Antibody response and soluble mediator profile in the first six months following acute SARS-CoV-2 infection

Abstract The COVID-19 pandemic has caused a severe global health and economic crisis, with significant consequences for human mortality and morbidity. Therefore, there is an urgent need for more studies on the immune response to SARS-CoV-2 infection, both to enhance its effectiveness and prevent its deleterious effects. This study presents the chronology of antibodies during six months after infection in hospitalized patients and the kinetics of serum soluble mediators of the cellular response triggered by SARS-CoV-2. Samples and clinical data from 330 patients hospitalized at the Hospital da Baleia in Belo Horizonte, Brazil, who were suspected of having COVID-19, were collected at the time of hospitalization and during 6 months after infection. The immune response was analyzed by enzyme-linked immunosorbent assay (ELISA) and flow cytometry. There was a significant difference in IgM specific antibody titers from the 7th to 60th days after infection between COVID-19 negative and positive patients. Soon after 60 days after infection, antibody levels started to reduce, becoming similar to the antibody levels of the COVID-19 negative patients. IgG specific antibodies started to be detectable after 9 days of infection and antibody levels were comparatively higher in positive patients as soon as after 7 days. Furthermore, IgG levels remained higher in these patients during the complete period of 180 days after infection. The study observed similar antibody profiles between different patient groups. The soluble systemic biomarkers evaluated showed a decrease during the six months after hospitalization, except for CCL11, CXCL8, CCL3, CCL4, CCL5, IL-6, IFN-g, IL-17, IL-5, FGF-basic, PDGF, VEGF, G-CSF, and GM-CSF. The results indicate that IgM antibodies are more prominent in the early stages of infection, while IgG antibodies persist for a longer period. Additionally, the study identified that patients with COVID-19 have elevated levels of biomarkers after symptom onset, which decrease over time

Bioprospecting for lipophilic-like components of five Phaeophyta macroalgae from the Portuguese coast

Lipophilic compounds present in dichloromethane extracts of five brown macroalgae from the Portuguese coast were analyzed by gas chromatography-mass spectrometry (GC-MS). Their dicarboxylic acids, long-chain aliphatic alcohols, and monoglyceride profile are reported for the first time. Additionally, other new compounds were also first reported: 24-methylene-cholesterol in Himanthalia elongata, Laminaria ochroleuca, and Undaria pinnatifida; desmosterol and brassicasterol in H. elongata, L. ochroleuca, Sargassum muticum, and U. pinnatifida; fucosterol and campesterol in S. muticum; and cholest-5-en-3-ol-(3β)-3-phenyl-2-propenoate in Cystoseira tamariscifolia. Brown macroalgae dichloromethane extracts are mainly composed of fatty acids (463.4–3089.0 mg kg−1 of dry material) and sterols (75.5–442.7 mg kg−1 of dry material). High amounts of polyunsaturated fatty acids were found, with the ω-6/ω-3 ratios of all species lower than 3. Cystoseira tamariscifolia, H. elongata, and S. muticum showed to be also promising sources of fucosterol. These results seem to uphold the incorporation of these macroalgae in a more balanced diet, as well as their use in the nutraceutical industry, as long as they are coupled with sustainable management of these natural resources

A Big-Data Variational Bayesian Framework for Supporting the Prediction of Functional Outcomes in Wake-Up Stroke Patients

Prognosis in Wake-up ischemic stroke (WUS) is important for guiding treatment and rehabilitation strategies, in order to improve recovery and minimize disability. For this reason, there is growing interest on models to predict functional recovery after acute ischemic events in order to personalize the therapeutic intervention and improve the final functional outcome. The aim of this preliminary study is to evaluate the possibility to predict a good functional outcome, in terms of modified Rankin Scale (mRS 64 2), in thrombolysis treated WUS patients by Bayesian analysis of clinical, demographic and neuroimaging data at admission. The study was conducted on 54 thrombolysis treated WUS patients. The Variational Bayesian logistic regression with Automatic Relevance Determination (VB-ARD) was used to produce model and select informative features to predict a good functional outcome (mRS 64 2) at discharge. The produced model showed moderately high 10\ua0 7\ua05-fold cross validation accuracy of 71% to predict outcome. The sparse model highlighted the relevance of NIHSS at admission, age, TACI stroke syndrome, ASPECTs, ischemic core CT Perfusion volume, hypertension and diabetes mellitus. In conclusion, in this preliminary study we assess the possibility to model the prognosis in thrombolysis treated WUS patients by using VB ARD. The identified features related to initial neurological deficit, history of diabetes and hypertension, together with necrotic tissue relate ASPECT and CTP core volume neuroimaging features, were able to predict outcome with moderately high accuracy