OBJECTIVEDiabetes is one of the most distinct comorbidities of COVID-19. Here, we describe the clinical characteristics of and outcomes in patients with diabetes in whom COVID-19 has been confirmed or clinically diagnosed (with typical features on lung imaging and symptoms), and their association with glucose-lowering or blood pressure-lowering medications.
RESEARCH DESIGN AND METHODSIn this retrospective study involving 904 patients with COVID-19 (136 with diabetes, mostly type 2 diabetes), clinical and laboratory characteristics were collected and compared between the group with diabetes and the group without diabetes, and between groups taking different medications. Logistic regression was used in order to explore risk factors associated with mortality or poor prognosis.
RESULTSThe proportion of comorbid diabetes is similar between cases of confirmed and of clinically diagnosed COVID-19. Risk factors for higher mortality of patients with diabetes and COVID-19 were older age (adjusted odds ratio [aOR] 1.09 [95% CI 1.04, 1.15] per year increase; P 5 0.001) and elevated C-reactive protein (aOR 1.12 [95% CI 1.00, 1.24]; P 5 0.043). Insulin usage (aOR 3.58 [95% CI 1.37, 9.35]; P 5 0.009) was associated with poor prognosis. Clinical outcomes of those who use an ACE inhibitor (ACEI) or angiotensin II type-I receptor blocker (ARB) were comparable with those of patients who do not use ACEI/ARB among patients with diabetes and hypertension who have COVID-19.
CONCLUSIONSC-reactive protein may help to identify patients with diabetes who are at greater risk of dying during hospitalization. Older patients with diabetes were prone to death
Limited energy supply is one of the major constraints in wireless sensor networks. A feasible strategy is to aggressively reduce the spatial sampling rate of sensors, i.e., the density of the measure points in a field. By properly scheduling, we want to retain the high fidelity of data collection. In this paper, we propose a data collection method that is based on a careful analysis of the surveillance data reported by the sensors. By exploring the spatial correlation of sensing data, we dynamically partition the sensor nodes into clusters so that the sensors in the same cluster have similar surveillance time series. They can share the workload of data collection in the future since their future readings may likely be similar. Furthermore, during a short time period, a sensor may report similar readings. Such a correlation in the data reported from the same sensor is called temporal correlation, which can be explored to further save energy. We develop a generic framework to address several important technical challenges, including how to partition the sensors into clusters, how to dynamically maintain the clusters in response to environmental changes, how to schedule the sensors in a cluster, how to explore temporal correlation, and how to restore the data in the sink with high fidelity. We conduct an extensive empirical study to test our method using both a real test bed system and a large-scale synthetic dataset.
Although prior studies have indicated the interrelationships between specific types of innovation strategy and specific elements of organizational culture, few studies simultaneously evaluate the relationship between the two multi-dimensional constructs in holistic perspective. Based on configuration theory, we conceptualize fit as 'profile deviation', and investigate the fit between an organization's culture and its innovation strategy. Data were collected from 183 Chinese organizations. We examine the hypothesis that greater fit between organizational culture and innovation strategy encourages superior innovation speed and innovation quality. Our results provide evidence that in the group of organizations exhibiting either exploratory or exploitative innovation strategy, the more similar the organizational culture configurations are to those of the top performers, the higher their innovation speed and innovation quality are. In the group of organizations exhibiting ambidextrous innovation strategy, the fit between organizational culture and innovation strategy is insignificantly associated with innovation speed and innovation quality. Implications for applying the culture-strategy fit in innovation management are discussed.
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