Multiple pro-viral effects of the host protein G3BP in SARS-CoV-2 Infection (preprint)

Ras-GTPase-activating protein SH3-domain-binding proteins (G3BP) are multifunctional RNA-binding proteins, pivotal in the initiation of stress granules (SGs). SARS-CoV-2 nucleocapsid (N) protein exhibits strong binding affinity for G3BP and inhibition of SG formation. However, pro-viral role(s) of the G3BP-N interaction have remained unclear. Here, we have comprehensively examined the importance of G3BP for SARS-CoV-2 infection both in vitro and in vivo. Using reverse genetics, we constructed a viral mutant, SARS-CoV-2 RATA, which exhibits stronger and more persistent SG response in infected cells. We also show that in SARS-CoV-2 infected cells, G3BP-N complexes are targeted to the pore complex of double membrane vesicles (DMV) from which nascent viral RNA emerges. Furthermore, through interaction with 40S ribosomal subunits, G3BP-N complexes promote highly localized translation of viral mRNAs at the viral factories and thus facilitate viral gene expression and replication. This work provides a mechanistic understanding of the roles of G3BP in SARS-CoV-2 infection.

Advances in developing ACE2 derivatives against SARS-CoV-2.

Extensive immune evasion of SARS-CoV-2 rendered therapeutic antibodies ineffective in the COVID-19 pandemic. Propagating SARS-CoV-2 variants are characterised by immune evasion capacity through key amino acid mutations, but can still bind human angiotensin-converting enzyme 2 (ACE2) through the spike protein and are, thus, sensitive to ACE2-mimicking decoys as inhibitors. In this Review, we examine advances in the development of ACE2 derivatives from the past 3 years, including the recombinant ACE2 proteins, ACE2-loaded extracellular vesicles, ACE2-mimicking antibodies, and peptide or mini-protein mimetics of ACE2. Several ACE2 derivatives are granted potent neutralisation efficacy against SARS-CoV-2 variants that rival or surpass endogenous antibodies by various auxiliary techniques such as chemical modification and practical recombinant design. The derivatives also represent enhanced production efficiency and improved bioavailability. In addition to these derivatives of ACE2, new effective therapeutics against SARS-CoV-2 variants are expected to be developed.

Advances in developing ACE2 derivatives against SARS-CoV-2

Extensive immune evasion of SARS-CoV-2 rendered therapeutic antibodies ineffective in the COVID-19 pandemic. Propagating SARS-CoV-2 variants are characterised by immune evasion capacity through key amino acid mutations, but can still bind human angiotensin-converting enzyme 2 (ACE2) through the spike protein and are, thus, sensitive to ACE2-mimicking decoys as inhibitors. In this Review, we examine advances in the development of ACE2 derivatives from the past 3 years, including the recombinant ACE2 proteins, ACE2-loaded extracellular vesicles, ACE2-mimicking antibodies, and peptide or mini-protein mimetics of ACE2. Several ACE2 derivatives are granted potent neutralisation efficacy against SARS-CoV-2 variants that rival or surpass endogenous antibodies by various auxiliary techniques such as chemical modification and practical recombinant design. The derivatives also represent enhanced production efficiency and improved bioavailability. In addition to these derivatives of ACE2, new effective therapeutics against SARS-CoV-2 variants are expected to be developed.

Vaccine supply chain coordination using blockchain and artificial intelligence technologies.

Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study's major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers' difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era.

"Re-visits and drug renewal" is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of "Re-visits and drug renewal" actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era.

Impact of Time-To-Surgery on the Prognosis of Patients with T1 Renal Cell Carcinoma: Implications for the COVID-19 Pandemic.

Background: During the COVID-19 pandemic, elective surgery has to undergo longer wait times, including nephrectomy for T1 renal cell carcinoma (RCC). This study aimed to investigate the time-to-surgery (TTS) of Chinese T1 RCC patients and its influencing factors, and to illustrate the impact of TTS on the prognosis of T1 RCC. Methods: We retrospectively enrolled 762 Chinese patients with pathological T1 RCC that underwent nephrectomy. To discover the impact of TTS on survival outcomes, we explored the possible delay intervals by week using the Kaplan-Meier method and Log-rank test. Cox proportional hazard models with inverse probability-treatment weighting (IPTW) were used to assess the association between TTS and disease-free survival (DFS) and overall survival (OS). Results: The median TTS of T1 RCC patients was 15 days. The Charlson comorbidity index, the Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) score, and the maximal tumor diameter on presentation were independent influencing factors for TTS. The cut-off point of TTS was selected as 5 weeks according to the Log-rank analysis. For T1a RCC, patients with TTS > 5 weeks had similar DFS (HR = 2.39; 95% CI, 0.82−6.94; p = 0.109) and OS (HR = 1.28; 95% CI, 0.23−7.16; p = 0.779) compared to patients with TTS ≤ 5 weeks. For T1b RCC, patients with TTS > 5 weeks had shorter DFS (HR = 2.90; 95% CI = 1.46−5.75; p = 0.002) and OS (HR = 2.49, 95% CI = 1.09−5.70; p = 0.030) than patients with TTS ≤ 5 weeks. Conclusions: Prolonged TTS had no impact on the prognosis of T1a RCC while surgery delayed for over 5 weeks may lead to worse survival in T1b RCC.

Vaccine supply chain coordination using blockchain and artificial intelligence technologies

Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a closed-loop vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous;the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study’s major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers’ difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.

Correlation coefficient-directed label-free characterization of native proteins by surface-enhanced Raman spectroscopy.

Investigation of proteins in their native state is the core of proteomics towards better understanding of their structures and functions. Surface-enhanced Raman spectroscopy (SERS) has shown its unique advantages in protein characterization with fingerprint information and high sensitivity, which makes it a promising tool for proteomics. It is still challenging to obtain SERS spectra of proteins in the native state and evaluate the native degree. Here, we constructed 3D physiological hotspots for a label-free dynamic SERS characterization of a native protein with iodide-modified 140 nm Au nanoparticles. We further introduced the correlation coefficient to quantitatively evaluate the variation of the native degree, whose quantitative nature allows us to explicitly investigate the Hofmeister effect on the protein structure. We realized the classification of a protein of SARS-CoV-2 variants in 15 min, which has not been achieved before. This study offers an effective tool for tracking the dynamic structure of proteins and biomedical research.

Impacts of detection and contact tracing on the epidemic spread in time-varying networks

In the situation of insufficient vaccines and rapid mutation of the virus, detection and contact tracing have been argued to be effective interventions in the containment of emergent epidemics. However, most of previous studies are devoted to data-driven, leading to insufficient understanding of quantifying their effectiveness, especially when individuals’ interactions evolve with time. Here, we aim at quantifying the effectiveness of detection and contact tracing interventions in suppressing the epidemic in time-varying networks. We propose the Susceptible-Exposed-Infected-Removed-Dead-Hospitalized (SEIRDH) model with detection and contact tracing. Under the framework of time-varying networks and with a mean-field approach, we analyze the epidemic thresholds under different situations. Experimental results show that detection can effectively suppress the epidemic spread with an increased epidemic threshold, while the role of tracing depends on the characteristics of the epidemic. When an epidemic is infectious in the incubation period, contact tracing has an obvious effect in suppressing the epidemic spread, but not when the epidemic is not infectious in the incubation. Thus, we apply this framework in real networks to explore possible contact tracing measures by taking use of individuals’ properties. We find that contact tracing based on activity and historical information is more efficient than random contact tracing. Moreover, individuals’ attractiveness and aging effects also affect the efficiency of detection and contact tracing. In conclusion, making full use of individuals’ properties can remarkably improve the effectiveness of detection and contact tracing. The proposed method is expected to provide theoretical guidance for coping with the COVID-19 or other emergent epidemics.

Co-evolution dynamics of epidemic and information under dynamical multi-source information and behavioral responses

In the absence of effective treatment programs and limited medical resources, multi-source information dynamically evolves with an epidemic and motivates people to adopt behavioral responses, which contributes much to reducing their infection risk and suppressing the epidemic spread. Here, we aim at studying the effects of dynamical multi-source information and behavioral responses on the co-evolution of epidemic and information in time-varying multiplex networks. We propose the UAU-SIS (unaware-aware-unaware susceptible-infected-susceptible) model with time-varying self-awareness and behavioral responses. Under the framework of time-varying multiplex networks and with a microscopic Markov chain approach, we analytically derive the epidemic thresholds for the proposed model. Experimental results for artificial networks show that time-varying behavioral responses can effectively suppress the epidemic spread with an increased epidemic threshold, while time-varying self-awareness can only reduce the scale of epidemic spread. In addition, the role of dynamical multi-source information in suppressing epidemic spread is limited. When the information transmission rate is beyond a certain critical value or the information efficiency is low, it will no longer affect the epidemic spread. Detailed analysis on the co-evolution of epidemic and information has to consider the heterogeneity of individuals in obtaining multi-source information and taking behavioral responses. Only when many people can obtain multi-source information and take behavioral responses, time-varying self-awareness and behavioral responses have a great impact on suppressing epidemic spread. Furthermore, we apply our proposed framework to two typical real-world networks and find that the results on real-world networks are consistent with those on artificial networks. Thus, the proposed method is expected to provide helpful guidance for coping with the COVID-19 or future emerging epidemics.

Green Transformation of Anti-Epidemic Supplies in the Post-Pandemic Era: An Evolutionary Approach.

Post-pandemic, the use of medical supplies, such as masks, for epidemic prevention remains high. The explosive growth of medical waste during the COVID-19 pandemic has caused significant environmental problems. To alleviate this, environment-friendly epidemic prevention measures should be developed, used, and promoted. However, contradictions exist between governments, production enterprises, and medical institutions regarding the green transformation of anti-epidemic supplies. Consequently, this study aimed to investigate how to effectively guide the green transformation. Concerning masks, a tripartite evolutionary game model, consisting of governments, mask enterprises, and medical institutions, was established for the supervision of mask production and use, boundary conditions of evolutionary stabilization strategies and government regulations were analyzed, and a dynamic system model was used for the simulation analysis. This analysis revealed that the only tripartite evolutionary stability strategy is for governments to deregulate mask production, enterprises to increase eco-friendly mask production, and medical institutions to use these masks. From the comprehensive analysis, a few important findings are obtained. First, government regulation can promote the green transformation process of anti-epidemic supplies. Government should realize the green transformation of anti-epidemic supplies immediately in order to avoid severe reputation damage. Second, external parameter changes can significantly impact the strategy selection process of all players. Interestingly, it is further found that the cost benefit for using environmentally friendly masks has a great influence on whether green transformation can be achieved. Consequently, the government should establish a favorable marketplace for, and promote the development of, inexpensive, high-quality, and effective environmentally friendly masks in order to achieve the ultimate goal of green transformation of anti-epidemic supplies in the post-pandemic era.

Behavioral problems of pediatric patients recovered from COVID-19 in Wuhan, China.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is profoundly affecting lives around the globe. Previous studies on COVID-19 mainly focused on epidemiological, clinical, and radiological features of patients with confirmed infection. Little attention has been paid to the follow-up of recovered patients. As a vulnerable population to adverse events, the health status of the COVID-19 recovered pediatric patients is of great concern. We aimed to investigate the prevalence of behavioral problems among pediatric patients recovered from the COVID-19 in Wuhan, China. METHODS: A total of 122 children who were suspected or confirmed COVID-19 cases and hospitalized for treatment were enrolled in the study between April 2020 and May 2020 in Wuhan, China. We collected related information about hospitalization and discharge of the children and emotional symptoms of their parents through electronic medical records and questionnaire. The behavioral problems of children were examined by applying the parent-reported the Strengths and Difficulties Questionnaire (SDQ). RESULTS: The participant children were discharged from hospital after about two months. Among them, 76 (62%) were boys, and the mean age was 6.71 years old. The highest prevalence of behavioral problems among pediatric children with COVID-19 was for prosocial behavior (15%), followed by total difficulties (13%), emotional symptoms (11%), hyperactivity (10%), conduct problems (9%), and peer problems (1%). With regarding to their parents, 26% reported having symptoms of anxiety and 23% as having symptoms of depression. The scores of SDQ were higher in those children whose parents have emotional problems compared to parents without. CONCLUSION: Long-term follow up studies on the psychological and behavioral problems of COVID-19 recovered children and their parents are warranted.

Behavioral problems of pediatric patients recovered from COVID-19 in Wuhan, China

Background Coronavirus disease 2019 (COVID-19) is profoundly affecting lives around the globe. Previous studies on COVID-19 mainly focused on epidemiological, clinical, and radiological features of patients with confirmed infection. Little attention has been paid to the follow-up of recovered patients. As a vulnerable population to adverse events, the health status of the COVID-19 recovered pediatric patients is of great concern. We aimed to investigate the prevalence of behavioral problems among pediatric patients recovered from the COVID-19 in Wuhan, China. Methods A total of 122 children who were suspected or confirmed COVID-19 cases and hospitalized for treatment were enrolled in the study between April 2020 and May 2020 in Wuhan, China. We collected related information about hospitalization and discharge of the children and emotional symptoms of their parents through electronic medical records and questionnaire. The behavioral problems of children were examined by applying the parent-reported the Strengths and Difficulties Questionnaire (SDQ). Results The participant children were discharged from hospital after about two months. Among them, 76 (62%) were boys, and the mean age was 6.71 years old. The highest prevalence of behavioral problems among pediatric children with COVID-19 was for prosocial behavior (15%), followed by total difficulties (13%), emotional symptoms (11%), hyperactivity (10%), conduct problems (9%), and peer problems (1%). With regarding to their parents, 26% reported having symptoms of anxiety and 23% as having symptoms of depression. The scores of SDQ were higher in those children whose parents have emotional problems compared to parents without. Conclusion Long-term follow up studies on the psychological and behavioral problems of COVID-19 recovered children and their parents are warranted.

Nightmares mediate the association between traumatic event exposure and suicidal ideation in frontline medical workers exposed to COVID-19.

BACKGROUND: Trauma experience increases the risk of suicidal ideation, but little is known about potentially psychological mechanisms underlying this relationship. This study aims to examine the relationship between coronavirus disease 2019 (COVID-19)-related traumatic event (CTE) exposure and suicidal ideation among hospital workers, and identify mediating roles of sleep disturbances in this relationship. METHODS: Workers in seven designated hospitals in Wuhan, China, were invited to participate in an online survey from May 27, 2020, to July 31, 2020. Participants completed a self-report questionnaire to evaluate demographic characteristics, level of CTE exposures, nightmare frequency, insomnia severity, symptoms of depression and anxiety, and suicidal ideation. A series of correlation analyses were performed, and a mediation model was generated to examine correlations between CTE exposure, sleep disturbances, and suicidal ideation. RESULTS: A total of 16,220 hospital workers were included in the final analysis, 13.3% of them reported suicidal ideation in the past month. CTE exposure was significantly associated with insomnia severity, nightmare frequency, and suicidal ideation. After controlling potential confounders, nightmares but not insomnia, depression, or anxiety were shown to be independent risk factors for suicidal ideation. Pathway analyses showed that the relationship between CTE exposure and suicidal ideation was fully mediated by nightmares (proportion mediated 66.4%) after adjusting for demographic characteristics and psychological confounders. LIMITATIONS: Cross-sectional design precluded the investigation of causal relationships. CONCLUSIONS: CTE exposure increases risk of hospital workers' suicidal ideation that is mediated by nightmares, suggesting nightmares intervention might be considered as a component when developing suicide prevention strategies.

Estimating the Effects of Public Health Measures by SEIR(MH) Model of COVID-19 Epidemic in Local Geographic Areas.

The COVID-19 pandemic of 2020-21 has been a major challenge to public health systems worldwide. Mathematical models of epidemic are useful tools for assessment of the situation and for providing decision-making support for relevant authorities. We developed and implemented SEIR(MH) model that extends the conventional SEIR model with parameters that define public lockdown (the level and start of lockdown) and the medical system capacity to contain patients. Comparative modeling of four regions in Europe that have similar population sizes and age structures, but different public health systems, was performed: Baden-Württemberg, Lombardy, Belgium, and Switzerland. Modeling suggests that the most effective measure for controlling epidemic is early lockdown (exponential effect), followed by the number of available hospital beds (linear effect if the capacity is insufficient, with diminishing returns when the capacity is sufficient). Dynamic management of lockdown levels is likely to produce better outcomes than strict lockdown.

Mental Health in Children in the Context of COVID-19: Focus on Discharged Children.

Introduction: To date, the mental health consequences of children hospitalized with COVID-19 remain unclear. We aimed to assess mental health status in children in the context of COVID-19, with a focus on discharged children. Methods: We recruited discharged children who recovered from COVID-19 and healthy controls between July and September 2020 in Wuhan Children's Hospital. Post-traumatic stress disorder (PTSD), anxiety, depression, and sleep problems were assessed in these children using questionnaires. Univariable and multivariable logistic and linear regressions were conducted to identify risk factors. Results: Totally, there were 152 children (61 discharged children and 91 healthy controls) aged 7-18 years old in our study. An increasing trend in the prevalence of PTSD, anxiety, and depression was observed in the discharged children compared with healthy controls (PTSD: 8.20 vs. 2.20%, anxiety: 22.95 vs. 13.19%; depression: 47.54 vs. 32.97%). Discharged children tended to report more depressive symptoms (ß = 0.39) and less sleep problems (ß = -0.37). Discharged children who lived in nuclear families and had longer hospital stays were more likely to report depression [odds ratio (OR) = 3.68 and 1.14, respectively]. Anxiety symptoms and the severity of sleep problems of discharged children were positively associated with caregivers' depression and PTSD symptoms (OR = 21.88 and 31.09, respectively). Conclusion: In conclusion, PTSD, anxiety, and depression symptoms were common among recovered children 4 months after COVID-19 hospitalization. Children from nuclear family and those had longer hospital stays need special attention. In addition, parental mental health had a significant impact on their children's mental resilience and recovery.

The Spring Festival Effect: The change in NO2 column concentration in China caused by the migration of human activities

The Spring Festival is the most important holiday in China, and human activity and population mobility may contribute greatly to air quality. According to the satellite-based tropospheric nitrogen dioxide (NO2) column and ground-based observational concentration of NO2 in megacities from 2013 to 2018 around the Spring Festival, we found that NO2 concentration obviously decreases, presenting a “tide phenomenon”, particularly in the megacities, with the tropospheric NO2 column density decreasing by 31.8%–44.5%. The tropospheric NO2 column density in Beijing decreased by 41.6% and rebounded by 22.3% after the festival. Vehicle sources were among the important causes of NOx emissions in the megacities, and traffic intensity decreased significantly during the festival. As the coronavirus disease 2019 (COVID-19) pandemic progresses, the traffic intensity in urban areas is decreasing significantly, with the tropospheric NO2 column density decreasing by 56.2% and rebounding by only 6.8% in 2020, without the “tide phenomenon”.