A survey of health status of healthcare providers in a square cabin hospital during the new corona omicron outbreak: A cross-sectional study

Background The coronavirus omicron variant outbroke in early 2022 in Shanghai. Although previous studies indicated that long working hours in a square cabin hospital might increase the risk of mental health among frontline healthcare providers, few studies have investigated whether the mental health risk could be reduced among well-trained professionals following the new guidelines. Objective This study aimed to investigate the health situation of frontline healthcare providers in Shanghai square cabin during the omicron variant circulation. Methods An online survey was used to evaluate those healthcare providers working in the square cabin hospitals from March 1, 2022, to May 31, 2022. The first online survey was conducted and emailed to the health providers on April 1. The second survey was conducted and sent to the nonrespondents on May 31. Overall, 142 frontline healthcare providers completed the online survey. Their mental health was assessed by the Insomnia Severity Index Scale, the Generalized Anxiety Disorder Scale, the Patient Health Questionnaire-9, and the Psychological Resilience Scale. We estimated multiple clinical systems and identified factors associated with those symptoms among participants. Multivariable logistic regression models were used to assess the risk factors of these symptoms. Results Overall, 66.20%, 45.07%, and 27.46% of frontline healthcare providers in Shanghai City reported symptoms of insomnia, depression, and anxiety, respectively. In addition, the most common symptoms included dry eyes (57.75%), lumbar muscle strain (47.18%), dry mouth (35.92%), itching (31.69%), headache (29.58%), and sore throat (28.87%) among the frontline healthcare providers. There was no statistical difference in symptoms by gender, age, personnel category, or job position (p > 0.05). Conclusion In the case of an unexpected pandemic, the mental health of healthcare providers is not optimistic. This situation still exists more than 2 years after the global outbreak of the COVID-19 pandemic. Therefore, the physical and mental health of long-term healthcare providers working in a square cabin hospital still needs monitoring.

AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era

In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., face-to-face (F2F)) communication. Consequently, schools, colleges, and universities worldwide have been forced to transition to different forms of online and virtual learning. Unlike F2F classes where the instructors could monitor and adjust lessons and content in tandem with the learners’ perceived emotions and engagement, in online learning environments (OLE), such tasks are daunting to undertake. In our modest contribution to ameliorate disruptions to education caused by the pandemic, this study presents an intuitive model to monitor the concentration, understanding, and engagement expected of a productive classroom environment. The proposed apposite OLE (i.e., AOLE) provides an intelligent 3D visualisation of the classroom atmosphere (CA), which could assist instructors adjust and tailor both content and instruction for maximum delivery. Furthermore, individual learner status could be tracked via visualisation of his/her emotion curve at any stage of the lesson or learning cycle. Considering the enormous emotional and psychological toll caused by COVID and the attendant shift to OLE, the emotion curves could be progressively compared through the duration of the learning cycle and the semester to track learners’ performance through to the final examinations. In terms of learning within the CA, our proposed AOLE is assessed within a class of 15 students and three instructors. Correlation of the outcomes reported with those from administered questionnaires validate the potential of our proposed model as a support for learning and counselling during these unprecedentedtimes that we find ourselves.

Parameter Estimation for the SEIR Model Using Recurrent Nets (preprint)

The standard way to estimate the parameters $\Theta_\text{SEIR}$ (e.g., the transmission rate $\beta$) of an SEIR model is to use grid search, where simulations are performed on each set of parameters, and the parameter set leading to the least $L_2$ distance between predicted number of infections and observed infections is selected. This brute-force strategy is not only time consuming, as simulations are slow when the population is large, but also inaccurate, since it is impossible to enumerate all parameter combinations. To address these issues, in this paper, we propose to transform the non-differentiable problem of finding optimal $\Theta_\text{SEIR}$ to a differentiable one, where we first train a recurrent net to fit a small number of simulation data. Next, based on this recurrent net that is able to generalize SEIR simulations, we are able to transform the objective to a differentiable one with respect to $\Theta_\text{SEIR}$, and straightforwardly obtain its optimal value. The proposed strategy is both time efficient as it only relies on a small number of SEIR simulations, and accurate as we are able to find the optimal $\Theta_\text{SEIR}$ based on the differentiable objective. On two COVID-19 datasets, we observe that the proposed strategy leads to significantly better parameter estimations with a smaller number of simulations.

Analysis on Action Tracking Reports of COVID-19 Informs Control Strategies and Vaccine Delivery in Post-Pandemic Era (preprint)

Understanding the spread of SARS-CoV-2 provides important insights for control policies such as social-distancing interventions and vaccine delivery in the post-pandemic era. In this work, we take the advantage of action tracking reports of confirmed COVID-19 patients, which contain details regarding the mobility trajectory of a patient, along with the people with whom the patient has interacted, the timing of diagnosis, and personal information (e.g., age and sex). We analyzed reports of 4,410 patients from April 2020 to February 2021 in China, a country where the residents are well-prepared for the "new normal" world following COVID-19 spread. We developed natural language processing (NLP) tools to transform the unstructured text of action-tracking reports to a structured network of social contacts. A SEIR model was built on top of the network, and was able to capture important aspects regarding coronavirus transmissions such as location category, age, sex and socioeconomic status. Our analysis provides important insights for the development of control policies. Under the "new normal" conditions, we find that restaurants, locations less protected by mask-wearing, have a greater risk than any other location categories, including locations where people are present at higher densities (e.g., flight). We find that discouraging railway transports is crucial to avoid another wave of breakout during the Chunyun season (a period of travel in China with extremely high traffic load around the Chinese New Year). By formalizing the challenge of finding the optimal vaccine delivery among various different population groups (e.g., sex, age and socioeconomic groups) as an optimization problem, our analysis helps to maximize the efficiency of vaccine delivery under the general situation of vaccine supply shortage. We are able to reduce the numbers of infections and deaths by 7.4% and 10.5% respectively with vaccine supply for only 1% of the population. Furthermore, with 10% vaccination rate, the numbers of infections and deaths further decrease by 52.6% and 78.1% respectively. Our work will be helpful in the design of effective policies regarding interventions, reopening, contact tracing and vaccine delivery in the "new normal" world following COVID-19 spread.

JDSR-GAN: Constructing A Joint and Collaborative Learning Network for Masked Face Super-Resolution (preprint)

With the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a high-quality high-resolution face image. The discriminator utilizes some carefully designed loss functions to ensure the quality of the recovered face images. Moreover, we incorporate the identity information and attention mechanism into our network for feasible correlated feature expression and informative feature learning. By jointly performing denoising and face super-resolution, the two tasks can complement each other and attain promising performance. Extensive qualitative and quantitative results show the superiority of our proposed JDSR-GAN over some comparable methods which perform the previous two tasks separately.

Structural basis for the inhibition of the SARS-CoV-2 RNA-dependent RNA polymerase by favipiravir-RTP (preprint)

The RNA polymerase inhibitor, favipiravir, is currently in clinical trials as a treatment for infection with SARS-CoV-2, despite limited information about the molecular basis for its activity. Here we report the structure of favipiravir ribonucleoside triphosphate (favipiravir-RTP) in complex with the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) bound to a template:primer RNA duplex, determined by electron cryomicroscopy (cryoEM) to a resolution of 2.5 Ang. The structure shows clear evidence for the inhibitor at the catalytic site of the enzyme, and resolves the conformation of key side chains and ions surrounding the binding pocket. Polymerase activity assays indicate that the inhibitor is weakly incorporated into the RNA primer strand, and suppresses RNA replication in the presence of natural nucleotides. The structure reveals an unusual, non-productive binding mode of favipiravir-RTP at the catalytic site of SARS-CoV2 RdRp which explains its low rate of incorporation into the RNA primer strand. Together, these findings inform current and future efforts to develop polymerase inhibitors for SARS coronaviruses.

Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions (preprint)

At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused severe impacts on society, economy and people's daily lives. People constantly express their opinions on various aspects of the pandemic on social media, making user-generated content an important source for understanding public emotions and concerns. In this paper, we perform a comprehensive analysis on the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. Specifically, by identifying people's sentiments, emotions (i.e., anger, disgust, fear, happiness, sadness, surprise) and the emotional triggers (e.g., what a user is angry/sad about) we are able to depict the dynamics of public affect in the time of COVID-19. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.

Blood glucose is a representative of the clustered indicators of multi-organ injury for predicting mortality of COVID-19 in Wuhan, China (preprint)

Background Concomitance with diabetes is associated with high mortality in critical conditions. Patients with previous diabetes are more vulnerable to COVID-19. However, new-onset COVID-19-related diabetes (CRD) and its relevance have scarcely been reported. This study investigates new-onset CRD and its correlation with poor outcomes or death in patients with COVID-19. Methods We performed a single center, retrospective case series study in 120 patients with laboratory confirmed COVID-19 at a university hospital. Fasting blood glucose (FBG) [≥]7.0 mmol/L for two times during hospitalization and without a history of diabetes were defined as CRD. The Critical status was defined as admitted to intensive care unit (ICU) or death. Results After excluding patients with a history of diabetes, chronic heart, kidney, and liver disease, 69 patients with COVID-19 were included in the final analysis. Of the 69 patients, 23 were Moderate, 20 were Severe, and 26 were Critical (including 16 deceased patients). The prevalence of CRD in Critical and Moderate+Severe patients was 53.85% and 13.95%, respectively. Kaplan-Meier survival analysis revealed a significantly higher mortality rate in patients with CRD (P=0.0019). Multivariable analysis indicated that CRD was an independent predictor for death (HR = 3.75, 95% CI 1.26-11.15). Cluster analysis suggested that indicators for multi-organ injury were interdependent, and more proximities of FBG with indicators for multi-organ injury was present. Conclusion Our results suggest that new onset COVID-19-related diabetes is an indicator of multi-organ injury and predictor for poor outcomes and death in COVID-19 patients. As it is easy to perform for clinical practices and even self-monitoring, glucose testing will be much helpful for predicting poor outcomes to facilitate appropriate intensive care in patients with COVID-19.

Strategies suggested for emergency diagnosis and treatment of traumatic orthopedicsin the epidemic periodof Corona Virus Disease 2019 / 中华创伤骨科杂志

Objective@#To suggest strategies for emergency diagnosis and treatment of trauma orthopedics in the epidemic period of Corona Virus Disease 2019(COVID-19).@*Methods@#In the epidemic of COVID-19 from January 21 to February 15, 2020, 128 patients with orthopaedic trauma sought emergency treatment at Department of Orthopedic Surgery, The People’s Hospital of Wuhan University. They were 71 males and 57 females with an average age of 48.7 years (from 5 to 88 years).Of them, 107 cases were treated at the outpatient department and 21 hospitalized. Emergency operations were carried out for 4 cases and selective operationsfor 17 cases. COVID-19 infections were recorded in the patients and medical staff as well. Measures taken and experiences learned were summarized since the epidemicoutbreak of COVID-19.@*Results@#Of the 107 cases treated at the outpatient department, 3 had a definite diagnosis of COVID-19 and 3 a suspected diagnosis of COVID-19. Of the 4 cases undergoing emergency surgery, one was suspected of having COVID-19. Of the 17 cases undergoing selective surgery, one was diagnosed definitely as COVID-19and 2 were suspected of COVID-19. Two nurses were diagnosed definitely as having mildCOVID-19.One doctor and one nurse were suspected of COVID-19. Since the COVID-19 infections in medical staff occurred all before the preventive and control measures for COVID-19 had been implemented,is was not ruled out that their infections might have come from communities.@*Conclusions@#It is particularly important for medical institutions of all levels to maintain safe and effective routine services while doing well in COVID-19 prevention. In the epidemic of COVID-19, front-line medical staff in emergency traumatic orthopedics is faced with great challenges in the process of diagnosing and treating patients. High-quality and safe medical services can be provided as long as nosocomial COVID-19infection is effectively controlled by rigid screening of patientsnewly admitted, classified management of inpatients, optimal management of inpatient wards, standard preventive measures in perioperative period, a perfect system for medical protection, and medical education for patients and their carers.

Higher severity and mortality in male patients with COVID-19 independent of age and susceptibility (preprint)

Importance: The recent outbreak of Novel Coronavirus (SARS-CoV-2) Disease (COVID-19) has put the world on alert, that is reminiscent of the SARS outbreak seventeen years ago. Objective: We aim to compare the severity and mortality between male and female patients with both COVID-19 and SARS, to explore the most useful prognostic factors for individualized assessment. Design, Setting, and Participants: We extracted the data from a case series of 43 hospitalized patients we treated, a public data set of the first 37 cases died of COVID-19 in Wuhan city and 1019 survived patients from six cities in China. We also analyzed the data of 524 patients with SARS, including 139 deaths, from Beijing city in early 2003. Main Outcomes and Measures: Severity and mortality. Results: Older age and high number of comorbidities were associated with higher severity and mortality in patients with both COVID-19 and SARS. The percentages of older age ([≥]65 years) were much higher in the deceased group than in the survived group in patients with both COVID-19 (83.8 vs. 13.2, P<0.001) and SARS (37.4 vs. 4.9, P<0.001). In the case series, men tend to be more serious than women (P=0.035), although age was comparable between men and women. In the public data set, age was also comparable between men and women in the deceased group or the survived group in patients with COVID-19. Meanwhile, gender distribution was exactly symmetrical in the 1019 survivors of COVID-19. However, the percentage of male were higher in the deceased group than in the survived group (70.3 vs. 50.0, P=0.015). The gender role in mortality was also observed in SARS patients. Survival analysis showed that men (hazard ratio [95% CI] 1.47 [1.05-2.06, P= 0.025) had a significantly higher mortality rate than women in patients with SARS. Conclusions and Relevance: Older age and male gender are risk factors for worse outcome in patients with COVID. While men and women have the same susceptibility to both SARS-CoV-2 and SARS-CoV, men may be more prone to have higher severity and mortality independent of age and susceptibility.