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Objective To explore the expression, correlation with clinicopathologic parameters, and clinical significance of MIS18 binding protein 1 (MIS18BP1) in bladder cancer. Methods TCGA and GEO databases were used to analyze the mRNA expression of MIS18BP1 in tumors and controls, and the results were verified via qRT-PCR. UALCAN online database was utilized in the analysis of the expression of MIS18BP1 and its correlation with clinicopathological parameters and the degree of immune cell infiltration. Immunohistochemistry was employed to analyze the expression of MIS18BP1 in bladder cancer and its relationship with clinicopathological features. The ROC curve was applied to evaluate the diagnostic value of MIS18BP1 mRNA in bladder cancer. Results Bioinformatics analysis and qRT-PCR results revealed the increased expression of MIS18BP1 mRNA in bladder cancer compared with that in the control group (P

Objective: The effect of tea seed saponin (TSS) combined with aerobic exercise (AE) on lipid metabolism and oxidative stress in mice on a high-fat diet (HFD) was investigated. Methods: Mice were randomly divided into a normal group and a model group. The normal group was fed on a maintenance diet and the model group on the HFD to establish an obesity model. After successful modeling, the mice were further divided into an HFD group, an HFD + TSS group, an HFD + AE group and an HFD + TSS-AE group. The levels of serum lipid metabolism and oxidative stress were determined by commercial kits. Hepatic steatosis was observed by hematoxylin-eosin (HE) staining. The protein levels of adenosine 5’-monophosphate (AMP)-activated protein kinase (AMPK), phosphorylated AMPK (p-AMPK), silent information regulator 1 (SIRT1), peroxisome proliferator-activated receptor γ coactlvator 1α (PGC-1α) and peroxisome proliferators-activated receptors γ (PPAR-γ) were detected by Western blot. Results: TSS combined with AE treatment significantly reduced body mass and subcutaneous, epididymal, perirenal and abdominal fat indexes in HFD mice (P < 0.05) and improved blood lipid disorders. The serum levels of total cholesterol (TC), triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) were, respectively, reduced by 27.80%, 75.15%, 78.36% in the HFD + TSS + AE group compared with the HFD group. Hepatic steatosis was significantly improved in the HFD + TSS, HFD + AE and HFD + TSS-AE groups in comparison with the HFD group. The expression level of hepatic p-AMPK was increased by 64.19%, 2.12 times and 2.64 times, SIRT1 by 47.01%, 1.48% and 67.02%, PGC-1α by 2.03 times, 62.93% and 2.10 times, and PPAR-γ in skeletal muscle by 64.08%, 2.12 times and 3.34 times, respectively. After treated by TSS and AE, the serum and liver levels of superoxide dismutase (SOD) and glutathione (GSH) were increased in HFD mice, and serum malondialdehyde (MDA) levels were decreased. Conclusion: TSS combined with AE can reduce fat accumulation and improve lipid metabolism disorders and oxidative stress induced by HFD in mice.

In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments. [ABSTRACT FROM AUTHOR]