Development of a multi-recombinase polymerase amplification assay for rapid identification of COVID-19, influenza A and B.

The coronavirus disease 2019 (COVID-19) pandemic caused extensive loss of life worldwide. Further, the COVID-19 and influenza mix-infection had caused great distress to the diagnosis of the disease. To control illness progression and limit viral spread within the population, a real-time reverse-transcription PCR (RT-PCR) assay for early diagnosis of COVID-19 was developed, but detection was time-consuming (4-6 h). To improve the diagnosis of COVID-19 and influenza, we herein developed a recombinase polymerase amplification (RPA) method for simple and rapid amplification of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19 and Influenza A (H1N1, H3N2) and B (influenza B). Genes encoding the matrix protein (M) for H1N1, and the hemagglutinin (HA) for H3N2, and the polymerase A (PA) for Influenza B, and the nucleocapsid protein (N), the RNA-dependent-RNA polymerase (RdRP) in the open reading frame 1ab (ORF1ab) region, and the envelope protein (E) for SARS-CoV-2 were selected, and specific primers were designed. We validated our method using SARS-CoV-2, H1N1, H3N2 and influenza B plasmid standards and RNA samples extracted from COVID-19 and Influenza A/B (RT-PCR-verified) positive patients. The method could detect SARS-CoV-2 plasmid standard DNA quantitatively between 102 and 105 copies/ml with a log linearity of 0.99 in 22 min. And this method also be very effective in simultaneous detection of H1N1, H3N2 and influenza B. Clinical validation of 100 cases revealed a sensitivity of 100% for differentiating COVID-19 patients from healthy controls when the specificity was set at 90%. These results demonstrate that this nucleic acid testing method is advantageous compared with traditional PCR and other isothermal nucleic acid amplification methods in terms of time and portability. This method could potentially be used for detection of SARS-CoV-2, H1N1, H3N2 and influenza B, and adapted for point-of-care (POC) detection of a broad range of infectious pathogens in resource-limited settings.

Factors Influencing Students' Career Intentions in the Hospitality and Tourism Industries: A Meta-Analysis.

This study adopted a meta-analysis to generalize and compare the effect of influencing factors on hospitality and tourism management students' career intentions from 34 studies. After 13 existing factors were classified into four categories, the results revealed that 11 of them significantly impact students' career intentions. In general, the influences of social identity and self-perception are more important, followed by school education and work conditions. A subgroup analysis further identified the sample students' grades as dynamics that partially moderate some of the influencing factors, while no statistical changes in the relative influences were observed after the COVID-19 outbreak. Finally, suggestions are provided for hospitality and tourism management education and industries.

Pre-existing humoral immunity to low pathogenic human coronaviruses exhibits limited cross-reactive antibodies response against SARS-CoV-2 in children.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes asymptomatic or mild symptoms, even rare hospitalization in children. A major concern is whether the pre-existing antibodies induced by low pathogenic human coronaviruses (LPH-CoVs) in children can cross-react with SARS-CoV-2. To address this unresolved question, we analyzed the pre-existing spike (S)-specific immunoglobin (Ig) G antibodies against LPH-CoVs and the cross-reactive antibodies against SARS-CoV-2 in 658 serum samples collected from children prior to SARS-CoV-2 outbreak. We found that the seroprevalence of these four LPH-CoVs reached 75.84%, and about 24.64% of the seropositive samples had cross-reactive IgG antibodies against the nucleocapsid, S, and receptor binding domain antigens of SARS-CoV-2. Additionally, the re-infections with different LPH-CoVs occurred frequently in children and tended to increase the cross-reactive antibodies against SARS-CoV-2. From the forty-nine serum samples with cross-reactive anti-S IgG antibodies against SARS-CoV-2, we found that seven samples with a median age of 1.4 years old had detected neutralizing activity for the wild-type or mutant SARS-CoV-2 S pseudotypes. Interestingly, all of the seven samples contained anti-S IgG antibodies against HCoV-OC43. Together, these data suggest that children's pre-existing antibodies to LPH-CoVs have limited cross-reactive neutralizing antibodies against SRAS-CoV-2.

Construction of Online Teaching Acceptance Model of University Library under the Epidemic Situation.

In order to improve the online teaching quality of university libraries, this paper puts forward the construction of the online teaching acceptance model of university libraries under the background of the epidemic situation, constructs the basic theoretical framework of the teaching evaluation system, establishes the basic evaluation indicators, takes the online teaching quality evaluation and the operability of the evaluation system as the constraint object, and determines the index parameter set after class review; the three modules were guided and trained. Based on the big data analysis of the evaluation index system, accurate teaching quality scores or grades were calculated, and the online teaching acceptance evaluation of university libraries was realized. The results of empirical analysis show that this method can realize the accurate evaluation of teaching quality, promote the deep integration of online teaching theory and practice in university libraries, and improve the acceptance level of online teaching quality.

The highly conserved RNA-binding specificity of nucleocapsid protein facilitates the identification of drugs with broad anti-coronavirus activity.

The binding of SARS-CoV-2 nucleocapsid (N) protein to both the 5'- and 3'-ends of genomic RNA has different implications arising from its binding to the central region during virion assembly. However, the mechanism underlying selective binding remains unknown. Herein, we performed the high-throughput RNA-SELEX (HTR-SELEX) to determine the RNA-binding specificity of the N proteins of various SARS-CoV-2 variants as well as other ß-coronaviruses and showed that N proteins could bind two unrelated sequences, both of which were highly conserved across all variants and species. Interestingly, both sequences are virtually absent from the human transcriptome; however, they exhibit a highly enriched, mutually complementary distribution in the coronavirus genome, highlighting their varied functions in genome packaging. Our results provide mechanistic insights into viral genome packaging, thereby increasing the feasibility of developing drugs with broad-spectrum anti-coronavirus activity by targeting RNA binding by N proteins.

Allosteric Hotspots in the Main Protease of SARS-CoV-2.

Inhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph-theoretical methods: Bond-to-bond propensity, which has been previously successful in identifying allosteric sites in extensive benchmark data sets without a priori knowledge, and Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. Using statistical bootstrapping, we score the highest ranking sites against random sites at similar distances, and we identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

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

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

Prediction of allosteric sites and signaling: Insights from benchmarking datasets.

Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.

Depression and anxiety and assciated factors among college students returned to campus during the outbreak of COVID-19

Objective: The purpose of this study was to investigate the state of depression and anxiety and associated factors of back-to-school college students during the outbreak of COVID-19, so as to provide theoretical basis for emotional counseling and psychological crisis intervention after long-term school closure due to epidemic outbreak.

Analysis of network usage and influencing factors among college students returning to a vocational college during COVID-19 epidemic

Objective: To understand the current situation and influencing factors of Internet addiction among college students after returning to school during the COVID-19 epidemic, and to provide scientific basis for timely intervention measures to adjust the risk factors of Internet addiction.

School adaptation and behavior of the first batch of college students returning to school during the COVID-19 epidemic

Objective: To analyze the school adaptation and behavioral performance of the first batch of students who return to school during the COVID-19 epidemic, and to provide a theoretical basis for further development of corresponding intervention measures.

Sleep quality and influencing factors of college students returning to school during COVID-19 epidemic

Objective: To investigate the sleep quality and influencing factors of the first batch of college students returning to school during COVID-19 epidemic, so as to provide scientific basis for taking corresponding measures.

Persistent viral RNA positivity during the recovery period of a patient with SARS-CoV-2 infection.

As an emerging infectious disease, the clinical course and virological course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remain to be further investigated. In this case report, we described a case of SARS-CoV-2 infection with the clinical course for more than 2 months. This patient had recovered from pneumonia after treatment. The viral RNA of throat swabs became negative and the viral-specific antibodies were produced during the recovery period. However, the viral RNA reappeared and additionally persisted in throat swabs for more than 40 days. In addition, the viral RNA was detected in multiple types of specimens with extremely high titers in the saliva. In conclusion, these findings indicate that SARS-CoV-2 can cause a long clinical course. The coexistence of viral RNA and viral-specific antibodies may imply an immune evasion of SARS-CoV-2 from the host's immune system.

The Association of Suicidal Ideation With Family Characteristics and Social Support of the First Batch of Students Returning to a College During the COVID-19 Epidemic Period: A Cross Sectional Study in China.

Objective: To investigate the prevalence of suicidal ideation among the first batch of students returning to a college during the COVID-19 epidemic, and to explore the correlation of suicidal ideation with family characteristics and social support. Methods: A cluster sampling survey with a self-designed questionnaire was conducted among the first batch of students returning to a college in Wuhu, China. The Positive and Negative Suicidal ideation (PANSI) and Social Support Scale (SSRS) were used to define students' suicidal ideation and social support, respectively. The influence of family characteristics and social support on the students' suicidal ideation was investigated using multivariate unconditional logistic regression analysis. Results: Two thousand seven hundred valid questionnaires were collected, including 673 males (24.9%) and 2,027 females (75.1%), in this study. A total of 146 students (5.4%) showed suicidal ideation. Male respondents reported higher rates (7.9%) than females (4.6%). Multivariate logistic regression analysis showed that a higher risk level of residence before returning to school and lower objective support were the risk factors for suicidal ideation in males. In contrast, a higher level of maternal education, a poorer relationship with the mother, and lower scores for subjective support and support availability had significant effects on females' suicidal ideation. Limitations: This is a cross-sectional study, and lacks comparison to the time point unaffected by COVID-19. Moreover, it was limited by COVID-19 epidemic prevention and control restrictions, and the differences in returning to school in different regions. Only one college was investigated in this study, and all of the respondents were sophomores, so there may be some limitations in the representativeness of the sample and extrapolation of the results. Conclusion: Family characteristics and social support have had an important influence on suicidal ideation among students returning to school during the COVID-19 epidemic. Some gender differences were identified. Targeted interventions are needed for early prevention and control.

Moderating effect of people-oriented public health services on depression among people under mandatory social isolation during the COVID-19 pandemic: a cross-sectional study in China.

BACKGROUND: Public health measures, such as social isolation, are vital to control the spread of the coronavirus disease 2019 (COVID-19), but such measures may increase the risk of depression. Thus, this study examines the influencing and moderating factors of depressive symptoms among individuals subjected to mandatory social isolation. METHODS: An online cross-sectional survey was conducted to collect data from people under mandatory home or centralized social isolation in Shenzhen, China, from February 28 to March 6, 2020. The perceived risk of infection with COVID-19, perceived tone of media coverage, perceived quality of people-oriented public health services, and their depressive symptoms were assessed. Three rounds of stepwise multiple regression were performed to examine the moderating effects after controlling various variables, such as demographics, duration and venue of mandatory social isolation, infection and isolation status of family, time spent on COVID-related news, and online social support. RESULTS: Among the 340 participants, 57.6% were men, the average age was 35.5 years old (SD = 8.37), and 55.6% held a bachelor's degree or above. Individuals subjected to mandatory social isolation generally reported low levels of depressive symptoms. Perceived susceptibility to infection was relatively low, whereas perceived tone of media coverage was mainly positive. In terms of perceived quality of public health services, 12 (3.5%), 103 (30.3%), and 225 (66.2%) participants reported low, medium, and high quality of people-oriented services, respectively. Perceived susceptibility was positively associated with depression, whereas perceived tone of media coverage was negatively associated. The quality of people-centered public health services moderated the association between perceived risk and depressive symptoms and between perceived tone of media coverage and depressive symptoms. CONCLUSIONS: This study revealed the depressive symptoms among individuals subjected to mandatory social isolation during the COVID-19 pandemic and highlighted that frontline public health workers play a critical role in protecting public mental health.

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19.

The systematic identification of host genetic risk factors is essential for the understanding and treatment of coronavirus disease 2019 (COVID-19). By performing a meta-analysis of two independent genome-wide association summary datasets (N = 680 128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2-IL10RB, odds ratio = 1.16, 95% confidence interval = 1.09-1.23, P = 2.57 × 10-6). The rs9976829 represents a strong splicing quantitative trait locus for both IFNAR2 and IL10RB genes, especially in lung tissue (P = 1.8 × 10-24). Integrative genomics analysis of combining genome-wide association study with expression quantitative trait locus data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.