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Context The long-term safety and benefit of pioglitazone use in combination with insulin are still uncertain. Objective This study compared the risks of all-cause mortality and major cardiovascular (CV) events between pioglitazone users and nonusers receiving insulin therapy. Design, Setting and Patients We conducted a 13-year retrospective cohort study by using data from the population-based National Health Insurance Research Database in Taiwan. A total of 20 376 patients with type 2 diabetes mellitus (T2DM) receiving insulin therapy were enrolled during 2000 to 2012. Overall, the incidence rates of all-cause mortality and CV events were compared between 2579 pioglitazone users and 2579 matched nonusers. Results After adjustment for age, sex, comorbidities, Diabetes Complications Severity Index scores, and drugs used, mortality rates were 30.26 and 15.02 per 1000 person-years for pioglitazone nonusers and users, respectively. The adjusted hazard ratio (aHR) of mortality was 0.47 (95% confidence interval [CI]: 0.38–0.58, P < 0.001) for pioglitazone users compared with nonusers. The aHRs of CV and non-CV deaths were 0.78 (95% CI: 0.51–1.19) and 0.50 (95% CI: 0.38–0.66), respectively. The aHRs of hospitalized coronary artery disease, hospitalized stroke, and incident heart failure were not significantly different between pioglitazone users and nonusers. Conclusions This nationwide cohort study demonstrated that pioglitazone use reduced the risks of all-cause mortality and non-CV death for patients with T2DM undergoing insulin therapy.

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At present, the synthesizing faces of different ages does not emphasize on feature alignment and rectification of twisted images. If these situations do happen, they might cause failure and inaccuracy on synthesizing images. In this paper, we propose a reversible human facial aging/rejuvenating synthesis system which is implemented by Active Shape Model (ASM) integrated with Log-Gabor Wavelet, which can be used to search for the dementia elderly. First, we use AdaBoost and ASM algorithm to extract the feature set of human face, and rectify them by the concept of facial geometric invariance. The invariant concepts are the distance between inner corners of both eyes and the distance between the nose and chin. Then, we find manually one target image which is similar to the test image from the database, and analyze age texture of this human image by Log-Gabor wavelet in order to retrieve decomposition maps. Finally, we can effectively simulate human facial images of people of different ages by controlling the number of decomposition map of images and objectively judge the results via the density of wrinkles.

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