Clinical outcomes of the severe acute respiratory syndrome coronavirus 2 Omicron and Delta variant: systematic review and meta-analysis of 33 studies covering 6 037 144 coronavirus disease 2019-positive patients.

BACKGROUND: Although the SARS-CoV-2 Omicron variant is considered to induce less severe disease, there have been no consistent results on the extent of the decrease in severity. OBJECTIVES: To compare the clinical outcomes of COVID-19-positive patients with Omicron and Delta variant infection. DATA SOURCES: Searches were implemented up to 8 November 2022 in PubMed, Web of Science, BioRvix, and MedRvix. STUDY ELIGIBILITY CRITERIA: Eligible studies were cohort studies reporting the clinical outcomes of COVID-19-positive patients with Omicron and Delta variant infection, including hospitalization, intensive care unit (ICU) admission, receiving invasive mechanical ventilation (IMV), and death. PARTICIPANTS: COVID-19-positive patients with Omicron and Delta variant infection. ASSESSMENT OF RISK OF BIAS: Risk of bias was assessed employing the Newcastle-Ottawa Scale. METHODS OF DATA SYNTHESIS: Random-effect models were employed to pool the ORs and 95% CIs to compare the risk of clinical outcome. I2 was employed to evaluate the heterogeneity between studies. RESULTS: A total of 33 studies with 6 037 144 COVID-19-positive patients were included in this meta-analysis. In the general population of COVID-19-positive patients, compared with Delta, Omicron variant infection resulted in a decreased risk of hospitalization (10.24% vs. 4.14%, OR = 2.91, 95% CI = 2.35-3.60), ICU admission (3.67% vs. 0.48%, OR = 3.64, 95% CI = 2.63-5.04), receiving IMV (3.93% vs. 0.34%, OR = 3.11, 95% CI = 1.76-5.50), and death (2.40% vs. 0.46%, OR = 2.97, 95% CI = 2.17-4.08). In the hospitalized patients with COVID-19, compared with Delta, Omicron variant infection resulted in a decreased risk of ICU admission (20.70% vs. 12.90%, OR = 1.63, 95% CI = 1.32-2.02), receiving IMV (10.90% vs. 5.80%, OR = 1.65, 95% CI = 1.28-2.14), and death (10.72% vs. 7.10%, OR = 1.44, 95% CI = 1.22-1.71). CONCLUSIONS: Compared with Delta, the severity of Omicron variant infection decreased.

COVID-19 vaccine uptake, reasons, and associated factors among older adults in Shenzhen, China.

Evidence is limited on the actual uptake of the coronavirus disease 2019 (COVID-19) vaccine among older adults, especially those with chronic diseases, during the pandemic. To examine COVID-19 vaccine uptake, reasons, and associated factor among older adults, a cross-sectional survey was conducted between September 24 and October 20, 2021 among older adults aged 60 and above in Shenzhen, China. Logistic regression analysis was used to examine associations of COVID-19 vaccine uptake with sociodemographic characteristics, pneumonia vaccination history, and participation in health education activities among older adults and among those with chronic diseases. Of the 951 participants, 82.8% reported being vaccinated against COVID-19 during the study period, but this proportion was relatively lower among adults aged 80 and above (62.7%) and those with chronic diseases (77.9%). The top-rated reasons for not being vaccinated included doctors not recommending it due to underlying diseases (34.1%), not being ready for it (18.3%), and failure to make an appointment (9.1%). General older adults who were aged below 70, had a high school and above education, were permanent residents of Shenzhen, were with good health and had pneumonia vaccination history were more likely to take the COVID-19 vaccination. Yet, among older adults with chronic diseases, other than age and permanent residency status, health status was the only significant indicator of COVID-19 vaccine uptake. Our study added to evidence that health condition is the critical barrier to the actual uptake of the COVID-19 vaccine among Chinese older adults, especially those aged 80 and above and those with chronic diseases.

Less Social Support for Patients With COVID-19: Comparison With the Experience of Nurses.

Context: Since December 2019, more than 80,000 patients have been diagnosed with coronavirus disease 2019 (COVID-19) in China. Social support status of COVID-19 patients, especially the impact of social support on their psychological status and quality of life, needs to be addressed with increasing concern. Objectives: In this study, we used social support rating scale (SSRS) to investigate the social support in COVID-19 patients and nurses. Methods: The present study included 186 COVID-19 patients at a Wuhan mobile cabin hospital and 234 nurses at a Wuhan COVID-19 control center. Responses to a mobile phone app-based questionnaire about social support, anxiety, depression, and quality of life were recorded and evaluated. Results: COVID-19 patients scored significantly lower than nurses did on the Social Support Rating Scale (SSRS). Among these patients, 33.9% had anxiety symptoms, while 23.7% had depression symptoms. Overall SSRS, subjective social support scores and objective support scores of patients with anxiety were lower than those of patients without anxiety. This result was also found in depression. In addition, all dimensions of social support were positively correlated with quality of life. Interestingly, in all dimensions of social support, subjective support was found to be an independent predictive factor for anxiety, depression, and quality of life, whereas objective support was a predictive factor for quality of life, but not for anxiety and depression via regression analysis. Conclusion: Medical staffs should pay attention to the subjective feelings of patients and make COVID-19 patients feel respected, supported, and understood from the perspective of subjective support, which may greatly benefit patients, alleviate their anxiety and depression, and improve their quality of life.

Close Contacts, Infected Cases, and the Trends of SARS-CoV-2 Omicron Epidemic in Shenzhen, China.

(1) The overall trends of the number of daily close contacts and infected cases as well as their association during an epidemic of Omicron Variant of SARS-CoV-2 have been poorly described. (2) Methods: This study was to describe the trends during the epidemic of the Omicron variant of SARS-CoV-2 in Shenzhen, China, including the number of close contacts and infected cases as well as their ratios by days and stages (five stages). (3) Results: A total of 1128 infected cases and 80,288 close contacts were identified in Shenzhen from 13 February 2022 to 1 April 2022. Before the citywide lockdown (14 March), the number of daily close contacts and infected cases gradually increased. However, the numbers showed a decrease after the lockdown was imposed. The ratio of daily close contacts to daily infected cases ranged from 20.2:1 to 63.4:1 and reached the lowest during the lockdown period. The growth rate of daily close contacts was consistent with those of infected cases observed 6 days later to some extent. (4) Conclusions: The Omicron variant epidemic was promptly contained by tracing close contacts and taking subsequent quarantine measures.

Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review.

BACKGROUND: The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases. METHODS: We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China's Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran's I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors. CONCLUSIONS: Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.

Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China

• The accuracy of the temperature, radiation and hybrid models improved by 12.05 %, 11.06% and 10.46% after being optimized by WOA. • The estimation accuracy of the temperature, radiation and hybrid models optimized by the whale algorithm were higher than the prediction result of the ELM model. • The empirical model with more input parameters has higher estimation accuracy than the empirical model with fewer parameters. The accurate estimation of reference crop evapotranspiration (ET 0) is of great significance to improve agricultural water use efficiency and optimize regional water resources management. At present, the applicability evaluation system of ET 0 models is still lacking in several climate regions in China, leading to the confusion in application of the ET 0 model in some specific regions. In this study, the daily meteorological data of 84 representative stations in four climate regions of China during the past 30 years (1991–2019) were selected to evaluate the ET 0 simulation results of twelve empirical models (four temperature models, five radiation models, and three hybrid models) on the daily scale, and the optimal models suitable for each climate region were screened. Whale optimization algorithm (WOA) was used to optimize the optimal model to improve the simulation accuracy, and the ET 0 results were compared with those predicted by extreme learning machine (ELM). The results showed that the estimation accuracy of the hybrid model was the best throughout China, followed by the radiation model, and the temperature model was relatively poor, with R2 ranges of 0.77–0.88, 0.60–0.86, and 0.58–0.82, respectively. Among the temperature-based models, Hargreaves-Samani and Improve Baier-Robertson model had the highest accuracy, with R2 of 0.80 and 0.79. Among the radiation-based models, Priestley-Taylor and Jensen-Haise models had the best accuracy, with R2 of 0.82 and 0.79. Among the hybrid models, Penman model had the highest accuracy, with R2 of 0.84. The accuracy of Hargreaves-Samani and Improve Baier-Robertson model in SMZ climate region was higher than TCZ, TMZ, and MPZ, and the accuracy of Jensen-Haise model in TCZ was the highest. The estimation accuracy of Priestley-Taylor and Penman models was similar in SMZ, TCZ, TMZ and MPZ. Using WOA to optimize the optimal temperature, radiation, and hybrid models, the prediction accuracy was improved by 12.05 %, 11.06 %, and 10.46 %, which were higher than the result of ELM model, with R2 of 0.90, 0.91, 0.95 and 0.90, respectively. Therefore, it is recommended to adopt WOA to optimize the empirical model to estimate the ET 0 all over China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Effect of Lockdown and Mass Testing for the SARS-CoV-2 Omicron Epidemic on Reducing New Infections in Shenzhen, China.

On 14 March 2022, China's tech hub Shenzhen, a mega-city with more than 18 million inhabitants, imposed a one-week citywide lockdown immediately after it observed a surge in infections. We assessed the effect of this one-week lockdown, coupled with mass testing, on reducing the daily number of new confirmed cases and asymptomatic cases during the Omicron wave, using an interrupted time series analysis approach. Our analysis suggests that the one-week citywide lockdown in Shenzhen was effective at lowering both daily new confirmed cases and asymptomatic cases during the Omicron wave. Early detection ensures timely isolation and treatment of infected patients in designated hospitals, and therefore helps lower the prevalence of confirmed cases and asymptomatic cases. Our findings of the immediate increase in asymptomatic cases after lockdown warrant further verifications in other city epidemic scenarios.

The interaction between black carbon and planetary boundary layer in the Yangtze River Delta from 2015 to 2020: Why O3 didn't decline so significantly as PM2.5.

Since the Air Pollution Prevention and Control Action Plan (air clean plan) issued in 2013, air quality has been in continuous improvement. The second stage of air clean plan since 2018 was focused on O3 controlling, but it still didn't decline so significantly as PM2.5. This study conducted a long-term observation on black carbon (BC) and utilized the observational data of other air pollutants (PM2.5, PM10, NO2, SO2, CO and O3), the meteorological elements and the vertical sounding data of PBL in Nanjing. In the daytime (08:00-20:00), PM2.5 kept decreasing from 2015 to 2020 at the rate of 4.8 µg⋅m-3⋅a-1, however, BC increased at the rate of 0.6 µg⋅m-3⋅a-1, which has led to the continuous growth of BC/PM2.5 (0.9%⋅a-1). However, during this period, O3 was relatively stable and, in 2020, it returned below its value in 2015 after slight increases in 2017 and 2018. Meanwhile, the average surface temperature had increased by around 1.0 °C during 2015-2019 at the rate of 0.3 °C⋅a-1. Also, the average height of the inversion layer had increased significantly by 494.0 and 176.7 m at 20:00 and 08:00, whose growth ratio was up to 57% and 25%, respectively. The above observation results have formed a set of chain reactions as follows. The growth of the surface BC caused the surface temperature to rise due to the increasing heating effect of BC. The continuous growth of the surface temperature made it easier for the PBL height to develop, which led to the lift of the inversion layer in the PBL and the larger atmospheric environment capacity. Ultimately, it is conducive to the diffusion of the near surface pollutants, thus helping reduce their concentrations, which offsets the increasing tendency of O3 and add to the decreasing trend of PM2.5. This phenomenon is the most remarkable in summer, with the fastest increasing rate of temperature (0.8 °C⋅a-1) and O3 (3.9 µg⋅m-3⋅a-1) during 2015-2019 (excluding 2020 to erase the great effect of COVID-19 lockdown on emissions).

Robust humoral and cellular immune responses in long-term convalescent COVID-19 individuals following one-dose SARS-CoV-2 inactivated vaccination

COVID-19, caused by SARS-CoV-2, has resulted in hundreds of millions of infections and millions of deaths worldwide. Preliminary results exhibited excellent efficacy of SARS-CoV-2 vaccine in preventing hospitalization and severe disease. However, data on inactivated vaccine-induced immune responses of naturally infected patients are limited. Here, we characterized SARS-CoV-2 RBD-specific IgG (anti-S-RBD IgG) and neutralizing antibodies (NAbs) against SARS-CoV-2 wild type and variants of concerns (VOCs), as well as RBD-specific IgG-secreting B cells and antigen-specific T cells respectively in 51 SARS-CoV-2 recovered subjects and 63 healthy individuals. In SARS-CoV-2 recovered patients, a single dose vaccine is sufficient to reactivate robust anti-S-RBD IgG and NAbs. The neutralizing capacity against VOCs increased significantly post-vaccination no matter healthy individuals or SARS-CoV-2 recovered patients. In addition, RBD-specific IgG-secreting B cells in SARS-CoV-2 recovered patients were significantly higher than that in healthy vaccine recipients. After the vaccine booster, the frequencies of specific IFN-γ+ CD4+ T cell, IL-2+ CD4+ T cell, and TNF-α+ CD4+ T cell responses were significantly increased in SARS-CoV-2 recovered patients. Our data highlighted the safety and utility of SARS-CoV-2 inactivated vaccine and demonstrated that robust humoral and cellular immune response can be reactivated by one-dose inactivated vaccine in SARS-CoV-2 recovered patients.

Role of cytokine storm in coronavirus infections: culprit or accomplice?

At present, there are seven known types of human coronaviruses (HCoVs), which can be further divided into two categories: low pathogenic and highly pathogenic. The low pathogenic HCoVs infect the upper respiratory tract, mainly causing mild, cold-like respiratory diseases. By contrast, highly pathogenic HCoVs mainly infect the lower respiratory tract and cause fatal types of pneumonia, which include severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), as well as the recent outbreak of coronavirus disease 2019 (COVID-19). Highly pathogenic HCoV infection has a high morbidity and mortality, which is usually related to the strong immune response induced by highly proinflammatory cytokines, which is also known as "cytokine storm". Therefore, it is particularly important to explore the role of cytokine storm in the process of highly pathogenic HCoV infection. We review the epidemiological and clinical manifestations of highly pathogenic HCoV infection, and reveal the pathology of cytokine storm and its role in the process of highly pathogenic HCoV infection.

Time-course analysis reveals that corticosteroids resuscitate diminished CD8+ T cells in COVID-19: a retrospective cohort study.

OBJECTIVE: To illustrate the effect of corticosteroids and heparin, respectively, on coronavirus disease 2019 (COVID-19) patients' CD8+ T cells and D-dimer. METHODS: In this retrospective cohort study involving 866 participants diagnosed with COVID-19, patients were grouped by severity. Generalized additive models were established to explore the time-course association of representative parameters of coagulation, inflammation and immunity. Segmented regression was performed to examine the influence of corticosteroids and heparin upon CD8+ T cell and D-dimer, respectively. RESULTS: There were 541 moderate, 169 severe and 156 critically ill patients involved in the study. Synchronous changes of levels of NLR, D-dimer and CD8+ T cell in critically ill patients were observed. Administration of methylprednisolone before 14 DFS compared with those after 14 DFS (ß = 0.154%, 95% CI=(0, 0.302), p=.048) or a dose lower than 40 mg per day compared with those equals to 40 mg per day (ß = 0.163%, 95% CI=(0.027, 0.295), p=.020) significantly increased the rising rate of CD8+ T cell in 14-56 DFS. CONCLUSIONS: The parameters of coagulation, inflammation and immunity were longitudinally correlated, and an early low-dose corticosteroid treatment accelerated the regaining of CD8+ T cell to help battle against SARS-Cov-2 in critical cases of COVID-19.

Optimization of extreme learning machine model with biological heuristic algorithms to estimate daily reference crop evapotranspiration in different climatic regions of China

• Hybrid ELM models (PSO-ELM, GA-ELM and ABC-ELM) were proposed for estimating ET 0 in different climate zones of China. • PSO-ELM model had the highest accuracy, followed by GA-ELM and ABC-ELM. • Hybrid ELM models outperformed standalone ELM and empirical models in different climate zones. • PSO-ELM model with T max , T min and RH obtained accurate ET 0 estimates in TCZ, SMZ and TMZ. • PSO-ELM model with only T max and T min was better performance on ET 0 estimates in MPZ. Accurate prediction of reference crop evapotranspiration (ET 0) is important for regional water resources management and optimal design of agricultural irrigation system. In this study, three hybrid models (PSO-ELM, GA-ELM and ABC-ELM) integrating the extreme learning machine model (ELM) with three biological heuristic algorithms, i.e., PSO, GA and ABC, were proposed for predicting daily ET 0 based on daily meteorological data from 2000 to 2019 at twelve representative stations in different climatic zones of China. The performances of the three hybrid ELM models were further compared with the standalone ELM model and three empirical models (Hargreaves, Priestley-Talor and Makkink models). The results showed that the hybrid ELM models (R 2 = 0.973–0.999) all performed better than the standalone ELM model (R 2 = 0.955–0.989) in four climatic regions in China. The estimation accuracy of the empirical models was relatively lower, with R2 of 0.822–0.887 and RMSE of 0.381–1.951 mm/d. The R 2 values of PSO-ELM, GA-ELM and ABC-ELM models were 0.993, 0.986 and 0.981 and the RMSE values were 0.266 mm/d, 0.306 mm/d and 0.404 mm/d, respectively, indicating that the PSO-ELM model had the best performance. When setting T max , T min and RH as the model inputs, the PSO-ELM model presented better performance in the temperate continental zone (TCZ), subtropical monsoon region (SMZ) and temperate monsoon zone (TMZ) climate zones, with R 2 of 0.892, 0866 and 0.870 and RMSE of 0.773 mm/d, 0.597 mm/d and 0.832 mm/d, respectively. The PSO-ELM model also performed in the mountain plateau region (MPZ) when only T max and T min data were available, with R2 of 0.808 and RMSE of 0.651 mm/d. All the three biological heuristic algorithms effectively improved the performance of the ELM model. Particularly, the PSO-ELM was recommended as a promising model realizing the high-precision estimation of daily ET 0 with fewer meteorological parameters in different climatic zones of China. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Air quality in Nanjing during COVID-19 lockdown period

In order to investigate the impact of COVID-19 lockdown on air quality in Nanjing, the air pollutants observed from January 25 to February 10, in 2020（COVID-19 lockdown period） in Nanjing and its surrounding cities was analyzed. During the lockdown period with poor atmospheric diffusion conditions, the concentrations of PM2.5, PM10, NO2, SO2, and CO decreased obviously, with the value of 36, 44, 5, 22μg/m3 and 1.1 mg/m3, whereasO3 increased by 4%. The net effectiveness of the emission reduction measures was calculated through comparisons of concentrations of air pollutants between and before COVID in the similar meteorological conditions. Concentrations of PM2.5, PM10, SO2, NO2 and CO decreased by 41.7%, 45.3%, 14.3%, 43.5% and 18.2%, respectively, whereasO3 increased by 4.8%. Compared to capital cities of the Yangtze River Delta in the same period, the largest decline of SO2 and the medium decline of the other pollutions were appeared in Nanjing. The diurnal variation concentration of PM2.5 and PM10 changed from double peak to single peak, due to the disappearance of nighttime sub-peak of particle. The concentration ofO3 increased significantly at night, which was resulted from that sharp reduction of traffic sources weaken the titration reaction of NO toO3. The peak ofO3 during the daytime depended on the variation of the ratio of VOCs to NOx due to the emission control.

A human-computer collaboration for COVID-19 differentiation: combining a radiomics model with deep learning and human auditing.

BACKGROUND: This study aimed to build a radiomics model with deep learning (DL) and human auditing and examine its diagnostic value in differentiating between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP). METHODS: Forty-three COVID-19 patients, whose diagnoses had been confirmed with reverse-transcriptase polymerase-chain-reaction (RT-PCR) tests, and 60 CAP patients, whose diagnoses had been confirmed with sputum cultures, were enrolled in this retrospective study. The candidate regions of interest (ROIs) on the computed tomography (CT) images of the 103 patients were determined using a DL-based segmentation model powered by transfer learning. These ROIs were manually audited and corrected by 3 radiologists (with an average of 12 years of experience; range 6-17 years) to check the segmentation acceptance for the radiomics analysis. ROI-derived radiomics features were subsequently extracted to build the classification model and processed using 4 different algorithms (L1 regularization, Lasso, Ridge, and Z test) and 4 classifiers, including the logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM), and extreme Gradient Boosting (XGboost). A receiver operating characteristic curve (ROC) analysis was conducted to evaluate the performance of the model. RESULTS: Quantitative CT measurements derived from human-audited segmentation results showed that COVID-19 patients had significantly decreased numbers of infected lobes compared to patients in the CAP group {median [interquartile range (IQR)]: 4 [3, 4] and 4 [4, 5]; P=0.031}. The infected percentage (%) of the whole lung was significantly more elevated in the CAP group [6.40 (2.77, 11.11)] than the COVID-19 group [1.83 (0.65, 4.42); P<0.001], and the same trend applied to each lobe, except for the superior lobe of the right lung [1.81 (0.09, 5.28) for COVID-19 vs. 1.32 (0.14, 7.02) for CAP; P=0.649]. Additionally, the highest proportion of infected lesions were observed in the CT value range of (-470, -370) Hounsfield units (HU) in the COVID-19 group. Conversely, the CAP group had a value range of (30, 60) HU. Radiomic model using corrected ROIs exhibited the highest area under ROC (AUC) of 0.990 [95% confidence interval (CI): 0.962-1.000] using Lasso for feature selection and MLP for classification. CONCLUSIONS: The proposed radiomics model based on human-audited segmentation made accurate differential diagnoses of COVID-19 and CAP. The quantification of CT measurements derived from DL could potentially be used as effective biomarkers in current clinical practice.

Declining Levels of Neutralizing Antibodies Against SARS-CoV-2 in Convalescent COVID-19 Patients One Year Post Symptom Onset.

Major advances have been made in understanding the dynamics of humoral immunity briefly after the acute coronavirus disease 2019 (COVID-19). However, knowledge concerning long-term kinetics of antibody responses in convalescent patients is limited. During a one-year period post symptom onset, we longitudinally collected 162 samples from 76 patients and quantified IgM and IgG antibodies recognizing the nucleocapsid (N) protein or the receptor binding domain (RBD) of the spike protein (S). After one year, approximately 90% of recovered patients still had detectable SARS-CoV-2-specific IgG antibodies recognizing N and RBD-S. Intriguingly, neutralizing activity was only detectable in ~43% of patients. When neutralization tests against the E484K-mutated variant of concern (VOC) B.1.351 (initially identified in South Africa) were performed among patients who neutralize the original virus, the capacity to neutralize was even further diminished to 22.6% of donors. Despite declining N- and S-specific IgG titers, a considerable fraction of recovered patients had detectable neutralizing activity one year after infection. However, neutralizing capacities, in particular against an E484K-mutated VOC were only detectable in a minority of patients one year after symptomatic COVID-19. Our findings shed light on the kinetics of long-term immune responses after natural SARS-CoV-2 infection and argue for vaccinations of individuals who experienced a natural infection to protect against emerging VOC.

Correlation between white blood cell count at admission and mortality in COVID-19 patients: a retrospective study.

BACKGROUND: Coronavirus disease-19 (COVID-19) has become a world health threaten. Its risk factors with death were still not known. White blood cells (WBC) count as a reflection of inflammation has played a vital role in COVID-19, however its level with death is not yet investigated. METHODS: In this retrospective, single-center study, all confirmed patients with COVID-19 at West Branch of Union Hospital from Jan 29 to Feb 28, 2020 were collected and analyzed. Demographic and clinical data including laboratory examinations were analyzed and compared between recovery and death patients. RESULTS: A total of 163 patients including 33 death cases were included in this study. Significant association was found between WBC count and death (HR = 1.14, 95%CI: 1.09-1.20, p < 0.001). The regression analysis results showed there was a significant association between WBC count and death (HR = 5.72, 95%CI: 2.21-14.82, p < 0.001) when use the second quartile as a cutoff value (> 6.16 × 10^9/L). The difference was still exist after adjusting for confounding factors (HR = 6.26, 95%CI: 1.72-22.77, p = 0.005). In addition, Kaplan-meier survival analysis showed that there was a significant decline of the cumulative survival rate (p < 0.001) in those with WBC count ≥6.16 × 10^9/L. CONCLUSION: WBC count at admission is significantly corelated with death in COVID-19 patients. Higher level of WBC count should be given more attention in the treatment of COVID-19.

The Severity of COVID-19 and Its Determinants: A Systematic Review and Meta-Analysis in China

To analyse the prevalence of severe and critical COVID-19 cases and its determinants, a systematic review and meta-analysis were conducted using Review Manager. Four English and two Chinese databases were used to identify and explore the relationships between the severity of COVID-19 and its determinants, with no restrictions on publication date. The odds ratio and 95% CI were combined to assess the influencing level of all factors. Twenty-three articles containing a total of 15,828 cases of COVID-19 were included in this systematic review. The prevalence of severe and critical COVID-19 cases was 17.84% and 4.9%, respectively. A total of 148 factors were identified, which included behavioural, symptom, comorbidity, laboratory, radiographic, exposure, and other factors. Among them, 35 factors could be included in the meta-analysis. Specifically, for example, the male (OR 1.55, 95% CI 1.42–1.69) and elderly (OR 1.06, 95% CI 1.03–1.10) populations tended to experience severe and critical illness. Patients with cough, dyspnea, fatigue, fever, and gastrointestinal symptoms could have severe and critical diseases. Regarding laboratory results, albumin, aspartate aminotransferase, creatinine, D-dimer, fibrinogen, neutrophils, procalcitonin, platelets, and respiratory rate were potential factors that could be used to predict the severity of COVID.

Scores based on neutrophil percentage and lactate dehydrogenase with or without oxygen saturation predict hospital mortality risk in severe COVID-19 patients.

BACKGROUND: Risk scores are needed to predict the risk of death in severe coronavirus disease 2019 (COVID-19) patients in the context of rapid disease progression. METHODS: Using data from China (training dataset, n = 96), prediction models were developed by logistic regression and then risk scores were established. Leave-one-out cross validation was used for internal validation and data from Iran (test dataset, n = 43) was used for external validation. RESULTS: A NSL model (area under the curve (AUC) 0.932) and a NL model (AUC 0.903) were developed based on neutrophil percentage and lactate dehydrogenase with and without oxygen saturation (SaO2) using the training dataset. AUCs of the NSL and NL models in the test dataset were 0.910 and 0.871, respectively. The risk scoring systems corresponding to these two models were established. The AUCs of the NSL and NL scores in the training dataset were 0.928 and 0.901, respectively. At the optimal cut-off value of NSL score, the sensitivity and specificity were 94% and 82%, respectively. The sensitivity and specificity of NL score were 94% and 75%, respectively. CONCLUSIONS: These scores may be used to predict the risk of death in severe COVID-19 patients and the NL score could be used in regions where patients' SaO2 cannot be tested.

Is SARS-CoV-2 vaccination safe and effective for elderly individuals with neurodegenerative diseases?

INTRODUCTION: Coronavirus Disease 2019 (COVID-19) poses a substantial threat to the lives of the elderly, especially those with neurodegenerative diseases, and vaccination against viral infections is recognized as an effective measure to reduce mortality. However, elderly patients with neurodegenerative diseases often suffer from abnormal immune function and take multiple medications, which may complicate the role of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. Currently, there is no expert consensus on whether SARS-CoV-2 vaccines are suitable for patients with neurodegenerative diseases. AREAS COVERED: We searched Pubmed to conduct a systematic review of published studies, case reports, reviews, meta-analyses, and expert guidelines on the impact of SARS-CoV-2 on neurodegenerative diseases and the latest developments in COVID-19 vaccines. We also summarized the interaction between vaccines and age-related neurodegenerative diseases. The compatibility of future SARS-CoV-2 vaccines with neurodegenerative diseases is discussed. EXPERT OPINION: Vaccines enable the body to produce immunity by activating the body's immune response. The pathogenesis and treatment of neurodegenerative diseases is complex, and these diseases often involve abnormal immune function, which can substantially affect the safety and effectiveness of vaccines. In short, this article provides recommendations for the use of vaccine candidates in patients with neurodegenerative diseases.