Left Atrial Function of COVID-19-recovered Patients During the Surge of Omicron Variants

Anxiety, depression, and insomnia among nurses during the full liberalization of COVID-19: A multicenter cross-sectional analysis of the high-income region in China (preprint)

Aims and objectives: This study demonstrates the impact of the full liberalization of COVID-19 on the psychological issues and the prevalence rate and associated factors of depressive symptoms, anxiety, and insomnia among frontline nurses. Background: It has been demonstrated that frontline nurses fighting against the epidemic were under great psychological stress. However, there is a lack of studies assessing the prevalence rates of anxiety, depression, and insomnia among frontline nurses after the full liberalization of COVID-19 in China. Design: Cross-sectional study. Methods: Of 1766 frontline nurses were invited to complete a self-reported online questionnaire by convenience sampling. The survey included six main sections: the Patient Health Questionnaire, the Generalized Anxiety Disorder Scale, the Insomnia Severity Index, the Perceived Stress Scale, sociodemographic information, and work information. Multiple logistic regression analyses were applied to identify the potential risk factors for psychological issues. Reporting of this research according to the STROBE checklist. Results: 90.83% of frontline nurses were infected with COVID-19, and 33.64% had to work while infected COVID-19. The overall prevalence of depressive symptoms, anxiety and insomnia among frontline nurses was 69.20%, 62.51%, and 76.78%, respectively. Multiple logistic analyses revealed that job satisfaction, attitude toward the current pandemic management, and perceived stress were associated with depressive symptoms, anxiety, and insomnia. Conclusions: This study demonstrated that the full liberalization of COVID-19 had a significant psychological impact on frontline nurses. Early detection of mental health issues and preventive and promotive interventions should be implemented according to the associated factors to improve mental health of nurses. Relevance to clinical practice: This study highlighted that nurses were suffering from varying degrees of depressive symptoms, anxiety, and insomnia, which needed early screening and preventive and promotive interventions for preventing a more serious psychological impact on frontline nurses.

The clinical and virological features of two children's coinfections with human adenovirus type 7 and human coronavirus-229E virus

Objective Human adenovirus (HAdV) coinfection with other respiratory viruses is common, but adenovirus infection combined with human coronavirus-229E (HCoV-229E) is very rare. Study design and setting Clinical manifestations, laboratory examinations, and disease severity were compared between three groups: one coinfected with HAdV-Ad7 and HCoV-229E, one infected only with adenovirus (mono-adenovirus), and one infected only with HCoV-229E (mono-HCoV-229E). Results From July to August 2019, there were 24 hospitalized children: two were coinfected with HAdV-Ad7 and HCoV-229E, and 21 were infected with a single adenovirus infection. Finally, one 14-year-old boy presented with a high fever, but tested negative for HAdV-Ad7 and HCoV-229E. Additionally, three adult asymptotic cases with HCoV-229E were screened. No significant difference in age was found in the coinfection and mono-adenovirus groups (11 vs. 8 years, p = 0.332). Both groups had the same incubation period (2.5 vs. 3 days, p = 0.8302), fever duration (2.5 vs. 2.9 days, p = 0.5062), and length of hospital stay (7 vs. 6.76 days, p = 0.640). No obvious differences were found in viral loads between the coinfection and mono-adenovirus groups (25.4 vs. 23.7, p = 0.570), or in the coinfection and mono-HCoV-229E groups (32.9 vs. 30.06, p = 0.067). All cases recovered and were discharged from the hospital. Conclusion HAdV-Ad7 and HCoV-229E coinfection in healthy children may not increase the clinical severity or prolong the clinical course. The specific interaction mechanism between the viruses requires further study.

Optimization, validation and analysis of a 2019-nCoV nucleic acid detection kit. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To optimize the sensitivity and specificity of a 2019-nCoV nucleic acid detection kit, so as to improve the positive detection rate and provide guidance for clinical use by comparison with different kits.

Service resource allocation problem in the IoT driven personalized healthcare information platform (preprint)

With real-time monitoring of the personalized healthcare condition, the IoT wearables collect the health data and transfer it to the healthcare information platform. The platform processes the data into healthcare recommendations and then delivers them to the users. The IoT structures in the personalized healthcare information service allows the users to engage in the loop in servitization more convenient in the COVID-19 pandemic. However, the uncertainty of the engagement behavior among the individual may result in inefficient of the service resource allocation. This paper seeks an efficient way to allocate the service resource by controlling the service capacity and pushing the service to the active users automatically. In this study, we propose a deep reinforcement learning method to solve the service resource allocation problem based on the proximal policy optimization (PPO) algorithm. Experimental results using the real world (open source) sport dataset reveal that our proposed proximal policy optimization adapts well to the users' changing behavior and with improved performance over fixed service resource policies.

Molecular basis of broad neutralization against SARS-CoV-2 variants including Omicron by a human antibody (preprint)

Omicron, a newly emerging SARS-CoV-2 variant, carried a large number of mutations in the spike protein leading to an unprecedented evasion from many neutralizing antibodies (nAbs). Here, we performed a head-to-head comparison of Omicron with other existing highly evasive variants in terms of their reduced sensitivities to antibodies, and found that Omicron variant is significantly more evasive than Beta and Mu variants. Of note, some key mutations occur in the conserved epitopes identified previously, especially in the binding sites of Class 4 nAbs, contributing to the increased Ab evasion. We also reported a broadly nAb (bnAb), VacW-209, which effectively neutralized all tested SARS-CoV-2 variants and even SARS-CoV. Finally, we determined six cryo-electron microscopy structures of VacW-209 complexed with the spike ectodomains of wild-type, Delta, Mu, C.1.2, Omicron, and SARS-CoV, and revealed the molecular basis of the broadly neutralizing activities of VacW-209 against SARS-CoV-2 variants. Overall, Omicron has once again raised the alarm over virus variation with significantly compromised neutralization. BnAbs targeting more conserved epitopes among variants will continue to play a key role in pandemic control and prevention. One sentence summaryStructural and functional analyses reveal that a human antibody named VacW-209 confers broad neutralization against SARS-CoV-2 variants including Omicron by recognizing a highly conserved epitope.

A Nonparametric Lack-of-Fit Test of Constant Regression in the Presence of Heteroscedastic Variances

We consider a k-nearest neighbor-based nonparametric lack-of-fit test of constant regression in presence of heteroscedastic variances. The asymptotic distribution of the test statistic is derived under the null and local alternatives for a fixed number of nearest neighbors. Advantages of our test compared to classical methods include: (1) The response variable can be discrete or continuous regardless of whether the conditional distribution is symmetric or not and can have variations depending on the predictor. This allows our test to have broad applicability to data from many practical fields;(2) this approach does not need nonlinear regression function estimation that often affects the power for moderate sample sizes;(3) our test statistic achieves the parametric standardizing rate, which gives more power than smoothing-based nonparametric methods for moderate sample sizes. Our numerical simulation shows that the proposed test is powerful and has noticeably better performance than some well known tests when the data were generated from high frequency alternatives or binary data. The test is illustrated with an application to gene expression data and an assessment of Richards growth curve fit to COVID-19 data. [ABSTRACT FROM AUTHOR] Copyright of Symmetry (20738994) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Assess the Impacts of Human Mobility Change on COVID-19 Dynamics in Arizona, U.S.: A Modeling Study Incorporating Google Community Mobility Reports (preprint)

In June 2020, Arizona, U.S., emerged as one of the world’s worst coronavirus disease 2019 (COVID-19) spots after the stay-at-home order was lifted in the middle of May. With the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three Arizona regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.Funding: N.Y. was partially supported by Nishihara Cultural Foundation. H.W. was partially supported by the National Natural Science Foundation (#1737861)Declaration of Interest: None to declare

Assess the impacts of human mobility change on COVID-19 dynamics in Arizona, U.S.: a modeling study incorporating Google Community Mobility Reports (preprint)

In June 2020, Arizona, U.S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May. However, with the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.

The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing (preprint)

Understanding the mechanism that leads to immune dysfunction induced by SARS-CoV2 virus is crucial to develop treatment for severe COVID-19. Here, using single cell RNA-seq, we characterized the peripheral blood mononuclear cells (PBMC) from uninfected controls and COVID-19 patients, and cells in paired broncho-alveolar lavage fluid (BALF). We found a close association of decreased dendritic cells (DC) and increased monocytes resembling myeloid-derived suppressor cells (MDSC) which correlated with lymphopenia and inflammation in the blood of severe COVID-19 patients. Those MDSC-like monocytes were immune-paralyzed. In contrast, monocyte-macrophages in BALFs of COVID-19 patients produced massive amounts of cytokines and chemokines, but secreted little interferons. The frequencies of peripheral T cells and NK cells were significantly decreased in severe COVID-19 patients, especially for innate-like T and various CD8+ T cell subsets, compared to health controls. In contrast, the proportions of various activated CD4+ T cell subsets, including Th1, Th2 and Th17-like cells were increased and more clonally expanded in severe COVID-19 patients. Patients peripheral T cells showed no sign of exhaustion or augmented cell death, whereas T cells in BALFs produced higher levels of IFNG, TNF, CCL4 and CCL5 etc. Paired TCR tracking indicated abundant recruitment of peripheral T cells to the patients lung. Together, this study comprehensively depicts how the immune cell landscape is perturbed in severe COVID-19.

Using A Partial Differential Equation with Google Mobility Data to Predict COVID-19 in Arizona (preprint)

The outbreak of COVID-19 disrupts the life of many people in the world. The state of Arizona in the U.S. emerges as one of the country's newest COVID-19 hot spots. Accurate forecasting for COVID-19 cases will help governments to implement necessary measures and convince more people to take personal precautions to combat the virus. It is difficult to accurately predict the COVID-19 cases due to many human factors involved. This paper aims to provide a forecasting model for COVID-19 cases with the help of human activity data from the Google Community Mobility Reports. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with the COVID-19 data from the New York Times at the county level in the state of Arizona in the U.S. The proposed model describes the combined effects of transboundary spread among county clusters in Arizona and human actives on the transmission of COVID-19. The results show that the prediction accuracy of this model is well acceptable (above 94\%). Furthermore, we study the effectiveness of personal precautions such as wearing face masks and practicing social distancing on COVID-19 cases at the local level. The localized analytical results can be used to help to slow the spread of COVID-19 in Arizona. To the best of our knowledge, this work is the first attempt to apply PDE models on COVID-19 prediction with the Google Community Mobility Reports.

Early viral clearance and antibody kinetics of COVID-19 among asymptomatic carriers (preprint)

BackgroundAsymptomatic carriers contribute to the spread of Coronavirus Disease 2019 (COVID-19), but their clinical characteristics, viral kinetics, and antibody responses remain unclear. MethodsA total of 56 COVID-19 patients without symptoms at admission and 19 age-matched symptomatic patients were enrolled. RNA of SARS-CoV-2 was tested using transcriptase quantitative PCR, and the total antibodies (Ab), IgG, IgA and IgM against the SARS-CoV-2 were tested using Chemiluminescence Microparticle Immuno Assay. ResultsAmong 56 patients without symptoms at admission, 33 cases displayed symptoms and 23 remained asymptomatic throughout the follow-up period. 43.8% of the asymptomatic carriers were children and none of the asymptomatic cases had recognizable changes in C-reactive protein or interleukin-6, except one 64-year-old patient. The initial threshold cycle value of nasopharyngeal SARS-CoV-2 in asymptomatic carriers was similar to that in pre-symptomatic and symptomatic patients, but the communicable period of asymptomatic carriers (9.63 days) was shorter than pre-symptomatic patients (13.6 days). There was no obvious differences of the seropositive conversion rate of total Ab, IgG, and IgA among the three groups, though the rates of IgM varied largely. The average peak IgG and IgM COI of asymptomatic cases was 3.5 and 0.8, respectively, which is also lower than those in symptomatic patients with peaked IgG and IgM COI of 4.5 and 2.4 (p <0.05). ConclusionYoung COVID-19 patients seem to be asymptomatic cases with early clearance of SARS-CoV-2 and low levels of IgM generation but high total Ab, IgG and IgA. Our findings provide empirical information for viral clearance and antibody kinetics of asymptomatic COVID-19 patients.

Exuberant elevation of IP-10, MCP-3 and IL-1ra during SARS-CoV-2 infection is associated with disease severity and fatal outcome (preprint)

The outbreak of Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, December 2019, and continuously poses a serious threat to public health. Our previous study has shown that cytokine storm occurred during SARS-CoV-2 infection, while the detailed role of cytokines in the disease severity and progression remained unclear due to the limited case number. In this study, we examined 48 cytokines in the plasma samples from 53 COVID-19 cases, among whom 34 were severe cases, and the others moderate. Results showed that 14 cytokines were significantly elevated upon admission in COVID-19 cases. Moreover, IP-10, MCP-3, and IL-1ra were significantly higher in severe cases, and highly associated with the PaO2/FaO2 and Murray score. Furthermore, the three cytokines were independent predictors for the progression of COVID-19, and the combination of IP-10, MCP-3 and IL-1ra showed the biggest area under the curve (AUC) of the receiver-operating characteristics (ROC) calculations. Serial detection of IP-10, MCP-3 and IL-1ra in 14 severe cases showed that the continuous high levels of these cytokines were associated with disease deterioration and fatal outcome. In conclusion, we report three cytokines that closely associated with disease severity and outcome of COVID-19. These findings add to our understanding of the immunopathologic mechanisms of SARS-CoV-2 infection, which suggested novel therapeutic targets and strategy.

Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 (preprint)

Summary Background The novel coronavirus SARS-CoV-2 is a newly emerging virus. The antibody response in infected patient remains largely unknown, and the clinical values of antibody testing have not been fully demonstrated. Methods A total of 173 patients with confirmed SARS-CoV-2 infection were enrolled. Their serial plasma samples (n = 535) collected during the hospitalization period were tested for total antibodies (Ab), IgM and IgG against SARS-CoV-2 using immunoassays. The dynamics of antibodies with the progress and severity of disease was analyzed. Findings Among 173 patients, the seroconversion rate for Ab, IgM and IgG was 93.1% (161/173), 82.7% (143/173) and 64.7% (112/173), respectively. Twelve patients who had not seroconverted were those only blood samples at the early stage of illness were collected. The seroconversion sequentially appeared for Ab, IgM and then IgG, with a median time of 11, 12 and 14 days, respectively. The presence of antibodies was < 40% among patients in the first 7 days of illness, and then rapidly increased to 100.0%, 94.3% and 79.8% for Ab, IgM and IgG respectively since day 15 after onset. In contrast, the positive rate of RNA decreased from 66.7% (58/87) in samples collected before day 7 to 45.5% (25/55) during days 15 to 39. Combining RNA and antibody detections significantly improved the sensitivity of pathogenic diagnosis for COVID-19 patients (p < 0.001), even in early phase of 1-week since onset (p = 0.007). Moreover, a higher titer of Ab was independently associated with a worse clinical classification (p = 0.006). Interpretation The antibody detection offers vital clinical information during the course of SARS-CoV-2 infection. The findings provide strong empirical support for the routine application of serological testing in the diagnosis and management of COVID-19 patients.