Is SARS-CoV-2 a concern in the largest wastewater treatment plant in middle east?

The surveillance of wastewater treatment plant (WWTP) as the end point of SARS-CoV-2 shed from infected people arise a speculation on transmission of this virus of concern from WWTP in epidemic period. To this end, the present study was developed to comprehensively investigate the presence of SARS-CoV-2 in raw wastewater, effluent and air inhaled by workers and employee in the largest WWTP in Tehran for one-year study period. The monthly raw wastewater, effluent and air samples of WWTP were taken and the SARS-CoV-2 RNA were detected using QIAamp Viral RNA Mini Kit and real-time RT-PCR assay. According to results, the speculation on the presence of SARS-CoV-2 was proved in WWTP by detection this virus in raw wastewater. However, no SARS-CoV-2 was found in both effluent and air of WWTP; this presents the low or no infection for workers and employee in WWTP. Furthermore, further research are needed for detection the SARS-CoV-2 in solid and biomass produced from WWTP processes due to flaks formation, followed by sedimentation in order to better understand the wastewater-based epidemiology and preventive measurement for other epidemics probably encountered in the future.

The impact of COVID-19 on the tourism and hospitality Industry: Evidence from international stock markets

COVID-19 seriously affects the tourism and hospitality industry. In this study, we investigate the behavior of 40 tourism and hospitality stock market indices worldwide from two perspectives. First, empowered by the Granger causality test and network analysis, we test the spillover effects among these stock markets and find that the dynamics of interconnectedness network structures differ significantly in the pre-pandemic and in-pandemic periods. Second, we employ econometric models to explore how the influence of COVID-19 on these stock markets varies by considering the interconnectedness structure, the government response stringency index, and other country-level characteristics. We find that the interconnectedness structure significantly and robustly affects stock returns in the tourism and hospitality markets. Our investigation provides a better understanding of the impact of COVID-19 on tourism and hospitality industry.

Blood transcriptome analysis revealed the crosstalk between COVID-19 and HIV.

Background: The severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body's ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients. Methods: COVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the "Deseq2" package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the "limma" package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification. Results: In this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients. Conclusion: In this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

Expectations of nursing home residents and their family members during the COVID-19 epidemic: A qualitative study (preprint)

Background COVID-19 has spread worldwide. Older people are at the greatest risk of contracting and dying from the virus. Nursing homes are densely populated places for older adults who are generally vulnerable and at high-risk. Although Chinese nursing homes have been trying to protect their residents, the needs and expectations of the residents and their families have been ignored. The aim of this study was to explore and better understand the expectations of nursing home residents and their family members during the COVID-19 epidemic in China.Methods Data was collected via face-to-face semi-structured interviews with nursing home residents and focus group online interviews with family members between June 2021 and February 2022. Data analysis followed inductive content analysis.Results 16 residents and 24 family members were interviewed. Four themes with 11 sub-themes were identified from the descriptions of participants. Their expectations were mainly focus on prevention and control measures for COVID-19, medical capacity of nursing homes, health education and expectations for some aged care policies.Conclusions In the face of concerns about the impact of COVID-19 on nursing homes, we sought to bring firsthand perspectives to the forefront by interviewing residents and their family members about their expectations to address this issue. Our findings provide important areas on which should be focused and may improve the sense of gain, happiness, and security of nursing home residents during the COVID-19 epidemic.

Spillover effects among crude oil, carbon, and stock markets: evidence from nonparametric causality-in-quantiles tests

This study investigates the spillovers and information transmission between carbon, crude oil, and stock markets under various market conditions in Phase III of the EU ETS. For this purpose, we use a novel causality-in-quantiles test method and quantile impulse response functions based on daily data of carbon futures, Brent spot, and three representative equity indices in the Europe over the period from 27 January 2014 to 18 September 2020. We find that crude oil market has a unidirectional spillover effect on carbon market, and this causality is significant under normal to bullish market conditions. Furthermore, the causality-in-quantiles between crude oil and stock markets varies with specific equality index, and the information transmission from crude oil to stock market is strong in the normal stock market but invalid when stock markets become extremely bearish or bullish. The COVID-19 epidemic may cause structural changes in the oil-carbon and oil-stock nexus.

Molecular recognition of SARS-CoV-2 spike protein with three essential partners: exploring possible immune escape mechanisms of viarl mutants (preprint)

The COVID-19 epidemic is raging around the world, including the emergence of viral mutant strains such as Delta and Omicron, posing severe challenges to people's health and quality of life. Full understanding life cycle of the virus in host cells helps to reveal inactivation mechanism of antibody and provide inspiration for the development of a new-generation vaccines. In this work, molecular recognitions and conformational changes of SARS-CoV-2 spike protein (SP) mutants (i.e., Delta, Mu and Omicron) and three essential partners (i.e., membrane receptor hACE2, protease TMPRSS2 and antibody C121) both were compared and analyzed using molecular simulations. Water basin and binding free energy calculations both show that the three mutants possess higher affinity for hACE2 than WT, exhibiting stronger virus transmission. The descending order of cleavage ability by TMPRSS2 is Mu, Delta, Omicron and WT, which is related to the new S1/S2 cutting site induced by transposition effect. The inefficient utilization of TMPRSS2 by Omicron is consistent with its primary entry into cells via the endosomal pathway. In addition, RBD-directed antibody C121 showed obvious resistance to Omicron, which may have originated from high fluctuation of approaching angles, high flexibility of I472-F490 loop and reduced binding ability. According to the overall characteristics of the three mutants, high infectivity, high immune escape and low virulence may be the future evolutionary selection of SARS-CoV-2. In a word, this work not only proposes the possible resistance mechanism of SARS-CoV-2 mutants, but also provides theoretical guidance for the subsequent drug design against COVID-19 based on SP structure.

A New iNKT-Cell Agonist-Adjuvanted SARS-CoV-2 Subunit Vaccine Elicits Robust Neutralizing Antibody Responses.

Adjuvants are essential components of vaccines. Invariant natural killer T (iNKT) cells are a distinct subset of T cells that function to bridge the innate and adaptive immunities and are capable of mediating strong and rapid responses to a range of diseases, including cancer and infectious disease. An increasing amount of evidence suggests that iNKT cells can help fight viral infection. In particular, iNKT-secreting IL-4 is a key mediator of humoral immunity and has a positive correlation with the levels of neutralizing antibodies. As iNKT cell agonists, αGC glycolipid (α-galactosylceramide, or KRN7000) and its analogues as vaccine adjuvants have begun to provide vaccinologists with a new toolset. Herein we found that a new iNKT-cell agonist αGC-CPOEt elicited a strong cytokine response with increased IL-4 production. Remarkably, after three immunizations, SARS-CoV-2 RBD-Fc adjuvanted by αGC-CPOEt evoked robust neutralizing antibody responses that were about 5.5-fold more than those induced by αGC/RBD-Fc and 25-fold greater than those induced by unadjuvanted RBD-Fc. These findings imply that αGC-CPOEt could be investigated further as a new COVID-19 vaccine adjuvant to prevent current and future infectious disease outbreaks.

Measurement of epicardial adipose tissue using non-contrast routine chest-CT: a consideration of threshold adjustment for fatty attenuation.

BACKGROUND: Epicardial adipose tissue (EAT) is known as an important imaging indicator for cardiovascular risk stratification. The present study aimed to determine whether the EAT volume (EV) and mean EAT attenuation (mEA) measured by non-contrast routine chest CT (RCCT) could be more consistent with those measured by coronary CT angiography (CCTA) by adjusting the threshold of fatty attenuation. METHODS: In total, 83 subjects who simultaneously underwent CCTA and RCCT were enrolled. EV and mEA were quantified by CCTA using a threshold of (N30) (- 190 HU, - 30 HU) as a reference and measured by RCCT using thresholds of N30, N40 (- 190 HU, - 40 HU), and N45 (- 190 HU, - 45 HU). The correlation and agreement of EAT metrics between the two imaging modalities and differences between patients with coronary plaques (plaque ( +)) and without plaques (plaque ( -)) were analyzed. RESULTS: EV obtained from RCCT showed very strong correlation with the reference (r = 0.974, 0.976, 0.972 (N30, N40, N45), P < 0.001), whereas mEA showed a moderate correlation (r = 0.516, 0.500, 0.477 (N30, N40, N45), P < 0.001). Threshold adjustment was able to reduce the bias of EV, while increase the bias of mEA. Data obtained by CCTA and RCCT both demonstrated a significantly larger EV in the plaque ( +) group than in the plaque ( -) group (P < 0.05). A significant difference in mEA was shown only by RCCT using a threshold of N30 (plaque ( +) vs ( -): - 80.0 ± 4.4 HU vs - 78.0 ± 4.0 HU, P = 0.030). The mEA measured on RCCT using threshold of N40 and N45 showed no significant statistical difference between the two groups (P = 0.092 and 0.075), which was consistent with the result obtained on CCTA (P = 0.204). CONCLUSION: Applying more negative threshold, the consistency of EV measurements between the two techniques improves and a consistent result can be obtained when comparing EF measurements between groups, although the bias of mEA increases. Threshold adjustment is necessary when measuring EF with non-contrast RCCT.

Rare Variants in Inborn Errors of Immunity Genes Associated With Covid-19 Severity

Host genetic factors have been shown to play an important role in SARS-CoV-2 infection and the course of Covid-19 disease. The genetic contributions of common variants influencing Covid-19 susceptibility and severity have been extensively studied in diverse populations. However, the studies of rare genetic defects arising from inborn errors of immunity (IEI) are relatively few, especially in the Chinese population. To fill this gap, we used a deeply sequenced dataset of nearly 500 patients, all of Chinese descent, to investigate putative functional rare variants. Specifically, we annotated rare variants in our call set and selected likely deleterious missense (LDM) and high-confidence predicted loss-of-function (HC-pLoF) variants. Further, we analyzed LDM and HC-pLoF variants between non-severe and severe Covid-19 patients by (a) performing gene- and pathway-level association analyses, (b) testing the number of mutations in previously reported genes mapped from LDM and HC-pLoF variants, and (c) uncovering candidate genes via protein-protein interaction (PPI) network analysis of Covid-19-related genes and genes defined from LDM and HC-pLoF variants. From our analyses, we found that (a) pathways Tuberculosis (hsa:05152), Primary Immunodeficiency (hsa:05340), and Influenza A (hsa:05164) showed significant enrichment in severe patients compared to the non-severe ones, (b) HC-pLoF mutations were enriched in Covid-19-related genes in severe patients, and (c) several candidate genes, such as IL12RB1, TBK1, TLR3, and IFNGR2, are uncovered by PPI network analysis and worth further investigation. These regions generally play an essential role in regulating antiviral innate immunity responses to foreign pathogens and in responding to many inflammatory diseases. We believe that our identified candidate genes/pathways can be potentially used as Covid-19 diagnostic markers and help distinguish patients at higher risk.

Carbon prices forecasting in quantiles

This paper proposes two new methods (the Quantile Group LASSO and the Quantile Group SCAD models) to evaluate the predictability of a large group of factors on carbon futures returns. The most powerful predictors are selected through the dimension-reduction mechanism of the two models, while potential differences of the statistically significant predictors for different quantiles of carbon returns are carefully considered. First, we find that the proposed models outperform a series of competing ones with respect to prediction accuracy. Second, impacts of the selected predictors over the carbon price distribution are estimated through a quantile approach, which outperforms the mean shrinkage model in our case with data featured by a non-normal distribution. Specifically, the Brent spot price, the crude oil closing stock in the UK, and the growth of natural gas production in the UK are found to impact carbon futures returns only in extreme conditions with a strong asymmetric feature. Importantly, our estimators remain robust against the extreme event caused by the Covid-19. Our findings reveal that the identification of appropriate carbon return predictors and their impacts hinge on the carbon market conditions, and should be of interest to various stakeholders.

Evaluation of an Antigen Detection Rapid Diagnostic Test for Detection of SARS-CoV-2 in Clinical Samples

Antigen detection rapid diagnostic tests have been developed for first-line large-scale screening given their rapidity, simplicity, and accuracy. This study evaluates the diagnostic performance of an antigen detection rapid diagnostic test (BLOK BioScience, London, UK) detecting SARS-CoV-2 nucleocapsid protein. Serially diluted SARS-CoV-2 isolate and 110 NPS from COVID-19 patients were tested to determine the test’s sensitivity, and other viral isolates and 20 NPS from non-infected individuals were, for specificity, also tested. Ten clinical samples from COVID-19 patients with SARS-CoV-2 variants, including alpha, beta, gamma, delta, and eta variants, were collected to evaluate the test’s potential application in detecting emerging variants. Overall sensitivity was 92%, and stratifying into viral loads yielded 100% for Ct < 25 samples including SARS-CoV-2 variants, but 11.11% for Ct ≥30 samples. The analytical sensitivity of log10 TCID50/mL 2.0 was identified for SARS-CoV-2. Ninety-seven percent specificity with only SARS-CoV cross-reactivity lead to the Youden index of 0.89. The rapid diagnostic test has a high sensitivity for detecting SARS-CoV-2 in high viral load samples, possibly including emerging SARS-CoV-2 variants, but reduced sensitivity in low viral load samples suggests its optimized usage as a complementary testing method to other tests, including RT-PCR or a point-of-care test for large-scale screening, particularly for pandemic areas or airport border infection control.

Investor Sentiment and Stock Returns During the COVID-19 Pandemic.

In this paper, we regard the Baidu index as an indicator of investors' attention to China's epidemic stocks. We believe that when seeking information to guide investment decisions, investor sentiment is usually affected by the information provided by the Baidu search engine, which may cause stock prices to fluctuate. Therefore, we constructed a GARCH extended model including the Baidu index to predict the return of epidemic stocks and compared it with the benchmark model. The empirical research in this paper finds that the forecast model including the Baidu index is significantly better than the benchmark model. This has important reference value both for investors in predicting stock trends and for the government's formulation of policies to prevent excessive stock market volatility.

Potent Neutralizing Antibodies Elicited by RBD-Fc-Based COVID-19 Vaccine Candidate Adjuvanted by the Th2-Skewing iNKT Cell Agonist.

The development of a safe and effective COVID-19 vaccine is of paramount importance to terminate the current pandemic. An adjuvant is crucial for improving the efficacy of the subunit COVID19 vaccine. α-Galactosylceramide (αGC) is a classical iNKT cell agonist which causes the rapid production of Th1- and Th2-associated cytokines; we, therefore, expect that the Th1- or Th2-skewing analogues of αGC can better enhance the immunogenicity of the receptor-binding domain in the spike protein of SARS-CoV-2 fused with the Fc region of human IgG (RBD-Fc). Herein, we developed a universal synthetic route to the Th1-biasing (α-C-GC) and Th2-biasing (OCH and C20:2) analogues. Immunization of mice demonstrated that αGC-adjuvanted RBD-Fc elicited a more potent humoral response than that observed with Alum and enabled the sparing of antigens. Remarkably, at a low dose of the RBD-Fc protein (2 µg), the Th2-biasing agonist C20:2 induced a significantly higher titer of the neutralizing antibody than that of Alum.

Epidemics, Public Sentiment, and Infectious Disease Equity Market Volatility.

Background: This article studies the relationship between the COVID-19 epidemic, public sentiment, and the volatility of infectious disease equities from the perspective of the United States. We use weekly data from January 3, 2020 to March 7, 2021. This provides a sufficient dataset for empirical analysis. Granger causality test results prove the two-way relationship between the fluctuation of infectious disease equities and confirmed cases. In addition, confirmed cases will cause the public to search for COVID-19 tests, and COVID-19 tests will also cause fluctuations in infectious disease equities, but there is no reverse correlation. The results of this research are useful to investors and policy makers. Investors can use the number of confirmed cases to predict the volatility of infectious disease equities. Similarly, policy makers can use the intervention of retrieved information to stabilize public sentiment and equity market fluctuations, and integrate a variety of information to make more scientific judgments on the trends of the epidemic.

Current status of antibody detection kits for SARS-CoV-2

Serological detection of specific antibody is an important method in the diagnosis of SARS-CoV-2 infection. However, numerous antibody detection kits for SARS-CoV-2 have been available within a short period, their accuracy and effectiveness are uncertain. This paper summarizes the current status of antibody detection kits for SARS-CoV-2 at home and abroad to provide reference on their uses by staffs in hospitals and CDCs.

The diagnostic value of joint detection of serum IgM and IgG antibodies to 2019-nCoV in 2019-nCoV infection

Objective: To investigate the diagnostic value of immunoglobulin M (IgM) and immunoglobulin G(IgG) antibodies to 2019 Novel Coronavirus (2019-nCoV) in 2019-nCoV infection. Methods: This is a retrospective study. Serum samples were collected from 284 patients including outpatients and inpatients in the Renmin Hospital of Wuhan University from January 20 to February 17 in 2020. Among them 205 cases were 2019-nCoV infected patients, including 186 cases confirmed with nucleic acid test and 19 cases diagnosed by clinical symptoms and CT characteristics according to "the New Coronavirus Pneumonia Control Protocol (5th edition)". A total of 79 subjects with other diseases but negative to 2019-nCoV infection were recruited as control group. Serum IgM and IgG antibodies to 2019-nCoV were measured with fully automated immunoassay technology for all subjects. Statistical significance between 2019-nCoV antibodies test and 2019-nCoV nucleic acid test was determined using the χ 2tests. Results: The sensitivity of serum IgM and IgG antibodies to 2019-nCoV were 70.24%(144/205) and 96.10%(197/205) respectively and the specificity were 96.20%(76/79) and 92.41%(73/79) respectively. The positive and negative predictive values of 2019-nCoV antibodies were 95.63%(197/206) and 91.03% (71/78) respectively, and the positive and negative predictive values of 2019-nCoV nucleic acid test were 100%(186/186) and 80.61%(79/98) respectively. The total coincidence rate of diagnosing 2019-nCoV infection between antibody tests and nucleic acid test for 2019-nCoV were 88.03%(250/284). Conclusion: Joint detection of serum IgM and IgG antibodies to 2019-nCoV is an effective screening and diagnostic indicators for 2019-nCoV infection, and an effective complement to the false negative results to nucleic acid test.