Immunoglobulin G Immune Complexes May Contribute to Neutrophil Activation in the Course of Severe Coronavirus Disease 2019.

Severe coronavirus disease 2019 (COVID-19) is associated with an overactive inflammatory response mediated by macrophages. Here, we analyzed the phenotype and function of neutrophils in patients with COVID-19. We found that neutrophils from patients with severe COVID-19 express high levels of CD11b and CD66b, spontaneously produce CXCL8 and CCL2, and show a strong association with platelets. Production of CXCL8 correlated with plasma concentrations of lactate dehydrogenase and D-dimer. Whole blood assays revealed that neutrophils from patients with severe COVID-19 show a clear association with immunoglobulin G (IgG) immune complexes. Moreover, we found that sera from patients with severe disease contain high levels of immune complexes and activate neutrophils through a mechanism partially dependent on FcγRII (CD32). Interestingly, when integrated in immune complexes, anti-severe acute respiratory syndrome coronavirus 2 IgG antibodies from patients with severe COVID-19 displayed a higher proinflammatory profile compared with antibodies from patients with mild disease. Our study suggests that IgG immune complexes might promote the acquisition of an inflammatory signature by neutrophils, worsening the course of COVID-19.

Development of simple and sensitive score to assess the risk of pneumonia in Covid-19 patients

ABSTRACT Background: Severe pneumonia is the most common cause of intensive care unit (ICU) admission and death due to novel coronavirus (SARS-CoV-2) respiratory disease (COVID-19). Due to its rapid outbreak, units for the evaluation of febrile patients in the pre-hospital setting were created. Objective: The objective of the study was to develop a sensitive and simple tool to assess the risk of pneumonia in COVID-19 patients and thus select which patients would require a chest imaging study. Materials and Methods: We conducted a cross-sectional study in a cohort of individuals with suspected COVID-19 evaluated in a public academic healthcare center in Buenos Aires city. All adult patients with positive RT-PCR assay for SARS-COV2 between April 24 and May 19 of 2020 were included in the study. Pneumonia was defined as the presence of compatible signs and symptoms with imaging confirmation. Univariate and multivariate logistic regression was performed. A risk indicator score was developed. Results: One hundred and forty-eight patients were included, 71 (48%) received the diagnosis of pneumonia. The final clinical model included four variables: age ≥ 40 years, cough, absence of sore throat, and respiratory rate ≥ 22. To create the score, we assigned values to the variables according to their ORs: 2 points for respiratory rate ≥ 22 and 1 point to the other variables. The AUC of the ROC curve was 0.80 (CI 95% 0.73-0.86). A cutoff value of 2 showed a sensitivity of 95.7% and a specificity of 43.24%. Conclusion: This sensible score may improve the risk stratification of COVID-19 patients in the pre-hospital setting. (REV INVEST CLIN. 2021;73(1):52-8)

Development of Simple and Sensitive Score to Assess the Risk of Pneumonia in COVID-19 Patients.

BACKGROUND: Severe pneumonia is the most common cause of intensive care unit (ICU) admission and death due to novel coronavirus (SARS-CoV-2) respiratory disease (COVID-19). Due to its rapid outbreak, units for the evaluation of febrile patients in the pre-hospital setting were created. OBJECTIVE: The objective of the study was to develop a sensitive and simple tool to assess the risk of pneumonia in COVID-19 patients and thus select which patients would require a chest imaging study. MATERIALS AND METHODS: We conducted a cross-sectional study in a cohort of individuals with suspected COVID-19 evaluated in a public academic healthcare center in Buenos Aires city. All adult patients with positive RT-PCR assay for SARS-COV2 between April 24 and May 19 of 2020 were included in the study. Pneumonia was defined as the presence of compatible signs and symptoms with imaging confirmation. Univariate and multivariate logistic regression was performed. A risk indicator score was developed. RESULTS: One hundred and forty-eight patients were included, 71 (48%) received the diagnosis of pneumonia. The final clinical model included four variables: age >- 40 years, cough, absence of sore throat, and respiratory rate >- 22. To create the score, we assigned values to the variables according to their ORs: 2 points for respiratory rate >- 22 and 1 point to the other variables. The AUC of the ROC curve was 0.80 (CI 95% 0.73-0.86). A cutoff value of 2 showed a sensitivity of 95.7% and a specificity of 43.24%. CONCLUSION: This sensible score may improve the risk stratification of COVID-19 patients in the pre-hospital setting.

Biomarkers of Progression after HIV Acute/Early Infection: Nothing Compares to CD4⁺ T-cell Count?

Progression of HIV infection is variable among individuals, and definition disease progression biomarkers is still needed. Here, we aimed to categorize the predictive potential of several variables using feature selection methods and decision trees. A total of seventy-five treatment-naïve subjects were enrolled during acute/early HIV infection. CD4⁺ T-cell counts (CD4TC) and viral load (VL) levels were determined at enrollment and for one year. Immune activation, HIV-specific immune response, Human Leukocyte Antigen (HLA) and C-C chemokine receptor type 5 (CCR5) genotypes, and plasma levels of 39 cytokines were determined. Data were analyzed by machine learning and non-parametric methods. Variable hierarchization was performed by Weka correlation-based feature selection and J48 decision tree. Plasma interleukin (IL)-10, interferon gamma-induced protein (IP)-10, soluble IL-2 receptor alpha (sIL-2Rα) and tumor necrosis factor alpha (TNF-α) levels correlated directly with baseline VL, whereas IL-2, TNF-α, fibroblast growth factor (FGF)-2 and macrophage inflammatory protein (MIP)-1ß correlated directly with CD4⁺ T-cell activation (p < 0.05). However, none of these cytokines had good predictive values to distinguish "progressors" from "non-progressors". Similarly, immune activation, HIV-specific immune responses and HLA/CCR5 genotypes had low discrimination power. Baseline CD4TC was the most potent discerning variable with a cut-off of 438 cells/µL (accuracy = 0.93, κ-Cohen = 0.85). Limited discerning power of the other factors might be related to frequency, variability and/or sampling time. Future studies based on decision trees to identify biomarkers of post-treatment control are warrantied.