Natural Evolution of Porcine Epidemic Diarrhea Viruses Isolated from Maternally Immunized Piglets

Simple SummaryDuring the long-term co-evolution of the virus and the host, even closely related vaccines may emerge with incomplete protective immunity due to the mutations or deletions of amino acids at specific antigenic sites. The mutation of PEDV was accelerated by the recombination of different strains and the mutation of the strains adapting to the environment. These mutations either cause immune escape from conventional vaccines or affect the virulence of the virus. Therefore, researching and developing new vaccines with cross-protection through continuous monitoring, isolation and sequencing are important to determine whether their genetic characteristics are changed and to evaluate the protective efficacy of current vaccines. The porcine epidemic diarrhea virus (PEDV) can cause severe piglet diarrhea or death in some herds. Genetic recombination and mutation facilitate the continuous evolution of the virus (PEDV), posing a great challenge for the prevention and control of porcine epidemic diarrhea (PED). Disease materials of piglets with PEDV vaccination failure in some areas of Shanxi, Henan and Hebei provinces of China were collected and examined to understand the prevalence and evolutionary characteristics of PEDV in these areas. Forty-seven suspicious disease materials from different litters on different farms were tested by multiplex PCR and screened by hematoxylin-eosin staining and immunohistochemistry. PEDV showed a positivity rate of 42.6%, infecting the small and large intestine and mesenteric lymph node tissues. The isolated strains infected Vero, PK-15 and Marc-145 multihost cells and exhibited low viral titers in all three cell types, as indicated by their growth kinetic curves. Possible putative recombination events in the isolates were identified by RDP4.0 software. Sequencing and phylogenetic analysis showed that compared with the classical vaccine strain, PEDV SX6 contains new insertion and mutations in the S region and belongs to genotype GIIa. Meanwhile, ORF3 has the complete amino acid sequence with aa80 mutated wild strains, compared to vaccine strains CV777, AJ1102, AJ1102-R and LW/L. These results will contribute to the development of new PEDV vaccines based on prevalent wild strains for the prevention and control of PED in China.

Infectious bronchitis virus nucleocapsid protein suppressed type I interferon production by interfering with the binding of MDA5-dsRNA and interacting with LGP2.

The type I interferon (IFN-I) is a critical component of the innate immune responses, and Coronaviruses (CoVs) from both the Alphacoronavirus and Betacoronavirus genera interfere with the IFN-I signaling pathway in various ways. Of the gammacoronaviruses that mainly infect birds, little is known about how infectious bronchitis virus (IBV), evades or interferes with the innate immune responses in avian hosts since few IBV strains have been adapted to grow in avian passage cells. Previously, we reported that a highly pathogenic IBV strain GD17/04 has adaptability in an avian cell line, providing a material basis for further study on the interaction mechanism. In the present work, we describe the suppression of IBV to IFN-I and the potential role of IBV-encoded nucleocapsid (N) protein. We show that IBV significantly inhibits the poly I: C-induced IFN-I production, accordingly the nuclear translocation of STAT1, and the expression of IFN-stimulated genes (ISGs). A detailed analysis revealed that N protein, acting as an IFN-I antagonist, significantly impedes the activation of the IFN-ß promoter stimulated by MDA5 and LGP2 but does not counteract its activation by MAVS, TBK1, and IRF7. Further results showed that IBV N protein, verified to be an RNA-binding protein, interferes with MDA5 recognizing double-stranded RNA (dsRNA). Moreover, we found that the N protein targets LGP2, which is required in the chicken IFN-I signaling pathway. Taken together, this study provides a comprehensive analysis of the mechanism by which IBV evades avian innate immune responses.

Comprehensive analysis of disinfectants on the horizontal transfer of antibiotic resistance genes.

The propagation of antimicrobial resistance (AMR) is constantly paralyzing our healthcare systems. In addition to the pressure of antibiotic selection, the roles of non-antibiotic compounds in disseminating antibiotic resistance genes (ARGs) are a matter of great concerns. This study aimed to explore the impact of different disinfectants on the horizontal transfer of ARGs and their underlying mechanisms. First, the effects of different kinds of disinfectants on the conjugative transfer of RP4-7 plasmid were evaluated. Results showed that quaternary ammonium salt, organic halogen, alcohol and guanidine disinfectants significantly facilitated the conjugative transfer. Conversely, heavy-metals, peroxides and phenols otherwise displayed an inhibitory effect. Furthermore, we deciphered the mechanism by which guanidine disinfectants promoted conjugation, which includes increased cell membrane permeability, over-production of ROS, enhanced SOS response, and altered expression of conjugative transfer-related genes. More critically, we also revealed that guanidine disinfectants promoted bacterial energy metabolism by enhancing the activity of electron transport chain (ETC) and proton force motive (PMF), thus promoting ATP synthesis and flagellum motility. Overall, our findings reveal the promotive effects of disinfectants on the transmission of ARGs and highlight the potential risks caused by the massive use of guanidine disinfectants, especially during the COVID-19 pandemic.

Computed tomography–based COVID–19 triage through a deep neural network using mask–weighted global average pooling

Background There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. Methods A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. Results The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. Conclusions This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.

Background: There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. Methods: A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. Results: The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. Conclusions: This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

Delay in seeking health care from community residents during a time with low prevalence of COVID-19: A cross-sectional national survey in China.

Background: The pandemic of COVID-19 has significant implications on health resources allocation and health care delivery. Patients with non-COVID illness may have to change their care seeking behaviors to mitigate the risk of infections. The research aimed to investigate potential delay of community residents in seeking health care at a time with an overall low prevalence of COVID-19 in China. Methods: An online survey was conducted in March 2021 on a random sample drawn from the registered survey participants of the survey platform Wenjuanxing. The respondents who reported a need for health care over the past month (n = 1,317) were asked to report their health care experiences and concerns. Logistic regression models were established to identify predictors of the delay in seeking health care. The selection of independent variables was guided by the Andersen's service utilization model. All data analyses were performed using SPSS 23.0. A two-sided p value of <0.05 was considered as statistically significant. Key results: About 31.4% of respondents reported delay in seeking health care, with fear of infection (53.5%) as a top reason. Middle (31-59 years) age (AOR = 1.535; 95% CI, 1.132 to 2.246), lower levels of perceived controllability of COVID-19 (AOR = 1.591; 95% CI 1.187 to 2.131), living with chronic conditions (AOR = 2.008; 95% CI 1.544 to 2.611), pregnancy or co-habiting with a pregnant woman (AOR = 2.115; 95% CI 1.154 to 3.874), access to Internet-based medical care (AOR = 2.529; 95% CI 1.960 to 3.265), and higher risk level of the region (AOR = 1.736; 95% CI 1.307 to 2.334) were significant predictors of the delay in seeking health care after adjustment for variations of other variables. Medical consultations (38.7%), emergency treatment (18.2%), and obtainment of medicines (16.5%) were the top three types of delayed care, while eye, nose, and throat diseases (23.2%) and cardiovascular and cerebrovascular diseases (20.8%) were the top two conditions relating to the delayed care. Self-treatment at home was the most likely coping strategy (34.9%), followed by Internet-based medical care (29.2%) and family/friend help (24.0%). Conclusions: Delay in seeking health care remained at a relatively high level when the number of new COVID-19 cases was low, which may present a serious health risk to the patients, in particular those living with chronic conditions who need continuous medical care. Fear of infection is the top reason for the delay. The delay is also associated with access to Internet-based medical care, living in a high risk region, and perceived low controllability of COVID-19.

Population genetic characteristics of Aedes aegypti in 2019 and 2020 under the distinct circumstances of dengue outbreak and the COVID-19 pandemic in Yunnan Province, China.

Introduction: Since Aedes aegypti invaded Yunnan Province in 2002, its total population has continued to expand. Shi et al. used microsatellite and mitochondrial molecular markers to study the Ae. aegypti populations in Yunnan Province in 2015 and 2016, found that it showed high genetic diversity and genetic structure. However, there are few studies on the population genetic characteristics of Ae. aegypti in Yunnan Province under different levels of human intervention. This study mainly used two common types of molecular markers to analyze the genetic characteristics of Ae. aegypti, revealing the influence of different input, prevention and control pressures on the genetic diversity and structure of this species. Understanding the genetic characteristics of Ae. aegypti populations and clarifying the diversity, spread status, and source of invasion are essential for the prevention, control and elimination of this disease vector. Methods: We analyzed the genetic diversity and genetic structure of 22 populations sampled in Yunnan Province in 2019 and 17 populations sampled in 2020 through nine microsatellite loci and COI and ND4 fragments of mitochondrial DNA. In 2019, a total of 22 natural populations were obtained, each containing 30 samples, a total of 660 samples. In 2020, a total of 17 natural populations were obtained. Similarly, each population had 30 samples, and a total of 510 samples were obtained. Results: Analysis of Ae. aegypti populations in 2019 and 2020 based on microsatellite markers revealed 67 and 72 alleles, respectively. The average allelic richness of the populations in 2019 was 3.659, while that in 2020 was 3.965. The HWE analysis of the 22 populations sampled in 2019 revealed significant departure only in the QSH-2 population. The 17 populations sampled in 2020 were all in HWE. The average polymorphic information content (PIC) values were 0.546 and 0.545, respectively, showing high polymorphism. The average observed heterozygosity of the 2019 and 2020 populations was 0.538 and 0.514, respectively, and the expected average heterozygosity was 0.517 and 0.519, showing high genetic diversity in all mosquito populations. By analyzing the COI and ND4 fragments in the mitochondrial DNA of Ae. aegypti, the populations sampled in 2019 had a total of 10 COI haplotypes and 17 ND4 haplotypes. A total of 20 COI haplotypes were found in the populations sampled in 2020, and a total of 24 ND4 haplotypes were obtained. STRUCTURE, UPGMA and DAPC cluster analyses and a network diagram constructed based on COI and ND4 fragments showed that the populations of Ae. aegypti in Yunnan Province sampled in 2019 and 2020 could be divided into two clusters. At the beginning of 2020, due to the impact of COVID-19, the flow of goods between the port areas of Yunnan Province and neighboring countries was reduced, and the sterilization was more effective when goods enter the customs, leading to different immigration pressures on Ae. aegypti population in Yunnan Province between 2019 and 2020, the source populations of the 2019 and 2020 populations changed. Mantel test is generally used to detect the correlation between genetic distance and geographical distance, the analysis indicated that population geographic distance and genetic distance had a moderately significant correlation in 2019 and 2020 (2019: p < 0.05 R2 = 0.4807, 2020: p < 0.05 R2 = 0.4233). Conclusion: Ae. aegypti in Yunnan Province maintains a high degree of genetic diversity. Human interference is one reason for the changes in the genetic characteristics of this disease vector.

Disinfectants facilitate the transformation of exogenous antibiotic resistance genes via multiple pathways.

The prevalence and spread of multidrug-resistant (MDR) bacteria pose a global challenge to public health. Natural transformation is one of the essential ways for horizontal transfer of antibiotic resistance genes (ARGs). Although disinfectants are frequently used during COVID-19, little is known about whether these disinfectants are associated with the transformation of plasmid-borne ARGs. In our study, we assessed the effect of some disinfectants on bacterial transformation using resistance plasmids as extracellular DNA and E. coli DH5α as the recipient bacteria. The results showed that these disinfectants at environmentally relevant concentrations, including benzalkonium bromide (BB), benzalkonium chloride (BC) and polyhexamethylene guanidine hydrochloride (PHMG), significantly enhanced the transformation of plasmid-encoded ARGs. Furthermore, we investigated the mechanisms underlying the promotive effect of disinfectants on transformation. We revealed that the addition of disinfectants significantly increased the membrane permeability and promoted membrane-related genes expression. Moreover, disinfectants led to the boosted bacterial respiration, ATP production and flagellum motility, as well as increased expression of bacterial secretion system-related genes. Together, our findings shed insights into the spread of ARGs through bacterial transformation and indicate potential risks associated with the widespread use of disinfectants.

From Immunogen to COVID-19 vaccines: Prospects for the post-pandemic era.

The COVID-19 pandemic has affected millions of people and posed an unprecedented burden on healthcare systems and economies worldwide since the outbreak of the COVID-19. A considerable number of nations have investigated COVID-19 and proposed a series of prevention and treatment strategies thus far. The pandemic prevention strategies implemented in China have suggested that the spread of COVID-19 can be effectively reduced by restricting large-scale gathering, developing community-scale nucleic acid testing, and conducting epidemiological investigations, whereas sporadic cases have always been identified in numerous places. Currently, there is still no decisive therapy for COVID-19 or related complications. The development of COVID-19 vaccines has raised the hope for mitigating this pandemic based on the intercross immunity induced by COVID-19. Thus far, several types of COVID-19 vaccines have been developed and released to into financial markets. From the perspective of vaccine use in globe, COVID-19 vaccines are beneficial to mitigate the pandemic, whereas the relative adverse events have been reported progressively. This is a review about the development, challenges and prospects of COVID-19 vaccines, and it can provide more insights into all aspects of the vaccines.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-ray.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration. In this work, we hereby propose a semantic-powered explainable model-free few-shot learning scheme to quickly and precisely diagnose COVID-19 with higher reliability and transparency. Specifically, we design a Report Image Explanation Cell (RIEC) to exploit clinically indicators derived from radiology reports as interpretable driver to introduce prior knowledge at training. Meanwhile, multi-task colla-borative diagnosis strategy (MCDS) is developed to construct [Formula: see text]-way [Formula: see text]-shot tasks, which adopts a cyclic and collaborative training approach for producing better generalization performance on new tasks. Extensive experiments demonstrate that the proposed scheme achieves competitive results (accuracy of 98.91%, precision of 98.95%, recall of 97.94% and F1-score of 98.57%) to diagnose COVID-19 and other pneumonia infected categories, even with only 200 paired CXR images and radiology reports for training. Furthermore, statistical results of comparative experiments show that our scheme provides an interpretable window into the COVID-19 diagnosis to improve the performance of the small sample size, the reliability and transparency of black-box deep learning mod-els. Our source codes will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/SPEMFSL-Diagnosis-COVID-19.

Rapid point-of-care detection of SARS-CoV-2 RNA with smartphone-based upconversion luminescence diagnostics.

Accurate COVID-19 screening via molecular technologies is still hampered by bulky instrumentation, complicated procedure, high cost, lengthy testing time, and the need for specialized personnel. Herein, we develop point-of-care upconversion luminescence diagnostics (PULD), and a streamlined smartphone-based portable platform facilitated by a ready-to-use assay for rapid SARS-CoV-2 nucleocapsid (N) gene testing. With the complementary oligo-modified upconversion nanoprobes and gold nanoprobes specifically hybridized with the target N gene, the luminescence resonance energy transfer effect leads to a quenching of fluorescence intensity that can be detected by the easy-to-use diagnostic system. A remarkable detection limit of 11.46 fM is achieved in this diagnostic platform without the need of target amplification, demonstrating high sensitivity and signal-to-noise ratio of the assay. The capability of the developed PULD is further assessed by probing 9 RT-qPCR-validated SARS-CoV-2 variant clinical samples (B.1.1.529/Omicron) within 20 min, producing reliable diagnostic results consistent with those obtained from a standard fluorescence spectrometer. Importantly, PULD is capable of identifying the positive COVID-19 samples with superior sensitivity and specificity, making it a promising front-line tool for rapid, high-throughput screening and infection control of COVID-19 or other infectious diseases.

A novel cartridge for nucleic acid extraction, amplification and detection of infectious disease pathogens with the help of magnetic nanoparticles

Nucleic acid detection (NAD) based on real-time polymerase chain reaction (real-time PCR) is gold standard for infectious disease detection. Magnetic nanoparticles (MNPs) are widely used for nucleic acid extraction (NAE) because of their excellent properties. Microfluidic technology makes automated NAD possible. However, most of the NAD microfluidic chips are too complex to be applied to point-of-care (POC) testing. In this paper, a simple-structure cartridge was developed for POC detection of infectious diseases. This self-contained cartridge can be divided into a magnetic-controlled NAE part, a valve-piston combined fluidic control part and a PCR chip, which is able to extract nucleic acid from up to 500 μL of liquid samples by MNPs and finish the detection process from "sample in” to "answer out” automatically. Performance tests of the cartridges show that it met the demands of automated NAD. Results of on-cartridge detection of hepatitis B virus (HBV) demonstrated that this system has good uniformity and no cross-contamination between different cartridges, and the limit of detection (LOD) of this system for HBV in serum is 50 IU/mL. Multiplex detections of severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) with a concentration of 500 copies/mL were carried out on the system and 100% positive detection rate was achieved.

How to reduce the exposure risk of medical staff from SARS-CoV-2 by reducing environmental contamination: Experience from designated hospitals in China.

Background: Using daily monitoring of environmental surfaces and personal protective equipment (PPE), we found an increase in environmental contamination since August 18, 2021, in a designated hospital for COVID-19 patients in China, which may lead to an increased risk of exposure to medical staff. Methods: To investigate the cause of increased environmental contamination and effect of our intervention, we obtained environmental samples at pre-intervention (August 18-21, 2021) and post-intervention (August 22-28, 2021) from six infection isolation rooms with windows for ventilation and other auxiliary areas at 105 and 129 sites before routine daily cleaning, respectively. In addition, we obtained PPE samples from 98 medical staff exiting the patient rooms/contaminated areas at 482 sites. Between August 22 and 24, 2021, we took measures to reduce environmental contamination based on sampling and inspection results. Findings: At pre-intervention, the positivity rates for contamination of environmental surfaces and PPE samples were significantly higher for critical patients (37.21 and 27.86%, respectively) than severely ill patients (25.00 and 12.50%, respectively) and moderately ill patients (0.00 and 0.00%, respectively) (Pearson's Chi-square: χ2 = 15.560, p = 0.000; Fisher's exact test: χ2 = 9.358, p = 0.007). Therefore, we inferred that the source of contamination of environmental surfaces and PPE was mainly the room of critically ill patients, likely through the hands of medical staff to the potentially contaminated areas. A critically ill patient had emergency tracheal intubation and rescue on August 18, 2021, due to worsened patient condition. The ventilator tube used for first aid did not match the ventilator, and the ventilator tube fell off multiple times on August 18-21, 2021, which may explain the increased contamination of environmental surfaces and PPE from critically ill patients, as well as lead to indirect contamination of potentially contaminated areas. The contamination positivity rates of environmental surfaces and PPE were reduced by replacing the appropriate ventilator catheter, limiting the number of people entering the isolation room simultaneously, increasing the frequency of environmental disinfection, standardizing the undressing process, setting up undressing monitoring posts to supervise the undressing process, and preventing the spread of virus infections in the hospital during an epidemic. Conclusions: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was spread on object surfaces in isolation rooms mainly by touch, and the contamination of environmental surfaces and PPE was greater in rooms of patients with greater disease severity and higher surface touch frequency. Therefore, strict protective measures for medical staff, frequent environmental cleaning for isolation rooms, and compliance with mask wearing by patients when conditions permit should be advised to prevent SARS-CoV-2 spread in hospitals.

Rapid point-of-care detection of SARS-CoV-2 RNA with smartphone-based upconversion luminescence diagnostics

Accurate COVID-19 screening via molecular technologies is still hampered by bulky instrumentation, complicated procedure, high cost, lengthy testing time, and the need for specialized personnel. Herein, we develop point-of-care upconversion luminescence diagnostics (PULD), and a streamlined smartphone-based portable platform facilitated by a ready-to-use assay for rapid SARS-CoV-2 nucleocapsid (N) gene testing. With the complementary oligo-modified upconversion nanoprobes and gold nanoprobes specifically hybridized with the target N gene, the luminescence resonance energy transfer effect leads to a quenching of fluorescence intensity that can be detected by the easy-to-use diagnostic system. A remarkable detection limit of 11.46 fM is achieved in this diagnostic platform without the need of target amplification, demonstrating high sensitivity and signal-to-noise ratio of the assay. The capability of the developed PULD is further assessed by probing 9 RT-qPCR-validated SARS-CoV-2 variant clinical samples (B.1.1.529/Omicron) within 20 mins, producing reliable diagnostic results consistent with those obtained from a standard fluorescence spectrometer. Importantly, PULD is capable of identifying the positive COVID-19 samples with superior sensitivity and specificity, making it a promising front-line tool for rapid, high-throughput screening and infection control of COVID-19 or other infectious diseases.

Coevolution of vaccination behavior and perceived vaccination risk can lead to a stag-hunt-like game.

Voluntary vaccination is effective to prevent infectious diseases from spreading. Both vaccination behavior and cognition of the vaccination risk play important roles in individual vaccination decision making. However, it is not clear how the coevolution of the two shapes population-wide vaccination behavior. We establish a coupled dynamics of epidemic, vaccination behavior, and perceived vaccination risk with three different time scales. We assume that the increase of vaccination level inhibits the rise of perceived vaccination risk, and the increase of perceived vaccination risk inhibits the rise of vaccination level. It is shown that the resulting vaccination behavior is similar to the stag-hunt game, provided that the basic reproductive ratio is moderate and that the epidemic dynamics evolves sufficiently fast. This is in contrast with the previous view that vaccination is a snowdriftlike game. And we find that epidemic breaks out repeatedly and eventually leads to vaccine scares if these three dynamics evolve on a similar time scale. Furthermore, we propose some ways to promote vaccination behavior, such as controlling side-effect bias and perceived vaccination costs. Our work sheds light on epidemic control via vaccination by taking into account the coevolutionary dynamics of cognition and behavior.

Rapid and ultrasensitive detection of SARS-CoV-2 spike protein based on upconversion luminescence biosensor for COVID-19 point-of-care diagnostics.

Here, we firstly introduce a detection system consisting of upconversion nanoparticles (UCNPs) and Au nanorods (AuNRs) for an ultrasensitive, rapid, quantitative and on-site detection of SARS-CoV-2 spike (S) protein based on Förster resonance energy transfer (FRET) effect. Briefly, the UCNPs capture the S protein of lysed SARS-CoV-2 in the swabs and subsequently they are bound with the anti-S antibodies modified AuNRs, resulting in significant nonradiative transitions from UCNPs (donors) to AuNRs (acceptors) at 480 nm and 800 nm, respectively. Notably, the specific recognition and quantitation of S protein can be realized in minutes at 800 nm because of the low autofluorescence and high Yb-Tm energy transfer in upconversion process. Inspiringly, the limit of detection (LOD) of the S protein can reach down to 1.06 fg mL-1, while the recognition of nucleocapsid protein is also comparable with a commercial test kit in a shorter time (only 5 min). The established strategy is technically superior to those reported point-of-care biosensors in terms of detection time, cost, and sensitivity, which paves a new avenue for future on-site rapid viral screening and point-of-care diagnostics.

Information quality evaluation of corona virus disease 2019 direct online reporting in Guangzhou

Objective: To evaluate the quality of direct online reporting of corona virus disease 2019 (COVID-19), sum up experience, find existing problems, identify influencing factors, and suggest improvement measures to better guide future epidemic information reporting.