High Levels of Serum Triglyceride, Low-density Lipoprotein Cholesterol, Total Bile Acid, and Total Bilirubin are Risk Factors for Gallstones.

BACKGROUND: This study aimed to identify the risk factors for gallstone disease in the Hakka population in the Meizhou area of China. METHODS: In total, 816 patients with gallstone disease and 818 control participants were included in the study, and their serum lipid levels were measured. Data on age, gender, and risk factors for gallstone disease (such as smoking and drinking history and the prevalence of hypertension) were recorded. RESULTS: Of the 1,634 enrolled individuals, age 13 - 101 years, 727 were men and 907 were women. Serum triglyceride (TG) (p < 0.001), low-density lipoprotein-cholesterol (LDL-C) (p = 0.043), total bile acid (TBA) (p < 0.001), and total bilirubin (T-BIL) (p < 0.001) levels showed significant differences between the patients and controls. However, age, the proportion history of drinking and smoking; the prevalence of hypertension and diabetes mellitus; and serum levels of total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), apolipoprotein A1 (Apo-A1), apolipoprotein B (Apo-B), and Apo-A1/Apo-B were similar between the two groups. The frequencies of gallstones in the common bile duct (χ2 = 13.909, p < 0.001) and intrahepatic bile ducts (χ2 = 8.289, p = 0.004) showed significant differences between male and female patients, but the distribution of gallstones of different sizes was similar between the two groups. Serum TBA (p < 0.001) and T-BIL (p < 0.001) levels were higher in patients with gallstones in the common bile duct than in those with gallstones in the gall bladder and intrahepatic bile ducts. Logistic regression analysis indicated that participants with high serum TG, LDL-C, TBA, and T-BIL levels had a significantly higher risk of gallstone disease. CONCLUSIONS: High serum levels of TG, LDL-C, TBA, and T-BIL are found to be the main risk factors for gallstone formation in our study.

Poisson Vector Graphics (PVG)-Guided Face Color Transfer in Videos.

This article presents a simple yet effective algorithm for automatically transferring face colors in portrait videos. We extract the facial features and vectorize the faces in the input video using Poisson vector graphics, which encodes the low-frequency colors as the boundary colors of diffusion curves, and the high-frequency colors as Poisson regions. Then, we transfer the face color of a reference image/video to the first frame of the input video by applying optimal mass transport between the boundary colors of diffusion curves. Next the boundary color of the first frame is transferred to the subsequent frames by matching the curves. Finally, with the original or modified Poisson regions, we render the video using an efficient random-access Poisson solver. Thanks to our efficient diffusion curve matching algorithm, transferring colors for the vectorized video takes less than 1 millisecond per frame. Our method is particularly desired for frequent transfer from multiple references due to its information reuse nature. The simple diffusion curve matching also greatly improves the performance of video vectorization, since we only need to solve an optimization problem for the first frame. Since our method does not require correspondence between the reference image/video and the input video, it is flexible and robust to handle faces with significantly different geometries and postures, which often pose challenges to the existing methods. Moreover, by manipulating Poisson regions, we can enhance or reduce the highlight and contrast so that the reference color can fit into the input video naturally. We demonstrate the efficacy of our method on image-to-video transfer and color swap in videos.