Sequence capture and next-generation resequencing of multiple tagged nucleic acid samples for mutation screening of urea cycle disorders

Molecular genetic testing is commonly used to confirm clinical diagnoses of inherited urea cycle disorders (UCDs); however, conventional mutation screenings encompassing only the coding regions of genes may not detect disease-causing mutations occurring in regulatory elements and introns. Microarray-based target enrichment and next-generation sequencing now allow more-comprehensive genetic screening. We applied this approach to UCDs and combined it with the use of DNA bar codes for more cost-effective, parallel analyses of multiple samples.

Primary bovine synoviocyte cultures: a useful tool for in vitro drug testing?

The aim of the study was to evaluate bovine synoviocyte culture as an in vitro model to test new intra-articular drugs. The inflammatory reaction pattern of synoviocytes as compared to fibroblasts was studied over nine passages. Expression of pro-inflammatory cytokines was assessed after stimulation with lipopolysaccharide. Immunohistochemical markers were used to identify synoviocyte populations. Primary synoviocytes expressed markedly higher amounts of interleukin-1beta mRNA and tumour necrosis factor-alpha mRNA than fibroblasts after stimulation. This difference was lost over two passages. CD68-positive macrophage-like synoviocytes diminished over three passages, which may explain the reduced pro-inflammatory cytokine response. Primary bovine synoviocytes appear to be an appropriate and optimised model for testing novel drugs for cattle, because their response may more closely reflect in vivo tissue responses compared to cultured cell lines.

Generation of HIV latency in humanized BLT mice

Here we demonstrate that a combination of tenofovir, emtricitabine, and raltegravir effectively suppresses peripheral and systemic HIV replication in humanized BLT mice. We also demonstrate that antiretroviral therapy (ART)-treated humanized BLT mice harbor latently infected resting human CD4+ T cells that can be induced ex vivo to produce HIV. We observed that the levels of infected resting human CD4+ T cells present in BLT mice are within the range of those observed circulating in patients undergoing suppressive ART. These results demonstrate the potential of humanized BLT mice as an attractive model for testing the in vivo efficacy of novel HIV eradication strategies.

A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations

In the context of expensive numerical experiments, a promising solution for alleviating the computational costs consists of using partially converged simulations instead of exact solutions. The gain in computational time is at the price of precision in the response. This work addresses the issue of fitting a Gaussian process model to partially converged simulation data for further use in prediction. The main challenge consists of the adequate approximation of the error due to partial convergence, which is correlated in both design variables and time directions. Here, we propose fitting a Gaussian process in the joint space of design parameters and computational time. The model is constructed by building a nonstationary covariance kernel that reflects accurately the actual structure of the error. Practical solutions are proposed for solving parameter estimation issues associated with the proposed model. The method is applied to a computational fluid dynamics test case and shows significant improvement in prediction compared to a classical kriging model.

A Stratified Model for Psychosis Prediction in Clinical Practice

Objective: Impaired cognition is an important dimension in psychosis and its at-risk states. Research on the value of impaired cognition for psychosis prediction in at-risk samples, however, mainly relies on study-specific sample means of neurocognitive tests, which unlike widely available general test norms are difficult to translate into clinical practice. The aim of this study was to explore the combined predictive value of at-risk criteria and neurocognitive deficits according to test norms with a risk stratification approach. Method: Potential predictors of psychosis (neurocognitive deficits and at-risk criteria) over 24 months were investigated in 97 at-risk patients. Results: The final prediction model included (1) at-risk criteria (attenuated psychotic symptoms plus subjective cognitive disturbances) and (2) a processing speed deficit (digit symbol test). The model was stratified into 4 risk classes with hazard rates between 0.0 (both predictors absent) and 1.29 (both predictors present). Conclusions: The combination of a processing speed deficit and at-risk criteria provides an optimized stratified risk assessment. Based on neurocognitive test norms, the validity of our proposed 3 risk classes could easily be examined in independent at-risk samples and, pending positive validation results, our approach could easily be applied in clinical practice in the future.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Is a grooved collar implant design superior to a machined design regarding bone level alteration? An observational pilot study.

OBJECTIVE This retrospective observational pilot study examined differences in peri-implant bone level changes (ΔIBL) between two similar implant types differing only in the surface texture of the neck. The hypothesis tested was that ΔIBL would be greater with machined-neck implants than with groovedneck implants. METHOD AND MATERIALS 40 patients were enrolled; n = 20 implants with machined (group 1) and n = 20 implants with a rough, grooved neck (group 2), all placed in the posterior mandible. Radiographs were obtained after loading (at 3 to 9 months) and at 12 to 18 months after implant insertion. Case number calculation with respect to ΔIBL was conducted. Groups were compared using a Brunner-Langer model, the Mann-Whitney test, the Wilcoxon signed rank test, and linear model analysis. RESULTS After the 12- to 18-month observation period, mean ΔIBL was -1.11 ± 0.92 mm in group 1 and -1.25 ± 1.23 mm in group 2. ΔIBL depended significantly on time (P < .001), but not on group. In both groups, mean marginal ΔIBL was significantly less than -1.5 mm. Only insertion depth had a significant influence on the amount of periimplant bone loss (P = .013). Case number estimate testing for a difference between group 1 and 2 with a power of 90% revealed a sample size per group of 1,032 subjects. CONCLUSION ΔIBL values indicated that both implant designs fulfilled implant success criteria, and the modification of implant neck texture had no significant influence on ΔIBL.

In vitro-ex vivo model systems for nanosafety assessment

Engineered nanomaterials have unique and novel properties enabling wide-ranging new applications in nearly all fields of research. As these new properties have raised concerns about potential adverse effects for the environment and human health, extensive efforts are underway to define reliable, cost- and time-effective, as well as mechanistic-based testing strategies to replace the current method of animal testing, which is still the most prevalent model used for the risk assessment of chemicals. Current approaches for nanomaterials follow this line. The aim of this review is to explore and qualify the relevance of new in vitro and ex vivo models in (nano)material safety assessment, a crucial prerequisite for translation into applications.