The relationship between COVID-19 event strength and job burnout among primary health care staff: The chain mediating effect of job stress and work engagement (preprint)

Background As a global pandemic, The Corona Virus Disease 2019 (COVID-19) has brought significant challenges to the primary health care (PHC) system. Health professionals are constantly affected by the pandemic's harmful impact on their mental health and are at significant risk of job burnout. Therefore, it is essential to gain a comprehensive understanding of how their burnout was affected. The study aimed to examine the relationship between COVID-19 event strength and job burnout among PHC providers and to explore the single mediating effect of job stress and work engagement and the chain mediating effect of these two variables on this relationship.Methods We used multilevel stratified convenience sampling to recruit participants from PHC institutions in Jilin Province, China. A total of 1148 medical professionals completed questionnaires regarding sociodemographic characteristics, COVID-19 event strength, job stress, work engagement, and job burnout. The chain mediation model was analysed using SPSS PROCESS 3.5 Macro Model 6.Results COVID-19 event strength not only positively predicted job burnout, but also indirectly influenced job burnout through the mediation of job stress and work engagement, thereby influencing job burnout through the "job stress → work engagement" chain.Conclusions This study extends the application of event systems theory and enriches the literature about how the COVID-19 pandemic impacted PHC medical staff job burnout. The findings derived from our study have critical implications for current and future emergency response and public policy in the long-term COVID-19 disease management period.

Systematic Mendelian randomization study of the effect of gut microbiome and plasma metabolome on severe COVID-19 (preprint)

Background COVID-19 could develop severe respiratory symptoms in certain infected patients, especially in the patients with immune disorders. Gut microbiome and plasma metabolome act important immunological modulators in the human body and could contribute to the immune responses impacting the progression of COVID-19.Methods Based on two-sample Mendelian randomization framework, the causal effects of 131 microbiota in genus or species level and 452 plasma metabolites on severe COVID-19 are estimated. Single nucleotide polymorphisms (SNPs) strongly associated with the abundance of intestinal bacteria in gut and the concentration of metabolites in plasma have been utilized as the instrument variables to infer whether they are causal factors of severe COVID-19. In addition, mediation analysis is conducted to find the potential link between the microbiota and metabolite which identified by polygenic Mendelian randomization analysis, while colocalization analysis has been performed to validate the causal relationships which identified by cis-Mendelian randomization analysis.Results Mendelian randomization support 13 microbiota and 53 metabolites, which are significantly causal association with severe COVID-19. Mediation analysis find 11 mediated relations, such as myo-inositol, 2-stearoylglycerophosphocholine and alpha-glutamyltyrosine, which appeared to mediate the association of Howardella and Ruminiclostridium 6 with severe COVID-19 respectively, while Butyrivibrio and Ruminococcus gnavus appeared to mediate the association of myo-inositol and N-acetylalanine respectively. Ruminococcus torques abundance was colocalized with severe COVID-19 (PP.H4 = 0.77) and the colon expression of permeability related protein RASIP1 (PP.H4 = 0.95).Conclusions Our study results highlight the causal relationships of gut microbiome and plasma metabolome for severe COVID-19, which have the promise to be served as clinical biomarkers for risk stratification and prognostication, and novel basis to unravel the pathophysiological mechanisms of severe COVID-19.

Targeting TRIM28-mediated SUMOylation of SARS-CoV-2 Nucleocapsid Protein Inhibits SARS-CoV-2 Infection (preprint)

Viruses, as opportunistic intracellular parasites, hijack the cellular machinery of host cells to support their survival and propagation. Consequently, numerous viral proteins are subjected to host-mediated post-translational modifications. Here, we demonstrate that the SARS-CoV-2 nucleocapsid protein (SARS2-NP) is modified by a small ubiquitin-like modifier (SUMO) on the lysine 65 residue. SARS2-NP SUMOylation is essential for executing efficiently SARS2-NP’s ability in homo-oligomerization, RNA association, liquid-liquid phase separation (LLPS), thereby the innate antiviral immune response is suppressed robustly both in vitro and in vivo . These roles played by SARS2-NP SUMOylation can be achieved through intermolecular association between SUMO conjugation and a newly identified SUMO-interacting motif (SIM) in SARS2-NP. Importantly, the widespread SARS2-NP R203K mutation in SARS-CoV-2 variants gains a novel site of SUMOylation which further increases SARS2-NP’s LLPS and immunosuppression. Notably, we discover that the SUMO E3 ligase TRIM28 is responsible for catalyzing SARS2-NP SUMOylation. An interfering peptide targeting the TRIM28 and SARS2-NP interaction was screened out to block SARS2-NP SUMOylation and LLPS, and consequently inhibit SARS-CoV-2 replication and rescue innate antiviral immunity. Collectively, these data support SARS2-NP SUMOylation as an essential modification for SARS-CoV-2 virulence, and therefore provide a strategy to antagonize SARS-CoV-2.

AND-gated ratiometric fluorescence biosensing of dual-emissive Ag nanoclusters based on transient recycling amplification of bivariate targeting DNA

Development of logic AND-gated multivariate assay systems of DNA-scaffolded green- and red-fluorescent Ag nanoclusters (g-AgNC and r-AgNC) is promising and remains challenging. By using two DNA segments (To and Tr) as dual-targeting inputs, herein we report an innovative strategy that utilizes targets-recycled amplification to create a sensitive AND-gated light-up ratiometric sensor with reversely changed fluorescence of g-AgNC and r-AgNC. The design is based on that: two hairpins (Ho and Hr) are individually recognizable to To and Tr, and two hybridized duplexes are resultantly implemented for transient dissipative base pairing, i.e. Ho·Hr, releasing To and Tr for repeated recycling. In an original stem-loop hairpin reporter (HR), one exposed overhang is to host g-AgNC. When HR is unfolded by the cooperative sticky toeholds in Ho∙Hr, its liberated stem merging with the g-AgNC template is for r-AgNC clustering. Only when meeting simultaneously the overexpressed To and Tr in SARS-CoV-2, the AND-gated ratio fluorescence of r-AgNC over g-AgNC can be lighted-up in a typical assay, enabling highly specific-inputs binding and sensitive dose-dependence down to 0.29 pM. This enzyme-free and label-free AND-logic ratiometirc strategy illustrates the potential of using multicolor AgNCs emitters for more accurate multivariate assay.

Impact of Epidemics on Enterprise Innovation: An Analysis of COVID-19 and SARS

This study analyzes the impact of SARS and COVID-19, the two most severe epidemics to occur in China since the 21st century, on corporate innovation, in order to find a path for sustained innovation growth under the epidemic. For COVID-19, the analysis used data from China's A-share-listed companies from 2019 to 2020;a longer period (1999–2006) and a wider sample of Chinese industrial enterprises were used for the SARS epidemic. The empirical model was constructed using the difference-in-differences method. Both COVID-19 and SARS were found to have significantly reduced enterprise innovation. However, the effect of SARS disappeared after two years. For COVID-19, information asymmetry, financing constraints, and economic policy uncertainty moderated the epidemic's effect on innovation. The results show that financing constraints and economic policy uncertainty reduce the epidemic's negative impact. However, while most previous studies have found that an epidemic reduces the information asymmetry between investors and enterprises in the short term, thus raising enterprise innovation, we found that information asymmetry aggravated the epidemic's negative impact. These findings can be applied to alleviate the current epidemic's negative impact as well as improve enterprise innovation thereafter.

Nursing experience of COVID-19 prevention and control in a regional central hospital in a non-Wuhan area in China

The objective of the article was to outline the practical nursing management strategies successfully followed in a general tertiary hospital involved in the of pre-screening 195458 patients, treatment of 316 suspected cases, and 4 confirmed COVID-19 cases from December 2019 to Mar 29, 2020, with no infection of medical staff. During the outbreak, the orderly management and distribution of personal protective equipment (PPE) were essential for COVID-19 prevention and control. A two-level warehouse management system for PPE was established. The hospital-level warehouse of the isolation hospital stored medical supplies. Input/output forms were used to record the usage of PPE. The wardlevel warehouse was equipped with daily requirements of protective supplies. Medical staff followed the policies and procedures of isolation precautions to use PPE. The nurse in charge reported the quantity of PPE used so that replenishment could be provided in time. Reasonable distribution and usage of PPE could be obtained through the two-level warehouse management system.

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes (preprint)

Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. This is in sharp contrast to most existing DAG learning methods assuming Gaussian noise with additional variance assumptions to attain exact DAG recovery. The proposed method leverages a novel concept of topological layer to facilitate the DAG learning. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes in each layer can also be consistently established. More importantly, the proposed method does not require the faithfulness or parental faithfulness assumption which has been widely assumed in the literature of DAG learning. Its advantage is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19.

Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification (preprint)

The COVID-19 pandemic has threatened global health. Many studies have applied deep convolutional neural networks (CNN) to recognize COVID-19 based on chest 3D computed tomography (CT). Recent works show that no model generalizes well across CT datasets from different countries, and manually designing models for specific datasets requires expertise; thus, neural architecture search (NAS) that aims to search models automatically has become an attractive solution. To reduce the search cost on large 3D CT datasets, most NAS-based works use the weight-sharing (WS) strategy to make all models share weights within a supernet; however, WS inevitably incurs search instability, leading to inaccurate model estimation. In this work, we propose an efficient Evolutionary Multi-objective ARchitecture Search (EMARS) framework. We propose a new objective, namely potential, which can help exploit promising models to indirectly reduce the number of models involved in weights training, thus alleviating search instability. We demonstrate that under objectives of accuracy and potential, EMARS can balance exploitation and exploration, i.e., reducing search time and finding better models. Our searched models are small and perform better than prior works on three public COVID-19 3D CT datasets.

Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans (preprint)

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people's lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3\_18) to establish the baseline performance on the three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification with the Gumbel Softmax technique to improve the searching efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis.

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (preprint)

COVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten people's lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at: https://github.com/arthursdays/HKBU\_HPML\_COVID-19.

The impact of goggle-associated harms to health and working status of nurses during management of COVID-19 (preprint)

Background To investigate the impact of goggles on their health and clinical practice during management of patients with COVID-19. Methods 231 nurse practitioners were enrolled who worked in isolation region in designated hospitals to admit patients with COVID-19 in China. Demographic data, goggle-associated symptoms and underlying reasons, incidence of medical errors or exposures, the effects of fog in goggles on practice were all collected. Data were stratified and analyzed by age or working experience. Risk factors of goggle-associated medical errors were analyzed by multivariable logistical regression analysis. Findings Goggle-associated symptoms and foggy goggles widely presented in nurses. The most common symptoms were headache, skin pressure injury and dizziness. Headache, vomit and nausea were significantly fewer reported in nurses with longer working experience while rash occurred higher in this group. The underlying reasons included tightness of goggles, unsuitable design and uncomfortable materials. The working status of nurses with more working experience was less impacted by goggles. 11.3% nurses occurred medical exposures in clinical practice while 19.5% nurses made medical errors on patients. The risk factors for medical errors were time interval before adapting to goggle-associated discomforts, adjusting goggles and headache. Interpretation Goggle-associated symptoms and fog can highly impact the working status and contribute to medical errors during management of COVID-19. Increased the experience with working in PPE through adequate training and psychological education may benefit for relieving some symptoms and improving working status. Improvement of goggle design during productive process was strongly suggested to reduce incidence of discomforts and medical errors.