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Kang H, Lee J, Jung J, Oh EJ. Humoral Response Kinetics and Cross-Immunity in Hospitalized Patients with SARS-CoV-2 WT, Delta, or Omicron Infections: A Comparison between Vaccinated and Unvaccinated Cohorts. Vaccines (Basel) 2023; 11:1803. [PMID: 38140207 PMCID: PMC10747008 DOI: 10.3390/vaccines11121803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023] Open
Abstract
With the ongoing evolution of severe acute respiratory virus-2 (SARS-CoV-2), the number of confirmed COVID-19 cases continues to rise. This study aims to investigate the impact of vaccination status, SARS-CoV-2 variants, and disease severity on the humoral immune response, including cross-neutralizing activity, in hospitalized COVID-19 patients. This retrospective cohort study involved 122 symptomatic COVID-19 patients hospitalized in a single center. Patients were categorized based on the causative specific SARS-CoV-2 variants (33 wild-type (WT), 54 Delta and 35 Omicron) and their vaccination history. Sequential samples were collected to assess binding antibody responses (anti-S/RBD and anti-N) and surrogate virus neutralization tests (sVNTs) against WT, Omicron BA.1, and BA.4/5. The vaccinated breakthrough infection group (V) exhibited higher levels of anti-S/RBD compared to the variant-matched unvaccinated groups (UVs). The Delta infection resulted in a more rapid production of anti-S/RBD levels compared to infections with WT or Omicron variants. Unvaccinated severe WT or Delta infections had higher anti-S/RBD levels compared to mild cases, but this was not the case with Omicron infection. In vaccinated patients, there was no difference in antibody levels between mild and severe infections. Both Delta (V) and Omicron (V) groups showed strong cross-neutralizing activity against WT and Omicron (BA.1 and BA.4/5), ranging from 79.3% to 97.0%. WT (UV) and Delta (UV) infections had reduced neutralizing activity against BA.1 (0.8% to 12.0%) and BA.4/5 (32.8% to 41.0%). Interestingly, patients who received vaccines based on the ancestral spike exhibited positive neutralizing activity against BA.4/5, even though none of the study participants had been exposed to BA.4/5 and it is antigenically more advanced. Our findings suggest that a previous vaccination enhanced the humoral immune response and broadened cross-neutralizing activity to SARS-CoV-2 variants in hospitalized COVID-19 patients.
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Affiliation(s)
- Hyunhye Kang
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (H.K.); (J.J.)
- Research and Development Institute for In Vitro Diagnostic Medical Devices, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Jihyun Lee
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul 06591, Republic of Korea;
| | - Jin Jung
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (H.K.); (J.J.)
- Research and Development Institute for In Vitro Diagnostic Medical Devices, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Eun-Jee Oh
- Department of Laboratory Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (H.K.); (J.J.)
- Research and Development Institute for In Vitro Diagnostic Medical Devices, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Herman B, Wong MCS, Chantharit P, Hannanu FF, Viwattanakulvanid P. Longitudinal study of disease severity and external factors in cognitive failure after COVID-19 among Indonesian population. Sci Rep 2023; 13:19405. [PMID: 37938599 PMCID: PMC10632387 DOI: 10.1038/s41598-023-46334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/31/2023] [Indexed: 11/09/2023] Open
Abstract
The COVID-19 infection is assumed to induce cognitive failure. Identifying the relationship between COVID-19, the effect of vaccination and medication, and accommodating non-COVID-19 factors to cognitive failure is essential. This study was conducted in Indonesia from September 2021 to January 2023. Demographic information, clinical data, comorbidities, vaccination, and medication during COVID-19 were obtained, as well as a 6-month cognitive assessment with Cognitive Failures Questionnaire/CFQ, Fatigue Severity Score, and Generalized Anxiety Disorder (GAD-7). A Structural Equation Model explains the relationship between potential predictors and cognitive failure. The average score of CFQ after 6 months was 45.6 ± 23.1 out of 100. The severity of the disease, which was associated with vaccination status, age, previous infection, and unit of treatment (p < 0.05), was not related to cognitive failure (p = 0.519), although there is a significant direct impact of worst vaccination status to cognitive failure(p < 0.001). However, age, fatigue, and current anxiety were associated with higher cognitive failure (p < 0.001), although comorbidities and recent headaches were not significant in other models (p > 0.05). This study concludes that cognitive failure after COVID-19 is a multifactorial event and does not solely depend on COVID-19 severity. It is crucial to re-address the factors related to the long-term efficacy of vaccination and medication and focus on non-health factors affecting cognitive failure.Trial Registration: NCT05060562.
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Affiliation(s)
- Bumi Herman
- College of Public Health Sciences, Chulalongkorn University, Bangkok, Thailand
- Department of Family and Preventive Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
| | - Martin Chi Sang Wong
- The Faculty of Medicine, JC School of Public Health, The Chinese University of Hongkong, Hong Kong, China
- The Faculty of Medicine, The Centre for Health Education and Health Promotion, The Chinese University of Hong Kong, Hong Kong, China
- School of Public Health, The Peking University, Beijing, China
- School of Public Health, Fudan University, Shanghai, China
- School of Public Health, The Chinese Academy of Medical Sciences and Peking Union Medical Colleges, Beijing, China
| | - Prawat Chantharit
- Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Firdaus Fabrice Hannanu
- Department of Family and Preventive Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia
- Department of Radiology, Brainstem Imaging Laboratory, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA
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Simayi A, Li C, Chen C, Wang Y, Dong C, Tian H, Kong X, Zhou L, Peng J, Zhang S, Zhu F, Hu J, Xu K, Jin H, Fan H, Bao C, Zhu L. Kinetics of SARS-CoV-2 neutralizing antibodies in Omicron breakthrough cases with inactivated vaccination: Role in inferring the history and duration of infection. Front Immunol 2023; 14:1083523. [PMID: 36761738 PMCID: PMC9902649 DOI: 10.3389/fimmu.2023.1083523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
Background The quantitative level and kinetics of neutralizing antibodies (NAbs) in individuals with Omicron breakthrough infections may differ from those of vaccinated individuals without infection. Therefore, we aimed to evaluate the difference in NAb levels to distinguish the breakthrough cases from the post-immunized population to identify early infected person in an outbreak epidemic when nasal and/or pharyngeal swab nucleic acid real-time PCR results were negative. Methods We collected 1077 serum samples from 877 individuals, including 189 with Omicron BA.2 breakthrough infection and 688 post-immunized participants. NAb titers were detected using the surrogate virus neutralization test, and were log(2)-transformed to normalize prior to analysis using Student's unpaired t-tests. Geometric mean titers (GMT) were calculated with 95% confidence intervals (CI). Linear regression models were used to identify factors associated with NAb levels. We further conducted ROC curve analysis to evaluate the NAbs' ability to identify breakthrough infected individuals in the vaccinated population. Results The breakthrough infection group had a consistently higher NAb levels than the post-immunized group according to time since the last vaccination. NAb titers in the breakthrough infection group were 6.4-fold higher than those in the post-immunized group (GMT: 40.72 AU/mL and 6.38 AU/mL, respectively; p<0.0001). In the breakthrough infection group, the NAbs in the convalescent phase were 10.9-fold higher than in the acute phase (GMT: 200.48 AU/mL and 18.46 AU/mL, respectively; p<0.0001). In addition, the time since infection, booster vaccination, and the time since last vaccination were associated with log(2)-transformed NAb levels in the breakthrough infection group. ROC curve analysis showed that ROC area was largest (0.728) when the cut-off value of log(2)-transformed NAb was 6, which indicated that NAb levels could identify breakthrough infected individuals in the vaccinated population. Conclusion Our study demonstrates that the NAb titers of Omicron BA.2 variant breakthrough cases are higher than in the post-immunized group. The difference in NAb levels could be used to identify cases of breakthrough infection from the post-immunized population in an outbreak epidemic.
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Affiliation(s)
- Aidibai Simayi
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.,Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Chuchu Li
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Cong Chen
- Department of Acute Infectious Disease Control and Prevention, Changzhou Center for Disease Control and Prevention, Changzhou, China
| | - Yin Wang
- Department of Acute Infectious Disease Control and Prevention, Yangzhou Center for Disease Control and Prevention, Yangzhou, China
| | - Chen Dong
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Hua Tian
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Xiaoxiao Kong
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Lu Zhou
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jiefu Peng
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Shihan Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.,Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Fengcai Zhu
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,National Health Commission (NHC) Key Laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jianli Hu
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Ke Xu
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Hui Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.,Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
| | - Huafeng Fan
- Department of Microbiological Laboratory, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Changjun Bao
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Jiangsu Province Engineering Research Center of Health Emergency, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Liguo Zhu
- Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,National Health Commission (NHC) Key Laboratory of Enteric Pathogenic Microbiology, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.,Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
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