Objective
Severe COVID-19 is characterized by a dysregulated immune response in which neutrophils play a critical role. Calprotectin reflects neutrophil activation and is involved in the self-amplifying thrombo-inflammatory storm in severe COVID-19. We aimed to evaluate the role of calprotectin in early prediction of severity in COVID-19 patients.
Methods
This was a multicenter prospective observational study enrolling consecutive adult COVID-19 patients. On arrival to emergency department, blood samples were collected for laboratory tests, including serum calprotectin. The primary outcome was severe respiratory failure requiring invasive mechanical ventilation and the secondary outcome was need for Intensive Care Unit (ICU) admission.
Results
Study population included 395 patients, 57 (14.4%) required invasive mechanical ventilation and 100 (25.3%) were admitted to ICU. Median serum calprotectin levels were significantly higher in intubated (3.73 mg/L vs. 2.63 mg/L;
p
< 0.001) and ICU patients (3.48 mg/L vs. 2.60 mg/L;
p
= 0.001). Calprotectin showed a significant accuracy to predict the need for invasive mechanical ventilation (ROC AUC 0.723) and ICU admission (ROC AUC 0.650). In multivariate analysis, serum calprotectin was an independent predictor of invasive mechanical ventilation (OR 1.161) and ICU admission (OR 1.068).
Conclusion
Serum calprotectin can be used as an early predictor of severity in COVID-19 patients.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00011-021-01516-4.
Socioeconomic status (SES) influences all the determinants of health, conditioning health throughout life. The aim of the present study was to explore the relationship between socioeconomic status and obesity in adolescence through an analysis of the patterns of contact between peers as a function of this parameter. A cross-sectional study was performed, analyzing a sample of 235 students aged 14 to 18 and 11 class networks. Social network analysis was used to analyze structural variables of centrality from a sociocentric perspective. We found that adolescents with a medium-low SES presented a two-fold higher probability of being overweight, but we did not detect any differences in the configuration of their social networks when compared with those of normal-weight adolescents. However, we did find significant differences in the formation of networks according to SES in the overall sample and disaggregated by gender, whereby adolescents with a high SES in general presented a higher capacity to form wider social networks. Elucidating the relationship between SES and overweight and its influence on social network formation can contribute to the design of preventative strategies against overweight and obesity in adolescents, since their social environment can provide them with several resources to combat excess weight.
Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.
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