Serum uric acid to serum creatinine ratio predicts neurological deterioration in branch atheromatous disease

Background and objectiveBranch atheromatous disease (BAD) makes patients prone to early neurological deterioration (END), resulting in poor prognosis. The aim of this study was to investigate the association between SUA/SCr and END in BAD stroke patients.MethodsWe conducted a retrospective study that included 241 patients with BAD-stroke within 48 h of symptom onset. We divided the patients into the END group and the no END group. END was defined as an NIHSS score increase of more than 2 points within 1 week. SUA/SCr was calculated by the concentration of serum uric acid and creatine (serum uric acid/serum creatine) on admission. Univariate and multivariate analyses were used to identify independent predictors of END in BAD-stroke patients.ResultsEND was observed in 24.1% (58/241) of the patients in our study. Multiple logistic regression analyses showed that SUA/SCr (aOR, 0.716; 95% CI, 0.538–0.952; P = 0.022) and female sex (aOR, 0.469; 95% CI, 0.245–0.898; P = 0.022) were associated with END after adjusting for confounding factors. The predicted value of SUA/Scr for END was a sensitivity of 79.3%, a specificity of 44.8%, and an AUC of 0.609 (95% CI, 0.527–0.691, P < 0.05). The optimal cut-off value was 4.76.ConclusionSUA/SCr was negatively associated with the risk of END in BAD stroke patients

Thermal ablation as an alternative to liver transplantation for hepatocellular carcinoma with clinically significant portal hypertension: propensity score matching study

PurposeThe objectives were to investigate the safety and efficacy of thermal ablation as an alternative to liver transplantation for hepatocellular carcinoma patients with clinically significant portal hypertension (CSPH).Materials and MethodsFrom July 2016 to September 2019, hepatocellular carcinoma patients with CSPH treated by liver transplantation (N=37) or thermal ablation (N=114) were enrolled. Cumulative intrahepatic recurrence, overall survival and major complications were compared by propensity score matching.ResultsIn the two matched groups, the 1-, 2-, and 3-year intrahepatic recurrence rates for the ablation group (22.3%, 50.0%, and 50.0%, respectively) were significantly higher than those for the transplantation group (4.5%, 4.5%, and 4.5%, respectively) (P=0.016). The 1-, 2-, and 3-year overall survival rates were comparable between the two groups [96.1%, 88.7%, and 88.7%, respectively (ablation group) vs. 84.6%, 76.2%, and 76.2%, respectively (transplantation group)] (P=0.07). The major complication rate for the ablation group [4.8% (3/62)] was significantly lower than that for the transplantation group [36.0% (9/25)] (P<0.001).ConclusionsThermal ablation is a safe and effective alternative for hepatocellular carcinoma patients with CSPH

Ultra-Sensitivity Glucose Sensor Based on Field Emitters

A new glucose sensor based on field emitter of ZnO nanorod arrays (ZNA) was fabricated. This new type of ZNA field emitter-based sensor shows high sensitivity with experimental limit of detection of 1 nM glucose solution and a detection range from 1 nM to 50 μM in air at room temperature, which is lower than that of glucose sensors based on surface plasmon resonance spectroscopy, fluorescence signal transmission, and electrochemical signal transduction. The new glucose sensor provides a key technique for promising consuming application in biological system for detecting low levels of glucose on single cells or bacterial cultures

Effect of Aspect Ratio on Field Emission Properties of ZnO Nanorod Arrays

ZnO nanorod arrays are prepared on a silicon wafer through a multi-step hydrothermal process. The aspect ratios and densities of the ZnO nanorod arrays are controlled by adjusting the reaction times and concentrations of solution. The investigation of field emission properties of ZnO nanorod arrays revealed a strong dependency on the aspect ratio and their density. The aspect ratio and spacing of ZnO nanorod arrays are 39 and 167 nm (sample C), respectively, to exhibit the best field emission properties. The turn-on field and threshold field of the nanorod arrays are 3.83 V/μm and 5.65 V/μm, respectively. Importantly, the sample C shows a highest enhancement of factorβ, which is 2612. The result shows that an optimum density and aspect ratio of ZnO nanorod arrays have high efficiency of field emission

Entropy SVM–Based Recognition of Transient Surges in HVDC Transmissions

Protection based on transient information is the primary protection of high voltage direct current (HVDC) transmission systems. As a major part of protection function, accurate identification of transient surges is quite crucial to ensure the performance and accuracy of protection algorithms. Recognition of transient surges in an HVDC system faces two challenges: signal distortion and small number of samples. Entropy, which is stable in representing frequency distribution features, and support vector machine (SVM), which is good at dealing with samples with limited numbers, are adopted and combined in this paper to solve the transient recognition problems. Three commonly detected transient surges—single-pole-to-ground fault (GF), lightning fault (LF), and lightning disturbance (LD)—are simulated in various scenarios and recognized with the proposed method. The proposed method is proved to be effective in both feature extraction and type classification and shows great potential in protection applications

Deep learning-based fault location of DC distribution networks

Compared with AC distribution networks, DC ones have a number of advantages. Intensive connections of distributed renewable energy can lead to large amount of power electronic converters and complex models. Underground cable is widely used in DC distribution networks. Accurate location of faults can help engineers find the fault points and shorten the time of maintenance. In DC distribution networks, where only a few measuring units are equipped and low sampling rates are adopted, there is limited data used for fault location. Also, for monopole grounding fault, the fault features are sometimes unobvious for recognition. Deep learning which provides feature hierarchy can learn experiences automatically and recognise raw data as human brain does. It reveals a high potential to solve location problems in DC distribution systems. This paper proposes a depth learning based fault location for DC distribution networks. First, a DC distribution network with radiant topology is modelled, and faults are added with different parameters to simulate various scenarios in practical projects. Then, a deep neural network is generated and trained with normalised fault currents. The parameters of network are discussed according to particular application. Finally, the location performance of deep neural network is tested

Characteristics, Toxic Effects, and Analytical Methods of Microplastics in the Atmosphere

Microplastics (MPs) (including nanoplastics (NPs)) are pieces of plastic smaller than 5 mm in size. They are produced by the crushing and decomposition of large waste plastics and widely distributed in all kinds of ecological environments and even in organisms, so they have been paid much attention by the public and scientific community. Previously, several studies have reviewed the sources, occurrence, distribution, and toxicity of MPs in water and soil. By comparison, the review of atmospheric MPs is inadequate. In particular, there are still significant gaps in the quantitative analysis of MPs and the mechanisms associated with the toxic effects of inhaled MPs. Thus, this review summarizes and analyzes the distribution, source, and fate of atmospheric MPs and related influencing factors. The potential toxic effects of atmospheric MPs on animals and humans are also reviewed in depth. In addition, the common sampling and analysis methods used in existing studies are introduced. The aim of this paper is to put forward some feasible suggestions on the research direction of atmospheric MPs in the future