Effect of serum triglyceride level on the prognosis of patients with hepatocellular carcinoma in the absence of cirrhosis

Abstract Background The liver plays an important role in the metabolism of lipid and lipoprotein. Dyslipidemia has been demonstrated to be related with several cancers, but the association between serum lipid and hepatocellular carcinoma (HCC) in the absence of cirrhosis remains unclear. Methods A total of 2528 patients with HCC at the Beijing Ditan Hospital between February 2008 and December 2017 were retrospectively included in the study. We identified 200 patients with HCC without cirrhosis by histopathology, imaging, endoscopic findings, and laboratory tests. Multivariate regression analysis was performed to determine the independent characteristics associated with HCC without cirrhosis and its prognosis. Results In the logistics regression analysis, compared to patients with HCC with cirrhosis, patients with HCC without cirrhosis were more likely to have elevated triglyceride (TG) levels (OR = 2.66; 95% CI, 1.18–6.01; P = 0.019). The Kaplan-Meier analysis revealed that a lower TG level was a risk factor regardless of the presence of cirrhosis. The results of the Cox proportional hazard regression analysis showed that a decreased TG level was significantly related to a worse overall survival (HR = 0.51; 95% CI, 0.29–0.89; P = 0.017). Conclusion Serum TG level may be an independent factor to predict the prognosis of patients with HCC in the absence of cirrhosis

Polarization Multiplexing Bifunctional Metalens Designed by Deep Neural Networks

Abstract As planar optical elements, metasurfaces confer an unprecedented potential to manipulate light, which benefits from the deep control of the interactions between nanostructures and light. In the past decade, considerable progress has been made in various metasurfaces for on‐demand functions, drawing great interest from the scientific community. However, it is a great challenge to integrate different functions into a single metasurface, due to the incapability of manipulating light at different dimensions and the lack of universal intelligent design strategy. Here, an intelligent design platform based on deep neural networks is proposed, which can map between structure parameters and optical response. The well‐trained network model can intelligently retrieve nanostructures to meet multidimensional optical requirements of metasurfaces. Four metalenses for chiral focusing are realized by the design platform and the simulation results are highly consistent with the design target. In addition, metalenses based on arbitrary polarization at various working wavelength are also demonstrated, showing that the method has powerful design ability. Various optical properties of nanostructures, such as phase shift and polarization, are manipulated by deep neural networks, which can greatly promote the development of multifunctional devices and further pave the way for optical display, communication, computing, sensing, and other applications