Mitochondrial N-formyl methionine peptides contribute to exaggerated neutrophil activation in patients with COVID-19.

Neutrophil dysregulation is well established in COVID-19. However, factors contributing to neutrophil activation in COVID-19 are not clear. We assessed if N-formyl methionine (fMet) contributes to neutrophil activation in COVID-19. Elevated levels of calprotectin, neutrophil extracellular traps (NETs) and fMet were observed in COVID-19 patients (n = 68), particularly in critically ill patients, as compared to HC (n = 19, p < 0.0001). Of note, the levels of NETs were higher in ICU patients with COVID-19 than in ICU patients without COVID-19 (p < 0.05), suggesting a prominent contribution of NETs in COVID-19. Additionally, plasma from COVID-19 patients with mild and moderate/severe symptoms induced in vitro neutrophil activation through fMet/FPR1 (formyl peptide receptor-1) dependent mechanisms (p < 0.0001). fMet levels correlated with calprotectin levels validating fMet-mediated neutrophil activation in COVID-19 patients (r = 0.60, p = 0.0007). Our data indicate that fMet is an important factor contributing to neutrophil activation in COVID-19 disease and may represent a potential target for therapeutic intervention.

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning.

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.

Convalescent Plasma Therapy, Therapeutic Formulations of Repurposed Drugs in 20th Century Epidemics against COVID-19: A Systematic Review.

Coronavirus 2019 disease (COVID-19) represents one of the largest pandemics the world has faced, and it is producing a global health crisis. To date, the availability of drugs to treat COVID-19 infections remains limited to supportive care although therapeutic options are being explored. Some of them are old strategies for treating infectious diseases. convalescent plasma (CP) therapy has been used successfully in other viral outbreaks in the 20th century. In this study, we systematically evaluated the effect and safety of CP therapy on hospitalized COVID-19 patients. A structured search was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines using Medline (PubMed), SciELO, Cochrane Library Plus, Web of Science, and Scopus. The search included articles published up to January 2022 and was restricted to English- and Spanish-language publications. As such, investigators identified six randomized controlled trials that met the search criteria. The results determined that in hospitalized COVID-19 patients the administration of CP therapy with a volume between 200-500 mL and a single transfusion performed in 1-2 h, compared to the control group, decreased viral load, symptomatology, the period of infection, and mortality, without serious adverse effects. CP did influence clinical outcomes and may be a possible treatment option, although further studies will be necessary.