Models for Trypanosoma evansi (surra), its control and economic impact on small-hold livestock owners in the Philippines

Simple demographic and infectious disease models of buffaloes and other domestic hosts for animal trypanosomosis (surra) caused by Trypanosoma evansi were developed. The animal models contained deterministic and stochastic elements and were linked to simulate the benefit of control regimes for surra in village domestic animal populations in Mindanao, Philippines. The impact of the disease on host fertility and mortality were key factors in determining the economic losses and net-benefit from the control regimes. If using a high (99%) efficacy drug in surra-moderate to high risk areas, then treating all animals twice each year yielded low prevalence in 2 years; targeted treatment of clinically sick animals, constantly monitored (monthly), required 75% fewer treatments but took longer to reach a low prevalence than treating all animals twice each year. At high drug efficacy both of these treatment strategies increased the benefit over untreated animals by 81%. If drug efficacy declined then the benefit obtained from twice yearly treatment of all animals declined rapidly compared with regular monitoring and targeting treatment to clinically sick animals. The current control regimen applied in the Philippines of annual sero-testing for surra and only treating sero-positive animals provided the lowest net-benefit of all the control options simulated and would not be regarded as effective control. The total net-benefit from effective surra control for a typical village in a moderate/high risk area was 7.9 million pesos per annum (US $158,000). The value added to buffaloes, cattle, horses, goats/sheep and pigs as a result of this control was US $88, $84, $151, $7, $114 per animal/year, respectively.     

Individual-based model of spatio-temporal dynamics of mixed forest stands

This research presents the results of constructing and parameterizing an individual-based model of spatiotemporal dynamics of mixed forest stands. The model facilitates computerized experiments with forest stands having different combinations of species and age structures. These forest stands grow on temperate areas where light is the main system-forming factor that shapes and develops forest ecosystems. The model TEMFORM (TEMperate FORests Model) is developed with few equations and parameters, most of which can be estimated using standard forest inventory data. Parameterization of the model used the growth tables of a set of basic forest-forming species in Far East Russia. Simulation results of the development of the natural single- and mixed-species stands and the effects of different types of disturbances on the stand dynamics and compositions are presented.     

Computational Oral Absorption Simulation for Low‐Solubility Compounds

Bile micelles play an important role in oral absorption of low‐solubility compounds. Bile micelles can affect solubility, dissolution rate, and permeability. For the pH–solubility profile in bile micelles, the Henderson–Hasselbalch equation should be modified to take bile‐micelle partition into account. For the dissolution rate, in the Nernst–Brunner equation, the effective diffusion coefficient in bile‐micelle media should be used instead of the monomer diffusion coefficient. The diffusion coefficient of bile micelles is 8‐ to 18‐fold smaller than that of monomer molecules. For permeability, the effective diffusion coefficient in the unstirred water layer adjacent to the epithelial membrane, and the free fraction at the epithelial membrane surface should be taken into account. The importance of these aspects is demonstrated here using several in vivo and clinical oral‐absorption data of low‐solubility model compounds. Using the theoretical equations, the food effect on oral absorption is further discussed.     

Stimulator of interferon genes (STING): A “new chapter” in virus-associated cancer research. Lessons from wild-derived mouse models of innate immunity

Thanks to the numerous studies that have been carried out recently in the field of cytosolic DNA sensing, STING (Stimulator of Interferon Genes) is now recognized as a key mediator of innate immune signaling. A substantial body of evidence derived from in vivo mouse models demonstrates that STING-regulated pathways underlie the pathogenesis of many diseases including infectious diseases and cancers. It has also become evident from these studies that STING is a promising therapeutic target for the treatment of cancer. However, mouse strains commonly used for modelling innate immune response against infections or tumors do not allow investigators to accurately reproduce certain specific characteristics of immune response observed in human cells. In this review, we will discuss recent data demonstrating that the use of wild-derived genetically distinct inbred mice as a model for investigation into the innate immunity signaling networks may provide valuable insight into the STING-regulated pathways specific for human cells. The maximum complexity of STING-mediated mechanisms can probably be seen in case of DNA virus-induced carcinogenesis in which STING may perform unexpected biological activities. Therefore, in another part of this review we will summarize emerging data on the role of STING in human DNA virus-related oncopathologies, with particular attention to HPV-associated cervical cancer, aiming to demonstrate that STING indeed “starts a new chapter” in research on this issue and that wild-derived mouse models of STING-mediated response to infections will probably be helpful in finding out molecular basis for clinical observations.     

Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data

Summary .  We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a   ℓ₂   penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial.