Association between serum creatinine and type 2 diabetes in the Chinese population: a retrospective cohort study.

The relationship between serum creatinine and type 2 diabetes is limited. We aimed to investigate the association of baseline serum creatinine and new-onset type 2 diabetes in Chinese population. This retrospective cohort study was conducted using data from the health screening program in China. The population were divided into four groups based on serum creatinine levels, and the outcome of interest was the occurrence of a diabetic event. Cox proportional risk model was used to assess the independent effect of baseline serum creatinine level on future diabetes risk. Sensitivity and subgroup analysis were used to verify the reliability of the results. After an average follow-up of 3.12 years, among 201,298 individuals aged ≥ 20 years, 3389 patients developed diabetes. Compared with participants in quartile 2-4 (> 51.6umol/L for female, > 71.8umol/L for male,), a significantly higher risk of new-onset Type 2 Diabetes (OR, 1.15; 95%CI: 1.07-1.23) was found in those in quartile 1 (< 51.6umol/L for female, < 71.8umol/L for male). Moreover, Similar results were found in various subgroups stratified by age, BMI, TG, TC, FPG and family history group. Low serum creatinine is independently associated with increased risk of type 2 diabetes in Chinese adults. It was also stable in various subgroups stratified.

Cryo-electron microscope image denoising based on the geodesic distance.

BACKGROUND: To perform a three-dimensional (3-D) reconstruction of electron cryomicroscopy (cryo-EM) images of viruses, it is necessary to determine the similarity of image blocks of the two-dimensional (2-D) projections of the virus. The projections containing high resolution information are typically very noisy. Instead of the traditional Euler metric, this paper proposes a new method, based on the geodesic metric, to measure the similarity of blocks. RESULTS: Our method is a 2-D image denoising approach. A data set of 2243 cytoplasmic polyhedrosis virus (CPV) capsid particle images in different orientations was used to test the proposed method. Relative to Block-matching and three-dimensional filtering (BM3D), Stein's unbiased risk estimator (SURE), Bayes shrink and K-means singular value decomposition (K-SVD), the experimental results show that the proposed method can achieve a peak signal-to-noise ratio (PSNR) of 45.65. The method can remove the noise from the cryo-EM image and improve the accuracy of particle picking. CONCLUSIONS: The main contribution of the proposed model is to apply the geodesic distance to measure the similarity of image blocks. We conclude that manifold learning methods can effectively eliminate the noise of the cryo-EM image and improve the accuracy of particle picking.