Dexmedetomidine alleviates high glucose-induced podocyte damage by inhibiting EDA2R

Purpose: To investigate the effect and mechanism of action of dexmedetomidine (Dex) on podocyte injury. Methods: Cells were incubated with high glucose (50 mM) to induce a podocyte injury model in vitro. Cell viability, apoptosis, the expression of related protein related in podocyte injury and albumin permeability were evaluated by 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT), flow cytometry, western blot and Transwell assays. Results: Dex administration enhanced HG-induced cell viability and the relative protein expression of Bcl-2, but reduced the HG-induced relative protein level of Bax and apoptosisrate in podocytes (p < 0.05). Besides, Dex incubation compensated HG-induced relative protein expressions of nephrin and podocin in podocytes but did the reverse with regard to relative protein expression of desmin and albumin permeability (p < 0.05). Moreover, Dex treatment resulted in a decrease in ectodysplasin A2 receptor (EDA2R) expression in HG-induced podocytes. The level of EDA2R was upregulated by the transfection of overexpression plasmid containing the EDA2R sequences. Overexpression of EDA2R reversed Dex-induced increase in cell viability, apoptosis, expression of nephrin, podocin and desmin, as well as albumin permeability in HG-stimulated podocytes (p < 0.05). Conclusion: Dex ameliorates HG-induced podocyte injury via inhibition of EDA2R, indicating that Dex is a potential alternative drug for the treatment of podocyte injury

A Modified Nonlinear Conjugate Gradient Method for Engineering Computation

A general criterion for the global convergence of the nonlinear conjugate gradient method is established, based on which the global convergence of a new modified three-parameter nonlinear conjugate gradient method is proved under some mild conditions. A large amount of numerical experiments is executed and reported, which show that the proposed method is competitive and alternative. Finally, one engineering example has been analyzed for illustrative purposes

Genetic and phenotypic profiling of single living circulating tumour cells from patients with microfluidics

Accurate prediction of the efficacy of immunotherapy for cancer patients through the characterization of both genetic and phenotypic heterogeneity in individual patient cells holds great promise in informing targeted treatments, and ultimately in improving care pathways and clinical outcomes. Here, we describe the nanoplatform for interrogating living cell host-gene and (micro-)environment (NICHE) relationships, that integrates micro- and nanofluidics to enable highly efficient capture of circulating tumor cells (CTCs) from blood samples. The platform uses a unique nanopore-enhanced electrodelivery system that efficiently and rapidly integrates stable multichannel fluorescence probes into living CTCs for in situ quantification of target gene expression, while on-chip coculturing of CTCs with immune cells allows for the real-time correlative quantification of their phenotypic heterogeneities in response to immune checkpoint inhibitors (ICI). The NICHE microfluidic device provides a unique ability to perform both gene expression and phenotypic analysis on the same single cells in situ, allowing us to generate a predictive index for screening patients who could benefit from ICI. This index, which simultaneously integrates the heterogeneity of single cellular responses for both gene expression and phenotype, was validated by clinically tracing 80 non–small cell lung cancer patients, demonstrating significantly higher AUC (area under the curve) (0.906) than current clinical reference for immunotherapy prediction

Algoritmos para calcular probabilidades exactas de inclusión para un diseño de muestreo no rechazable pi*pt

AP-design, an efficient non-rejective implementation of the ps sampling design, was proposed in the literature as an alternative Poisson sampling scheme. In this paper, we have updated inclusion probabilities formulas in the AP sampling design. The formulas of these inclusion probabilities have been greatly simplified. The proposed results show that the AP design and the algorithms to calculate inclusion probabilities are simple and effective, and the design is possible to be used in practice. Three real examples have also been included to illustrate the performance of these designs.Una implementación del diseño de muestreo pt, que no es de rechazo, ha sido recientemente propuesta como alternativa al esquema de Poisson. En este trabajo, hemos adaptado las formulas de probabilidades de inclusión en el diseño de muestreo Poisson alternativo (AP por sus siglas en inglés). Estas fórmulas han sido significativamente simplificadas. Los resultados propuestos muestran que el diseño AP y los algoritmos para calcular las probabilidades de inclusión son simples y efectivos, y que el diseño se puede usar en la práctica. Se incluyen tres ejemplos reales para ilustrar el desempeño de la propuesta

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators

Algorithms to calculate exact inclusion probabilities for a non-rejective approximate ps sampling design

Una implementación del diseño de muestreo pt, que no es de rechazo,ha sido recientemente propuesta como alternativa al esquema de Poisson. Eneste trabajo, hemos adaptado las formulas de probabilidades de inclusión enel diseño de muestreo Poisson alternativo (AP por sus siglas en inglés). Estasfórmulas han sido significativamente simplificadas. Los resultados propuestosmuestran que el diseño AP y los algoritmos para calcular las probabilidadesde inclusión son simples y efectivos, y que el diseño se puede usar en lapráctica. Se incluyen tres ejemplos reales para ilustrar el desempeño de lapropuesta

Random search algorithm for solving the nonlinear Fredholm integral equations of the second kind.

In this paper, a randomized numerical approach is used to obtain approximate solutions for a class of nonlinear Fredholm integral equations of the second kind. The proposed approach contains two steps: at first, we define a discretized form of the integral equation by quadrature formula methods and solution of this discretized form converges to the exact solution of the integral equation by considering some conditions on the kernel of the integral equation. And then we convert the problem to an optimal control problem by introducing an artificial control function. Following that, in the next step, solution of the discretized form is approximated by a kind of Monte Carlo (MC) random search algorithm. Finally, some examples are given to show the efficiency of the proposed approach

E-Bayesian and H-Bayesian Inferences for a Simple Step-Stress Model with Competing Failure Model under Progressively Type-II Censoring

In this paper, we discuss the statistical analysis of a simple step-stress accelerated competing failure model under progressively Type-II censoring. It is assumed that there is more than one cause of failure, and the lifetime of the experimental units at each stress level follows exponential distribution. The distribution functions under different stress levels are connected through the cumulative exposure model. The maximum likelihood, Bayesian, Expected Bayesian, and Hierarchical Bayesian estimations of the model parameters are derived based on the different loss function. Based on Monte Carlo Simulations. We also get the average length and the coverage probability of the 95% confidence intervals and highest posterior density credible intervals of the parameters. From the numerical studies, it can be seen that the proposed Expected Bayesian estimations and Hierarchical Bayesian estimations have better performance in terms of the average estimates and mean squared errors, respectively. Finally, the methods of statistical inference discussed here are illustrated with a numerical example

Reliability analysis of inverse Gaussian processes with two-stage degenerate paths

For randomly degraded products undergoing a two-stage degradation process, traditional random effects models assume that the degradation rate follows a symmetrically normal distribution. However, certain products exhibit asymmetric degradation rates. In light of this, this paper proposes an approach for reliability analysis based on the inverse Gaussian (IG) degeneration process, which considers both asymmetric random effects and the two-stage nature simultaneously. To begin with, we establish a two-stage IG degradation process model that incorporates a skew normal random effect. Subsequently, we determine the location of change points using the Schwarz Information Criterion (SIC). The estimation of parameters is then conducted by combining Maximum Likelihood Estimations (MLEs) with the Genetic Algorithm (GA). Finally, we validate and demonstrate the practicality for the proposed model through Monte Carlo (MC) simulation and examples involving lithium batteries

A new exponential ratio-type estimator with linear combination of two auxiliary variables.

In sample surveys, it is usual to make use of auxiliary information to increase the precision of estimators. We propose a new exponential ratio-type estimator of a finite population mean using linear combination of two auxiliary variables and obtain mean square error (MSE) equation for proposed estimator. We find theoretical conditions that make proposed estimator more efficient than traditional multivariate ratio estimator using information of two auxiliary variables, the estimator of Bahl and Tuteja and the estimator proposed by Abu-Dayeh et al. In addition, we support these theoretical results with the aid of two numerical examples