Measurement of SARS-CoV-2 Antibody Titers Improves the Prediction Accuracy of COVID-19 Maximum Severity by Machine Learning in Non-Vaccinated Patients

Inactivated COVID-19 vaccines: potential concerns of antibody-dependent enhancement and original antigenic sin

Inactivated vaccine is one of the platforms employed in COVID-19 vaccines. Inactivated vaccines have been associated with concerns of antibody-dependent enhancement (ADE) and original antigenic sin (OAS), which are related to non-neutralising or poorly neutralising antibodies against the pathogen. Since inactivated COVID-19 vaccines use whole-SARS-CoV-2 virus as the immunogen, they are expected to generate antibodies against non-spike structural proteins, which are highly conservative across variants of SARS-CoV-2. These antibodies against non-spike structural proteins have found to be largely non-neutralising or poorly neutralising in nature. Hence, inactivated COVID-19 vaccines could possibly be associated with ADE and OAS, especially as novel variants emerge. This article explores the potential concern of ADE and OAS in the context of inactivated COVID-19 vaccine, and outlines the future research directions.

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning

Background

COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning.

Methods

A dataset comprising symptoms and feedback from 257 persons, of whom 203 were vaccinated and 54 unvaccinated, was used for the investigation. Three machine learning algorithms - Deep Multilayer Perceptron (Deep MLP), XGBoost, and Logistic Regression - were trained with the original (imbalanced) dataset and the balanced dataset created by using the Random Oversampling Technique (ROT), and the Synthetic Minority Oversampling Technique (SMOTE). We compared the performance of the classification algorithms when the features highly correlated with breakthrough infection were used and when all features in the dataset were used.

Result

The results show that when highly correlated features were considered as predictors, with Random Oversampling to address data imbalance, the XGBoost classifier has the best performance (F1 = 0.96; accuracy = 0.96; AUC = 0.98; G-Mean = 0.98; MCC = 0.88). The Deep MLP had the second best performance (F1 = 0.94; accuracy = 0.94; AUC = 0.92; G-Mean = 0.70; MCC = 0.42), while Logistic Regression had less accurate performance (F1 = 0.89; accuracy = 0.88; AUC = 0.89; G-Mean = 0.89; MCC = 0.68). We also used Shapley Additive Explanations (SHAP) to investigate the interpretability of the models. We found that body temperature, total cholesterol, glucose level, blood pressure, waist circumference, body weight, body mass index (BMI), haemoglobin level, and physical activity per week are the most critical variables indicating a higher risk of breakthrough infection.

Conclusion

These results, evident from our unique data source derived from apparently healthy volunteers with cardiovascular risk factors, follow the expected pattern of positive or negative correlations previously reported in the literature. This information strengthens the body of knowledge currently applied in public health guidelines and may also be used by medical practitioners in the future to reduce the risk of SARS-CoV-2 breakthrough infection.

Relationship between Anti-SARS-CoV-2 S Abs and IFN-λ3 Levels in the Administration of Oxygen following COVID-19 Vaccination

Although the effectiveness of vaccination at preventing hospitalization and severe coronavirus disease (COVID-19) has been reported in numerous studies, the detailed mechanism of innate immunity occurring in host cells by breakthrough infection is unclear. One hundred forty-six patients were included in this study. To determine the effects of vaccination and past infection on innate immunity following SARS-CoV-2 infection, we analyzed the relationship between anti-SARS-CoV-2 S Abs and biomarkers associated with the deterioration of COVID-19 (IFN-λ3, C-reactive protein, lactate dehydrogenase, ferritin, procalcitonin, and D-dimer). Anti-S Abs were classified into two groups according to titer: high titer (≥250 U/ml) and low titer (<250 U/ml). A negative correlation was observed between anti-SARS-CoV-2 S Abs and IFN-λ3 levels (r = -0.437, p < 0.001). A low titer of anti-SARS-CoV-2 S Abs showed a significant association with oxygen demand in patients, excluding aspiration pneumonia. Finally, in a multivariate analysis, a low titer of anti-SARS-CoV-2 S Abs was an independent risk factor for oxygen demand, even after adjusting for age, sex, body mass index, aspiration pneumonia, and IFN-λ3 levels. In summary, measuring anti-SARS-CoV-2 S Abs and IFN-λ3 may have clinical significance for patients with COVID-19. To predict the oxygen demand of patients with COVID-19 after hospitalization, it is important to evaluate the computed tomography findings to determine whether the pneumonia is the result of COVID-19 or aspiration pneumonia.

Decrease in serum levels of autotaxin in COVID-19 patients

INTRODUCTION

In order to identify therapeutic targets in Coronavirus disease 2019 (COVID-19), it is important to identify molecules involved in the biological responses that are modulated in COVID-19. Lysophosphatidic acids (LPAs) are involved in the pulmonary inflammation and fibrosis are one of the candidate molecules. The aim of this study was to evaluate the association between the serum levels of autotaxin (ATX), which are enzymes involved in the synthesis of lysophosphatidic acids.

MATERIAL AND METHODS

We enrolled 134 subjects with COVID-19 and 58 normal healthy subjects for the study. We measured serum ATX levels longitudinally in COVID-19 patients and investigated the time course and the association with severity and clinical parameters.

RESULTS

The serum ATX levels were reduced in all patients with COVID-19, irrespective of the disease severity, and were negatively associated with the serum CRP, D-dimer, and anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody levels.

DISCUSSION

Considering the biological properties of LPAs in the pulmonary inflammation and fibrosis, modulation of ATX might be compensatory biological responses to suppress immunological overreaction especially in the lung, which is an important underlying mechanism for the mortality of the disease.

CONCLUSIONS

COVID-19 patients showed a decrease in the serum levels of ATX, irrespective of the disease severity. Key MessagesAutotaxin (ATX) is an enzyme involved in the synthesis of lysophosphatidic acid (LPA), which has been reported to be involved in pulmonary inflammation and fibrosis. Patients with COVID-19 show decrease in the serum levels of ATX. Modulation of ATX might be compensatory biological responses to suppress immunological overreaction.

Non-neutralizing antibodies: Deleterious or propitious during SARS-CoV-2 infection?

Antibody-dependent enhancement (ADE) is a complex phenomenon mediated by antibodies, frequently pre-existing non-neutralizing or sub-neutralizing antibodies. In the course of infectious diseases, ADE may be responsible for worsening the clinical course of the disease by increasing the virulence of pathogens (ADE of infection) or enhancing disease severity (ADE of disease). Here we reviewed the mechanisms thought to be behind the ADE phenomenon and its potential relationship with COVID-19 severity. Since the early COVID-19 epidemics, ADE has been mentioned as a possible mechanism involved in severe COVID-19 disease and, later, as a potential risk in the case of infection after vaccination. However, current data do not support its role in disease severity, both after infection and reinfection.

Evaluation of the Anti-Spike (RDB) IgG Titer among Workers Employed at the University of Pisa Vaccinated with Different Types of SARS-CoV-2 Vaccines

With the development of SARS-CoV-2 vaccines, many authors started evaluating the immunization efficacy of the available vaccines mainly through sero-positivity tests or by a quantitative assessment of the IgG against the spike protein of SARS-CoV-2 virus in vaccinated subjects. In this work, we compared the titers resulting from vaccination and tried to understand the potential factors affecting the immune response to the available SARS-CoV-2 vaccines. This study was conducted on 670 volunteers employed at the University of Pisa and undergoing a health surveillance program at the University Hospital of Pisa. For each participant, 10 mL of blood, information about contacts with confirmed cases of COVID-19, age, sex, SARS-CoV-2 vaccination status, previous SARS-CoV-2 infection and symptoms, type of vaccine and the date of administration were collected. In the multivariate analysis, the type of vaccine, the presence of symptoms in SARS-CoV-2 positive individuals, and the distance from the second dose significantly affected the antibody titer; the combined vaccination resulted in a faster decay over time compared with the other types of vaccination. No significant differences were observed between Spikevax and Comirnaty (p > 0.05), while the antibody levels remain more stable in subjects undergoing Vaxzevria vaccination (p < 0.01) compared with mRNA-based ones.