G0S2 regulates innate immunity in Kawasaki disease via lncRNA HSD11B1-AS1

Background Kawasaki disease (KD) is a systemic vasculitis that is currently the most common cause of acquired heart disease in children. However, its etiology remains unknown. Long non-coding RNAs (lncRNAs) contribute to the pathophysiology of various diseases. Few studies have reported the role of lncRNAs in KD inflammation;thus, we investigated the role of lncRNA in KD inflammation. Methods A total of 50 patients with KD (median age, 19 months;29 males and 21 females) were enrolled. We conducted cap analysis gene expression sequencing to determine differentially expressed genes in monocytes of the peripheral blood of the subjects. Results About 21 candidate lncRNA transcripts were identified. The analyses of transcriptome and gene ontology revealed that the immune system was involved in KD. Among these genes, G0/G1 switch gene 2 (G0S2) and its antisense lncRNA, HSD11B1-AS1, were upregulated during the acute phase of KD (P < 0.0001 and <0.0001, respectively). Moreover, G0S2 increased when lipopolysaccharides induced inflammation in THP-1 monocytes, and silencing of G0S2 suppressed the expression of HSD11B1-AS1 and tumor necrosis factor-α. Conclusions This study uncovered the crucial role of lncRNAs in innate immunity in acute KD. LncRNA may be a novel target for the diagnosis of KD. Impact This study revealed the whole aspect of the gene expression profile of monocytes of patients with Kawasaki disease (KD) using cap analysis gene expression sequencing and identified KD-specific molecules: G0/G1 switch gene 2 (G0S2) and long non-coding RNA (lncRNA) HSD11B1-AS1. We demonstrated that G0S2 and its antisense HSD11B1-AS1 were associated with inflammation of innate immunity in KD. lncRNA may be a novel key target for the diagnosis of patients with KD.

The Effect of the Imacoco Care Psychoeducation Website on Improving Psychological Distress Among Workers During the COVID-19 Pandemic: Randomized Controlled Trial.

BACKGROUND: The prolonged COVID-19 pandemic has affected mental health among workers. Psychoeducational intervention via a website could be effective for primary prevention of mental illness among workers in the current COVID-19 pandemic. OBJECTIVE: The aim of this randomized controlled trial is to examine the effect of a newly developed online psychoeducational website named Imacoco Care on reducing psychological distress and fear about COVID-19 infection among workers. METHODS: Participants in the study were recruited from registered members of a web survey company in Japan. Participants who fulfilled the eligibility criteria were randomly allocated to the intervention or control group. Participants in the intervention group were invited to access the Imacoco Care program within a month after the baseline survey. The Kessler Psychological Distress Scale (K6) and the Fear of COVID-19 Scale (FCV-19S) scores were obtained at baseline and at 1- and 3-month follow-ups. RESULTS: A total of 1200 workers were randomly allocated to the intervention and control groups (n=600 [50%] per group). The Imacoco Care intervention group showed a significant favorable effect on K6 scores (P=.03) with a small effect size (ES; Cohen d=-0.14) and an adverse effect on FCV-19S scores (P=.01) with a small ES (Cohen d=0.16) at 3-month follow-up. In the per protocol analysis (including only participants who had read the Imacoco Care content at least 1 time), the Imacoco Care intervention group also showed a significant favorable effect on reducing K6 scores (P=.03), while an adverse effect on FCV-19S scores was not significant (P=.06) in the intervention group at 3-month follow-up. CONCLUSIONS: A web-based psychoeducation approach may be effective for improving psychological distress among workers; however, it may be important not only to distribute information but also to encourage active engagement with the content of the program to prevent adverse effects of psychoeducational intervention. TRIAL REGISTRATION: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) UMIN000042556; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000048548.

Effectiveness of an Internet-Based Machine-Guided Stress Management Program Based on Cognitive Behavioral Therapy for Improving Depression Among Workers: Protocol for a Randomized Controlled Trial.

BACKGROUND: The effect of an unguided internet-based cognitive behavioral therapy (iCBT) stress management program on depression may be enhanced by applying artificial intelligence (AI) technologies to guide participants adopting the program. OBJECTIVE: The aim of this study is to describe a research protocol to investigate the effect of a newly developed iCBT stress management program adopting AI technologies on improving depression among healthy workers during the COVID-19 pandemic. METHODS: This study is a two-arm, parallel, randomized controlled trial. Participants (N=1400) will be recruited, and those who meet the inclusion criteria will be randomly allocated to the intervention or control (treatment as usual) group. A 6-week, six-module, internet-based stress management program, SMART-CBT, has been developed that includes machine-guided exercises to help participants acquire CBT skills, and it applies machine learning and deep learning technologies. The intervention group will participate in the program for 10 weeks. The primary outcome, depression, will be measured using the Beck Depression Inventory II at baseline and 3- and 6-month follow-ups. A mixed model repeated measures analysis will be used to test the intervention effect (group × time interactions) in the total sample (universal prevention) on an intention-to-treat basis. RESULTS: The study was at the stage of recruitment of participants at the time of submission. The data analysis related to the primary outcome will start in January 2022, and the results might be published in 2022 or 2023. CONCLUSIONS: This is the first study to investigate the effectiveness of a fully automated machine-guided iCBT program for improving subthreshold depression among workers using a randomized controlled trial design. The study will explore the potential of a machine-guided stress management program that can be disseminated online to a large number of workers with minimal cost in the post-COVID-19 era. TRIAL REGISTRATION: UMIN Clinical Trials Registry（UMIN-CTR) UMIN000043897; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000050125. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30305.

Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan.

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.