Metasurfaces provide unprecedented routes to manipulations on electromagnetic waves, which can realize many exotic functionalities. Despite the rapid development of metasurfaces in recent years, the design process of metasurface is still time‐consuming and computational resource‐consuming. Moreover, it is quite complicated for layman users to design metasurfaces as plenty of specialized knowledge is required. In this work, a metasurface design method named REACTIVE is proposed on the basis of deep learning, as deep learning method has shown its natural advantages and superiorities in mining undefined rules automatically in many fields. REACTIVE is capable of calculating metasurface structure directly through a given design target; meanwhile, it also shows the advantage in making the design process automatic, more efficient, less time‐consuming, and less computational resource‐consuming. Besides, it asks for less professional knowledge, so that engineers are required only to pay attention to the design target. Herein, a triple‐band absorber is designed using the REACTIVE method, where a deep learning model computes the metasurface structure automatically through inputting the desired absorption rate. The whole design process is achieved 200 times faster than the conventional one, which convincingly demonstrates the superiority of this design method. REACTIVE is an effective design tool for designers, especially for laymen users and engineers.

Background: Epidemiological studies have shown that hyperuricemia is associated with all-cause and cardiovascular mortality in chronic kidney disease (CKD) and hemodialysis patients. Our study investigated the influence of serum uric acid (UA) levels on survival in peritoneal dialysis (PD) patients. Methods: This was a retrospective study involving 156 subjects who had undergone PD. The patient demographics, etiology of ESRD, comorbid conditions and other laboratory parameters were collected. The subjects were divided into three groups according to their serum UA concentrations (group 1, the lowest quartile; group 2, the middle quartiles; group 3, the highest quartile). The risk of death was calculated using a multivariate Cox regression model. Results: There were 41 deaths during a follow-up period of 31.3±17.5 months. Compared with group 2, which had a mortality rate of 5.7 per 1000 person-months, the mortality rates were higher in group 1 (14.3 per 1000 person-months, p<0.05) and group 3 (13.3 per 1000 person-months, p<0.05). A multivariable Cox regression model revealed that age, serum albumin, diabetes mellitus (DM), hypertensive nephropathy, residual renal function and UA group were factors associated with mortality in the PD patients. Using group 2 as a reference, the hazard ratio (HR) of mortality was found to be 1.15 (95% confidence interval [CI] 0.20-2.57, p>0.05) for group 1 and 2.96 (95% CI 1.29-6.80, p=0.01) for group 3. Conclusions: In PD patients, a higher serum UA level is related to increased mortality and is an independent risk factor for all-cause mortality. Uric acid levels and all-cause mortality in peritoneal dialysis patients.

Conventional metasurface design methods usually require a lot of computational resources and time, meaning they fail to satisfy the efficient, rapid design on demand. On account of this, we branch out of the conventional metasurface design methods by attempting to relate the emerging discipline of artificial intelligence to a traditional physical area. With our method, named AMID, metasurface structures are designed inversely where they can be computed directly by simply proposing and inputting the desired design targets into AMID. AMID greatly simplifies conventional methods that call for not only sufficient professional knowledge but also trial and error through simulation softwares. According to the design results, unit cells of metasurfaces are successfully computed, which verifies the availability of AMID and improves the design efficiency in the meanwhile.

Background/Aims: This study aimed to investigate potential risk factors for calcification in aortic and mitral valves in maintenance peritoneal dialysis (MPD) patients. Methods: We enrolled MPD patients who had undergone over 18 months of dialysis in our dialysis center, examined their cardiac valve calcification status by echocardiography, and recorded their biochemical data and dialysis-related indicators. These results were compared by logistic regression analyses to identify the risk factors associated with calcification in aortic and mitral valves. Results: Among the 117 enrolled MPD patients, 41 exhibited calcification in aortic or mitral valves, including 38 with aortic valve calcification (AVC) and 17 with mitral valve calcification (MVC); 14 of them had calcification in both aortic and mitral valves. Multivariate logistic regression analysis revealed that age (OR=1.965, p=0.01), diabetes history (OR=4.693, p=0.029), calcium-phosphorus product (OR=2.373, p=0.001) and prealbumin (OR=0.908, p=0.012) were independently related to AVC, whereas age (OR=3.179, p=0.023), calcium-phosphorus product (OR=6.512, p=0.001), prealbumin (OR=0.885, p=0.033), high-density lipoprotein (OR=19.540, p=0.011) and diabetes history (OR=6.948, p=0.038) were independently related to MVC. Conclusions The incidence of cardiac valve calcification in MPD patients is high, and the incidence of AVC is higher than MVC. Age, diabetes history, calcium-phosphorus product and hypo-prealbuminemia are independent risk factors for AVC, whereas age, calcium-phosphorus product and hypo-prealbuminemia are independent risk factors for MVC.