The Copper-cysteamine (Cu-Cy) nanoparticle is a novel sensitizer with a potential to increase the effectiveness of radiation therapy for cancer treatment. In this work, the effect of nanoparticle size and the energy of X-rays on the effectiveness of radiation therapy are investigated. The effect of the particle size on their performance is very complicated. The nanoparticles with an average size of 300 nm have the most intense photoluminescence, the nanoparticles with the average size of 100 nm have the most reactive oxygen species production upon X-ray irradiation, while the nanoparticles with the average size of 40 nm have the best outcome in the tumor suppression in mice upon X-ray irradiation. For energy, 90 kVp radiation resulted in smaller tumor sizes than 250 kVp or 350 kVp radiation energies. Overall, knowledge of the effect of nanoparticle size and radiation energy on radiation therapy outcomes could be useful for future applications of Cu-Cy nanoparticles.

This study explores natural direct and joint natural indirect effects (JNIE) of prenatal opioid exposure on neurodevelopmental disorders (NDDs) in children mediated through pregnancy complications, major and minor congenital malformations, and adverse neonatal outcomes, using Medicaid claims linked to vital statistics in Rhode Island, United States, 2008–2018. A Bayesian mediation analysis with elastic net shrinkage prior was developed to estimate mean time to NDD diagnosis ratio using posterior mean and 95% credible intervals (CrIs) from Markov chain Monte Carlo algorithms. Simulation studies showed desirable model performance. Of 11,176 eligible pregnancies, 332 had ≥2 dispensations of prescription opioids anytime during pregnancy, including 200 (1.8%) having ≥1 dispensation in the first trimester (T1), 169 (1.5%) in the second (T2), and 153 (1.4%) in the third (T3). A significant JNIE of opioid exposure was observed in each trimester (T1, JNIE = 0.97, 95% CrI: 0.95, 0.99; T2, JNIE = 0.97, 95% CrI: 0.95, 0.99; T3, JNIE = 0.96, 95% CrI: 0.94, 0.99). The proportion of JNIE in each trimester was 17.9% (T1), 22.4% (T2), and 56.3% (T3). In conclusion, adverse pregnancy and birth outcomes jointly mediated the association between prenatal opioid exposure and accelerated time to NDD diagnosis. The proportion of JNIE increased as the timing of opioid exposure approached delivery.

Human Immunodeficiency Virus (HIV) interventions among people who use drugs (PWUD) often have spillover, also known as interference or dissemination, which occurs when one participant’s exposure affects another participant’s outcome. PWUD are often members of networks defined by social, sexual, and drug-use partnerships and their receipt of interventions can affect other members in their network. For example, HIV interventions with possible spillover include educational training about HIV risk reduction, pre-exposure prophylaxis, or treatment as prevention. In turn, intervention effects frequently depend on the network structure, and intervention coverage levels and spillover can occur even if not measured in a study, possibly resulting in an underestimation of intervention effects. Recent methodological approaches were developed to assess spillover in the context of network-based studies. This tutorial provides an overview of different study designs for network-based studies and related methodological approaches for assessing spillover in each design. We also provide an overview of other important methodological issues in network studies, including causal influence in networks and missing data. Finally, we highlight applications of different designs and methods from studies of PWUD and conclude with an illustrative example from the Transmission Reduction Intervention Project (TRIP) in Athens, Greece.

Missing data are frequently encountered in longitudinal clinical trials. To better monitor and understand the progress over time, one must handle the missing data appropriately and examine whether the missing data mechanism is ignorable or nonignorable. In this article, we develop a new probit model for longitudinal binary response data. It resolves a challenging issue for estimating the variance of the random effects, and substantially improves the convergence and mixing of the Gibbs sampling algorithm. We show that when improper uniform priors are specified for the regression coefficients of the joint multinomial model via a sequence of one-dimensional conditional distributions for the missing data indicators under nonignorable missingness, the joint posterior distribution is improper. A variation of Jeffreys prior is thus established as a remedy for the improper posterior distribution. In addition, an efficient Gibbs sampling algorithm is developed using a collapsing technique. Two model assessment criteria, the deviance information criterion (DIC) and the logarithm of the pseudomarginal likelihood (LPML), are used to guide the choices of prior specifications and to compare the models under different missing data mechanisms. We report on extensive simulations conducted to investigate the empirical performance of the proposed methods. The proposed methodology is further illustrated using data from an HIV prevention clinical trial.

BACKGROUND: Low testosterone at the time of diagnosis of prostate cancer has been associated with a worse prognosis. Whether this is true and how to define the best treatment approach at the time of first prostate‐specific antigen (PSA) failure to the authors' knowledge has not been elucidated to date and was studied herein.METHODS: Between 1995 and 2001, a total of 58 men with unfavorable‐risk PC who were treated on clinical trials with radiotherapy and androgen deprivation therapy (ADT) had available testosterone levels at the time of PSA failure. Cox and Fine and Gray regressions were performed to ascertain whether low versus normal testosterone was associated with the risk of PC‐specific mortality, other‐cause mortality, and all‐cause mortality adjusting for age, salvage ADT, and known PC prognostic factors.RESULTS: After a median follow‐up of 6.68 years after PSA failure, 31 men (53.4%) had died; 10 of PC (32.3%), of which 8 of 11 (72.7%) versus 2 of 47 (4.3%) deaths occurred in men with low versus normal testosterone at the time of PSA failure, respectively. A significant increase in the risk of all‐cause mortality (adjusted hazard ratio [AHR], 2.54; 95% confidence interval [95% CI], 1.04‐6.21 [P = .04]) and PC‐specific mortality (AHR, 13.71; 95% CI, 2.4‐78.16 [P = .003]), with a reciprocal trend toward a decreased risk of other‐cause mortality (AHR, 0.18; 95% CI, 0.02‐1.55 [P = .12]) was observed in men with low versus normal testosterone.CONCLUSIONS: Low, but not necessarily castrate, testosterone levels at the time of PSA failure confer a very poor prognosis. These observations provide evidence to support testosterone testing at the time of PSA failure. Given prolonged survival when abiraterone or docetaxel is added to ADT in men with castrate‐sensitive metastatic PC and possibly localized high‐risk PC provides a rationale supporting their use with ADT in men with low testosterone in the setting of a phase 2 trial. Cancer 2018;124:1383‐90. © 2017 American Cancer Society.