Study on the Changes of Liver and Kidney Function-Related Indicators and Clinical Significance in Patients with OSAHS.

Purpose: To study the changes of liver and kidney function-related indexes in patients with obstructive sleep apnea hypopnea syndrome (OSAHS) and analyze their clinical significance. Method: Ninety OSAHS patients treated in our hospital from April 2019 to April 2021 were selected. According to the apnea-hypopnea Index (AHI), they were divided into mild OSAHS group (5 ≤ AHI < 15 times/h, 35 people), moderate OSAHS group (15 ≤ AHI < 30 times/h, 35 people), and severe OSAHS group (AHI ≥ 30 times/h, 20 people). In addition, 50 healthy people who underwent physical examination in our hospital at the same time were selected as the control group, and the liver and kidney function and polysomnography (PSG)-related indexes of the above subjects were detected, and the comparison between the groups was carried out. Result: The serum BUN and SCR levels of the severe group were significantly higher than those of the moderate group, the moderate group had significantly higher levels than the mild group, and the mild group had significantly higher levels than the control group (P < 0.05). The blood AST level of the severe group was significantly lower than that of the moderate group, the moderate group had a significantly lower level than the mild group, and the mild group had a significantly lower level than the control group (P < 0.05). The blood ALT level of the severe group was significantly higher than that of the moderate group, the moderate group had significantly a higher level than the mild group, and the mild group had a significantly higher level than the control group (P < 0.05). The proportions of abnormal liver and kidney function in the control group, mild group, moderate group, and severe group were significantly different (P < 0.05). The AHI of the severe group was significantly higher than that of the moderate group, the moderate group had a higher value than the mild group, and the mild group had a higher value than the control group (P < 0.05). The ASpO2 and MSpO2 of the severe group were significantly lower than those of the moderate group, the moderate group had significantly lower values than the mild group, and the mild group had significantly lower values than the control group (P < 0.05). Spearman correlation analysis showed that the liver and kidney function indexes of OSAHS patients were significantly correlated with PSG indexes (P < 0.05). Conclusion: Patients with OSAHS will have obvious liver and kidney dysfunction, and the monitoring of liver and kidney function in such patients should be strengthened. If abnormality occurs, early intervention is recommended.

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest.

Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusions: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.

Sharp selective scattering of red, green, and blue light achieved via gain material's loss compensation.

Frequency-selective scattering of light can be achieved by metallic nanoparticle's localized surface plasmon resonance (LSPR). And this property may find an application in a transparent projection screen: ideally, specially designed metallic nanoparticles dispersed in a transparent matrix only selectively scatter red, green and blue light and transmit the visible light of other colors. However, optical absorption and surface dispersion of a metallic nanoparticle, whose size is comparable or smaller than mean free path of electrons in the constituent material, degenerate the desired performance by broadening the resonance peak width (i.e., decreasing frequency-selectivity) and decreasing light scattering intensity. In this work, it is shown that the problem can be solved by introducing gain material. Numerical simulations are performed on nanostructures based on silver (Ag), gold (Au) or aluminum (Al) with or without gain material, to examine the effect of gain material and to search for suitable structures for sharp selective scattering of red, green and blue light. And it is found that introducing gain material greatly improves performance of the structures based on Ag or Au except the structures based on Al. The most suitable structures for sharp selective scattering of red, green and blue light are, respectively, found to be the core-shell structures of silica/Au (core/shell), silica/Ag and Ag/silica, all with gain material.

Preparation of LiFePO4/C Cathode Materials via a Green Synthesis Route for Lithium-Ion Battery Applications.

In this work, LiFePO4/C composite were synthesized via a green route by using Iron (III) oxide (Fe2O3) nanoparticles, Lithium carbonate (Li2CO3), glucose powder and phosphoric acid (H3PO4) solution as raw materials. The reaction principles for the synthesis of LiFePO4/C composite were analyzed, suggesting that almost no wastewater and air polluted gases are discharged into the environment. The morphological, structural and compositional properties of the LiFePO4/C composite were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), Raman and X-ray photoelectron spectroscopy (XPS) spectra coupled with thermogravimetry/Differential scanning calorimetry (TG/DSC) thermal analysis in detail. Lithium-ion batteries using such LiFePO4/C composite as cathode materials, where the loading level is 2.2 mg/cm², exhibited excellent electrochemical performances, with a discharge capability of 161 mA h/g at 0.1 C, 119 mA h/g at 10 C and 93 mA h/g at 20 C, and a cycling stability with 98.0% capacity retention at 1 C after 100 cycles and 95.1% at 5 C after 200 cycles. These results provide a valuable approach to reduce the manufacturing costs of LiFePO4/C cathode materials due to the reduced process for the polluted exhaust purification and wastewater treatment.