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      Seroprevalence and dynamics of anti-SARS-CoV-2 antibodies: a longitudinal study based on patients with underlying diseases in Wuhan

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          Abstract

          Background

          Assessing the humoral immunity of patients with underlying diseases after being infected with SARS-CoV-2 is essential for adopting effective prevention and control strategies. The purpose of this study is to analyze the seroprevalence of people with underlying diseases and the dynamic change features of anti-SARS-CoV-2 antibodies.

          Methods

          We selected 100 communities in Wuhan using the probability-proportional-to-size sampling method. From these 100 communities, we randomly selected households according to a list provided by the local government. Individuals who have lived in Wuhan for at least 14 days since December 2019 and were ≥ 40 years old were included. From April 9–13, 2020, community staff invited all selected individuals to the community healthcare center in batches by going door-to-door or telephone. All participants completed a standardized electronic questionnaire simultaneously. Finally, 5 ml of venous blood was collected from all participants. Blood samples were tested for the presence of pan-immunoglobulins, IgM, IgA, and IgG antibodies against SARS-CoV-2 nucleocapsid protein and neutralising antibodies were assessed. During the period June 11–13, 2020 and October 9–December 5, 2020, all family members of a positive family and matched negative families were followed up twice.

          Results

          The seroprevalence of anti-SARS-CoV-2 antibodies in people with underlying diseases was 6.30% (95% CI [5.09–7.52]), and that of people without underlying diseases was 6.12% (95% CI [5.33–6.91]). A total of 313 people were positive for total antibodies at baseline, of which 97 had underlying disease. At the first follow-up, a total of 212 people were positive for total antibodies, of which 66 had underlying disease. At the second follow-up, a total of 238 people were positive for total antibodies, of which 68 had underlying disease. A total of 219 participants had three consecutive serum samples with positive total antibodies at baseline. The IgG titers decreased significantly with or without underlying diseases ( P < 0.05) within the 9 months at least, while the neutralizing antibody titer remained stable. The titer of asymptomatic patients was lower than that of symptomatic patients (baseline, P = 0.032, second follow-up, P = 0.018) in the underlying diseases group.

          Conclusion

          Our research focused on the serological changes of people with and without underlying diseases in a state of single natural infection. Regardless of the underlying diseases, the IgG titer decreased significantly over time, while there was no significant difference in the decline rate of IgG between with and without underlying diseases. Moreover, the neutralizing antibody titer remained relatively stable within the 9 months at least.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12931-022-02096-5.

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          Most cited references22

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          Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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            OpenSAFELY: factors associated with COVID-19 death in 17 million patients

            COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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              Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study

              Abstract Objective To delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) who died. Design Retrospective case series. Setting Tongji Hospital in Wuhan, China. Participants Among a cohort of 799 patients, 113 who died and 161 who recovered with a diagnosis of covid-19 were analysed. Data were collected until 28 February 2020. Main outcome measures Clinical characteristics and laboratory findings were obtained from electronic medical records with data collection forms. Results The median age of deceased patients (68 years) was significantly older than recovered patients (51 years). Male sex was more predominant in deceased patients (83; 73%) than in recovered patients (88; 55%). Chronic hypertension and other cardiovascular comorbidities were more frequent among deceased patients (54 (48%) and 16 (14%)) than recovered patients (39 (24%) and 7 (4%)). Dyspnoea, chest tightness, and disorder of consciousness were more common in deceased patients (70 (62%), 55 (49%), and 25 (22%)) than in recovered patients (50 (31%), 48 (30%), and 1 (1%)). The median time from disease onset to death in deceased patients was 16 (interquartile range 12.0-20.0) days. Leukocytosis was present in 56 (50%) patients who died and 6 (4%) who recovered, and lymphopenia was present in 103 (91%) and 76 (47%) respectively. Concentrations of alanine aminotransferase, aspartate aminotransferase, creatinine, creatine kinase, lactate dehydrogenase, cardiac troponin I, N-terminal pro-brain natriuretic peptide, and D-dimer were markedly higher in deceased patients than in recovered patients. Common complications observed more frequently in deceased patients included acute respiratory distress syndrome (113; 100%), type I respiratory failure (18/35; 51%), sepsis (113; 100%), acute cardiac injury (72/94; 77%), heart failure (41/83; 49%), alkalosis (14/35; 40%), hyperkalaemia (42; 37%), acute kidney injury (28; 25%), and hypoxic encephalopathy (23; 20%). Patients with cardiovascular comorbidity were more likely to develop cardiac complications. Regardless of history of cardiovascular disease, acute cardiac injury and heart failure were more common in deceased patients. Conclusion Severe acute respiratory syndrome coronavirus 2 infection can cause both pulmonary and systemic inflammation, leading to multi-organ dysfunction in patients at high risk. Acute respiratory distress syndrome and respiratory failure, sepsis, acute cardiac injury, and heart failure were the most common critical complications during exacerbation of covid-19.
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                Author and article information

                Contributors
                renliliipb@163.com
                yangweizhong@cams.cn
                wangchen@pumc.edu.cn
                Journal
                Respir Res
                Respir Res
                Respiratory Research
                BioMed Central (London )
                1465-9921
                1465-993X
                15 July 2022
                15 July 2022
                2022
                : 23
                : 188
                Affiliations
                [1 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, School of Population Medicine and Public Health, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, China
                [2 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Institute of Pharmaceutical and Medical Devices Supervision, , National Medical Products Administration-Chinese Academy of Medical Sciences, ; Beijing, China
                [3 ]GRID grid.443385.d, ISNI 0000 0004 1798 9548, Department of Respiratory and Critical Care Medicine, , Affiliated Hospital of Guilin Medical University, ; Guilin, China
                [4 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, National Health Commission Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory, Institute of Pathogen Biology, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, China
                [5 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Key Laboratory of Respiratory Disease Pathogenomics, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, China
                Article
                2096
                10.1186/s12931-022-02096-5
                9284953
                35841095
                ea8e0ca5-8a8a-41e1-a03c-b15d09eb6c97
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 9 October 2021
                : 22 June 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Respiratory medicine
                covid-19,sars-cov-2,underlying diseases,antibody,single natural infection
                Respiratory medicine
                covid-19, sars-cov-2, underlying diseases, antibody, single natural infection

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