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Background: HER2-positive breast cancer (BC) is a subtype of breast cancer. Increased ERBB3 expression has been implicated as a potential cause of resistance to other HER-targeted therapies. Our study aimed to screen and validate prognostic markers associated with ERBB3 expression by bioinformatics and affecting the prognosis of HER2 staging. Methods: Analyzing differences in ERBB3-related groups. ERBB3 expression-related differentially expressed genes (DEGs) were identified and intersected with survival status-related DEGs to obtain intersected genes. Three algorithms, LASSO, RandomForest and XGBoost were combined to identify the signature genes. we construct risk models and generate ROC curves for prediction. Furthermore, we delve into the immunological traits, correlations, and expression patterns of signature genes by conducting a comprehensive analysis that encompasses immune infiltration analysis, correlation analysis, and differential expression analysis. Results: Significant variability in ERBB3 expression and prognosis in high and low ERBB3 expression groups. Twenty-five candidate DEGs were identified by intersecting ERBB3-related DEGs with survival-related DEGs. Utilizing three distinct machine learning algorithms, we identified three signature genes-PBX1, IGHM, and CXCL13-that exhibited significant diagnostic value within the diagnostic model. In addition, the risk model had better prognostic and predictive effects, and the immune infiltration analysis showed that IGHM, CXCL13 might affect the proliferation of BC cells through immune cells. Functional studies demonstrated that interference with PBX1 inhibited the proliferation, migration, and epithelial-mesenchymal transition process of HER2-positive BC cells. Conclusion: PBX1, IGHM and CXCL13 are associated with the expression level of the ERBB3 and are prognostic markers for HER2-positive in BC, which may play an important role in the development and progression of BC. [ABSTRACT FROM AUTHOR]

Due to the merits of high flexibility, large driving force, and light weight, the pneumatic artificial muscle (PAM) has been widely applied in robot. However, the inherent asymmetric hysteresis nonlinear characteristics affect the control performance of PAM seriously. This paper presents an improved generalized Prandtl–Ishlinskii (GPI-M) model for the asymmetric hysteresis characterization of PAM, which adopts the simplified Magic Formula (SMF) as the envelop function instead of hyperbolic tangent function (GPI) or arc tangent function (GPI-A). With regard to the effect of parameters identification, the Levenberg-Marquardt (LM) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) algorithm are studied, which shows the LM algorithm is far superior to the other three intelligent algorithms in terms of computational efficiency and identification accuracy. Furthermore, a crosswise comparative study on three models and two envelop functions investigating modeling accuracy reveals that the newly introduced GPI-M model exhibits better performance than the modified symmetric generalized Prandtl-Ishlinskii (MSGPI) model and the GPI-A model presented previously. Meanwhile, the influence of the operator numbers on the modeling accuracy of three models is discussed, which finds that the GPI-M model possess the best accuracy with the least operator numbers. [ABSTRACT FROM AUTHOR]

Using oxalic acid and choline chloride as raw materials, the deep eutectic solvent was prepared by vacuum evaporation. The effect of the molar ratio of oxalic acid and choline chloride on the appearance and refractive index of the deep eutectic solvent, and the effect of temperature on the viscosity and conductivity of the deep eutectic solvent were studied. The results showed that the deep eutectic solvent prepared in this experiment were all yellowish, transparent, viscous and uniform stable liquid. With the decrease of the amount of substance ratio of oxalic acid and choline chloride, the refractive index of the deep eutectic solvent increased gradually. At the same amount of substance ratio of oxalic acid and choline chloride, the viscosity of the deep eutectic solvent decreased with the increase of temperature. The conductivity of the deep eutectic solvent increased with the increase of temperature. At the same temperature, the viscosity of the deep eutectic solvent increased with the decrease of the amount of substance ratio of oxalic acid and choline chloride, while the conductivity of the deep eutectic solvent increased first and then decreased with the decrease of the amount of substance ratio of oxalic acid and choline chloride. [ABSTRACT FROM AUTHOR]