Aims: To describe characteristics of COVID-19 patients with type 2 diabetes and to analyze risk factors for severity. Methods: Demographics, comorbidities, symptoms, laboratory findings, treatments and outcomes of COVID-19 patients with diabetes were collected and analyzed. Results: Seventy-four COVID-19 patients with diabetes were included. Twenty-seven patients (36.5%) were severe and 10 patients (13.5%) died. Higher levels of blood glucose, serum amyloid A (SAA), C reactive protein and interleukin 6 were associated with severe patients compared to non-severe ones (P b 0.05). Levels of albumin, cholesterol, high density lipoprotein, small and dense low density lipoprotein and CD4 + T lymphocyte counts in severe patients were lower than those in non-severe patients (P b 0.05). Logistic regression analysis identified decreased CD4 + T lymphocyte counts (odds ratio [OR] = 0.988, 95%Confidence interval [95%CI] 0.979-0.997) and increased SAA levels (OR = 1.029, 95%CI 1.002-1.058) as risk factors for severity of COVID-19 with diabetes (P b 0.05). Conclusions: Type 2 diabetic patients were more susceptible to COVID-19 than overall population, which might be associated with hyperglycemia and dyslipidemia. Aggressive treatment should be suggested, especially when these patients had low CD4 + T lymphocyte counts and high SAA levels.

This diagnostic study develops and prospectively validates a deep learning algorithm that uses ocular fundus images to recognize numerous retinal diseases in a clinical setting at 65 screening centers in 19 Chinese provinces.

Realizing the diagnosis of lung cancer at an inchoate stage is significant to get valuable time to conduct curative surgery. In this work, we relied on a density functional theory (DFT)-proposed Ru−SnS 2 monolayer as a novel, promising biosensor for lung cancer diagnosis through exhaled gas analysis. The results indicated that the Ru−SnS 2 monolayer has admirable adsorption performance for three typical volatile organic compounds (VOCs) of lung cancer patients, which therefore results in a remarkable change in the electronic behavior of the Ru-doped surface. As a consequence, the conductivity of the Ru−SnS 2 monolayer increases after gas adsorption based on frontier molecular orbital theory. This provides the possibility to explore the Ru−SnS 2 monolayer as a biosensor for lung cancer diagnosis at an early stage. In addition, the desorption behavior of three VOCs from the Ru−SnS 2 surface is studied as well. Our calculations aim at proposing novel sensing nanomaterials for experimentalists to facilitate the progress in lung cancer prognosis.

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Results indicate that Th17 and Treg cells take roles in the pathogenesis of SLE. Th17 cells might suppress the differentiation of Treg cells, and feedback effects might exist between them during SLE pathogenesis. The measure of plasma level of IL-17A may be useful for evaluation of disease activity in new-onset SLE patients.