Scaling of hysteresis loops at phase transitions into a quasiabsorbing state

Models undergoing a phase transition to an absorbing state weakly broken by the addition of a very low spontaneous nucleation rate are shown to exhibit hysteresis loops whose width $\Delta\lambda$ depends algebraically on the ramp rate $r$. Analytical arguments and numerical simulations show that $\Delta\lambda \sim r^{\kappa}$ with $\kappa = 1/(\beta'+1)$, where $\beta'$ is the critical exponent governing the survival probability of a seed near threshold. These results explain similar hysteresis scaling observed before in liquid crystal convection experiments. This phenomenon is conjectured to occur in a variety of other experimental systems.Comment: 4 pages, 4 figures, 1 tabl

Semiparametric Relative-risk Regression for Infectious Disease Data

This paper introduces semiparametric relative-risk regression models for infectious disease data based on contact intervals, where the contact interval from person i to person j is the time between the onset of infectiousness in i and infectious contact from i to j. The hazard of infectious contact from i to j is \lambda_0(\tau)r(\beta_0^T X_{ij}), where \lambda_0(\tau) is an unspecified baseline hazard function, r is a relative risk function, \beta_0 is an unknown covariate vector, and X_{ij} is a covariate vector. When who-infects-whom is observed, the Cox partial likelihood is a profile likelihood for \beta maximized over all possible \lambda_0(\tau). When who-infects-whom is not observed, we use an EM algorithm to maximize the profile likelihood for \beta integrated over all possible combinations of who-infected-whom. This extends the most important class of regression models in survival analysis to infectious disease epidemiology.Comment: 38 pages, 5 figure

The Bernstein-Von Mises Theorem in Semiparametric Competing Risks Models

Semiparametric Bayesian models are nowadays a popular tool in survival analysis. An important area of research concerns the investigation of frequentist properties of these models. In this paper, a Bernstein-von Mises theorem is derived for semiparametric Bayesian models of competing risks data. The cause-specific hazard is taken as the product of the conditional probability of a failure type and the overall hazard rate. We model the conditional probability as a smooth function of time and leave the cumulative overall hazard unspecified. A prior distribution is defined on the joint parameter space, which includes a beta process prior for the cumulative overall hazard. We show that the posterior distribution for any differentiable functional of interest is asymptotically equivalent to the sampling distribution derived from maximum likelihood estimation. A simulation study is provided to illustrate the coverage properties of credible intervals on cumulative incidence functions.Bayesian nonparametrics, Bernstein-von Mises theorem, beta process, competing risks, conditional probability of a failure type, semiparametric inference.

Beta-blockers have no impact on survival in pancreatic ductal adenocarcinoma prior to cancer diagnosis

Previous studies have suggested that β-adrenergic signaling may regulate the growth of various cancers. The aim of our study is to investigate the association between the incidental use of beta-blockers for various conditions on the overall survival of patients with pancreatic ductal adenocarcinoma (PDAC). Patients with histologically-confirmed PDAC between 2007 and 2011 were extracted from Surveillance, Epidemiology, and End Results registry (SEER)-Medicare linked database. Kaplan Meier and multivariable Cox Proportional-Hazard models were used to examine the association between beta-blocker usage before diagnosis and overall survival adjusting for appropriate confounders. As an additional analysis we also examined continuous beta-blocker use before and after diagnosis. From 2007 to 2011, 13,731 patients were diagnosed with PDAC. Of these, 7130 patients had Medicare Part D coverage in the 6-month period before diagnosis, with 2564 (36%) of these patients using beta-blockers in this period. Patients receiving beta-blockers had a mean survival time of 5.1 months compared to 6 months for non-users (p < 0.01). In multivariable analysis, beta-blockers usage was not associated with improved survival (Hazard Ratio (HR) 1.04, 95%, Confidence Interval (CI) 0.98–1.1, p = 0.2). When patients were stratified by conditions with indications for beta-blocker usage, such as hypertension, coronary artery disease and cardiac arrhythmia, differences in survival were insignificant compared to non-users in all groups (p > 0.05). After stratification by receptor selectivity, this lack of association with survival persisted (p > 0.05 for all). As a subgroup analysis, looking at patients with continuous Medicare Part D coverage who used beta-blockers in the 6-month period before and after cancer diagnosis, we identified 7085 patients, of which 1750 (24.7%) had continuous beta blocker use. In multivariable analysis, continuous beta-blockers usage was associated with improved survival (Hazard Ratio (HR) 0.86, 95%, Confidence Interval (CI) 0.8–0.9, p < 0.01). Beta-blocker usage before diagnosis does not confer a survival advantage in patients with PDAC, though continuous use before and after diagnosis did confer a survival advantage. Prospective studies into the mechanism for this advantage are needed

A Bayesian estimation on right censored survival data with mixture and non-mixture cured fraction model based on beta-Weibull distribution

Models for survival data that includes the proportion of individuals who are not subject to the event under study are known as a cure fraction models or simply called long-term survival models. The two most common models used to estimate the cure fraction are the mixture model and the non-mixture model. in this work, we present mixture and the non-mixture cure fraction models for survival data based on the beta-Weibull distribution. This four parameter distribution has been proposed as an alternative extension of the Weibull distribution in the analysis of lifetime data. This approach allows the inclusion of covariates in the models, where the estimation of the parameters was obtained under a Bayesian approach using Gibbs sampling methods

Intracranial AAV-IFN-beta gene therapy eliminates invasive xenograft glioblastoma and improves survival in orthotopic syngeneic murine model

The highly invasive property of glioblastoma (GBM) cells and genetic heterogeneity are largely responsible for tumor recurrence after the current standard-of-care treatment and thus a direct cause of death. Previously, we have shown that intracranial interferon-beta (IFN-beta) gene therapy by locally administered adeno-associated viral vectors (AAV) successfully treats noninvasive orthotopic glioblastoma models. Here, we extend these findings by testing this approach in invasive human GBM xenograft and syngeneic mouse models. First, we show that a single intracranial injection of AAV encoding human IFN-beta eliminates invasive human GBM8 tumors and promotes long-term survival. Next, we screened five AAV-IFN-beta vectors with different promoters to drive safe expression of mouse IFN-beta in the brain in the context of syngeneic GL261 tumors. Two AAV-IFN-beta vectors were excluded due to safety concerns, but therapeutic studies with the other three vectors showed extensive tumor cell death, activation of microglia surrounding the tumors, and a 56% increase in median survival of the animals treated with AAV/P2-Int-mIFN-beta vector. We also assessed the therapeutic effect of combining AAV-IFN-beta therapy with temozolomide (TMZ). As TMZ affects DNA replication, an event that is crucial for second-strand DNA synthesis of single-stranded AAV vectors before active transcription, we tested two TMZ treatment regimens. Treatment with TMZ prior to AAV-IFN-beta abrogated any benefit from the latter, while the reverse order of treatment doubled the median survival compared to controls. These studies demonstrate the therapeutic potential of intracranial AAV-IFN-beta therapy in a highly migratory GBM model as well as in a syngeneic mouse model and that combination with TMZ is likely to enhance its antitumor potency

Inhibition of TGF beta 1 and TGF beta 3 promotes hematopoiesis in Fanconi anemia

Fanconi anemia (FA) is a chromosome instability syndrome with congenital abnormalities, cancer predisposition and bone marrow failure (BMF). Although hematopoietic stem and progenitor cell (HSPC) transplantation is the recommended therapy, new therapies are needed for FA patients without suitable donors. BMF in FA is caused, at least in part, by a hyperactive growth-suppressive transforming growth factor beta (TGF beta) pathway, regulated by the TGF beta 1, TGF beta 2, and TGF beta 3 ligands. Accordingly, the TGF beta pathway is an attractive therapeutic target for FA. While inhibition of TGF beta 1 and TGF beta 3 promotes blood cell expansion, inhibition of TGF beta 2 is known to suppress hematopoiesis. Here, we report the effects of AVID200, a potent TGF beta 1- and TGF beta 3-specific inhibitor, on FA hematopoiesis. AVID200 promoted the survival of murine FA HSPCs in vitro. AVID200 also promoted in vitro the survival of human HSPCs from patients with FA, with the strongest effect in patients progressing to severe aplastic anemia or myelodysplastic syndrome (MDS). Previous studies have indicated that the toxic upregulation of the nonhomologous end-joining (NHEJ) pathway accounts, at least in part, for the poor growth of FA HSPCs. AVID200 downregulated the expression of NHEJ-related genes and reduced DNA damage in primary FA HSPC in vitro and in in vivo models. Collectively, AVID200 exhibits activity in FA mouse and human preclinical models. AVID200 may therefore provide a therapeutic approach to improving BMF in FA. (c) 2020 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.Peer reviewe