccinated were significantly associated with vaccination intention. Several sources of information about COVID-19 were also significantly associated with vaccination intention, including physicians, nurses, and television, medical information sites with lower odds of being unsure or unwilling, and internet news sites, YouTube, family members, and scientists and researchers with higher odds. The higher the level of trust in television as a source of COVID-19 information, the higher the odds of responding 'not sure' (odds ratio 1.11, 95% confidence interval 1.01-1.21). We also demonstrated that many respondents presented concerns about the side effects and safety of a COVID-19 vaccine as a major reason for being unsure or unwilling. To decide whether or not to get the vaccine, many respondents requested more information about the compatibilities between the vaccine and their personal health conditions, whether other people had been vaccinated, the effectiveness of vaccines against variants, and doctors' recommendations. INTERPRETATION: Our findings suggest that public health messaging based on the sociodemographic and psychological characteristics of those who are unsure or unwilling regarding intention to be vaccinated against COVID-19 vaccine may help to increase vaccine uptake amongst this population. FUNDING: The present work was supported in part by a grant from the Ministry of Health, Labour and Welfare of Japan (H29-Gantaisaku-ippan-009).

Effect of emergency declaration on mental health during the COVID-19 pandemic in Japan: A social network service-based difference-in-differences approach.

Strong lockdowns to control COVID-19 pandemic have been enforced globally and strongly restricted social activities with consequent negative effects on mental health. Japan has effectively implemented a unique voluntary policy to control COVID-19, but the mental health impact of the policy has not been examined on a large scale. In this study, we examined the effect of the first declaration on the mental health of affected residents. We used population-level questionnaire data of 17,400 people living under the state of emergency and 9208 who were not through a social-networking-service app and applied a difference-in-differences regression model to estimate the causal effect of the declaration of the state of emergency on psychological wellbeing, stratified by job category. No statistically significant effect of the declaration was observed among all job categories. This suggests that residents' psychological situation has gradually changed, possibly influenced by other factors such as the surrounding environment, rather than the declaration itself. Given that Japan has a unique policy to control COVID-19 instead of a strict lockdown, our results showed the Japanese-style policy may serve as a form of harm reduction strategy, to control the epidemic with minimal psychological harm, and enable a policy that balances disease control and mental health. Caution is necessary that this study used self-reported data from a limited time period before and after the first declaration in April 2020.

Trends in Healthcare Access in Japan during the First Wave of the COVID-19 Pandemic, up to June 2020.

We evaluated the impact of the new coronavirus disease (COVID-19) on healthcare access in Japan in terms of the number of outpatients and hospitalized patients as well as the length of hospital stays, during the first wave of the pandemic, up to June 2020. This observational study evaluated the monthly average number of outpatients per day at hospitals, the average number of hospitalized patients per day, and the average length of hospital stays per patient, from December 2010 to June 2020, using the hospital reports data, which are open aggregated data on the utilization of hospitals from the Ministry of Health, Labour and Welfare. These numbers were compared with those from the same period of previous years, using a quasi-Poisson regression model. We found a nationwide decrease in the number of outpatients in general hospitals and hospitalized patients, particularly in long-term care beds in Japan, as well as the excess length of hospital stays among psychiatric care patients during the first wave of the COVID-19. This limited access to healthcare demonstrated the importance of the long-term health monitoring of vulnerable populations and the need for urgent management support to healthcare facilities in preparation for possible prolonged pandemics in the future.

Whole-organ analysis of TGF-ß-mediated remodelling of the tumour microenvironment by tissue clearing.

Tissue clearing is one of the most powerful strategies for a comprehensive analysis of disease progression. Here, we established an integrated pipeline that combines tissue clearing, 3D imaging, and machine learning and applied to a mouse tumour model of experimental lung metastasis using human lung adenocarcinoma A549 cells. This pipeline provided the spatial information of the tumour microenvironment. We further explored the role of transforming growth factor-ß (TGF-ß) in cancer metastasis. TGF-ß-stimulated cancer cells enhanced metastatic colonization of unstimulated-cancer cells in vivo when both cells were mixed. RNA-sequencing analysis showed that expression of the genes related to coagulation and inflammation were up-regulated in TGF-ß-stimulated cancer cells. Further, whole-organ analysis revealed accumulation of platelets or macrophages with TGF-ß-stimulated cancer cells, suggesting that TGF-ß might promote remodelling of the tumour microenvironment, enhancing the colonization of cancer cells. Hence, our integrated pipeline for 3D profiling will help the understanding of the tumour microenvironment.

Excess All-Cause Deaths during Coronavirus Disease Pandemic, Japan, January-May 20201.

To provide insight into the mortality burden of coronavirus disease (COVID-19) in Japan, we estimated the excess all-cause deaths for each week during the pandemic, January-May 2020, by prefecture and age group. We applied quasi-Poisson regression models to vital statistics data. Excess deaths were expressed as the range of differences between the observed and expected number of all-cause deaths and the 95% upper bound of the 1-sided prediction interval. A total of 208-4,322 all-cause excess deaths at the national level indicated a 0.03%-0.72% excess in the observed number of deaths. Prefecture and age structure consistency between the reported COVID-19 deaths and our estimates was weak, suggesting the need to use cause-specific analyses to distinguish between direct and indirect consequences of COVID-19.

Impact of domestic travel restrictions on transmission of COVID-19 infection using public transportation network approach.

The international spread of COVID-19 infection has attracted global attention, but the impact of local or domestic travel restriction on public transportation network remains unclear. Passenger volume data for the domestic public transportation network in Japan and the time at which the first confirmed COVID-19 case was observed in each prefecture were extracted from public data sources. A survival approach in which a hazard was modeled as a function of the closeness centrality on the network was utilized to estimate the risk of importation of COVID-19 in each prefecture. A total of 46 prefectures with imported cases were identified. Hypothetical scenario analyses indicated that both strategies of locking down the metropolitan areas and restricting domestic airline travel would be equally effective in reducing the risk of importation of COVID-19. While caution is necessary that the data were limited to June 2020 when the pandemic was in its initial stage and that no other virus spreading routes have been considered, domestic travel restrictions were effective to prevent the spread of COVID-19 on public transportation network in Japan. Instead of lockdown that might seriously damage the economy, milder travel restrictions could have the similar impact on controlling the domestic transmission of COVID-19.

Assessing the regional impact of Japan's COVID-19 state of emergency declaration: a population-level observational study using social networking services.

OBJECTIVE: On 7 April 2020, the Japanese government declared a state of emergency in response to the novel coronavirus outbreak. To estimate the impact of the declaration on regional cities with low numbers of COVID-19 cases, large-scale surveillance to capture the current epidemiological situation of COVID-19 was urgently conducted in this study. DESIGN: Cohort study. SETTING: Social networking service (SNS)-based online survey conducted in five prefectures of Japan: Tottori, Kagawa, Shimane, Tokushima and Okayama. PARTICIPANTS: 127 121 participants from the five prefectures surveyed between 24 March and 5 May 2020. INTERVENTIONS: An SNS-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was launched. It asks questions regarding postcode, personal information, preventive actions, and current and past symptoms related to COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES: Empirical Bayes estimates of age-sex-standardised incidence rate (EBSIR) of symptoms and the spatial correlation between the number of those who reported having symptoms and the number of COVID-19 cases were examined to identify the geographical distribution of symptoms in the five prefectures. RESULTS: 97.8% of participants had no subjective symptoms. We identified several geographical clusters of fever with significant spatial correlation (r=0.67) with the number of confirmed COVID-19 cases, especially in the urban centres of prefectural capital cities. CONCLUSIONS: Given that there are still several high-risk areas measured by EBSIR, careful discussion on which areas should be reopened at the end of the state of emergency is urgently required using real-time SNS system to monitor the nationwide epidemic.

CUBIC-Cloud provides an integrative computational framework toward community-driven whole-mouse-brain mapping.

Recent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution. With the expansion of this experimental technique, however, a scalable and easy-to-use computational tool is in demand to effectively analyze and integrate whole-brain mapping datasets. To that end, here we present CUBIC-Cloud, a cloud-based framework to quantify, visualize, and integrate mouse brain data. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analyses, and publish the results. We demonstrate the generality of CUBIC-Cloud by a variety of applications. First, we investigated the brain-wide distribution of five cell types. Second, we quantified Aß plaque deposition in Alzheimer's disease model mouse brains. Third, we reconstructed a neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudotyped rabies virus. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping.

Travel restrictions and SARS-CoV-2 transmission: an effective distance approach to estimate impact.

OBJECTIVE: To estimate the effect of airline travel restrictions on the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) importation. METHODS: We extracted passenger volume data for the entire global airline network, as well as the dates of the implementation of travel restrictions and the observation of the first case of coronavirus disease (COVID-19) in each country or territory, from publicly available sources. We calculated effective distance between every airport and the city of Wuhan, China. We modelled the risk of SARS-CoV-2 importation by estimating survival probability, expressing median time of importation as a function of effective distance. We calculated the relative change in importation risk under three different hypothetical scenarios that all resulted in different passenger volumes. FINDINGS: We identified 28 countries with imported cases of COVID-19 as at 26 February 2020. The arrival time of the virus at these countries ranged from 39 to 80 days since identification of the first case in Wuhan. Our analysis of relative change in risk indicated that strategies of reducing global passenger volume and imposing travel restrictions at a further 10 hub airports would be equally effective in reducing the risk of importation of SARS-CoV-2; however, this reduction is very limited with a close-to-zero median relative change in risk. CONCLUSION: The hypothetical variations in observed travel restrictions were not sufficient to prevent the global spread of SARS-CoV-2; further research should also consider travel by land and sea. Our study highlights the importance of strengthening local capacities for disease monitoring and control.

Early SNS-Based Monitoring System for the COVID-19 Outbreak in Japan: A Population-Level Observational Study.

BACKGROUND: The World Health Organization declared the novel coronavirus outbreak (COVID-19) to be a pandemic on March 11, 2020. Large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan would improve preparation for and prevention of a massive outbreak. METHODS: A chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was developed using the LINE app to evaluate the current Japanese epidemiological situation. LINE users could participate in the system either though a QR code page in the prefectures' websites or a banner at the top of the LINE app screen. COOPERA asked participants questions regarding personal information, preventive actions, and non-specific symptoms related to COVID-19 and their duration. We calculated daily cross correlation functions between the reported number of infected cases confirmed using polymerase chain reaction and the symptom-positive group captured by COOPERA. RESULTS: We analyzed 206,218 participants from three prefectures reported between March 5 and 30, 2020. The mean age of participants was 44.2 (standard deviation, 13.2) years. No symptoms were reported by 96.93% of participants, but there was a significantly positive correlation between the reported number of COVID-19 cases and self-reported fevers, suggesting that massive monitoring of fever might help to estimate the scale of the COVID-19 epidemic in real time. CONCLUSIONS: COOPERA is the first real-time system being used to monitor trends in COVID-19 in Japan and provides useful insights to assist political decisions to tackle the epidemic.