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Accelerated life test provides a feasible and effective way to rapidly derive lifetime information by exposing products to higher-than-normal operating conditions. However, most of the previous research on accelerated life test has focused on the application of a single stress factor and a traditional censoring scheme. This article considers the reliability inference for a dual constant-stress accelerated life test model with exponential distribution and progressively Type-II censoring. Point estimates for model parameters are provided using maximum likelihood estimation and the weighted least squares method based on random variable transformation. In addition, we construct asymptotic confidence intervals, approximate confidence intervals, and bootstrap confidence intervals for the parameters of interest. Finally, extensive simulation studies and an illustrative example are presented to investigate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

To inference the reliability of product at normal stress conditions, accelerated life test (ALT) is usually used to obtain failure information rapidly. However, most of the researches on ALT are focused on the statistical inference with single stress, which makes it difficult to gain accurate reliability through applying ALT method in single stress case. In this article, statistical inference is considered on dual constant-stress accelerated life test (DCSALT) model for the Weibull distribution with Type-I censored data. On the assumption that the Weibull distribution has constant shape parameter, and a log-linear acceleration model between its scale parameter and the test stress level combination. Using the profile maximum likelihood estimation (PMLE) method to estimate the unknown parameters in DCSALT model for Weibull distribution, and this process is completed by Newton-Raphson algorithm. Moreover, the observed information matrix is obtained in order to construct asymptotic confidence intervals of the unknown parameters. Finally, the numerical study is presented to illustrate the effectiveness of the maximum likelihood estimators (MLEs). [ABSTRACT FROM AUTHOR]

Multidrug resistance (MDR) of cancer cells is a significant challenge in chemotherapy, highlighting the urgent medical need for simple and reproducible strategies to reverse this process. Here, we report the development of an active tumor-targeting and redox-responsive nanoplatform (PA-ss-NP) using hyaluronic acid-g-cystamine dihydrochloride-poly-ε-(benzyloxycarbonyl)-L-lysine (HA-ss-PLLZ) to co-deliver paclitaxel (PTX) and apatinib (APA) for effective reversal of MDR. This smart nanoplatform specifically bound to CD44 receptors, leading to selective accumulation at the tumor site and uptake by MCF-7/ADR cells. Under high concentrations of cellular glutathione (GSH), the nanocarrier was degraded rapidly with complete release of its encapsulated drugs. Released APA effectively inhibited the function of the P-glycoprotein (P-gp) drug pump and improved the sensitivity of MDR cells to chemotherapeutic agents, leading to the recovery of PTX chemosensitivity in MDR cells. As expected, this newly developed intelligent drug delivery system could effectively control MDR, both in vitro and in vivo. [ABSTRACT FROM AUTHOR]