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.

Background: Adverse drug outcomes in the elderly have led to the development of lists of potentially inappropriate medications (PIMs), such as the Beers criteria, and these PIMs have been studied widely; however, it is still unclear whether PIM use is predictive of adverse outcomes in older people. Objective: To qualitatively examine the associations between exposure to PIMs from the general Beers criteria and the Screening Tool of Older Persons’ Prescriptions list and adverse outcomes, such as adverse drug reactions (ADRs)/adverse drug events (ADEs), hospitalization, and mortality. Methods: Specified databases were searched from inception to February 1, 2018. Two reviewers independently selected studies that met the inclusion criteria, assessed study quality, and extracted data. Data were pooled using Stata 12.0. The outcomes were ADRs/ADEs, hospitalization, and mortality. Results: A total of 33 studies met the inclusion criteria. The combined analysis revealed a statistically significant association between ADRs/hospitalizations and PIMs (odds ratio [OR] = 1.44, 95% CI = 1.33-1.56; OR = 1.27, 95% CI = 1.20-1.35), but no statistically significant association was found between mortality and PIMs (OR = 1.04; 95% CI = 0.75-1.45). It is interesting to note that the results changed when different continents/criteria were used for the analysis. Compared with the elderly individuals exposed to 1 PIM, the risk of adverse health outcomes was much higher for those who took ≥2 PIMs. Conclusion and Relevance: We recommend that clinicians avoid prescribing PIMs for older adults whenever feasible. In addition, the observed associations should be generalized to other countries with different PIM criteria with caution.

Certain uterine prostaglandins (PGs) are elevated at implantation sites and are needed to trigger the events of blastocyst implantation that include blastocyst-uterine attachment and stromal decidualization with vascular permeability changes. Several decades of investigations showed that treatment with PG synthesis inhibitors, prior to or during the time of implantation, resulted in either complete inhibition or a delay in implantation or reduction in the number of implantation sites with diminished decidual tissue. Consistent with these findings, we observed that whereas a selective PG endoperoxide synthase (Ptgs) 1 inhibitor SC-560 failed to inhibit implantation, a selective Ptgs2 inhibitor SC-236 showed significantly reduced number and size of implantation sites in progesterone-treated ovariectomized pregnant hamsters. It is known that Ptgs2 expression and Ptgs2-derived prostacyclin (PGI 2 ) synthesis at implantation sites are needed for implantation in the mouse (a rodent that needs ovarian estrogen for implantation). However, it is unknown which Ptgs and PG synthases produce which PGs at implantation sites of the hamster (a rodent that does not need ovarian estrogen for implantation). Here we demonstrate that as blastocyst implantation proceeds, a reduction in Ptgs1 expression from uterine luminal epithelial cells and a gradual induction in Ptgs2 expression exclusively in luminal epithelial and adjacent decidual cells occurred at implantation sites of hamsters. Results also reveal that PGE 2 , but not PGI 2 , is the major PG at implantation sites where Ptgs2 and microsomal type PGE synthases but not PGI synthases are co-expressed. This elevated uterine PGE 2 at implantation sites may serve to initiate or amplify physiological signals required for specific aspects of the implantation process in hamsters.

Knowledge of vehicle headway distribution is very important for intelligent transportation and intelligent vehicle simulations. Various headway distribution models have been proposed, but most of them only fit for a certain traffic situation. To solve this problem, we study the dependence of headway distributions on traffic status in this paper. Results show that the log-normal distribution model is adequate in fitting headway data when the traffic is in free flow status; while the log-logistic distribution model is more suitable in fitting headway data when the traffic is in congestion status. This conclusion is useful in the traffic signal optimization algorithm, since it indicates that we should apply different delay estimation models during different traffic status so as to design optimal timing plan.

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