IL-13 determines specific IgE responses and SARS-CoV-2 immunity after mild COVID-19 and novel mRNA vaccination.

After recovery, mild and severe COVID-19 diseases are associated with long-term effects on the host immune system, such as prolonged T-cell activation or accumulation of autoantibodies. In this study, we show that mild SARS-CoV-2 infections, but not SARS-CoV-2 spike mRNA vaccinations, cause durable atopic risk factors such as a systemic Th2- and Th17-type environment as well as activation of B cells responsive of IgE against aeroallergens from house dust mite and mold. At an average of 100 days post mild SARS-CoV-2 infections, anti-mold responses were associated with low IL-13 levels and increased pro-inflammatory IL-6 titers. Acutely severely ill COVID-19 patients instead showed no evidence of atopic reactions. Considering convalescents of mild COVID-19 courses and mRNA-vaccinated individuals together, IL-13 was the predominant significantly upregulated factor, likely shaping SARS-CoV-2 immunity. Application of multiple regression analysis revealed that the IL-13 levels of both groups were determined by the Th17-type cytokines IL-17A and IL-22. Taken together, these results implicate a critical role for IL-13 in the aftermath of SARS-CoV-2 mild infections and mRNA vaccinations, conferring protection against airway directed, atopic side reactions that occur in mildly experienced COVID-19.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Unique autoantibody prevalence in long-term recovered SARS-CoV-2-infected individuals.

The variability in resolution of SARS-CoV-2-infections between individuals neither is comprehended, nor are the long-term immunological consequences. To assess the long-term impact of a SARS-CoV-2-infection on the immune system, we conducted a prospective study of 80 acute and former SARS-CoV-2 infected individuals and 39 unexposed donors to evaluate autoantibody responses and immune composition. Autoantibody levels against cyclic citrullinated peptide (CCP), a specific predictor for rheumatoid arthritis (RA), were significantly (p = 0.035) elevated in convalescents only, whereas both acute COVID-19 patients and long-term convalescents showed critically increased levels of anti-tissue transglutaminase (TG), a specific predictor of celiac disease (CD) (p = 0.002). Both, anti-CCP and anti-TG antibody levels were still detectable after 4-8 months post infection. Anti-TG antibodies occurred predominantly in aged patients in a context of a post-SARS-CoV-2-specific immune composition (R2 = 0.31; p = 0.044). This study shows that increased anti-CCP and anti-TG autoantibody levels can remain long-term after recovering even from mildly experienced COVID-19. The inter-relationship of the lung as viral entry side and RA- and CD-associated autoimmunity indicates that a SARS-CoV-2-infection could be a relevant environmental factor in their pathogenesis.