Programmed Cell Death Protein-1 Regulation in Response to SARS-CoV-2 in Paediatric Multisystem Inflammatory Syndrome Temporally Associated with SARS-CoV-2: A Prospective Cohort Study.

The role of programmed death cell protein 1 (PD-1) has already been described in a range of various diseases, including COVID-19. This study provides new, innovative data, related to the expression of PD-1 and the risk of Paediatric Inflammatory Multisystem Syndrome, temporally associated with SARS-CoV-2 infection (PIMS-TS)-a rare, but potentially life-threatening complication of COVID-19. In this study, we evaluated the expression of PD-1 protein in patients with PIMS. Blood samples were taken from patients at the time of diagnosis (n = 33), after 6 weeks (n = 33), 3 months (n = 24), 6 months (n = 24) and 12 months (n = 8). The immunophenotypes were evaluated in flow cytometry. The control group consisted of 35 healthy children with negative SARS-CoV-2 antigen/PCR test, who were asymptomatic and had no history of allergic, autoimmune or oncological diseases. The associations between immunophenotypes, biochemical findings and clinical data were analysed. Significant increases in the expression of PD-1 for CD4+ and CD8+ T cells, compared to the control group, were observed in the day of admission, with a gradual decrease during the first weeks from initiation of treatment. This study sheds new light on the pathogenesis of PIMS-TS, emphasizing the role of PD-1 protein. Future research is essential for early risk prediction in SARS-CoV-2 patients and for devising effective clinical prevention and management strategies.

Artificial neural network based prediction of the lung tissue involvement as an independent in-hospital mortality and mechanical ventilation risk factor in COVID-19.

INTRODUCTION: During COVID-19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in-hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN-analyzed lung inflammation data. METHODS: A data set of 4317 COVID-19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models. RESULTS: Overall in-hospital mortality associated with ANN-assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4-7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID-19 pneumonia), age category (HR: 5.34, 95% CI: 3.32-8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59-2.76, p < 0.001, C-reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25-3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37-2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69-2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN-based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65-20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14-3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2-2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91-3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38-4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44-3.7, p < 0.001). CONCLUSIONS: ANN-based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID-19 and represents a valuable support tool for clinical decisions.

Programmed Cell Death-1/Programmed Cell Death-1 Ligand as Prognostic Markers of Coronavirus Disease 2019 Severity.

Current research proves that immune dysregulation is a common feature of coronavirus disease 2019 (COVID-19), and immune exhaustion is associated with increased disease mortality. Immune checkpoint molecules, including the programmed cell death-1 (PD-1)/PD-1 ligand (PD-L1) axis, may serve as markers of disease severity. Accordingly, in this study, we evaluated the expression of PD-1/PD-L1 in patients with COVID-19. Blood immunophenotypes of hospitalized patients with moderate (n = 17, requiring oxygen support) and severe (n = 35, requiring mechanical ventilation in the intensive care setting) COVID-19 were compared and associated with clinical, laboratory, and survival data. The associations between severity and lymphocyte profiles were analysed at baseline and after 7 and 14 days of in-hospital treatment. Forty patients without COVID-19 infection were used as controls. For PD-1-positive T and B lymphocyte subsets, notable increases were observed between controls and patients with moderate or severe COVID-19 for CD4+PD-1+ T cells, CD8+PD-1+ T and CD19+PD-1+ B cells. Similar trends were observed for PD-L1-positive lymphocytes, namely, CD4+PD-L1+ T cells, CD8+PD-L1+ T cells and CD19+PD-L1+ B cells. Importantly, all markers associated with PD-1 and PD-L1 were stable over time for the analysed time points in the moderate and severe COVID-19 groups. Increased abundances of PD-1+ and PD-L1+ lymphocytes were associated with disease severity and mortality and were stable over time in patients with moderate to severe COVID-19. These immune exhaustion parameters may be attractive biomarkers of COVID-19 severity.

Immune Signature of COVID-19: In-Depth Reasons and Consequences of the Cytokine Storm.

In the beginning of the third year of the fight against COVID-19, the virus remains at least still one step ahead in the pandemic "war". The key reasons are evolving lineages and mutations, resulting in an increase of transmissibility and ability to evade immune system. However, from the immunologic point of view, the cytokine storm (CS) remains a poorly understood and difficult to combat culprit of the extended number of in-hospital admissions and deaths. It is not fully clear whether the cytokine release is a harmful result of suppression of the immune system or a positive reaction necessary to clear the virus. To develop methods of appropriate treatment and therefore decrease the mortality of the so-called COVID-19-CS, we need to look deeply inside its pathogenesis, which is the purpose of this review.

Interplay between Neutrophils, NETs and T-Cells in SARS-CoV-2 Infection-A Missing Piece of the Puzzle in the COVID-19 Pathogenesis?

Since the end of 2019, a new, dangerous virus has caused the deaths of more than 3 million people. Efforts to fight the disease remain multifaceted and include prophylactic strategies (vaccines), the development of antiviral drugs targeting replication, and the mitigation of the damage associated with exacerbated immune responses (e.g., interleukin-6-receptor inhibitors). However, numerous uncertainties remain, making it difficult to lower the mortality rate, especially among critically ill patients. While looking for a new means of understanding the pathomechanisms of the disease, we asked a question-is our immunity key to resolving these uncertainties? In this review, we attempt to answer this question, and summarize, interpret, and discuss the available knowledge concerning the interplay between neutrophils, neutrophil extracellular traps (NETs), and T-cells in COVID-19. These are considered to be the first line of defense against pathogens and, thus, we chose to emphasize their role in SARS-CoV-2 infection. Although immunologic alterations are the subject of constant research, they are poorly understood and often underestimated. This review provides background information for the expansion of research on the novel, immunity-oriented approach to diagnostic and treatment possibilities.

Molecular Evolution and Epidemiological Characteristics of SARS COV-2 in (Northwestern) Poland.

The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) evolved into a worldwide outbreak, with the first Polish cases in February/March 2020. This study aimed to investigate the molecular epidemiology of the circulating virus lineages between March 2020 and February 2021. We performed variant identification, spike mutation pattern analysis, and phylogenetic and evolutionary analyses for 1106 high-coverage whole-genome sequences, implementing maximum likelihood, multiple continuous-time Markov chain, and Bayesian birth-death skyline models. For time trends, logistic regression was used. In the dataset, virus B.1.221 lineage was predominant (15.37%), followed by B.1.258 (15.01%) and B.1.1.29 (11.48%) strains. Three clades were identified, being responsible for 74.41% of infections over the analyzed period. Expansion in variant diversity was observed since September 2020 with increasing frequency of the number in spike substitutions, mainly H69V70 deletion, P681H, N439K, and S98F. In population dynamics inferences, three periods with exponential increase in infection were observed, beginning in March, July, and September 2020, respectively, and were driven by different virus clades. Additionally, a notable increase in infections caused by the B.1.1.7 lineage since February 2021 was noted. Over time, the virus accumulated mutations related to optimized transmissibility; therefore, faster dissemination is reflected by the second wave of epidemics in Poland.