<div><b>OBJECTIVE:</b> Diabetes is one of the most distinct comorbidities of COVID-19. Here, we described the clinical characteristics and outcomes in diabetic patients with confirmed or clinically diagnosed (with typical lung imaging features and symptoms) COVID-19, and their association with glucose lowering or blood pressure lowering medications.</div><div><br></div><div><b>RESEARCH DESIGN AND METHODS: </b>In this retrospective study involving 904 COVID-19 patients (136 with diabetes, mostly type 2 diabetes), clinical and laboratory characteristics were collected and compared between diabetic vs non-diabetic groups, and different medication groups. Logistic regression was used to explore risk factors associated with mortality or poor prognosis.</div><div><br></div><div><b>RESULTS:</b> Proportion of comorbid diabetes is similar between confirmed and clinically diagnosed COVID-19 cases. Risk factors for higher mortality of diabetic patients with COVID-19 were elder age (adjusted OR [aOR] 1.09 [95% CI 1.04, 1.15], per year increase, P = 0.001) and elevated C-reactive protein (aOR 1.12 [95% CI 1.00, 1.24], mg/dL, P = 0.043). Insulin usage (aOR 3.58 [95%CI 1.37, 9.35], P = 0.009) was associated with poor prognosis. Clinical outcomes of ACE inhibitor or angiotensin II type-I receptor blocker (ACEI/ARB) users were comparable to non-ACEI/ARB users in COVID-19 patients with diabetes and hypertension.</div><div><br></div><div><b>CONCLUSIONS: </b>C-reactive protein may help to identify diabetic patients with greater risk. Elder diabetic patients were prone to COVID-19-related fatality. Attentions need to be paid on insulin-using diabetic patients with COVID-19. ACEI/ARB showed no significant impact on COVID-19 patients with diabetes and hypertension.</div>

<div><b>OBJECTIVE:</b> Diabetes is one of the most distinct comorbidities of COVID-19. Here, we described the clinical characteristics and outcomes in diabetic patients with confirmed or clinically diagnosed (with typical lung imaging features and symptoms) COVID-19, and their association with glucose lowering or blood pressure lowering medications.</div><div><br></div><div><b>RESEARCH DESIGN AND METHODS: </b>In this retrospective study involving 904 COVID-19 patients (136 with diabetes, mostly type 2 diabetes), clinical and laboratory characteristics were collected and compared between diabetic vs non-diabetic groups, and different medication groups. Logistic regression was used to explore risk factors associated with mortality or poor prognosis.</div><div><br></div><div><b>RESULTS:</b> Proportion of comorbid diabetes is similar between confirmed and clinically diagnosed COVID-19 cases. Risk factors for higher mortality of diabetic patients with COVID-19 were elder age (adjusted OR [aOR] 1.09 [95% CI 1.04, 1.15], per year increase, P = 0.001) and elevated C-reactive protein (aOR 1.12 [95% CI 1.00, 1.24], mg/dL, P = 0.043). Insulin usage (aOR 3.58 [95%CI 1.37, 9.35], P = 0.009) was associated with poor prognosis. Clinical outcomes of ACE inhibitor or angiotensin II type-I receptor blocker (ACEI/ARB) users were comparable to non-ACEI/ARB users in COVID-19 patients with diabetes and hypertension.</div><div><br></div><div><b>CONCLUSIONS: </b>C-reactive protein may help to identify diabetic patients with greater risk. Elder diabetic patients were prone to COVID-19-related fatality. Attentions need to be paid on insulin-using diabetic patients with COVID-19. ACEI/ARB showed no significant impact on COVID-19 patients with diabetes and hypertension.</div>

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SARS-CoV-2 has affected over 9 million patients with more than 460,000 deaths in about 6 months. Understanding the factors that contribute to efficient SARS-CoV-2 infection of human cells, which are not previously reported, may provide insights on SARS-CoV-2 transmissibility and pathogenesis, and reveal targets of intervention. Here, we reported key host and viral determinants that were essential for efficient SARS-CoV-2 infection in the human lung. First, we identified heparan sulfate as an important attachment factor for SARS-CoV-2 infection. Second, we demonstrated that while cell surface sialic acids significantly restricted SARS-CoV infection, SARS-CoV-2 could largely overcome sialic acid-mediated restriction in both human lung epithelial cells and ex vivo human lung tissue explants. Third, we demonstrated that the inserted furin-like cleavage site in SARS-CoV-2 spike was required for efficient virus replication in human lung but not intestine tissues. Overall, these findings contributed to our understanding on efficient SARS-CoV-2 infection of human lungs.

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Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited kernel size of convolution layers in FCNNs, their performance of modeling long-range information is sub-optimal, and this can lead to deficiencies in the segmentation of tumors with variable sizes. On the other hand, transformer models have demonstrated excellent capabilities in capturing such longrange information in multiple domains, including natural language processing and computer vision. Inspired by the success of vision transformers and their variants, we propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is reformulated as a sequence to sequence prediction problem wherein multi-modal input data is projected into a 1D sequence of embedding and used as an input to a hierarchical Swin transformer as the encoder. The swin transformer encoder extracts features at five different resolutions by utilizing shifted windows for computing self-attention and is connected to an FCNN-based decoder at each resolution via skip connections. We have participated in BraTS 2021 segmentation challenge, and our proposed model ranks among the top-performing approaches in the validation phase. Code: https://monai.io/research/swin-unetr

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The synthesis of dual-emissive carbon dots (CDs) with a longer emission wavelength by using a facile strategy is of great importance for the fabrication of ratiometric fluorescent nanoprobes. Herein, red/green dual-emissive carbon dots (RGDE CDs) were synthesized in one step using 2,5-diaminotoluene sulfate (DATS) as a carbon source. The as-prepared RGDE CDs not only exhibited dual emission fluorescence peaks (525 nm, 603 nm) at the single excitation wavelength of 370 nm, but also possessed good water solubility and excellent fluorescence stability. Moreover, the as-prepared RGDE CDs could be directly utilized as a ratiometric fluorescent probe for the determination of trace ONOO- due to the electron transfer process from ONOO- to the excited RGDE CDs. Under optimal conditions, the linear range was 0.03-60 μM with the limit of detection of 11.6 nM. Importantly, this RGDE CD probe could be applied for the detection of intracellular ONOO- with excellent biocompatibility and cellular imaging capability, indicating great promise in biomedical applications.