In vivo knockdown of the androgen receptor results in growth inhibition and regression of well-established, castration-resistant prostate tumours

Purpose: Progression to the castration-resistant state is the incurable and lethal end stage of prostate cancer, and there is strong evidence that androgen receptor (AR) still plays a central role in this process. We hypothesize that knocking down AR will have a major effect on inhibiting growth of castration-resistant tumors. Experimental Design: Castration-resistant C4-2 human prostate cancer cells stably expressing a tetracycline-inducible AR-targeted short hairpin RNA (shRNA) were generated to directly test the effects of AR knockdown in C4-2 human prostate cancer cells and tumors. Results:In vitro expression of AR shRNA resulted in decreased levels of AR mRNA and protein, decreased expression of prostate-specific antigen (PSA), reduced activation of the PSA-luciferase reporter, and growth inhibition of C4-2 cells. Gene microarray analyses revealed that AR knockdown under hormone-deprived conditions resulted in activation of genes involved in apoptosis, cell cycle regulation, protein synthesis, and tumorigenesis. To ensure that tumors were truly castration-resistant in vivo, inducible AR shRNA expressing C4-2 tumors were grown in castrated mice to an average volume of 450 mm3. In all of the animals, serum PSA decreased, and in 50% of them, there was complete tumor regression and disappearance of serum PSA. Conclusions: Whereas castration is ineffective in castration-resistant prostate tumors, knockdown of AR can decrease serum PSA, inhibit tumor growth, and frequently cause tumor regression. This study is the first direct evidence that knockdown of AR is a viable therapeutic strategy for treatment of prostate tumors that have already progressed to the castration-resistant state.

Iteratively specified tree-based regression : theory and trip generation example

Focuses on a study which introduced an iterative modeling method that combines properties of ordinary least squares (OLS) with hierarchical tree-based regression (HTBR) in transportation engineering. Information on OLS and HTBR; Comparison and contrasts of OLS and HTBR; Conclusions.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

Good results for early treatment of clinically isolated syndrome prior to multiple sclerosis with interferon beta-1b and glatiramer

Background: The first sign of developing multiple sclerosis is a clinically isolated syndrome that resembles a multiple sclerosis relapse. Objective/methods: The objective was to review the clinical trials of two medicines in clinically isolated syndromes (interferon β and glatiramer acetate) to determine whether they prevent progression to definite multiple sclerosis. Results: In the BENEFIT trial, after 2 years, 45% of subjects in the placebo group developed clinically definite multiple sclerosis, and the rate was lower in the interferon β-1b group. Then all subjects were offered interferon β-1b, and the original interferon β-1b group became the early treatment group, and the placebo group became the delayed treatment group. After 5 years, the number of subjects with clinical definite multiple sclerosis remained lower in the early treatment than late treatment group. In the PreCISe trial, after 2 years, the time for 25% of the subjects to convert to definite multiple sclerosis was prolonged in the glatiramer group. Conclusions: Interferon β-1b and glatiramer acetate slow the progression of clinically isolated syndromes to definite multiple sclerosis. However, it is not known whether this early treatment slows the progression to the physical disabilities experienced in multiple sclerosis.

A two-compartment mechanochemical model of the roles of transforming growth factor-beta and tissue tension in dermal wound healing

The repair of dermal tissue is a complex process of interconnected phenomena, where cellular, chemical and mechanical aspects all play a role, both in an autocrine and in a paracrine fashion. Recent experimental results have shown that transforming growth factor-beta (TGF-beta) and tissue mechanics play roles in regulating cell proliferation, differentiation and the production of extracellular materials. We have developed a 1D mathematical model that considers the interaction between the cellular, chemical and mechanical phenomena, allowing the combination of TGF-beta and tissue stress to inform the activation of fibroblasts to myofibroblasts. Additionally, our model incorporates the observed feature of residual stress by considering the changing zero-stress state in the formulation for effective strain. Using this model, we predict that the continued presence of TGF-beta in dermal wounds will produce contractures due to the persistence of myofibroblasts; in contrast, early elimination of TGF-beta significantly reduces the myofibroblast numbers resulting in an increase in wound size. Similar results were obtained by varying the rate at which fibroblasts differentiate to myofibroblasts and by changing the myofibroblast apoptotic rate. Taken together, the implication is that elevated levels of myofibroblasts is the key factor behind wounds healing with excessive contraction, suggesting that clinical strategies which aim to reduce the myofibroblast density may reduce the appearance of contractures.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.