Background
The purpose of this study was to explore the effect of changing treatment to high-flux hemodialysis (HFHD) on mortality rate in patients with long-term low flux hemodialysis (LFHD).
Methods
The patients with end-stage renal disease (ESRD) who underwent LFHD with dialysis age more than 36 months and stable condition in our hospital before December 31, 2014 were included in this study. They were divided into control group and observation group. Propensity score matched method was used to select patients in the control group. The hemodialysis was performed 3 times a week for 4 h. The deadline for follow-up is December 31, 2018. End-point event is all-cause death. The survival rates of the two groups were compared and multivariate Cox regression analysis was carried out.
Results
K-M survival analysis showed that the 1-year, 2-year, 3-year and 4-year survival rates of HFHD group were 98, 96, 96 and 96%, respectively. The 1-year, 2-year, 3-year and 4-year survival rates of LFHD group were 95, 85, 80 and 78%, respectively. Log-rank test showed that the survival rate of HFHD group was significantly higher than that of LFHD group (x2= 7.278, P = 0.007). Multivariate Cox regression analysis showed that male, age, hemoglobin and low-throughput dialysis were independent predictors of death (P < 0.05). Compared with LFHD, HFHD can significantly reduce the mortality risk ratio of patients, as high as 86%.
Conclusion
The prognosis of patients with ESRD who performed long-term LFHD can be significantly improved after changing to HFHD.
As a medium for transmitting visual information, image is a direct reflection of the objective existence of the natural world. Grayscale images lack more visual information than color images. Therefore, it is of great significance to study the colorization of grayscale images. At present, the problems of semantic ambiguity, boundary overflow and lack of color saturation exist in both traditional and deep learning methods. To solve the above problems, an adversarial image colorization method based on semantic optimization and edge preservation is proposed. By improving generative and discriminative networks and designing loss functions, deeper semantic information and sharper edges of images can be learned by our network. Our experiments are carried out on the public datasets Place365 and ImageNet. The experimental results show that the method in this paper can reduce the color anomaly caused by semantic ambiguity, suppress the color blooming in the image boundary area and improve the saturation of the image. Our work achieves competitive results on objective indicators of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and learned perceptual image patch similarity (LPIPS), with values of 30.903 dB, 0.956 and 0.147 on Place365 and 30.545 dB, 0.946 and 0.150 on ImageNet, which proves that this method can effectively colorize grayscale images.
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