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Zhou Z, Li D, Zhao Z, Shi S, Wu J, Li J, Zhang J, Gui K, Zhang Y, Ouyang Q, Mei H, Hu Y, Li F. Dynamical modelling of viral infection and cooperative immune protection in COVID-19 patients. PLoS Comput Biol 2023; 19:e1011383. [PMID: 37656752 PMCID: PMC10501599 DOI: 10.1371/journal.pcbi.1011383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/14/2023] [Accepted: 07/24/2023] [Indexed: 09/03/2023] Open
Abstract
Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.
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Affiliation(s)
- Zhengqing Zhou
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Dianjie Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Ziheng Zhao
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Shuyu Shi
- Peking University Third Hospital, Peking University, Beijing, China
| | - Jianghua Wu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianwei Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Jingpeng Zhang
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Ke Gui
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Yu Zhang
- Department of Immunology, School of Basic Medical Sciences, NHC Key Laboratory of Medical Immunology, Peking University, Beijing, China
| | - Qi Ouyang
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
| | - Heng Mei
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fangting Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing, China
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Wan YI, Puthucheary ZA, Pearse RM, Prowle JR. Characterising biological mechanisms underlying ethnicity-associated outcomes in COVID-19 through biomarker trajectories: a multicentre registry analysis. Br J Anaesth 2023; 131:491-502. [PMID: 37198030 PMCID: PMC10121108 DOI: 10.1016/j.bja.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Differences in routinely collected biomarkers between ethnic groups could reflect dysregulated host responses to disease and to treatments, and be associated with excess morbidity and mortality in COVID-19. METHODS A multicentre registry analysis from patients aged ≥16 yr with SARS-CoV-2 infection and emergency admission to Barts Health NHS Trust hospitals during January 1, 2020 to May 13, 2020 (wave 1) and September 1, 2020 to February 17, 2021 (wave 2) was subjected to unsupervised longitudinal clustering techniques to identify distinct phenotypic patient clusters based on trajectories of routine blood results over the first 15 days of hospital admission. Distribution of trajectory clusters across ethnic categories was determined, and associations between ethnicity, trajectory clusters, and 30-day survival were assessed using multivariable Cox proportional hazards modelling. Secondary outcomes were ICU admission, survival to hospital discharge, and long-term survival to 640 days. RESULTS We included 3237 patients with hospital length of stay ≥7 days. In patients who died, there was greater representation of Black and Asian ethnicity in trajectory clusters for C-reactive protein and urea-to-creatinine ratio associated with increased risk of death. Inclusion of trajectory clusters in survival analyses attenuated or abrogated the higher risk of death in Asian and Black patients. Inclusion of C-reactive protein went from hazard ratio (HR) 1.36 [0.95-1.94] to HR 0.97 [0.59-1.59] (wave 1), and from HR 1.42 [1.15-1.75]) to HR 1.04 [0.78-1.39] (wave 2) in Asian patients. Trajectory clusters associated with reduced 30-day survival were similarly associated with worse secondary outcomes. CONCLUSIONS Clinical biochemical monitoring of COVID-19 and progression and treatment response in SARS-CoV-2 infection should be interpreted in the context of ethnic background.
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Affiliation(s)
- Yize I Wan
- William Harvey Research Institute, Queen Mary University of London, London, UK; Acute Critical Care Research Unit, Royal London Hospital, Barts Health NHS Trust, London, UK.
| | - Zudin A Puthucheary
- William Harvey Research Institute, Queen Mary University of London, London, UK; Acute Critical Care Research Unit, Royal London Hospital, Barts Health NHS Trust, London, UK
| | - Rupert M Pearse
- William Harvey Research Institute, Queen Mary University of London, London, UK; Acute Critical Care Research Unit, Royal London Hospital, Barts Health NHS Trust, London, UK
| | - John R Prowle
- William Harvey Research Institute, Queen Mary University of London, London, UK; Acute Critical Care Research Unit, Royal London Hospital, Barts Health NHS Trust, London, UK
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Ahmed T, Hasan SMT, Akter A, Tauheed I, Akhtar M, Rahman SIA, Bhuiyan TR, Ahmed T, Qadri F, Chowdhury F. Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh. Front Med (Lausanne) 2023; 10:1111037. [PMID: 37293303 PMCID: PMC10244648 DOI: 10.3389/fmed.2023.1111037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Background Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection. Methodology In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models. Result The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection. Conclusion Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.
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Affiliation(s)
- Tasnuva Ahmed
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - S. M. Tafsir Hasan
- Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Afroza Akter
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Imam Tauheed
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Marjahan Akhtar
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Sadia Isfat Ara Rahman
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Taufiqur Rahman Bhuiyan
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Tahmeed Ahmed
- Nutrition and Clinical Services Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
- Office of the Executive Director, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Firdausi Qadri
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Fahima Chowdhury
- Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. J Cardiovasc Dev Dis 2023; 10:jcdd10020039. [PMID: 36826535 PMCID: PMC9967447 DOI: 10.3390/jcdd10020039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
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Ding Y, Sun Y, Liu C, Jiang Q, Chen F, Cao Y. SERS-Based Biosensors Combined with Machine Learning for Medical Application. ChemistryOpen 2023; 12:e202200192. [PMID: 36627171 PMCID: PMC9831797 DOI: 10.1002/open.202200192] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.
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Affiliation(s)
- Yan Ding
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yang Sun
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Cheng Liu
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Qiao‐Yan Jiang
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Feng Chen
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
| | - Yue Cao
- Department of Forensic MedicineNanjing Medical UniversityNanjing211166P.R. China
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Mellado-Artigas R, Zattera L, Barbeta E, Ferrando C. Comment to “Very late intubation in COVID-19 patients: A forgotten prognosis factor?”. Crit Care 2022; 26:212. [PMID: 35818055 PMCID: PMC9272866 DOI: 10.1186/s13054-022-04033-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/11/2022] [Indexed: 11/25/2022] Open
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Sanche S, Cassidy T, Chu P, Perelson AS, Ribeiro RM, Ke R. A simple model of COVID-19 explains disease severity and the effect of treatments. Sci Rep 2022; 12:14210. [PMID: 35988008 PMCID: PMC9392071 DOI: 10.1038/s41598-022-18244-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/08/2022] [Indexed: 12/23/2022] Open
Abstract
Considerable effort has been made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue by focusing on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation, identifying the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and reveal conditions that can increase the likelihood of desired clinical outcome from treatment administration. In particular, the analysis suggest that antivirals need to be administered early during infection to have an impact on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.
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Battaglini D, Lopes-Pacheco M, Castro-Faria-Neto HC, Pelosi P, Rocco PRM. Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19. Front Immunol 2022; 13:857573. [PMID: 35572561 PMCID: PMC9091347 DOI: 10.3389/fimmu.2022.857573] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/31/2022] [Indexed: 01/08/2023] Open
Abstract
Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.
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Affiliation(s)
- Denise Battaglini
- Anesthesia and Intensive Care, San Martino Policlinico Hospital, Instituto di Ricovero e Cura a Carattere Scientifico (IRCCS) for Oncology and Neuroscience, Genoa, Italy.,Department of Surgical Science and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy.,Department of Medicine, University of Barcelona, Barcelona, Spain
| | - Miquéias Lopes-Pacheco
- Laboratory of Pulmonary Investigation, Carlos Chagas Filho Biophysics Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Paolo Pelosi
- Anesthesia and Intensive Care, San Martino Policlinico Hospital, Instituto di Ricovero e Cura a Carattere Scientifico (IRCCS) for Oncology and Neuroscience, Genoa, Italy.,Department of Surgical Science and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Patricia R M Rocco
- Laboratory of Pulmonary Investigation, Carlos Chagas Filho Biophysics Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,COVID-19 Virus Network from Brazilian Council for Scientific and Technological Development, Brasília, Brazil.,COVID-19 Virus Network from Foundation Carlos Chagas Filho Research Support of the State of Rio de Janeiro, Rio de Janeiro, Brazil
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