OBJECTIVEDiabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics and outcomes and to analyze the risk factors for in-hospital mortality of COVID-19 patients with diabetes. RESEARCH DESIGN AND METHODSThis two-center retrospective study was performed at two tertiary hospitals in Wuhan, China. Confirmed COVID-19 patients with diabetes (N 5 153) who were discharged or died from 1 January 2020 to 8 March 2020 were identified. One sexand age-matched COVID-19 patient without diabetes was randomly selected for each patient with diabetes. Demographic, clinical, and laboratory data were abstracted. Cox proportional hazards regression analyses were performed to identify the risk factors associated with the mortality in these patients. RESULTSOf 1,561 COVID-19 patients, 153 (9.8%) had diabetes, with a median age of 64.0 (interquartile range 56.0-72.0) years. A higher proportion of intensive care unit admission (17.6% vs. 7.8%, P 5 0.01) and more fatal cases (20.3% vs. 10.5%, P 5 0.017) were identified in COVID-19 patients with diabetes than in the matched patients. Multivariable Cox regression analyses of these 306 patients showed that hypertension (hazard ratio [HR] 2.50, 95% CI 1.30-4.78), cardiovascular disease (HR 2.24, 95% CI 1.19-4.23), and chronic pulmonary disease (HR 2.51, 95% CI 1.07-5.90) were independently associated with in-hospital death. Diabetes (HR 1.58, 95% CI 0.84-2.99) was not statistically significantly associated with in-hospital death after adjustment. Among patients with diabetes, nonsurvivors were older (76.0 vs. 63.0 years), most were male (71.0% vs. 29.0%), and were more likely to have underlying hypertension (83.9% vs. 50.0%) and cardiovascular disease (45.2% vs. 14.8%) (all P values <0.05). Age ‡70 years (HR 2.39, 95% CI 1.03-5.56) and hypertension (HR 3.10, 95% CI 1.14-8.44) were independent risk factors for in-hospital death of patients with diabetes. CONCLUSIONSCOVID-19 patients with diabetes had worse outcomes compared with the sex-and age-matched patients without diabetes. Older age and comorbid hypertension independently contributed to in-hospital death of patients with diabetes.

<div><b>OBJECTIVE: </b>Diabetes is common in COVID-19 patients and associated with unfavorable outcomes. We aimed to describe the characteristics, outcomes and analyze the risk factors for in-hospital mortality of COVID-19 patients with diabetes.</div><div><b><br></b></div><div><b>RESEARCH DESIGN AND METHODS: </b>This two-center, retrospective study was performed at two tertiary hospitals in Wuhan, China. Confirmed COVID-19 patients with diabetes (N=153) who were discharged or died from January 1, 2020, to March 8, 2020, were identified. One sex- and age-matched COVID-19 patient without diabetes was randomly selected for each patient with diabetes. Demographic, clinical, and laboratory data were abstracted. Cox proportional hazards regression analyses were performed to identify the risk factors associated with the mortality in these patients.</div><div><br></div><div><b>RESULTS:</b> Of 1561 COVID-19 patients, 153 (9.8%) had diabetes, with a median age of 64.0 (IQR, 56.0-72.0) years. A higher proportion of ICU admission (17.6% vs 7.8%, P=0.01) and more fatal cases (20.3% vs 10.5%, P=0.017) were identified in COVID-19 patients with diabetes than in the matched patients. Multivariable Cox regression analyses of these 306 patients showed that hypertension (hazards ratio [HR] 2.50, 95% CI 1.30-4.78), cardiovascular disease (HR 2.24, 95% CI 1.19-4.23) and chronic pulmonary disease (HR 2.51, 95% CI 1.07-5.90) were independently associated with in-hospital death. Diabetes (HR 1.58, 95% CI 0.84-2.99) was not statistically significantly associated with in-hospital death after adjustment. Among patients with diabetes, nonsurvivors were older (76.0 vs 63.0 years), most were male (71.0% vs 29.0%), and were more likely to have underlying hypertension (83.9% vs 50.0%) and cardiovascular disease (45.2% vs 14.8%) (all P-values<0.05). Age ≥70 years (HR 2.39, 95% CI 1.03-5.56) and hypertension (HR 3.10, 95% CI 1.14-8.44) were independent risk factors for in-hospital death of patients with diabetes.</div><div><br></div><div><b>CONCLUSIONS: </b>COVID-19 patients with diabetes had worse outcomes compared with the sex- and age-matched patients without diabetes. Older age and comorbid hypertension independently contributed to in-hospital death of patients with diabetes.</div>

Stable information of a sky light polarization pattern can be used for navigation with various advantages such as better performance of anti-interference, no "error cumulative effect," and so on. But the existing method of sky light polarization measurement is weak in real-time performance or with a complex system. Inspired by the navigational capability of a Cataglyphis with its compound eyes, we introduce a new approach to acquire the all-sky image under different polarization directions with one camera and without a rotating polarizer, so as to detect the polarization pattern across the full sky in a single snapshot. Our system is based on a handheld light field camera with a wide-angle lens and a triplet linear polarizer placed over its aperture stop. Experimental results agree with the theoretical predictions. Not only real-time detection but simple and costless architecture demonstrates the superiority of the approach proposed in this paper.

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The sky light polarization navigator has many advantages, such as low cost, no decrease in accuracy with continuous operation, etc. However, current celestial polarization measurement methods often suffer from low performance when the sky is covered by clouds, which reduce the accuracy of navigation. In this paper we introduce a new method and structure based on a handheld light field camera and a radial polarizer, composed of an S-wave plate and a linear polarizer, to detect the sky light polarization pattern across a wide field of view in a single snapshot. Each micro-subimage has a special intensity distribution. After extracting the texture feature of these subimages, stable distribution information of the angle of polarization under a cloudy sky can be obtained. Our experimental results match well with the predicted properties of the theory. Because the polarization pattern is obtained through image processing, rather than traditional methods based on mathematical computation, this method is less sensitive to errors of pixel gray value and thus has better anti-interference performance.