ABSTRACT
Background: Knowledge on the immunopathobiology of COVID-19 is rapidly increasing but most studies analyzed relatively small series of patients and immune features predictive of fatal outcome are unavailable for routine stratification. Furthermore, an increased risk of death in patients with hematological cancer infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been identified, but it remains unknown if this is related to possible immunosuppression caused by cancer itself and cytotoxic treatment. Aims: Characterize the immune response to SARS-CoV-2 in a large cohort of patients to identify high-risk immune biomarkers and evaluate the association between COVID-19 severity and immunosuppression in patients with hematological cancer. Methods: Multidimensional flow cytometry was used to conduct holistic and unbiased analyses of17 immune cell types on 780 peripheral blood samples obtained from 513 COVID-19 patients, 24 cases with non-SARS-CoV-2 infection and 36 age-matched healthy adults.167 COVID-19 patients had 207 longitudinal samples collected over time. RNA sequencing on FACSorted cells and high-resolution flow cytometry were used to perform a deeper characterization of various myeloid and lymphoid subsets in14 COVID-19 patients and4 healthy adults. Results: Immune profiles of COVID-19 patients were generally similar to those of age-matched patients with non-SARS-CoV-2 infection, but significantly different from those of age-matched healthy adults. When compared to the later, COVID-19 patients showed increased percentages of neutrophils, CD4+CD56+ T-cells, and plasmablasts, whereas levels of basophils, eosinophils, and non-classical monocytes, as well as double-negative, CD8loCD56-, CD8-/loCD56+ and CD8hiCD56- T-cells, and B-cells were decreased. Both transcriptional and immunophenotypic data in myeloid and lymphoid subsets suggested an association between COVID-19 severity and neutrophil activation, as well as significantly reduced levels of specific adaptive immune cell types. Unsupervised clustering analysis of 513 patients revealed three immunotypes in response to SARS-CoV-2 infection. One of them, present in14% of patients (n=74), was characterized by significantly lower percentages of all immune cell types except neutrophils and plasmablasts, and was significantly associated with more severe disease. Of note, 50% of COVID-19 patients with blood cancer displayed this immunotype. Accordingly, hematological patients showed a significantly higher frequency of admission into intensive care units (50% vs 5%, P<.001) and death (30% vs4%, P<.001) than patients without tumor did. On multivariate analysis incorporating age and comorbidities, the frequency of B-cells and non-classical monocytes were independent prognostic factors for overall survival. Indeed, <1% B-cells in peripheral blood was most strongly associated with risk of death. Among patients with immune monitoring during follow-up, significant changes in the relative distribution of eight immune cell types, including basophils, CD8loCD56- T-cells, and B-cells, were observed from the first to last peripheral blood sample between patients who survived or died. Summary/Conclusion: Our results accelerate our understanding of the immunopathobiology of COVID-19 and unveil an association between altered immune profiles in patients with hematological cancer and their poorer outcome. Reduced percentages of B-cells and non-classical monocytes are high-risk immune biomarkers that could be readily implemented in routine practice for risk-stratification of COVID-19.
ABSTRACT
The paper presents an attempt for a critical analysis of e-learning as a mono-educational technology in a pandemic. The subject of the study is the impact of the critical epidemic situation caused by COVID-19 in 2020 on the theoretical university training of students-pedagogues. The indisputable possibilities, advantages, but also limitations in the course of the e-learning, realized through various forms of electronic environment, are emphasized. Based on the analysis, the following conclusions can be drawn:indisputable advantages of e-learning as an assistive technology for the theoretical university training of students pedagogues;indisputable advantages of e-learning as a mono technology for the theoretical university preparation of students pedagogues in conditions of crisis and impossibility for face-to-face training;e-learning, implemented as mono technology, is unsuitable for the theoretical university training of students pedagogues in normal conditions, due to a number of limitations and shortcomings.
ABSTRACT
This paper presents a new clustering algorithm to identify groups of countries. First, a layer of several clustering methods is applied to the original dataset. Then, after performing dimensionality reduction techniques like t-SNE or SOM on the resulting data, a second clustering layer (K-Means) is applied to identify the final clusters. This method is applied to a dataset from 163 countries, considering the following variables population, area, Gross Domestic Product (GDP), Gross Domestic Product adjusted for Purchase Power Parity (GDP-PPP), and COVID-19 related data (Confirmed, Recovered, and Deaths). The implementation with SOM dimensionality reduction outperformed the one with t-SNE for the considered dataset. We expect that using this information, countries can have an insight on which measures against COVID-19 replicate or avoid, based on the results in countries from the same cluster. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.