Abstract Objectives The pathological mechanisms of patients with Renal Cell Carcinoma (RCC) remain defined. This study aimed to evaluate relationships between the landscape of gene mutations and their clinical significance in RCC patients. Methods Tissue and peripheral blood samples of 42 patients with RCC were collected and performed for the Next Generation Sequencing (NGS) with Geneseeq PrimeTM 425-gene panel probes. Their landscapes of gene mutation were analyzed. We also carried out an evaluation of Tumor-Node-Metastasis (TNM) staging, RENAL nephelometry score, surgery, and targeted drug treatment of patients. Then we compared the correlations of landscape in gene mutations and the prognosis. Results The most common gene alternations, including BAP1, PBRM1, SETD2, CSF1R, NPM1, EGFR, POLE, RB1, and VHL genes, were identified in tissue and blood samples of 75% of patients. EGFR, POLE, and RB1 gene mutations frequently occurred in relapsed and metastatic patients. BAP1, CCND2, KRAS, PTPN11, ERBB2/3, JAK2, and POLE were presented in the patients with > 9 RENAL nephelometry score. Univariable analysis indicated that SETD2, BAP1, and PBRM1 genes were key factors for Disease-Free Survival (DFS). Multivariable analysis confirmed that mutated SETD1, NPM1, and CSF1R were critical factors for the Progression Free Survival (PFS) of RCC patients with target therapy. Conclusions Wild-type PBRM1 and mutated BAP1 in patients with RCC were strongly associated with the outcomes of the patient. The PFS of the patients with SETD2, NPM1, and CSF1R mutations were significantly shorter than those patients without variants.

Abstract As an alternative type of tea product, tartary buckwheat tea (TBT) has become popular in the current market because of its rich taste, natural healthiness and convenience of use. Because sensory evaluation is easily affected by the individual differences and external factors that affect human assessors, the present study used intelligent sensory systems (electronic nose, electronic tongue and electronic eye) to analyze different types of TBT to establish a scientific basis for its quality characteristics. Results showed that obvious differences in color, aroma and taste were found among the different types of teas. The taste differences in the TBT were mainly acquired by the CTS sensor (saltiness) and ANS sensor (sweetness), with the greatest number of volatile substance was detected in the whole plant tea. Overall, intelligent sensory technology could accurately evaluate TBT thus providing the capability to meet the needs of different types of consumers and of different occasions.

Abstract Ginkgo biloba leaves are commonly used in traditional Chinese medicine. Owing to its wide distribution across China with varying geographical environments and climatic characteristics, G. biloba leaf products have different active ingredients and degrees of activities, which poses a challenge for quality control and standardization. We aimed to identify the key factors contributing to the quality of G. biloba based on assessment of anti-aging and antioxidative components. G. biloba leaf samples collected from 14 different regions in China were analyzed for antioxidant activities by the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radical scavenging assay, and the levels of terpene lactones and flavonoids were quantified by ultra-performance liquid chromatography-tandem mass spectroscopy and high performance liquid chromatography. Correlation analysis attributed the antioxidant activity of G. biloba leaf samples of different origins to isoginkgetin and ginkgetin, which also affected the matrix metalloproteinase-1 levels of fibroblasts, determined using enzyme-linked immunosorbent assay and reverse transcription-polymerase chain reaction. Therefore, appropriate quality control of a collected G. biloba sample could be achieved by determining the levels of isoginkgetin and ginkgetin as anti-aging and antioxidant components. The results further provide a foundation for the application of G. biloba leaves as an antioxidant raw material.

Abstract Background: Atherosclerosis (AS) is the main pathological basis of coronary heart disease, cerebral infarction and peripheral vascular disease, which seriously endanger people's life and health. In recent years, long non-coding RNA (lncRNA) has been found to be involved in gene expression regulation, but the research on AS is still in the initial stage. In this study, we mainly studied the role of HCG11 in patients with AS. Quantitative Real-time Polymerase Chain Reaction (QRT-PCR) was used to detect the expression of HCG11 and miR-144 in the serum of AS patients and healthy volunteers. Oxidation Low Lipoprotein (Ox-LDL), interleukin-6 (IL-6) and tumor necrosis factor α (TNF α) radiation were used to establish human vascular smooth muscle cells (VSMCs) in vitro model. Cell proliferation was determined by Cell Counting Kit-8 (CCK-8) assay. The apoptosis rate was determined by flow cytometry (FACS) and terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay (TUNEL) staining. The expression levels of Forkhead box protein F1 (FOXF1), B cell lymphoma-2 (Bcl-2) and BCL2-Associated X (Bax) were detected by qRT-PCR. Luciferase gene reporter and RNA pull down experiments confirmed the relationship between HCG11 and miR-144, miR-144 and FOXF1. Results: This study showed that HCG11 was significantly upregulated in patients with AS, while miR-144 was down-regulated in patients with AS. Ox-LDL and IL-6 in VSMCs induced up-regulation of HCG11 and down-regulation of miR-144. Overexpression of HCG11 promoted the proliferation and inhibited apoptosis of VSMCs. Luciferase gene reporter gene assay showed that HCG11 could bind to miR-144, and miR-144 could bind to FOXF1. Overexpression of miR-144 reversed the effect of HCG11 on VSMCs. Conclusions: LncRNA HCG11 regulates proliferation and apoptosis of vascular smooth muscle cell through targeting miR-144-3p/FOXF1 axis.

Efficient evaluation of biotoxicity of organics is of vital significance to resource utilization and environmental protection. In this study, toxicity of 110 alcohol compounds to tadpoles of Rana temporaria is adopted as the dependent variable and 1388 physiochemical parameters (features) calculated by PCLIENT are used for representing each compound. A feature selection pipeline with three steps is developed to refine the feature subset: 282 features that significantly correlated with biotoxicity of chemical compounds are preliminarily selected via the maximum information coefficient (MIC); 138 descriptors that have positive contribution to the model’s performance are reserved after a support vector regression (SVR) based backward elimination; 18 descriptors are finally selected via a forward selection process that integrated minimal redundancy maximal relevance (mRMR), MIC and SVR. In terms of feature subsets with different numbers of variables, quantitative structure activity relationship (QSAR) models are built using multiple linear regression (MLR), partial least square regression (PLS) and SVR, respectively. The independent prediction evaluation index, Q2, increases from -74.787, 0.824 and 0.868 to 0.892, 0.878 and 0.940, for the three regression models, respectively. Results suggest that nonlinear feature selection methods involved in MIC and SVR can effectively eliminate irrelevant descriptors. SVR outperforms classical statistical models to QSAR modeling on high-dimensional data containing nonlinear relationship between features. The methods proposed in this study have a potential application in the QSAR research field such as biotoxicity compounds.