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Objective: Serum uric acid is associated with tumor progression and hepatocarcinogenesis. Here, we aimed to determine whether serum uric acid is related to the survival time of patients with hepatocellular carcinoma (HCC) and whether the inhibition of uric acid production affects the progression and survival of rats with HCC.Methods: The follow-up data of 288 patients with advanced HCC were analyzed. Ten purine metabolites in serum and liver samples of diethylnitrosamine (DEN)-induced HCC rats were quantitatively determined by an established UPLC-MS/MS method. On this basis, febuxostat, a specific inhibitor of xanthine oxidase (XOD), was used to interfere with HCC rats.Results: The serum uric acid level of HCC patients was significantly negatively correlated with survival days (r = -0.155). The median survival time was 133.5 days in the high uric acid group (>360 μmol/L, n = 80) and 176.0 days in the normal serum uric acid group (n = 208, p = 0.0013). The levels of hypoxanthine, guanine, and uric acid; XOD activity; and xanthine dehydrogenase mRNA expression in the serum or liver samples of HCC rats were significantly upregulated compared with those in the control group. After febuxostat intervention in DEN-induced HCC rats, the number of atypical cells and inflammatory cells decreased significantly; the serum alpha fetoprotein level and Fisher’s ratio tended to return to normal; the median survival time increased from 36 to 96 days (p = 0.08). In addition, serum malondialdehyde, superoxide dismutase, and glutathione activity nearly returned to the level of the healthy control group.Conclusion: The elevation of serum uric acid implies a risk of poor survival in advanced HCC patients and Febuxostat can reduce the generation of reactive oxygen species, thereby playing a role in delaying the progression of liver cancer.

BackgroundPD-1 inhibitors have been routinely used in the treatment of advanced non-small cell lung cancer (NSCLC), and have demonstrated to significantly improve survivorship when combining with other conventional therapies, such as chemotherapy and anti-angiogenesis therapy. PD-L1 is the most commonly used biomarker to select benefiting groups, while not all patients with high PD-L1 expression benefit from immunotherapy. Therefore, identifying other prognostic and predictive biomarkers, including peripheral blood indexes, is essential.MethodsWe retrospectively collected medical records and hematological data of 151 patients with advanced NSCLC treated with PD-1 inhibitor-based combination therapy in our hospital. The peripheral blood indexes of interest were NLR, PLR, PAR, Hb, LDH, CEA, and NSE. The association between peripheral blood indexes and treatment responses or survival outcomes was examined by multivariable logistic regression and Cox regression, respectively.ResultsThe decreased CEA at week 6 (OR = 4.209, 95%CI: 1.287-13.758) or 12 (OR = 7.267, 95%CI: 1.508-35.006) post-treatment was related to a higher disease control rate. The decrease or NLR at week 6 (OR = 3.081, 95%CI: 1.464-6.483) or 12 (OR = 3.304, 95%CI: 1.560-7.001) post-treatment, or CEA at week 12 post-treatment (OR = 2.469, 95%CI: 1.134-5.375), was associated with a higher objective response rate. Patients whose NLR (HR = 0.610, 95%CI: 0.411-0.907) or CEA (HR = 0.477, 95%CI: 0.320-0.710) decreased at week 6 post-treatment tended to have longer progression-free survival, and similar results were found in those with decreased NLR (HR = 0.587, 95%CI: 0.388-0.886) or CEA (HR = 0.406, 95%CI: 0.270-0.609) at week 12 post-treatment. Patients whose CEA (HR = 0.543, 95%CI: 0.339-0.871) or NSE (HR = 0.619, 95%CI: 0.386-0.994) decreased after 6 weeks post-treatment appeared to have longer overall survival, and the same was found for those whoseCEA (HR = 0.620, 95%CI: 0.390-0.986) or NSE (HR = 0.578, 95%CI: 0.353-0.947) was decreased at 12 weeks after treatment.ConclusionPost-treatment NLR, CEA and NSE changes are suggestive indicators for the prognosis of NSCLC patients after immunotherapy.

A 2-stage predictive decision solution was proposed to solve the inventory control problem in this paper, which consists of forecasting the product demand and making inventory decisions for commodities based on the demand prediction. In the prediction module, the long short-term memory network is adopted to predict the demand for commodities. Associated with the demand predictor, an inventory decision rule was newly proposed. Unlike the conventional methodology to deal with the prediction and decision tasks independently, the demand predictor is trained by minimizing the inventory cost instead of optimizing accuracy-oriented metrics, e.g. mean squared error (MSE). Moreover, an empirical study was performed for the e-commerce sales data from Alibaba. The decision-oriented predictor training was realized and compared with the MSE-oriented training, and the results show our solution can significantly reduce the inventory cost.

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874&ndash, 1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

Critical metals are used increasingly in vehicle manufacturing. For more sustainable use of these metals, it is important to understand their distribution in vehicles. In this paper, we present a comparative study examining the distribution of critical metals in conventional passenger vehicles. We identified two existing approaches to estimate the amounts of critical metals used in one passenger vehicle: input-driven and output-driven approach, and compared the results of 25 metals among five studies. In general, the results were found to be scattered. Cu, Mn, Sr and Sb were found with the highest median masses per vehicle. The median masses of eight metals (Nb, Zr, Co, La, Mo, Nd, Ce, and Ag in descending order) were around or below 10 g per vehicle and those of 13 metals (Pd, Ta, Pr, Ga, Sm, Y, W, Au, Gd, Dy, In, Pt, and Tb in descending order) were below 1 g per vehicle. Top three subsystems and parts containing the largest mass of critical metals in sum were presented. Our research provides a consolidated summary of existing information on the critical metal distribution in conventional passenger vehicles and suggests improvements for future studies on this topic.

Concurrent with the demand for cleaner, lighter, and more efficient vehicles, many scarce metals (SMs) are used in passenger vehicles because of their unique physical and chemical properties. To explore the recycling potential of these metals, it is important to understand their distribution in the vehicles as well as their fate at the vehicles' end-of-life. However, this information remains very scattered and sparse. In this paper, we present a study investigating the distribution of 31 SMs in selected electrical and electronic (EE) components of conventional passenger vehicles and in the end-of-life vehicle shredder fractions from a shredder plant in Switzerland. The results of the chemical analyses show that the mass fractions of Co, Sn, Sr, Ta, Y, and Zr were dominant with20,000 g/t in the selected EE components and Ag, Ga, Mo, Sb, Sn, Sr, and Zr with50 g/t in the analyzed shredder fractions. The largest masses of 17 SMs were found in the shredder light fraction, which is incinerated in municipal waste treatment plants mainly in Switzerland; thus, these SMs are currently not recovered. The SM mass fractions in both the EE components and the shredder fractions were projected to their total masses in 100 hypothetical midrange passenger vehicles. The resulting mass balance showed a mismatch of50% for 23 metals, which indicates other important SM sources such as alloys.

The rare earth elements (REE) are a group of fifteen elements with unique properties that make them indispensable for a wide variety of emerging and conventional established technologies. However, quantitative knowledge of REE remains sparse, despite the current heightened interest in future availability of the resources. Mining is heavily concentrated in China, whose monopoly position and potential restriction of exports render primary supply vulnerable to short term disruption. We have drawn upon the published literature and unpublished materials in different languages to derive the first quantitative annual domestic production by end use of individual rare earth elements from 1995 to 2007. The information is illustrated in Sankey diagrams for the years 1995 and 2007. Other years are available in the supporting information. Comparing 1995 and 2007, the production of the rare earth elements in China, Japan, and the US changed dramatically in quantities and structure. The information can provide a solid foundation for industries, academic institutions and governments to make decisions and develop strategies.