Echinococcus metacestodes as laboratory models for the screening of drugs against cestodes and trematodes

Among the cestodes, Echinococcus granulosus, Echinococcus multilocularis and Taenia solium represent the most dangerous parasites. Their larval stages cause the diseases cystic echinococcosis (CE), alveolar echinococcosis (AE) and cysticercosis, respectively, which exhibit considerable medical and veterinary health concerns with a profound economic impact. Others caused by other cestodes, such as species of the genera Mesocestoides and Hymenolepis, are relatively rare in humans. In this review, we will focus on E. granulosus and E. multilocularis metacestode laboratory models and will review the use of these models in the search for novel drugs that could be employed for chemotherapeutic treatment of echinococcosis. Clearly, improved therapeutic drugs are needed for the treatment of AE and CE, and this can only be achieved through the development of medium-to-high throughput screening approaches. The most recent achievements in the in vitro culture and genetic manipulation of E. multilocularis cells and metacestodes, and the accessability of the E. multilocularis genome and EST sequence information, have rendered the E. multilocularis model uniquely suited for studies on drug-efficacy and drug target identification. This could lead to the development of novel compounds for the use in chemotherapy against echinococcosis, and possibly against diseases caused by other cestodes, and potentially also trematodes.

I want to be my own person: the meaning-making and psychosocial processes of legacy students at Bucknell University

This study examined the meaning-making and psychosocial processes of five female legacy students at Bucknell University, each of whom having had at least one parent graduate from the institution. With a research philosophy, design, and methodology rooted in qualitative inquiry and phenomenology, inductive data analysis led to three primary categories that underscored legacy identity development. The first, Paradox of Influence and Identity, revealed through six themes nuanced experiences of separation-individuation. Second, Teaching and Learning, comprised of five themes, illuminated the impact of family — and of Bucknell parent alumni in particular — on their children’s internal working models. Lastly, Bucknell — the Environmental Contextand the five themes grouped therein highlighted the contributions of University community members, and of the campus culture and climate itself, to the co-construction of psychosocial formation. A tentative outline of grounded theory was offered, which explored categorical relationships; Paradox of Influence and Identity emerged as thedominant phenomenon, informing and being reinforced by the data of Teaching and Learning and Bucknell — the Environmental Context. Provisional intervention strategies for student affairs practice, in the contexts of academics, residential life, and career development, were discussed. Further, triangulated research is needed to substantiate and evolve the findings and theoretical model of this thesis.

USE OF HIDDEN MARKOV MODELS FOR QTL MAPPING

An important aspect of the QTL mapping problem is the treatment of missing genotype data. If complete genotype data were available, QTL mapping would reduce to the problem of model selection in linear regression. However, in the consideration of loci in the intervals between the available genetic markers, genotype data is inherently missing. Even at the typed genetic markers, genotype data is seldom complete, as a result of failures in the genotyping assays or for the sake of economy (for example, in the case of selective genotyping, where only individuals with extreme phenotypes are genotyped). We discuss the use of algorithms developed for hidden Markov models (HMMs) to deal with the missing genotype data problem.

Quantification of atelectatic lung volumes in two different porcine models of ARDS

BACKGROUND: Cyclic recruitment during mechanical ventilation contributes to ventilator associated lung injury. Two different pathomechanisms in acute respiratory distress syndrome (ARDS) are currently discussed: alveolar collapse vs persistent flooding of small airways and alveoli. We compare two different ARDS animal models by computed tomography (CT) to describe different recruitment and derecruitment mechanisms at different airway pressures: (i) lavage-ARDS, favouring alveolar collapse by surfactant depletion; and (ii) oleic acid ARDS, favouring alveolar flooding by capillary leakage. METHODS: In 12 pigs [25 (1) kg], ARDS was randomly induced, either by saline lung lavage or oleic acid (OA) injection, and 3 animals served as controls. A respiratory breathhold manoeuvre without spontaneous breathing at different continuous positive airway pressure (CPAP) was applied in random order (CPAP levels of 5, 10, 15, 30, 35 and 50 cm H(2)O) and spiral-CT scans of the total lung were acquired at each CPAP level (slice thickness=1 mm). In each spiral-CT the volume of total lung parenchyma, tissue, gas, non-aerated, well-aerated, poorly aerated, and over-aerated lung was calculated. RESULTS: In both ARDS models non-aerated lung volume decreased significantly from CPAP 5 to CPAP 50 [oleic acid lung injury (OAI): 346.9 (80.1) to 96.4 (48.8) ml, P<0.001; lavage-ARDS: 245 17.6) to 42.7 (4.8) ml, P<0.001]. In lavage-ARDS poorly aerated lung volume decreased at higher CPAP levels [232 (45.2) at CPAP 10 to 84 (19.4) ml at CPAP 50, P<0.001] whereas in OAI poorly aerated lung volume did not vary at different airway pressures. CONCLUSIONS: In both ARDS models well-aerated and non-aerated lung volume respond to different CPAP levels in a comparable fashion: Thus, a cyclical alveolar collapse seems to be part of the derecruitment process also in the OA-ARDS. In OA-ARDS, the increase in poorly aerated lung volume reflects the specific initial lesion, that is capillary leakage with interstitial and alveolar oedema.

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are illustrated with two examples and their validity for cases with practical sample sizes is demonstrated via a simulation study.

Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models

Suppose that we are interested in establishing simple, but reliable rules for predicting future t-year survivors via censored regression models. In this article, we present inference procedures for evaluating such binary classification rules based on various prediction precision measures quantified by the overall misclassification rate, sensitivity and specificity, and positive and negative predictive values. Specifically, under various working models we derive consistent estimators for the above measures via substitution and cross validation estimation procedures. Furthermore, we provide large sample approximations to the distributions of these nonsmooth estimators without assuming that the working model is correctly specified. Confidence intervals, for example, for the difference of the precision measures between two competing rules can then be constructed. All the proposals are illustrated with two real examples and their finite sample properties are evaluated via a simulation study.