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1.
International Journal of Environmental Research and Public Health ; 19(16):10344, 2022.
Article in English | MDPI | ID: covidwho-1997592

ABSTRACT

Background: In the period of the COVID-19 pandemic, the level of college students' physical exercise, the detection rate of negative emotions, and their correlation should attract extensive attention. Therefore, this study aimed to explore the correlation between college students' physical exercise and negative emotions. Methods: Data were collected via a web-based cross-sectional survey. A questionnaire survey was conducted among 3118 college students from five universities in Shanghai in March 2022. In addition to sociodemographic information, measures included Physical Activity Rating Scale (PARS-3) and Depression Anxiety Stress Scale (DASS). The chi-squared test and logistic regression were used to analyze the differences and test the relative risk of negative emotions caused by different amounts of physical exercise. Results: Most students (66.1%) performed a small amount of physical exercise. Male students' physical-exercise level was higher than female students', and the detection rate of negative emotions was lower than that of female students. Moderate and low physical-exercise levels were associated with a higher risk of depression (beta of 0.289 and 0.345, respectively) and anxiety (beta of 0.301 and 0.418) symptoms than high physical-exercise level. Conclusions: The anxiety symptoms of college students were significant during the COVID-19 pandemic period. The physical-exercise behavior of college students was closely related to negative emotions, and the weakening of physical-exercise behavior was one of the factors that induced negative emotions in college students.

3.
iScience ; : 104886, 2022 Aug 09.
Article in English | MEDLINE | ID: covidwho-1977403

ABSTRACT

The emergence of the SARS-CoV-2 Omicron BA.1 (B.1.1.529) variant has raised questions regarding resistance to neutralizing antibodies elicited by natural infection or immunization. We examined the neutralization activity of sera collected from previously SARS-CoV-2-infected individuals and SARS-CoV-2 naïve individuals who received BBIBP-CorV or CoronaVac to BA.1 and the earlier variants Alpha, Beta, and Delta. Both sera from convalescent patients over three months after infection and two-dose BBIBP-CorV or CoronaVac vaccine recipients barely inhibited BA.1, less effectively neutralized Beta and Delta, and moderately neutralized Alpha. However, administering a single dose of BBIBP-CorV or CoronaVac in previously infected individuals or a third dose booster vaccination of BBIBP-CorV or CoronaVac in previously vaccinated individuals enhances neutralizing activity against BA.1 and other variants, albeit with a lower antibody titer for BA.1. Our data suggest that a booster vaccination is important to broaden neutralizing antibody responses against the variants.

4.
Sci Immunol ; : eabp9962, 2022 Aug 04.
Article in English | MEDLINE | ID: covidwho-1973781

ABSTRACT

The rapid evolution of SARS-CoV-2 viruses, such as the Omicron variants which are highly transmissible and immune evasive, underscores the need to develop therapeutic antibodies with broad neutralizing activities. Here, we used the LIBRA-seq technology, which identified SARS-CoV-2 specific B cells via DNA-barcoding and subsequently single cell sequenced BCRs, to identify an antibody, SW186, which could neutralize major SARS-CoV-2 variants of concern, including Beta, Delta, and Omicron, as well as SARS-CoV-1. The cryo-EM structure of SW186 bound to the receptor-binding domain (RBD) of the viral spike protein showed that SW186 interacted with an epitope of the RBD that is not at the interface of its binding to the ACE2 receptor but highly conserved among SARS coronaviruses. This epitope encompasses a glycosylation site (N343) of the viral spike protein. Administration of SW186 in mice after they were infected with SARS-CoV-2 Alpha, Beta, or Delta variants reduced the viral loads in the lung. These results demonstrated that SW186 neutralizes diverse SARS coronaviruses by binding to a conserved RBD epitope, which could serve as a target for further antibody development.

5.
Comput Ind Eng ; 171: 108389, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1914242

ABSTRACT

In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.

6.
PLoS One ; 17(6): e0269882, 2022.
Article in English | MEDLINE | ID: covidwho-1892328

ABSTRACT

INTRODUCTION: Coronavirus Disease 2019 (COVID-19) has made a serious public health threat worldwide. Recent evidence has indicated that COVID-19 patients in convalescence frequently experience insomnia, which reduces their quality of life and causes unknown risks. The positive effect of cognitive behavior on insomnia has been well addressed in previous studies. Given the high infectivity and epidemicity of COVID-19, Internet-delivered intervention may be safer than face-to-face treatment. However, whether Internet-delivered cognitive behavioral therapy can effectively improve the insomnia of COVID-19 patients in convalescence has not been completely determined yet. Therefore, we conducted a meta-analysis and systematic review to evaluate the effects of Internet-delivered cognitive behavioral therapy on insomnia in COVID-19 patients in convalescence, with the aim to confer some guidance for its clinical application. METHODS AND ANALYSIS: This systematic review and meta-analysis has been registered in the International Prospective Register of Systematic Reviews (PROSPERO). Two researchers will retrieve the relevant literature on Internet-delivered cognitive behavioral therapy for insomnia in convalescent patients with COVID-19 in PubMed, Web of Science, Embase, MEDLINE, Cochrane Library, Clinical Trials gov, Chinese Biomedical Literature Database (CBM), and Chinese National Knowledge Infrastructure (CNKI) from inception to 11th of December. In addition, we will review the relevant trials and references of the included literature and manually searched the grey literature. The two researchers will independently extracted data and information and evaluated the quality of the included literature. The Review Manager software (version 5.3) and Stata software (version 14.0) will be used for data analysis. The mean difference or the standardized mean difference of 95% CI will be used to calculate continuous variables to synthesize the data. In addition, I2 and Cochrane will be used for heterogeneity assessment. TRIAL REGISTRATION: PROSPERO registration number CRD42021271278.


Subject(s)
COVID-19 , Cognitive Behavioral Therapy , Sleep Initiation and Maintenance Disorders , COVID-19/complications , COVID-19/therapy , Convalescence , Humans , Internet , Meta-Analysis as Topic , Quality of Life , Research Design , Sleep Initiation and Maintenance Disorders/complications , Sleep Initiation and Maintenance Disorders/therapy , Systematic Reviews as Topic
7.
Cardiol Rev ; 2022 Jun 09.
Article in English | MEDLINE | ID: covidwho-1891079

ABSTRACT

COVID-19 was declared a global pandemic in March 2020, and since then it has had a significant impact on healthcare including on solid-organ transplantation. Based on age, immunosuppression and prevalence of chronic comorbidities, heart transplant recipients are at high risk of adverse outcomes associated with COVID-19. In our center, 31 heart transplant patients were diagnosed with COVID-19 from March 2020 to September 2021. They required: hospitalization (39%), intensive care (10%) and mechanical ventilation (6%) of patients with overall short-term mortality of 3%. Early outpatient use of anti-SARS-CoV-2 monoclonal antibodies in our heart transplant recipients was associated with a reduction in the risk of hospitalization, need for intensive care, and death related to COVID-19. In prior multicenter studies, completed in different geographic areas and pandemic timeframes, diverse rates of hospitalization (38-91%), mechanical ventilation (4-38%) and death (16-33%) have been reported. Progression of disease and adverse outcomes were most significantly associated with severity of lymphopenia, chronic co-morbid conditions like older age, chronic allograft vasculopathy, increased body mass index as well as intensity of baseline immune-suppression. In this article, we also review the current roles and limitations of vaccination, anti-viral agents, and anti-SARS-CoV-2 monoclonal antibodies in the management of heart transplant recipients. Our single center experience, considered together with other studies indicates a trend toward improved outcomes among heart transplant patients with COVID-19.

8.
Environ Pollut ; 307: 119510, 2022 Aug 15.
Article in English | MEDLINE | ID: covidwho-1851033

ABSTRACT

Atmospheric nitrogen dioxide (NO2) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO2 monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO2 from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO2 with high accuracy, with a coefficient of determination (R2) of 0.89 and a root mean squared error of 5.8 µg/m3 for sample-based 10-fold cross-validation. Based on the surface NO2 concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO2 pollution in north China. We found substantial drops in surface NO2 concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO2 exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO2 among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO2 concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO2 dataset, can benefit air quality monitoring, forecasting, and health burden assessments.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , China , Communicable Disease Control , Environmental Monitoring/methods , Humans , Neural Networks, Computer , Nitrogen Dioxide/analysis , Particulate Matter/analysis
9.
Comput Struct Biotechnol J ; 20: 2212-2222, 2022.
Article in English | MEDLINE | ID: covidwho-1814300

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide as a severe pandemic and caused enormous global health and economical damage. Since December 2019, more than 197 million cases have been reported, causing 4.2 million deaths. In the settings of pandemic it is an urgent necessity for the development of an effective COVID-19 treatment. While in-vitro screening of hundreds of antibodies isolated from convalescent patients is challenging due to its high cost, use of computational methods may provide an attractive solution in selecting the top candidates. Here, we developed a computational approach (SARS-AB) for binding prediction of spike protein SARS-CoV-2 with monoclonal antibodies. We validated our approach using existing structures in the protein data bank (PDB), and demonstrated its prediction power in antibody-spike protein binding prediction. We further tested its performance using antibody sequences from the literature where crystal structure is not available, and observed a high prediction accuracy (AUC = 99.6%). Finally, we demonstrated that SARS-AB can be used to design effective antibodies against novel SARS-CoV-2 mutants that might escape the current antibody protections. We believe that SARS-AB can significantly accelerate the discovery of neutralizing antibodies against SARS-CoV-2 and its mutants.

10.
Chem Phys Lett ; 800: 139663, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-1814291

ABSTRACT

In order to control COVID-19, rapid and accurate detection of the pathogenic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an urgent task. The target spike proteins of SARS-CoV-2 have been detected experimentally via Raman spectroscopy. However, there lacks high-accuracy theoretical Raman spectra of the spike proteins to as a standard reference for the clinic diagnostic purpose. In this paper, we propose a large fragment method to construct the high-precision Raman spectra for the spike proteins. The large fragment method not only reduces the calculation error but also improves the accuracy of the protein Raman spectra by completely calculating the interactions within the large fragment. The Pearson correlation coefficient of theoretical Raman spectra is greater than 0.929 or more. Compared with the experimental spectra, the characteristic patterns are easily visible. This work provides a detection standard for the spike proteins which shall bring a step closer to the fast recognition of SARS-CoV-2 via Raman spectroscopy method.

11.
Risk Manag Healthc Policy ; 15: 643-655, 2022.
Article in English | MEDLINE | ID: covidwho-1809141

ABSTRACT

Purpose: Considering high risk of imported epidemic in port cities, it is necessary to estimate COVID-19 vaccine acceptability and to promote vaccination coverage of high-risk occupations. Methods: A cross-sectional survey was carried out among the occupations in Yantai city, China, using an online questionnaire service platform. Targeted strategies were developed based on the survey results. In addition, periodic monitoring of the vaccination rate was provided in order to evaluate the effectiveness of the strategies. Results: A total of 2231 (73.22%) of 3047 participants were willing to accept the vaccine, while 2.53% refused and 24.25% were not sure. Frontline port workers (133/152, 87.50%) and healthcare workers (999/1155, 86.49%) had higher intentions to accept, while public places and commercial service staff (584/1011, 57.76%) had the lowest. The reasons for refusal and hesitation were mainly "doubt of safety or effectiveness" (661/816, 81.00%) and "hearing previous news about vaccines" (455/816, 55.76%). Multilevel strategies such as adequate organizations, health education and promotion, and easy access to vaccination were promoted by local authorities in collaboration with schools, hospitals, enterprises and institutions. The study showed a significant increase in vaccination rate among these occupations after the implementation of these strategies (p<0.001), reaching 87.96%. Conclusion: COVID-19 vaccine acceptability among high-risk occupations was unsatisfactory before the stage of emergency vaccination. An advanced understanding of vaccine attitudes and acceptance can aid in the development of focused immunization promotion programs. It is worth emphasizing that wide strategies with the strong support and enthusiastic cooperation of the government and the industry executive can contribute to increasing occupations' acceptance of the ongoing COVID-19 immunization project.

12.
Signal Transduct Target Ther ; 7(1): 137, 2022 04 25.
Article in English | MEDLINE | ID: covidwho-1805598

ABSTRACT

Whether and how innate antiviral response is regulated by humoral metabolism remains enigmatic. We show that viral infection induces progesterone via the hypothalamic-pituitary-adrenal axis in mice. Progesterone induces downstream antiviral genes and promotes innate antiviral response in cells and mice, whereas knockout of the progesterone receptor PGR has opposite effects. Mechanistically, stimulation of PGR by progesterone activates the tyrosine kinase SRC, which phosphorylates the transcriptional factor IRF3 at Y107, leading to its activation and induction of antiviral genes. SARS-CoV-2-infected patients have increased progesterone levels, and which are co-related with decreased severity of COVID-19. Our findings reveal how progesterone modulates host innate antiviral response, and point to progesterone as a potential immunomodulatory reagent for infectious and inflammatory diseases.


Subject(s)
COVID-19 , SARS-CoV-2 , Animals , Antiviral Agents , COVID-19/genetics , Humans , Hypothalamo-Hypophyseal System , Immunity, Innate/genetics , Mice , Pituitary-Adrenal System , Progesterone/pharmacology
13.
J Med Virol ; 94(5): 1967-1975, 2022 05.
Article in English | MEDLINE | ID: covidwho-1777577

ABSTRACT

We aimed to assess whether blood glucose control can be used as predictors for the severity of 2019 coronavirus disease (COVID-19) and to improve the management of diabetic patients with COVID-19. A two-center cohort with a total of 241 confirmed cases of COVID-19 with definite outcomes was studied. After the diagnosis of COVID-19, the clinical data and laboratory results were collected, the fasting blood glucose levels were followed up at initial, middle stage of admission and discharge, the severity of the COVID-19 was assessed at any time from admission to discharge. Hyperglycemia patients with COVID-19 were divided into three groups: good blood glucose control, fair blood glucose control, and blood glucose deterioration. The relationship of blood glucose levels, blood glucose control status, and severe COVID-19 were analyzed by univariate and multivariable regression analysis. In our cohort, 21.16% were severe cases and 78.84% were nonsevere cases. Admission hyperglycemia (adjusted odds ratio [aOR], 1.938; 95% confidence interval [95% CI], 1.387-2.707), mid-term hyperglycemia (aOR, 1.758; 95% CI, 1.325-2.332), and blood glucose deterioration (aOR, 22.783; 95% CI, 2.661-195.071) were identified as the risk factors of severe COVID-19. Receiver operating characteristic (ROC) curve analysis, reaching an area under ROC curve of 0.806, and a sensitivity and specificity of 80.40% and 68.40%, respectively, revealed that hyperglycemia on admission and blood glucose deterioration of diabetic patients are potential predictive factors for severe COVID-19. Our results indicated that admission hyperglycemia and blood glucose deterioration were positively correlated with the risk factor for severe COVID-19, and deterioration of blood glucose may be more likely to the occurrence of severe illness in COVID-19.


Subject(s)
COVID-19 , Diabetes Mellitus , Hyperglycemia , Blood Glucose/analysis , COVID-19/complications , COVID-19/epidemiology , Cohort Studies , Diabetes Mellitus/epidemiology , Humans , Hyperglycemia/epidemiology , Prognosis , Retrospective Studies , Risk Factors
14.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-331560

ABSTRACT

Since the outbreak of COVID-19 in 2019, the 2019-nCov coronavirus has appeared diverse mutational characteristics due to its own flexible conformation. One multiple-mutant strain (Omicron) with surprisingly infective activity outburst, and affected the biological activities of current drugs and vaccines, making the epidemic significantly difficult to prevent and control, and seriously threaten health around the world. Importunately exploration of mutant characteristics for novel coronavirus Omicron can supply strong theoretical guidance for learning binding mechanism of mutant viruses. What’s more, full acknowledgement of key mutated-residues on Omicron strain can provide new methodology of the novel pathogenic mechanism to human ACE2 receptor, as well as the subsequent vaccine development. In this research, 3D structures of 32 single-point mutations of 2019-nCov were firstly constructed, and 32-sites multiple-mutant Omicron were finally obtained based one the wild-type virus by homology modeling method. One total number of 33 2019-nCov/ACE2 complex systems were acquired by protein-protein docking, and optimized by using preliminary molecular dynamics simulations. Binding free energies between each 2019-nCov mutation system and human ACE2 receptor were calculated, and corresponding binding patterns especially the regions adjacent to mutation site were analyzed. The results indicated that one total number of 6 mutated sites on the Omicron strain played crucial role in improving binding capacities from 2019-nCov to ACE2 protein. Subsequently, we performed long-term molecular dynamic simulations and protein-protein binding energy analysis for the selected 6 mutations. 3 infected individuals, the mutants T478K, Q493R and G496S with lower binding energies − 66.36, -67.98 and − 67.09 kcal/mol also presents the high infectivity. These findings indicated that the 3 mutations T478K, Q493R and G496S play the crucial roles in enhancing binding affinity of Omicron to human ACE2 protein. All these results illuminate important theoretical guidance for future virus detection of the Omicron epidemic, drug research and vaccine development.

15.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326491

ABSTRACT

We propose a Bayesian hierarchical model to simultaneously estimate mean based changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak and strong spatial correlation, and weak and strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study.

16.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-311718

ABSTRACT

Background: The COVID-19 epidemic has had an extreme impact on society. This study aimed to discuss this epidemic in the U.S. and explore the association between COVID-19 daily incidence rate and influencing factors including people’s implementation of states’ quarantine policy and environmental factors including temperature, humidity and so on. Methods: . Data of 50 states in U.S. were used as the research subjects. A panel data model was established based on the daily incidence rate and influencing factors from 15 March to 30 September, 2020. The period was analyzed both unsegmented and segmented. The k-means clustering method was used to cluster the states, and panel linear regression method was used for correlation analysis. Results: . The characteristics of the daily incidence rate and factors of the three categories were different after clustering. The daily residents at home, proportion of travel people, humidity and incidence rate were negatively correlated, while the daily temperature and incidence rate were positively correlated after unsegmented multivariate analysis. While after segmented analysis, the air pressure and the temperature showed a trend that was negatively correlated with the daily incidence rate respectively in the first and the fifth segment, other indicators showed the analogous results. At the same time, this study also completed the regression analysis after classification of the three groups. Compared with results without classification, there was a decrease of the number of significant independent variables. Conclusions: . The spread of COVID-19 in 50 states in U.S. was related to quarantine measures, temperature and humidity. The progress of the epidemic would be relatively slow if people chose to stay at home. Besides, the increase in temperature (<84.2℉) could be conducive to the spread of the epidemic, while the increase in relative humidity (40~70%) might inhibit the spread of the virus to a certain degree.

17.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311717

ABSTRACT

The Coronavirus Disease of 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) threatens global public health and economy. Therapeutic options such as monoclonal antibodies (mAbs) against SARS-CoV-2 are in urgent need. We have identified potent monoclonal antibodies binding to SARS-CoV-2 Spike protein from COVID-19 convalescent patients and one of these antibodies, P4A1, interacts directly and covers the majority of the Receptor Binding Motif (RBM) of Spike receptor-binding domain (RBD), shown by high-resolution complex structure analysis. We further demonstrated P4A1 binding and neutralizing activities against wild type and mutant spike proteins. P4A1 was subsequently engineered to reduce the potential risk for antibody-dependent enhancement (ADE) of infection and to extend its half-life. The engineered mAb exhibits optimized pharmacokinetic and safety profile, and results in complete viral clearance in a rhesus monkey model of COVID-19 following a single injection.

18.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315938

ABSTRACT

Early prediction of disease severity is important for effective treatment of COVID-19. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets {Age, WBC, LYMC, NEUT}, {Age, WBC, LMYC} and {Age, NEUT, LYMC}. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. This result indicated that using age and the indicators selected by the MCDM algorithm from blood routine test results can effectively predict the severity of COVID-19 at an early stage.

19.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315155

ABSTRACT

Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.

20.
Sci China Chem ; 65(3): 630-640, 2022.
Article in English | MEDLINE | ID: covidwho-1669939

ABSTRACT

Outbreaks of both influenza virus and the novel coronavirus SARS-CoV-2 are serious threats to human health and life. It is very important to establish a rapid, accurate test with large-scale detection potential to prevent the further spread of the epidemic. An optimized RPA-Cas12a-based platform combined with digital microfluidics (DMF), the RCD platform, was established to achieve the automated, rapid detection of influenza viruses and SARS-CoV-2. The probe in the RPA-Cas12a system was optimized to produce maximal fluorescence to increase the amplification signal. The reaction droplets in the platform were all at the microliter level and the detection could be accomplished within 30 min due to the effective mixing of droplets by digital microfluidic technology. The whole process from amplification to recognition is completed in the chip, which reduces the risk of aerosol contamination. One chip can contain multiple detection reaction areas, offering the potential for customized detection. The RCD platform demonstrated a high level of sensitivity, specificity (no false positives or negatives), speed (≤30 min), automation and multiplexing. We also used the RCD platform to detect nucleic acids from influenza patients and COVID-19 patients. The results were consistent with the findings of qPCR. The RCD platform is a one-step, rapid, highly sensitive and specific method with the advantages of digital microfluidic technology, which circumvents the shortcomings of manual operation. The development of the RCD platform provides potential for the isothermal automatic detection of nucleic acids during epidemics. Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s11426-021-1169-1.

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