Early Prediction of Severe COVID-19 in Patients by a Novel Immune-Related Predictive Model

Notable correlation between serum epidermal growth factor values and inflammatory status in patients with COVID-19

INTRODUCTION

Despite its crucial role in Epidermal Growth Factor Receptor (EGFR) activation, and the resulting impact on the health-disease process, epidermal growth factor (EGF) is an underexplored molecule in relation to how its serum concentrations relate to other analytes and clinical variables in pathological contexts.

OBJECTIVE

To clarify the possible correlation between EGF and clinical and analytical variables in the context of COVID-19.

METHODS

Cross-sectional observational and analytical study, in patients with virological and clinical diagnosis of COVID-19, selected by simple random sampling, admitted between August and September 2021. UMELISA-EGF commercial kits were used.

RESULTS

Differences in overall EGF values were observed between groups (566.04 vs. 910.53 pg/ml, p = .0430). In COVID-19 patients, no notable correlations were observed for neutrophil, platelet, triglyceride or liver enzyme values (p > .05). Significant correlations were observed with the neutrophil-lymphocyte indicator (r = 0.4711, p = .0128) as well as with the platelet-lymphocyte index (r = 0.4553, p = .0155). Statistical results of multivariate regression analysis suggest NLR (β = .2232, p = .0353) and PLR (β = .2117, p = .0411) are predictors of inflammation in patients with COVID-19.

CONCLUSIONS

Serum EGF concentrations in COVID-19 correlate positively with prognostic inflammatory markers of severity and could presumably act as an independent risk factor for the development of inflammation in response to new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort

BACKGROUND

Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

METHODS

Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections.

RESULTS

Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms.

CONCLUSION

SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

A predictive model for disease severity among COVID-19 elderly patients based on IgG subtypes and machine learning

Objective

Due to the increased likelihood of progression of severe pneumonia, the mortality rate of the elderly infected with coronavirus disease 2019 (COVID-19) is high. However, there is a lack of models based on immunoglobulin G (IgG) subtypes to forecast the severity of COVID-19 in elderly individuals. The objective of this study was to create and verify a new algorithm for distinguishing elderly individuals with severe COVID-19.

Methods

In this study, laboratory data were gathered from 103 individuals who had confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using a retrospective analysis. These individuals were split into training (80%) and testing cohort (20%) by using random allocation. Furthermore, 22 COVID-19 elderly patients from the other two centers were divided into an external validation cohort. Differential indicators were analyzed through univariate analysis, and variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression. The severity of elderly patients with COVID-19 was predicted using a combination of five machine learning algorithms. Area under the curve (AUC) was utilized to evaluate the performance of these models. Calibration curves, decision curves analysis (DCA), and Shapley additive explanations (SHAP) plots were utilized to interpret and evaluate the model.

Results

The logistic regression model was chosen as the best machine learning model with four principal variables that could predict the probability of COVID-19 severity. In the training cohort, the model achieved an AUC of 0.889, while in the testing cohort, it obtained an AUC of 0.824. The calibration curve demonstrated excellent consistency between actual and predicted probabilities. According to the DCA curve, it was evident that the model provided significant clinical advantages. Moreover, the model performed effectively in an external validation group (AUC=0.74).

Conclusion

The present study developed a model that can distinguish between severe and non-severe patients of COVID-19 in the elderly, which might assist clinical doctors in evaluating the severity of COVID-19 and reducing the bad outcomes of elderly patients.

Distinct anti-NP, anti-RBD and anti-Spike antibody profiles discriminate death from survival in COVID-19

Introduction

Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces rapid production of IgM, IgA, and IgG antibodies directed to multiple viral antigens that may have impact diverse clinical outcomes.

Methods

We evaluated IgM, IgA, and IgG antibodies directed to the nucleocapsid (NP), IgA and IgG to the Spike protein and to the receptor-binding domain (RBD), and the presence of neutralizing antibodies (nAb), in a cohort of unvaccinated SARS-CoV-2 infected individuals, in the first 30 days of post-symptom onset (PSO) (T1).

Results

This study included 193 coronavirus disease 2019 (COVID-19) participants classified as mild, moderate, severe, critical, and fatal and 27 uninfected controls. In T1, we identified differential antibody profiles associated with distinct clinical presentation. The mild group presented lower levels of anti-NP IgG, and IgA (vs moderate and severe), anti-NP IgM (vs severe, critical and fatal), anti-Spike IgA (vs severe and fatal), and anti-RBD IgG (vs severe). The moderate group presented higher levels of anti-RBD IgA, comparing with severe group. The severe group presented higher levels of anti-NP IgA (vs mild and fatal) and anti-RBD IgG (vs mild and moderate). The fatal group presented higher levels of anti-NP IgM and anti-Spike IgA (vs mild), but lower levels of anti-NP IgA (vs severe). The levels of nAb was lower just in mild group compared to severe, critical, and fatal groups, moreover, no difference was observed among the more severe groups. In addition, we studied 82 convalescent individuals, between 31 days to 6 months (T2) or more than 6 months (T3), PSO, those: 12 mild, 26 moderate, and 46 severe plus critical. The longitudinal analyzes, for the severe plus critical group showed lower levels of anti-NP IgG, IgA and IgM, anti-Spike IgA in relation T3. The follow-up in the fatal group, reveals that the levels of anti-spike IgG increased, while anti-NP IgM levels was decreased along the time in severe/critical and fatal as well as anti-NP IgG and IgA in several/critical groups.

Discussion

In summary, the anti-NP IgA and IgG lower levels and the higher levels of anti-RBD and anti-Spike IgA in fatal compared to survival group of individuals admitted to the intensive care unit (ICU). Collectively, our data discriminate death from survival, suggesting that anti-RBD IgA and anti-Spike IgA may play some deleterious effect, in contrast with the potentially protective effect of anti-NP IgA and IgG in the survival group.

High Neutrophil-to-Lymphocyte Ratio Facilitates Cancer Growth-Currently Marketed Drugs Tadalafil, Isotretinoin, Colchicine, and Omega-3 to Reduce It: The TICO Regimen

Simple Summary

Several elements that are composed of, or related to, neutrophils, have been shown to inhibit strong immune responses to cancer and promote cancers’ growth. This paper presents the collected data showing these elements and how their coordinated actions as an ensemble facilitate growth in the common cancers. The paper goes on to present a drug regimen, TICO, designed to reduce the cancer growth enhancing effects of the neutrophil related elements. TICO uses four already marketed, readily available generic drugs, repurposed to inhibit neutrophil centered growth facilitation of cancer.

Abstract

This paper presents remarkably uniform data showing that higher NLR is a robust prognostic indicator of shorter overall survival across the common metastatic cancers. Myeloid derived suppressor cells, the NLRP3 inflammasome, neutrophil extracellular traps, and absolute neutrophil count tend to all be directly related to the NLR. They, individually and as an ensemble, contribute to cancer growth and metastasis. The multidrug regimen presented in this paper, TICO, was designed to decrease the NLR with potential to also reduce the other neutrophil related elements favoring malignant growth. TICO is comprised of already marketed generic drugs: the phosphodiesterase 5 inhibitor tadalafil, used to treat inadequate erections; isotretinoin, the retinoid used for acne treatment; colchicine, a standard gout (podagra) treatment; and the common fish oil supplement omega-3 polyunsaturated fatty acids. These individually impose low side effect burdens. The drugs of TICO are old, cheap, well known, and available worldwide. They all have evidence of lowering the NLR or the growth contributing elements related to the NLR when clinically used in general medicine as reviewed in this paper.

Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods

The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients hospitalized due to COVID-19 during the first wave in France (spring of 2020) with clinical and biological features. Machine learning and explainability methods were used to construct an aggravation risk score and analyzed feature effects. The model had a robust AUC ROC Score of 81%. Most important features were age, chest CT Severity and biological variables such as CRP, O2 Saturation and Eosinophils. Several features showed strong non-linear effects, especially for CT Severity. Interaction effects were also detected between age and gender as well as age and Eosinophils. Clustering techniques stratified inpatients in three main subgroups (low aggravation risk with no risk factor, medium risk due to their high age, and high risk mainly due to high CT Severity and abnormal biological values). This in-depth analysis determined significantly distinct typologies of inpatients, which facilitated definition of medical protocols to deliver the most appropriate cares for each profile.

Graphical Abstract