Liberté, Égalité, Crédibilité: An experimental study of citizens' perceptions of government responses to COVID-19 in eight countries.

During a global pandemic, individual views of government can be linked to citizens' trust and cooperation with government and their propensity to resist state policies or to take action that influences the course of a pandemic. This article explores citizens' assessments of government responses to COVID-19 as a function of policy substance (restrictions on civil liberties), information about performance, and socioeconomic inequity in outcomes. We conducted a survey experiment and analyzed data on over 7000 respondents from eight democratic countries. We find that across countries, citizens are less favorable toward COVID-19 policies that are more restrictive of civil liberties. Additionally, citizens' views of government performance are significantly influenced by objective performance information from reputable sources and information on the disproportionate impacts of COVID-19 on low-income groups. This study reinforces the importance of policy design and outcomes and the consideration of multiple public values in the implementation of public policies.

A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection.

Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.

A Deep Learning Approach for the Morphological Recognition of Reactive Lymphocytes in Patients with COVID-19 Infection

Laboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.

[High frequency of endoluminal thrombus in patients with ischaemic stroke following COVID-19]. / Alta Frecuencia de Trombo Endoluminal en pacientes con ictus isquémico tras la infección por Coronavirus 2019.

BACKGROUND: Ischaemic stroke may be a major complication of SARS-CoV-2 infection.Studying and characterising the different aetiological subtypes, clinical characteristics, and functional outcomes may be valuable in guiding patient selection for optimal management and treatment. METHODS: Data were collected retrospectively on consecutive patients with COVID-19 who developed acute focal brain ischaemia (between 1 March and 19 April 2020) at a tertiary university hospital in Madrid (Spain). RESULTS: During the study period, 1594 patients were diagnosed with COVID-19. We found 22 patients with ischaemic stroke (1.38%), 6 of whom did not meet the inclusion criteria. The remaining 16 patients were included in the study (15 cases of ischaemic stroke and one case of transient ischaemic attack).Median baseline National Institutes of Health Stroke Scale score was 9 (interquartile range: 16), and mean (standard deviation) age was 73 years (12.8). Twelve patients (75%) were men. Mean time from COVID-19 symptom onset to stroke onset was 13 days. Large vessel occlusion was identified in 12 patients (75%).We detected elevated levels of D-dimer in 87.5% of patients and C-reactive protein in 81.2%. The main aetiology was atherothrombotic stroke (9 patients, 56.3%), with the predominant subtype being endoluminal thrombus (5 patients, 31.2%), involving the internal carotid artery in 4 cases and the aortic arch in one. The mortality rate in our series was 44% (7 of 16 patients). CONCLUSIONS: In patients with COVID-19, the most frequent stroke aetiology was atherothrombosis, with a high proportion of endoluminal thrombus (31.2% of patients). Our clinical and laboratory data support COVID-19-associated coagulopathy as a relevant pathophysiological mechanism for ischaemic stroke in these patients.

Atypical lymphoid cells circulating in blood in COVID-19 infection: morphology, immunophenotype and prognosis value.

AIMS: Atypical lymphocytes circulating in blood have been reported in COVID-19 patients. This study aims to (1) analyse if patients with reactive lymphocytes (COVID-19 RL) show clinical or biological characteristics related to outcome; (2) develop an automatic system to recognise them in an objective way and (3) study their immunophenotype. METHODS: Clinical and laboratory findings in 36 COVID-19 patients were compared between those showing COVID-19 RL in blood (18) and those without (18). Blood samples were analysed in Advia2120i and stained with May Grünwald-Giemsa. Digital images were acquired in CellaVisionDM96. Convolutional neural networks (CNNs) were used to accurately recognise COVID-19 RL. Immunophenotypic study was performed throughflow cytometry. RESULTS: Neutrophils, D-dimer, procalcitonin, glomerular filtration rate and total protein values were higher in patients without COVID-19 RL (p<0.05) and four of these patients died. Haemoglobin and lymphocyte counts were higher (p<0.02) and no patients died in the group showing COVID-19 RL. COVID-19 RL showed a distinct deep blue cytoplasm with nucleus mostly in eccentric position. Through two sequential CNNs, they were automatically distinguished from normal lymphocytes and classical RL with sensitivity, specificity and overall accuracy values of 90.5%, 99.4% and 98.7%, respectively. Immunophenotypic analysis revealed COVID-19 RL are mostly activated effector memory CD4 and CD8 T cells. CONCLUSION: We found that COVID-19 RL are related to a better evolution and prognosis. They can be detected by morphology in the smear review, being the computerised approach proposed useful to enhance a more objective recognition. Their presence suggests an abundant production of virus-specific T cells, thus explaining the better outcome of patients showing these cells circulating in blood.