Renal clearance of graphene oxide: glomerular filtration or tubular secretion and selective kidney injury association with its lateral dimension.

BACKGROUND: Renal excretion is one of the major routes of nanomaterial elimination from the body. Many previous studies have found that graphene oxide nanosheets are excreted in bulk through the kidneys. However, how the lateral size affects GO disposition in the kidneys including glomerular filtration, active tubular secretion and tubular reabsorption is still unknown. RESULTS: The thin, two-dimensional graphene oxide nanosheets (GOs) was observed to excrete in urine through the kidneys, but the lateral dimension of GOs affects their renal clearance pathway and renal injury. The s-GOs could be renal excreted via the glomerular filtration, while the l-GOs were predominately excreted via proximal tubular secretion at a much faster renal clearance rate than the s-GOs. For the tubular secretion of l-GOs, the mRNA level of basolateral organic anion transporters Oat1 and Oat2 in the kidney presented dose dependent increase, while no obvious alterations of the efflux transporters such as Mdr1 and Mrp4 mRNA expression levels were observed, suggesting the accumulation of l-GOs. During the GO renal elimination, mostly the high dose of 15 mg/kg s-GO and l-GO treatment showed obvious kidney injuries but at different renal compartment, i.e., the s-GOs induced obvious glomerular changes in podocytes, while the l-GOs induced more obvious tubular injuries including necrosis of renal tubular epithelial cells, loss of brush border, cast formation and tubular dilatation. The specifically tubular injury biomarkers KIM1 and NGAL were shown slight increase with mRNA levels in l-GO administrated mice. CONCLUSIONS: This study shows that the lateral size of GOs affected their interactions with different renal compartments, renal excretion pathways and potential kidney injuries.

Asymptomatic SARS-CoV-2 Infection by Age: A Global Systematic Review and Meta-analysis.

BACKGROUND: Asymptomatic SARS-CoV-2 infections have raised concerns for public health policies to manage epidemics. This systematic review and meta-analysis aimed to estimate the age-specific proportion of asymptomatic SARS-CoV-2 infected persons globally by year of age. METHODS: We searched PubMed, Embase, medRxiv and Google Scholar on September 10, 2020, and March 1, 2021. We included studies conducted during January to December 2020, before routine vaccination against COVID-19. Because we expected the relationship between the asymptomatic proportion and age to be nonlinear, multilevel mixed-effects logistic regression (QR decomposition) with a restricted cubic spline was used to model asymptomatic proportions as a function of age. RESULTS: A total of 38 studies were included in the meta-analysis. In total, 6556 of 14,850 cases were reported as asymptomatic. The overall estimate of the proportion of people who became infected with SARS-CoV-2 and remained asymptomatic throughout infection was 44.1% (6556/14,850, 95% CI: 43.3%-45.0%). The predicted asymptomatic proportion peaked in children (36.2%, 95% CI: 26.0%-46.5%) at 13.5 years, gradually decreased by age and was lowest at 90.5 years of age (8.1%, 95% CI: 3.4%-12.7%). CONCLUSIONS: Given the high rates of asymptomatic carriage in adolescents and young adults and their active role in virus transmission in the community, heightened vigilance and public health strategies are needed among these individuals to prevent disease transmission.

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

• Quantify the effectiveness of detection and contact tracing in time-varying networks. • Propose the SEIRDH model with detection and contact tracing. • Derive the epidemic thresholds under different conditions of detection and contact tracing. • Analyze the effect of detection and contact tracing in suppressing epidemic spread. 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. [ FROM AUTHOR]

[Characteristics and Evolutionary Advantages of Coronavirus].

Coronaviruses are a major source of emerging infectious diseases in recent years.With a variety of family members,wide host spectrum,and diverse mutant strains,coronaviruses have demonstrated unique advantages in evolution.This paper reviews the research progress of coronaviruses from genome characteristics,host animals,distribution of receptorsand gene mutations,summarizes the advantages of coronaviruses in evolution and transmission,aiming to draw attention to the prevention and control of such viruses.

Understanding metabolic alterations after SARS-CoV-2 infection: insights from the patients' oral microenvironmental metabolites.

BACKGROUND: Coronavirus disease 2019 is a type of acute infectious pneumonia and frequently confused with influenza since the initial symptoms. When the virus colonized the patient's mouth, it will cause changes of the oral microenvironment. However, few studies on the alterations of metabolism of the oral microenvironment affected by SARS-CoV-2 infection have been reported. In this study, we explored metabolic alterations of oral microenvironment after SARS-CoV-2 infection. METHODS: Untargeted metabolomics (UPLC-MS) was used to investigate the metabolic changes between oral secretion samples of 25 COVID-19 and 30 control participants. To obtain the specific metabolic changes of COVID-19, we selected 25 influenza patients to exclude the metabolic changes caused by the stress response of the immune system to the virus. Multivariate analysis (PCA and PLS-DA plots) and univariate analysis (students' t-test) were used to compare the differences between COVID-19 patients and the controls. Online hiplot tool was used to perform heatmap analysis. Metabolic pathway analysis was conducted by using the MetaboAnalyst 5.0 web application. RESULTS: PLS-DA plots showed significant separation of COVID-19 patients and the controls. A total of 45 differential metabolites between COVID-19 and control group were identified. Among them, 35 metabolites were defined as SARS-CoV-2 specific differential metabolites. Especially, the levels of cis-5,8,11,14,17-eicosapentaenoic acid and hexanoic acid changed dramatically based on the FC values. Pathway enrichment found the most significant pathways were tyrosine-related metabolism. Further, we found 10 differential metabolites caused by the virus indicating the body's metabolism changes after viral stimulation. Moreover, adenine and adenosine were defined as influenza virus-specific differential metabolites. CONCLUSIONS: This study revealed that 35 metabolites and tyrosine-related metabolism pathways were significantly changed after SARS-CoV-2 infection. The metabolic alterations of oral microenvironment in COVID-19 provided new insights into its molecular mechanisms for research and prognostic treatment.

A Systematic Review and Meta-Analysis on the Real-World Effectiveness of COVID-19 Vaccines against Infection, Symptomatic and Severe COVID-19 Disease Caused by the Omicron Variant (B.1.1.529).

Real-world data on the effectiveness of COVID-19 vaccines against the Omicron variant (B.1.1.529) is limited. This systematic review aimed to investigate the real-world effectiveness and durability of protection conferred by primary course and booster vaccines against confirmed Omicron infection, and severe outcomes. We systematically searched literature up to 1 August 2022. Meta-analysis was performed with the DerSimonian-Laird random-effects model to estimate the pooled vaccine effectiveness (VE). Overall, 28 studies were included representing 11 million individuals. The pooled VE against Omicron infection was 20.4% (95%CI: 12.1-28.7%) and 23.4% (95%CI: 13.5-33.3%) against symptomatic infection with variation based on vaccine type and age groups. VE sharply declined from 28.1% (95%CI: 19.1-37.1%) at three months to 3.9% (95%CI: -24.8-32.7%) at six months. Similar trends were observed for symptomatic Omicron infection. A booster dose restored protection against Omicron infection up to 51.1% (95%CI: 43.8-58.3%) and 57.3% (95%CI: 54.0-60.5%) against symptomatic infection within three months; however, this waned to 32.8% (95%CI: 16.8-48.7%) within six months. VE against severe Omicron infection following the primary course was 63.6% (95%CI: 57.5-69.7%) at three months, decreased to 49% (95%CI: 35.7-63.4%) within six months, and increased to 86% after the first or second booster dose.

Epidemiological and genetic characteristics of respiratory syncytial virus infection in children from Hangzhou after the peak of COVID-19.

BACKGROUND: Respiratory syncytial virus (RSV) is one of the main pathogens that causes acute lower respiratory tract infection (ARTI) in infants. During the Coronavirus Disease 2019 (COVID-19) pandemic, although strict interventions have been implemented, RSV infection has not decreased. OBJECTIVES: To study the epidemiological and genetic characteristics of RSV circulating in Hangzhou after the peak of COVID-19. METHODS: A total of 1225 nasopharyngeal swabs were collected from outpatients with ARTIs from July 2021 to January 2022 in The Children's Hospital, Zhejiang University School of Medicine. RESULTS: A total of 267 (21.79%) of the 1225 samples were RSV positive. There was no gender bias. However, an obvious age preference for infection was observed, and children aged 3-6 years were more susceptible, which was very different from previous RSV pandemic seasons. Phylogenetic analysis of 115 sequenced RSV isolates showed that all the RSV-A viruses belong to the ON1 subtype, which could be clustered into three clusters. While all the RSV-B viruses belong to BA9. Further analysis of the mutations highlights the fixation of ten mutations, which should be given extra attention regarding their biological properties. CONCLUSION: The incidence of RSV infection in preschool children reported in this study is high. Phylogenetic analysis showed that the subtype A ON1 genotype was the dominant strain in Hangzhou from July 2021 to January 2022.

Effect of Hand Hygiene Intervention in Community Kindergartens: A Quasi-Experimental Study.

This study aimed to evaluate the effect of hand hygiene interventions on the overall hand hygiene (HH) status of teaching instruction of hand hygiene in kindergartens, given the vulnerability of kindergarten children and their high risk due to infectious diseases and the current COVID-19 epidemic. We investigated the HH status of teachers from two kindergartens in the same community. The participants were recruited from 28 classes in both kindergartens. After completing the baseline survey, the intervention program consisted of three components: lectures on infectious diseases, lectures on HH, and seven-step hand washing techniques conducted in two kindergartens. The intervention program effectively increased teachers' perceived disease susceptibility (p < 0.05), reduced the total bacterial colonization of children's hands (p < 0.001), and improved the HH environment (p < 0.01). We recommend that health authorities or kindergartens adopt this HH intervention program to effectively improve the HH status in kindergartens and allow for preventive responses to the COVID-19 epidemic or other emerging infectious diseases.

Assessing quality of online learning platforms for in-service teachers’ professional development: The development and application of an instrument

To help optimize online learning platforms for in-service teachers’ professional development, this study aims to develop an instrument to assess the quality of this type of platforms on teacher satisfaction. After reliability and validity tests and expert empowerment, the 27-item instrument was formed. Based on the information systems (IS) success model, this instrument was designed to measure teacher perceptions of the quality of online learning platforms from three dimensions, namely, content quality, technical quality, and service quality. Moreover, the developed instrument was used to analyze the effects of the National Teacher Training Platform amid the COVID-19 outbreak in China. The findings revealed that the improvement of the platform’s style, tool function, operating efficiency, and teaching methods could enhance teachers’ experience of online training.

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.

Prevalence of Sarcopenia in Patients With COVID-19: A Systematic Review and Meta-Analysis

Background It has been speculated that patients with sarcopenia are aggravated by the current novel coronavirus disease 2019 (COVID-19) epidemic. However, there is substantial uncertainty regarding the prevalence of sarcopenia in patients with COVID-19. Objectives The purpose of the study was to systematically evaluate the prevalence of sarcopenia in patients with COVID-19, including stratification by gender, study location, study population, study design, and diagnostic criteria. Design This is the systematic literature review and meta-analysis. Methods An electronic search was performed in MEDLINE/PubMed, Embase, Cochrane Library, and Web of Science and Scopus to identify observational studies reporting a prevalence estimate for sarcopenia in patients with COVID-19. Studies were reviewed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and a meta-analysis was performed. Risk of bias (RoB) was assessed using the Newcastle–Ottawa Scale (NOS) for cohort studies and Joanna Briggs Institute (JBI) manual for cross-sectional studies, and Stata 14.0 was used to perform meta-analyses. Results A total of 4,639 studies were initially identified. After removing the duplicates and applying the selection criteria, we reviewed 151 full-text studies. A total of 21 studies, including 5,407 patients, were eligible for inclusion in this review finally. The prevalence of sarcopenia in patients with COVID-19 in individual studies varied from 0.8 to 90.2%. The pooled prevalence of sarcopenia in COVID-19 was 48.0% (95% confidence interval, CI: 30.8 to 65.1%, I2 = 99.68%, p = 0.000). We did not find any significant differences in the prevalence estimates between gender specificity (OR = 1.34;95% CI = 0.80–2.26;p = 0.001). By sex, the prevalence was 42.5% (95% CI: 31.7 to 53.4%) in men and 35.7% (95% CI: 24.2 to 47.2%) in women. The prevalence estimates significantly varied based on population settings and different diagnostic criteria of sarcopenia. ICU patients (69.7, 95% CI: 51.7 to 85.2%) were more likely to suffer from sarcopenia compared to other population settings. Conclusion To our knowledge, this is the first meta-analysis reporting on the prevalence of sarcopenia in patients with COVID-19. Sarcopenia is frequently observed in patients with COVID-19, with varying prevalence across population settings. This study would be useful for clinicians to prompt the increasing awareness of identifying sarcopenia and developing interventions at patients with COVID-19 with high risk of sarcopenia. Further prospective longitudinal studies to define the association of sarcopenia and its prognostic outcomes in COVID-19 survivors are urgently needed to propose the most appropriate treatment strategies during their admission and discharge. Systematic Review Registration [www.crd.york.ac.uk/prospero/], identifier [CRD42022300431].

Intervention of resource allocation strategies on spatial spread of epidemics.

Medical resources are crucial in mitigating epidemics, especially during pandemics such as the ongoing COVID-19. Thereby, reasonable resource deployment inevitably plays a significant role in suppressing the epidemic under limited resources. When an epidemic breaks out, people can produce resources for self-protection or donate resources to help others for treatment. That is, the exchange of resources also affects the transmission between individuals, thus, altering the epidemic dynamics. To understand factors on resource deployment and the interplay between resource and transmission we construct a metapopulation network model with resource allocation. Our results indicate actively or promptly donating resources is not helpful to suppress the epidemic under both homogeneous population distribution and heterogeneous population distribution. Besides, strengthening the speed of resources production can significantly increase the recovery rate so that they reduce the final outbreak size. These results may provide policy guidance toward epidemic containment.

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.

Intervention strategies for epidemic spreading on bipartite metapopulation networks.

Intervention strategies are of great significance for controlling large-scale outbreaks of epidemics. Since the spread of epidemic depends largely on the movement of individuals and the heterogeneity of the network structure, understanding potential factors that affect the epidemic is fundamental for the design of reasonable intervention strategies to suppress the epidemic. So far, most of previous studies mainly consider intervention strategies on the network composed of a single type of locations, while ignoring the movement behavior of individuals to and from locations that are composed of different types, i.e., residences and public places, which often presents heterogeneous structure. In addition, the transmission rate in public places with different population flows is heterogeneous. Inspired by the above observation, we build a bipartite metapopulation network model and propose intervention strategies based on the importance of public places. With the Markovian Chain approach, we derive the epidemic threshold under intervention strategies. Experimental results show that, compared with the uniform intervention to residences or public places, nonuniform intervention to public places is more effective for suppressing the epidemic with an increased epidemic threshold. Specifically, interventions to public places with large degree can further suppress the epidemic. Our study opens a new path for understanding the spatial epidemic spread and provides guidance for the design of intervention strategies for epidemics in the future.

Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China.

The COVID-19 restrictions in 2020 have led to distinct variations in NO2 and O3 concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China's 2018-2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO2 and O3 are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO2 concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018-2020 to a reduction in NO2 by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO2 of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO2 reductions, respectively. For O3 the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and -0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP's contribution highlights the effectiveness of the Chinese government's air-quality regulations on NO2 reduction.