Immune response to mRNA-based COVID-19 booster vaccination in people living with HIV.

OBJECTIVES: Our objective was to assess immune responses and their influencing factors in people living with HIV after messenger RNA (mRNA)-based COVID-19 booster vaccination (third dose). METHODS: This was a retrospective cohort study of people living with HIV who received booster vaccination with BNT-162b2 or mRNA-1273 between October 2021 and January 2022. We assessed anti-spike receptor-binding domain (RBD) immunoglobulin G (IgG), virus neutralizing activity (VNA) titres reported as 100% inhibitory dilution (ID100 ), and T-cell response (using interferon-gamma-release-assay [IGRA]) at baseline and quarterly follow-up visits. Patients with reported COVID-19 during follow-up were excluded. Predictors of serological immune response were analyzed using multivariate regression models. RESULTS: Of 84 people living with HIV who received an mRNA-based booster vaccination, 76 were eligible for analysis. Participants were on effective antiretroviral therapy (ART) and had a median of 670 CD4+ cells/µL (interquartile range [IQR] 540-850). Following booster vaccination, median anti-spike RBD IgG increased by 705.2 binding antibody units per millilitre (BAU/mL) and median VNA titres increased by 1000 ID100 at the follow-up assessment (median 13 weeks later). Multivariate regression revealed that time since second vaccination was a predictor of stronger serological responses (p < 0.0001). No association was found for other factors, including CD4+ status, choice of mRNA vaccine, or concomitant influenza vaccination. In total, 45 patients (59%) had a reactive baseline IGRA, of whom two lost reactivity during follow-up. Of 31 patients (41%) with non-reactive baseline IGRA, 17 (55%) converted to reactive and seven (23%) remained unchanged following booster vaccination. CONCLUSIONS: People living with HIV with ≥500 CD4+ cells/µL showed favourable immune responses to mRNA-based COVID-19 booster vaccination. A longer time (up to 29 weeks) since second vaccination was associated with higher serological responses, whereas choice of mRNA vaccine or concomitant influenza vaccination had no impact.

COVID-19 Severity and Thrombo-Inflammatory Response Linked to Ethnicity.

Although there is strong evidence that SARS-CoV-2 infection is associated with adverse outcomes in certain ethnic groups, the association of disease severity and risk factors such as comorbidities and biomarkers with racial disparities remains undefined. This retrospective study between March 2020 and February 2021 explores COVID-19 risk factors as predictors for patients' disease progression through country comparison. Disease severity predictors in Germany and Japan were cardiovascular-associated comorbidities, dementia, and age. We adjusted age, sex, body mass index, and history of cardiovascular disease comorbidity in the country cohorts using a propensity score matching (PSM) technique to reduce the influence of differences in sample size and the surprisingly young, lean Japanese cohort. Analysis of the 170 PSM pairs confirmed that 65.29% of German and 85.29% of Japanese patients were in the uncomplicated phase. More German than Japanese patients were admitted in the complicated and critical phase. Ethnic differences were identified in patients without cardiovascular comorbidities. Japanese patients in the uncomplicated phase presented a suppressed inflammatory response and coagulopathy with hypocoagulation. In contrast, German patients exhibited a hyperactive inflammatory response and coagulopathy with hypercoagulation. These differences were less pronounced in patients in the complicated phase or with cardiovascular diseases. Coagulation/fibrinolysis-associated biomarkers rather than inflammatory-related biomarkers predicted disease severity in patients with cardiovascular comorbidities: platelet counts were associated with severe illness in German patients. In contrast, high D-dimer and fibrinogen levels predicted disease severity in Japanese patients. Our comparative study indicates that ethnicity influences COVID-19-associated biomarker expression linked to the inflammatory and coagulation (thrombo-inflammatory) response. Future studies will be necessary to determine whether these differences contributed to the less severe disease progression observed in Japanese COVID-19 patients compared with those in Germany.

Corrigendum to "Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases"[Artificial Intelligence in Life Sciences] 1(2021), 100020.

[This corrects the article DOI: 10.1016/j.ailsci.2021.100020.].

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.