Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

Lasso, Gorka; Khan, Saad; Allen, Shelby; Mariano, Margarette C.; Florez, Catalina; Orner, Erika P.; Quiroz, Jose A.; Quevedo, Gregory; Massimi, Aldo; Hegde, Aditi; Wirchnianski, Ariel S.; Bortz, Robert H.; Malonis, Ryan J.; Georgiev, George I; Tong, Karen; Herrera, Natalia G.; Morano, Nicholas C.; Garforth, S.; Malaviya, Avinash; Khokhar, Ahmed; Laudermilch, Ethan; Dieterle, M. Eugenia; Fels, J. Maximilian; Haslwanter, Denise; Jangra, Rohit K.; Barnhill, Jason; Almo, Steven C.; Chandran, Kartik; Lai, Jonathan R.; Kelly, Libusha; Daily, Johanna P.; Vergnolle, Olivia

doi:10.1371/journal.pcbi.1009778

Cited by 13 publications

(12 citation statements)

References 72 publications

(135 reference statements)

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“…While it is likely that many factors are involved in severe disease development, our model represents a high-level description of some of the physiological pathways, in particular, the innate immune response pathways, that may be involved in disease progression. This is consistent with the data from Li et al and Lasso et al that many immune markers predict disease severity better than other features, such as demographic characteristics 59 , 60 . Although we investigated more complex models, we decided against the modeling of individual cytokines or cells, as they often exhibit overlapping functions, and because some of these functions have been poorly studied leading to uncertainties in parameter values.…”

Section: Discussionsupporting

confidence: 92%

A simple model of COVID-19 explains disease severity and the effect of treatments

Sanche

Cassidy

Chu

et al. 2022

Sci Rep

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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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Section: Discussionsupporting

confidence: 92%

A simple model of COVID-19 explains disease severity and the effect of treatments

Sanche

Cassidy

Chu

et al. 2022

Sci Rep

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“…Most predictive models rely on demographic and clinical variables. However, biomarkers have recently shown good correlation with severity of disease and mortality in COVID-19 modeling ( 111 ). One example was a large study of 2,895 consecutive patients with COVID-19 in whom three biomarkers measured at admission were found to reflect pathobiological axes of myocardial injury, altered coagulation, and inflammation.…”

Section: Future Perspectives: Metabolomic and Proteomic Biomarkers An...mentioning

confidence: 99%

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

et al. 2022

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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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“…ML is expected to be able to specify accurate medical solutions like excellent medical doctors. [ 20 , 21 , 22 , 23 , 24 ]…”

Section: Introductionmentioning

confidence: 99%

SERS‐Based Biosensors Combined with Machine Learning for Medical Application**

et al. 2023

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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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Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

Cited by 13 publications

References 72 publications

A simple model of COVID-19 explains disease severity and the effect of treatments

A simple model of COVID-19 explains disease severity and the effect of treatments

Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

SERS‐Based Biosensors Combined with Machine Learning for Medical Application**

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