Increased circulating microparticles contribute to severe infection and adverse outcomes of COVID-19 in patients with diabetes.

Patients with diabetes infected with COVID-19 have greater mortality than those without comorbidities, but the underlying mechanisms remain unknown. This study aims to identify the mechanistic interactions between diabetes and severe COVID-19. Microparticles (MPs), the cell membrane-derived vesicles released on cell activation, are largely increased in patients with diabetes. To date, many mechanisms have been postulated for increased severity of COVID-19 in patients with underlying conditions, but the contributions of excessive MPs in patients with diabetes have been overlooked. This study characterizes plasma MPs from normal human subjects and patients with type 2 diabetes in terms of amount, cell origins, surface adhesive properties, ACE2 expression, spike protein binding capacity, and their roles in SARS-CoV-2 infection. Results showed that over 90% of plasma MPs express ACE2 that binds the spike protein of SARS-CoV-2. MPs in patients with diabetes increase 13-fold in quantity and 11-fold in adhesiveness when compared with normal subjects. Perfusion of human plasma with pseudo-typed SARS-CoV-2 virus or spike protein-bound MPs into human endothelial cell-formed microvessels-on-a chip demonstrated that MPs from patients with diabetes, not normal subjects, interact with endothelium and carry SARS-CoV-2 into cells through endocytosis, providing additional virus entry pathways and enhanced infection. Results also showed a large percentage of platelet-derived tissue factor-bearing MPs in diabetic plasma, which could contribute to thrombotic complications with SARS-CoV-2 infection. This study reveals a dual role of diabetic MPs in promoting SARS-CoV-2 entry and propagating vascular inflammation. These findings provide novel mechanistic insight into the high prevalence of COVID-19 in patients with diabetes and their propensity to develop severe vascular complications.NEW & NOTEWORTHY This study provides the first evidence that over 90% of human plasma microparticles express ACE2 that binds SARS-CoV-2 S protein with high affinity. Thus, the highly elevated adhesive circulating microparticles identified in patients with diabetes not only have greater SARS-CoV-2 binding capacity but also enable additional viral entry through virus-bound microparticle-endothelium interactions and enhanced infection. These findings reveal a novel mechanistic insight into the adverse outcomes of COVID-19 in patients with diabetes.

Utilisation of outpatient medical services by people with diagnosed diabetes during the COVID-19 pandemic in Germany.

People with diabetes regularly need outpatient medical care due to their disease and possible concomitant and secondary illnesses. Using data from the nationwide GEDA 2019/2020-EHIS survey conducted from April 2019 to September 2020, the present study examines developments in outpatient utilisation behaviour during the measures put in place to contain the SARS-CoV-2 pandemic. During the observation period, people with diabetes had a significantly higher rate of utilisation of medical services provided by general practitioners (GPs) and specialists than the population as a whole. In the spring of 2020, when the restrictions were put in place, utilisation of specialist medical services by people with diabetes decreased temporarily by 46% compared to the 2019 reference period. In contrast, no relevant decline in the utilisation of medical services provided by GPs was observed, but this could be related to adaptations of care provision through telephone consultations for people with regularly requiring GP office visits. The issue examined here requires further observations in view of the renewed containment measures.

Rhabdomyolysis is Associated with In-Hospital Mortality in Patients with COVID-19.

PURPOSE: Rhabdomyolysis (RM) has been associated with many viral infectious diseases, and associated with poor outcomes. We aim to evaluate the clinical features and outcomes of RM in patients with coronavirus disease 2019 (COVID-19). METHOD: This was a single-center, retrospective, cohort study of 1,014 consecutive hospitalized patients with confirmed COVID-19 at the Huoshenshan Hospital in Wuhan, China, between February 17 and April 12, 2020. RESULTS: The overall incidence of RM was 2.2%. Compared with patients without RM, those with RM tended to have a higher risk of deterioration. Patients with RM also constituted a greater percentage of patients admitted to the intensive care unit (90.9% vs. 5.3%, P < 0.001) and a greater percentage of patients undergoing mechanical ventilation (86.4% vs. 2.7% P < 0.001). Moreover, patients with RM had laboratory test abnormalities, including the presence of markers of inflammation, activation of coagulation, and kidney injury. Patients with RM also had a higher risk of in-hospital death (P < 0.001). Cox's proportional hazard regression model analysis confirmed that RM indicators, including peak creatine kinase levels > 1,000 IU/L (HR = 6.46, 95% CI: 3.02-13.86) and peak serum myoglobin concentrations > 1,000 ng/mL (HR = 9.85, 95% CI: 5.04-19.28), were independent risk factors for in-hospital death. Additionally, patients with COVID-19 that developed RM tended to have delayed viral clearance. CONCLUSION: RM might be an important contributing factor to adverse outcomes in COVID-19 patients. The early detection and effective intervention of RM may help reduce mortality among COVID-19 patients.

Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models.

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.