Virological characteristics of SARS-CoV-2 Omicron BA.5.2.48 (preprint)

With the prevalence of sequentially-emerged sublineages including BA.1, BA.2 and BA.5, SARS-CoV-2 Omicron infection has transformed into a regional epidemic disease. As a sublineage of BA.5, the BA.5.2.48 outbreak and evolved into multi-subvariants in China without clearly established virological characteristics, especially the pathogenicity. Though reduced airborne transmission and pathogenicity of former Omicron sublineages have been revealed in animal models, the virological characteristics of BA.5.2.48 was unidentified. Here, we evaluated the in vitro and in vivo virological characteristics of two isolates of the prevalent BA.5.2.48 subvariant, DY.2 and DY.1.1 (a subvariant of DY.1). DY.2 replicates more efficiently than DY.1.1 in HelahACE2+ cells and Calu-3 cells. The A570S mutation (of DY.1) in a normal BA.5 spike protein (DY.2) leads to a 20% improvement in the hACE2 binding affinity, which is slightly reduced by a further K147E mutation (of DY.1.1). Compared to the normal BA.5 spike, the double-mutated protein demonstrates efficient cleavage and reduced fusogenicity. BA.5.2.48 demonstrated enhanced airborne transmission capacity in hamsters than BA.2. The pathogenicity of BA.5.2.48 is greater than BA.2, as revealed in K18-hACE2 rodents. Under immune selection pressure, DY.1.1 shows stronger fitness than DY.2 in hamster turbinates. Thus the outbreaking prevalent BA.5.2.48 multisubvariants exhibites divergent virological features.

Semiparametric estimation of expected shortfall and its application in finance

Measuring risk effectively is crucial for managing risk in financial markets. The expected shortfall has become an increasingly popular metric for risk in recent years. How to estimate it is important in statistics and financial econometrics. Based on the single index quantile regression, we introduce a new semiparametric approach, namely, weighted single index quantile regression. We assess the performance of the proposed expected shortfall estimator with backtesting. Our simulation results indicate that the estimator has a good finite sample performance and often outperforms existing methods. By applying the new method to both a market index and individual stocks, we show that it not only exhibits the best performance but also reveals an insight about the effect of the COVID pandemic, that is, the pandemic increases the market risk.

The initial attempt at home hemodialysis in mainland China.

BACKGROUND: Observational studies have shown home hemodialysis (HHD) to be associated with better survival than facility hemodialysis (HD) and peritoneal dialysis (PD). Patients on HHD have reported higher quality of life and independence. HHD is considered to be an economical way to manage end-stage kidney disease (ESKD). The coronavirus disease 2019 pandemic has had a significant impact on patients with ESKD. Patients on HHD may have an advantage over in-center HD patients because of a lower risk of exposure to infection. PARTICIPANTS AND METHODS: We enrolled HD patients from our dialysis center. We first established the HHD training center. The training center was approved by the Chinese government. Doctors, nurses and engineers train and assess patients separately. There are three forms of patient monitoring: home visits, internet remote monitoring, and outpatient services. Demographic and medical data included age, sex, blood pressure, and dialysis-related data. Laboratory tests were conducted in our central testing laboratory, including hemoglobin (Hgb), serum creatinine (Cr), urea nitrogen (BUN), uric acid (UA), albumin (Alb), calcium (Ca), phosphorus (P), parathyroid hormone (PTH), and brain natriuretic peptide (BNP) levels. RESULTS: Six patients who underwent regular dialysis in the HD center of our hospital were selected for HHD training. We enrolled 6 patients, including 4 males and 2 females. The mean age of the patients was 47.5 (34.7-55.7) years, and the mean dialysis age was 33.5 (11.2-41.5) months. After an average of 16.0 (11.2-25.5) months of training, Alb, P and BNP levels were improved compared with the baseline values. After training, three patients returned home to begin independent HD. During the follow-up, there were no serious adverse events leading to hospitalization or death, but there were several adverse events. They were solved quickly by extra home visits of the technicians or online by remote monitoring. During the follow-up time, the laboratory indicators of all the patients, including Hgb, Alb, Ca, P, PTH, BNP, and ß2-MG levels, remained stable before and after HHD treatment. CONCLUSION: HHD is feasible and safe for ESKD in China, but larger-scale and longer-term studies are needed for further confirmation.

Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies.

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.

The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach

In emergency medical facilities location, the hierarchical diagnosis and treatment system plays an obvious role in the rational allocation of medical resources and improving the use efficiency of medical resources. However, few studies have investigated the operational mechanism of hierarchical medical systems in uncertain environments. To address this research gap, this paper proposes a hybrid approach for emergency medical facilities' location-allocation. In the first stage, in order to concentrate on the utilization of medical resources, we choose alternative facility points from the whole facilities through the entropy weight method (EWM). In the second stage, uncertainty sets are used to describe the uncertain number of patients at emergency medical points more accurately. We propose a robust model to configure large base hospitals based on the robust optimization method. Furthermore, the proposed robust models are applied to the emergency management of Huanggang City under COVID-19. The results show that the optimal emergency medical facility location-allocation scheme meets the actual treatment needs. Simultaneously, the disturbance ratio and uncertainty level have a significant impact on the configuration scheme.

Semiparametric estimation of expected shortfall and its application in finance

Measuring risk effectively is crucial for managing risk in financial markets. The expected shortfall has become an increasingly popular metric for risk in recent years. How to estimate it is important in statistics and financial econometrics. Based on the single index quantile regression, we introduce a new semiparametric approach, namely, weighted single index quantile regression. We assess the performance of the proposed expected shortfall estimator with backtesting. Our simulation results indicate that the estimator has a good finite sample performance and often outperforms existing methods. By applying the new method to both a market index and individual stocks, we show that it not only exhibits the best performance but also reveals an insight about the effect of the COVID pandemic, that is, the pandemic increases the market risk.

Changing ozone sensitivity in the South Coast Air Basin during the COVID-19 period

The South Coast Air Basin (SoCAB), which includes the city of Los Angeles and is home to more than 15 million people, frequently experiences ozone (O3) levels that exceed ambient air quality standards. While strict regulation of O3 precursors has dramatically improved air quality over the past 50 years, the region has seen limited improvement in O3 over the past decade despite continued reductions in precursor emissions. One contributing factor to the recent lack of improvement is a gradual transition of the underlying photochemical environment from a VOC-limited regime (where VOC denotes volatile organic compound) towards an NOx-limited one. The changes in human activity prompted by COVID-19-related precautions in spring and summer of 2020 exacerbated these existing changes in the O3 precursor environment. Analyses of sector-wide changes in activity indicate that emissions of NOx decreased by 15 %–20 % during spring (April–May) and by 5 %–10 % during summer (June–July) relative to expected emissions for 2020, largely due to changes in mobile-source activity. Historical trend analysis from two indicators of O3 sensitivity (the satellite HCHO/NO2 ratio and the O3 weekend/weekday ratio) revealed that spring of 2020 was the first year on record to be on averageNOx-limited, while the “transitional” character of recent summers became NOx-limited due to COVID-19-related NOx reductions in 2020. Model simulations performed with baseline and COVID-19-adjusted emissions capture this change to an NOx-limited environment and suggest that COVID-19-related emission reductions were responsible for a 0–2 ppb decrease in O3 over the study period. Reaching NOx-limited territory is an important regulatory milestone, and this study suggests that deep reductions in NOx emissions (in excess of those observed in this study) would be an effective pathway toward long-term O3 reductions.

An antibody that neutralizes SARS-CoV-1 and SARS-CoV-2 by binding to a conserved spike epitope outside the receptor binding motif.

The rapid evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), such as the Omicron variants that 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 is 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.

Estimated effects of economic policies for COVID-19 on the leisure and recreation industry under public health interventions. (Special Issue: COVID-19 and tourism.)

The appropriate policy responses for COVID-19 are being vigorously debated, especially regarding whether there is a trade-off between containing the spread of the virus and reducing the economic recession. The aim of the paper is to examine the effect of various economic policies on the leisure and recreation industry under public health interventions during the pandemic. The researchers collect data for 131 countries/regions from February to October 2020 and employ fixed-effects models to examine the impact of economic policies after controlling for public health interventions and country- and time-fixed effects. Results show that, with an impact lag, economic policies significantly promote current-date leisure and recreation activities under public health intervention;this effect peaks after around one month, and the two policies mutually reinforce each other in the medium term. The positive effect of economic policies ranges from 5 to 11%, depending on the magnitude of public health interventions. With regard to the different categories of measures, monetary policies have an immediate positive announcement effect while fiscal policies significantly promote leisure and recreation activities, though with a response lag. In addition, this study discusses the implications for the recovery of the leisure and recreation industry under pandemic crisis from a policy perspective.

Quantum Deep Learning for Mutant COVID-19 Strain Prediction (preprint)

New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period. Early prediction of possible variants (especially spike protein) of COVID-19 epidemic strains based on available mutated SARS-CoV-2 RNA sequences may lead to early prevention and treatment. Here, combining the advantage of quantum and quantum-inspired algorithms with the wide application of deep learning, we propose a development tool named DeepQuantum, and use this software to realize the goal of predicting spike protein variation structure of COVID-19 epidemic strains. In addition, this hybrid quantum-classical model for the first time achieves quantum-inspired blur convolution similar to classical depthwise convolution and also successfully applies quantum progressive training with quantum circuits, both of which guarantee that our model is the quantum counterpart of the famous style-based GAN. The results state that the fidelities of random generating spike protein variation structure are always beyond 96% for Delta, 94% for Omicron. The training loss curve is more stable and converges better with multiple loss functions compared with the corresponding classical algorithm. At last, evidences that quantum-inspired algorithms promote the classical deep learning and hybrid models effectively predict the mutant strains are strong.

Lymphocyte blood levels that remain low can predict the death of patients with COVID-19.

ABSTRACT: The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly spreading on a global scale and poses a great threat to human health. However, efficient indicators for disease severity have not been fully investigated. Here, we aim to investigate whether dynamic changes of lymphocyte counts can predict the deterioration of patients with COVID-19.We collected data from 2923 patients with laboratory-confirmed COVID-19. Patients were then screened, and we focused on 145 severe cases and 60 critical cases (29 recovered cases, 31 deaths). The length of hospitalization was divided into five time points, namely admission, 25%, 50%, 75% and discharge or death, according to the principle of interquartile distance. A series of laboratory findings and clinical data were collected and analyzed during hospitalization. The results showed that there were differences in levels of leukocytes, neutrophils and lymphocytes at almost every time point in the severe cases and 60 critical cases (29 recovered cases, 31 deaths). Further analysis showed that 70.2% of the COVID-19 cases had low circulating lymphocyte count, of which 64.1% were severe cases and 85.0% were critical cases (75.9% recovered cases and 93.5% died). Moreover, the lymphocyte count in dead cases was significantly lower than that of critical cases who recovered, at almost every time point in the critical groups. We also divided critical patients into group A (<1.1 × 109/L) and group B (>1.1 × 109/L) according to number of lymphocytes. Through survival analysis, we found that there was no significant difference in survival between group A and group B at admission (P = .3065). However, the survival rate according to lymphocyte levels in group A was significantly lower than that of group B at 25% hospital stay (on average day 6.5), 50% and 75% time points (P < .001).Lymphocyte counts that remain lower after the first week following symptom onset are highly predictive of in-hospital death of adults with COVID-19. This predictor may help clinicians identify patients with a poor prognosis and may be useful for guiding clinical decision-making at an early stage.

The immediate and subsequent effects of public health interventions for COVID-19 on the leisure and recreation industry.

Public health interventions to combat COVID-19 can be viewed as an exogenous shock to the economy, especially for industries-such as leisure, recreation, and tourism-that rely heavily on human mobility. This study investigates whether and how exactly the economic impact of government public health policies varies over time. Focusing on the leisure and recreation industry, we use data for 131 countries/regions from February to May 2020 and employ generalized difference-in-differences models to investigate the short- and longer-term effects of public health policies. We find that stricter policies lead, on average, to an immediate 9.2-percentage-point drop in leisure and recreation participation. Even so, that industry recovers in about seven weeks after a COVID-19 outbreak in countries/regions that undertake active interventions. After thirteen weeks, leisure and recreation involvement recovers to 70% of pre-pandemic levels in a place that actively intervened but stagnates at about 40% in one that did not.

Effects of (Combined) Chinese Medicine on the Risk of Death From COVID-19: A Retrospective Data Analysis Based on 4567 Patients (preprint)

Background The coronavirus disease 2019 (COVID-19) epidemic is still ongoing, but the optimal treatment remains unclear. China adopted a series of measures, including widespread screening, strict quarantine and early treatment, combining western medicine with Chinese medicine, leading to rapid control of its spread. Nevertheless, the effects of ( combined ) Chinese medicine in reducing the toll of COVID-19 lack proof from statistics.Objective We conducted a retrospective data analysis to determine whether ( combined ) Chinese medicine is able to affect patient outcomes and to decrease the risk of death in COVID-19 patients.Methods The data were acquired by outputting the formatting information from the HIS system and then extracting and recording it in the database for complete cases. The demographics, disease onset, treatment, survival/death and all of the clinical classifications, groups and definitions were verified by specialists in the clinic, along with the research methodology and statistics, before conducting the statistical analysis. The characteristics of the cohort and the clinical symptoms and signs, prescriptions and outcomes were described and analyzed by the mean ± SD, median, interquartile range and composition ratio. Analysis of variance was used for comparisons between the measurement data sets; otherwise, the rank sum test was used. Counting data were compared between groups using the chi square test and Fisher’s exact test. Tendency matching was adopted to make the general data balance between groups. A Cox proportional hazard model was used to compare the risk of death among the different groups.Results Four centers were included in our study, and a total of 6,076 patients' clinical records were obtained after combining the data. We included 4567 cases for the descriptive statistics, and the crude case fatality rate was 3.0%. Compared with using only western medicine, (combined) Chinese medicine reduced the risk of death from COVID-19 after adjusting for other prognostic risk factors (HR = 0.135, 95% CI (0.088, 0.208)). Multivariate Cox regression also indicated that when applying the clinical classification of severe/critical, age ≥ 65 years old, coronary heart disease or chronic kidney disease and a time from onset to hospital admission of fewer than 14 days, all of these factors increased the risk of death.Conclusion (Combined) Chinese medicine can significantly reduce the risk of death from COVID-19, but the specific strategy/solution, effects and amount need further exploration in future studies.

Evaluation of the Risk Prediction Tools for Patients With Coronavirus Disease 2019 in Wuhan, China: A Single-Centered, Retrospective, Observational Study.

OBJECTIVES: To evaluate and compare the efficacy of National Early Warning Score, National Early Warning Score 2, Rapid Emergency Medicine Score, Confusion, Respiratory rate, Blood pressure, Age 65 score, and quick Sepsis-related Organ Failure Assessment on predicting in-hospital death in patients with coronavirus disease 2019. DESIGN: A retrospective, observational study. SETTING: Single center, West Campus of Wuhan Union hospital-a temporary center to manage critically ill patients with coronavirus disease 2019. PATIENTS: A total of 673 consecutive adult patients with coronavirus disease 2019 between January 30, 2020, and March 14, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data on demography, comorbidities, vital signs, mental status, oxygen saturation, and use of supplemental oxygen at admission to the ward were collected from medical records and used to score National Early Warning Score, National Early Warning Score 2, Rapid Emergency Medicine Score, Confusion, Respiratory rate, Blood pressure, Age 65 score, and quick Sepsis-related Organ Failure Assessment. Total number of patients was 673 (51% male) and median (interquartile range) age was 61 years (50-69 yr). One-hundred twenty-one patients died (18%). For predicting in-hospital death, the area under the receiver operating characteristics (95% CI) for National Early Warning Score, National Early Warning Score 2, Rapid Emergency Medicine Score, Confusion, Respiratory rate, Blood pressure, Age 65 score, and quick Sepsis-related Organ Failure Assessment were 0.882 (0.847-0.916), 0.880 (0.845-0.914), 0.839 (0.800-0.879), 0.766 (0.718-0.814), and 0.694 (0.641-0.746), respectively. Among the parameters of National Early Warning Score, the oxygen saturation score was found to be the most significant predictor of in-hospital death. The area under the receiver operating characteristic (95% CI) for oxygen saturation score was 0.875 (0.834-0.916). CONCLUSIONS: In this single-center study, the discrimination of National Early Warning Score/National Early Warning Score 2 for predicting mortality in patients with coronavirus disease 2019 admitted to the ward was found to be superior to Rapid Emergency Medicine Score, Confusion, Respiratory rate, Blood pressure, Age 65 score, and quick Sepsis-related Organ Failure Assessment. Peripheral oxygen saturation could independently predict in-hospital death in these patients. Further validation of our finding in multiple settings is needed to determine its applicability for coronavirus disease 2019.

Analysis and validation of a highly sensitive one-step nested quantitative real-time polymerase chain reaction assay for specific detection of severe acute respiratory syndrome coronavirus 2.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is posing a serious threat to global public health. Reverse transcriptase real-time quantitative polymerase chain reaction (qRT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. Due to technical limitations, the reported positive rates of qRT-PCR assay of throat swab samples vary from 30 to 60%. Therefore, the evaluation of alternative strategies to overcome the limitations of qRT-PCR is required. A previous study reported that one-step nested (OSN)-qRT-PCR revealed better suitability for detecting SARS-CoV-2. However, information on the analytical performance of OSN-qRT-PCR is insufficient. METHOD: In this study, we aimed to analyze OSN-qRT-PCR by comparing it with droplet digital PCR (ddPCR) and qRT-PCR by using a dilution series of SARS-CoV-2 pseudoviral RNA and a quality assessment panel. The clinical performance of OSN-qRT-PCR was also validated and compared with ddPCR and qRT-PCR using specimens from COVID-19 patients. RESULT: The limit of detection (copies/ml) of qRT-PCR, ddPCR, and OSN-qRT-PCR were 520.1 (95% CI: 363.23-1145.69) for ORF1ab and 528.1 (95% CI: 347.7-1248.7) for N, 401.8 (95% CI: 284.8-938.3) for ORF1ab and 336.8 (95% CI: 244.6-792.5) for N, and 194.74 (95% CI: 139.7-430.9) for ORF1ab and 189.1 (95% CI: 130.9-433.9) for N, respectively. Of the 34 clinical samples from COVID-19 patients, the positive rates of OSN-qRT-PCR, ddPCR, and qRT-PCR were 82.35% (28/34), 67.65% (23/34), and 58.82% (20/34), respectively. CONCLUSION: In conclusion, the highly sensitive and specific OSN-qRT-PCR assay is superior to ddPCR and qRT-PCR assays, showing great potential as a technique for detection of SARS-CoV-2 in patients with low viral loads.

Mental distress and its associations with behavioral outcomes during the COVID-19 pandemic: A national survey of Chinese adults (preprint)

Background This study primarily aimed to evaluate the associations between mental distress and COVID-19-related changes in behavioral outcomes, and potential modifiers (age, gender, and educational attainment) of such associations.Methods An online survey using anonymous network sampling was conducted in China during April-May, 2020 using a 74-item questionnaire distributed through social media. A national sample of 10,545 adults in 31 provinces provided data on socio-demographic characteristics, COVID-19-related mental distress, and changes in behavioral outcomes. Structural equation models were used for data analyses.Results About 13% of adults reported experiencing at least one symptom of mental distress. After adjusting for covariates, greater mental distress was associated with increased smoking and alcohol consumption (among current smokers and drinkers) and with both increased and decreased physical activity. Underweight adults were more likely to lose body weight (≥ 1 kg) whereas overweight adults were more likely to gain weight by the same amount. Association between mental distress and change in physical activity was stronger in adults aged 40 and above and those with high education.Conclusions Mental distress was associated with increased smoking in males but not females. These findings inform the design of tailored public health interventions aimed to mitigate long-term negative consequences of mental distress on outcomes.

Can Masks Be Reused After Hot Water Decontamination During the COVID-19 Pandemic?

Masks have become one of the most indispensable pieces of personal protective equipment and are important strategic products during the coronavirus disease 2019 (COVID-19) pandemic. Due to the huge mask demand-supply gap all over the world, the development of user-friendly technologies and methods is urgently needed to effectively extend the service time of masks. In this article, we report a very simple approach for the decontamination of masks for multiple reuse during the COVID-19 pandemic. Used masks were soaked in hot water at a temperature greater than 56 °C for 30 min, based on a recommended method to kill COVID-19 virus by the National Health Commission of the People's Republic of China. The masks were then dried using an ordinary household hair dryer to recharge the masks with electrostatic charge to recover their filtration function (the so-called "hot water decontamination + charge regeneration" method). Three kinds of typical masks (disposable medical masks, surgical masks, and KN95-grade masks) were treated and tested. The filtration efficiencies of the regenerated masks were almost maintained and met the requirements of the respective standards. These findings should have important implications for the reuse of polypropylene masks during the COVID-19 pandemic. The performance evolution of masks during human wear was further studied, and a company (Zhejiang Runtu Co., Ltd.) applied this method to enable their workers to extend the use of masks. Mask use at the company was reduced from one mask per day per person to one mask every three days per person, and 122 500 masks were saved during the period from 20 February to 30 March 2020. Furthermore, a new method for detection of faulty masks based on the penetrant inspection of fluorescent nanoparticles was established, which may provide scientific guidance and technical methods for the future development of reusable masks, structural optimization, and the formulation of comprehensive performance evaluation standards.