Randomized control trial on impact of clinical pharmacist led interventions on antibiotic stewardship program in children 0 to 18 years age in a tertiary care hospital in southern India.

A prospective randomized controlled interventional study was conducted at a quaternary care multispecialty hospital in South India with the primary objective of identifying opportunities to improve antibiotic prescribing patterns in neonates, children, and adolescents. The hospital has a robust clinical pharmacist program wherein antibiotic prescriptions were checked for appropriateness of antibiotic dose, route, formulation, duration of therapy, and IV to oral switch. These characteristics of antibiotic use were therefore similar in the 75 children in the control and 75 in the intervention group. The additional clinical pharmacist interventions analyzed in this study included checking if a provisional diagnosis had been made before initiating antibiotics, ensuring that appropriate cultures were sent before starting antibiotics, time taken to optimize antibiotic therapy in accordance to the culture sensitivity report and whether the indication for antibiotics use was as per Indian Acdemy of Pediatrics (IAP) Drug Formulary recommendations. The main outcomes were that all these parameters except the first (all children had a clinical diagnosis before initiating antibiotic/s) were better in the intervention group and there was a significant reduction in the total antibiotic days, an increase in antibiotic-free days, and an improvement in the appropriateness of duration of antibiotic therapy and frequency of the antibiotic dosing. However, since the incidence of severe sepsis was higher in the intervention group, broad-spectrum and restricted antibiotics were used, and thus treatment costs were higher in this group. The results indicate that despite already existing clinical pharmacist interventions, additional ones could further improve antibiotic prescription accuracy significantly; and re-emphasized the need to employ trained pediatric general and subspecialty clinical pharmacists in sufficient numbers to implement a successful pediatric antibiotic stewardship program in a hospital.

Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models.

Cellular decision making in differentiation, proliferation, or cell death is mediated by molecular signaling processes, which control the regulation and expression of genes. Vice versa, the expression of genes can trigger the activity of signaling pathways. We introduce and describe a statistical method called Dynamic Nested Effects Model (D-NEM) for analyzing the temporal interplay of cell signaling and gene expression. D-NEMs are Bayesian models of signal propagation in a network. They decompose observed time delays of multiple step signaling processes into single steps. Time delays are assumed to be exponentially distributed. Rate constants of signal propagation are model parameters, whose joint posterior distribution is assessed via Gibbs sampling. They hold information on the interplay of different forms of biological signal propagation. Molecular signaling in the cytoplasm acts at high rates, direct signal propagation via transcription and translation act at intermediate rates, while secondary effects operate at low rates. D-NEMs allow the dissection of biological processes into signaling and expression events, and analysis of cellular signal flow. An application of D-NEMs to embryonic stem cell development in mice reveals a feed-forward loop dominated network, which stabilizes the differentiated state of cells and points to Nanog as the key sensitizer of stem cells for differentiation stimuli.

Analyzing gene perturbation screens with nested effects models in R and bioconductor.

UNLABELLED: Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. NEMs reverse engineer upstream/downstream relations of cellular signaling cascades. NEMs take as input a set of candidate pathway genes and phenotypic profiles of perturbing these genes. NEMs return a pathway structure explaining the observed perturbation effects. Here, we describe the package nem, an open-source software to efficiently infer NEMs from data. Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. The methods we present summarize the current state-of-the-art in NEMs. AVAILABILITY: Our software is written in the R language and freely avail-able via the Bioconductor project at http://www.bioconductor.org.

Detecting hierarchical structure in molecular characteristics of disease using transitive approximations of directed graphs.

MOTIVATION: Molecular diagnostics aims at classifying diseases into clinically relevant sub-entities based on molecular characteristics. Typically, the entities are split into subgroups, which might contain several variants yielding a hierarchical model of the disease. Recent years have introduced a plethora of new molecular screening technologies to molecular diagnostics. As a result molecular profiles of patients became complex and the classification task more difficult. RESULTS: We present a novel tool for detecting hierarchical structure in binary datasets. We aim for identifying molecular characteristics, which are stochastically implying other characteristics. The final hierarchical structure is encoded in a directed transitive graph where nodes represent molecular characteristics and a directed edge from a node A to a node B denotes that almost all cases with characteristic B also display characteristic A. Naturally, these graphs need to be transitive. In the core of our modeling approach lies the problem of calculating good transitive approximations of given directed but not necessarily transitive graphs. By good transitive approximation we understand transitive graphs, which differ from the reference graph in only a small number of edges. It is known that the problem of finding optimal transitive approximation is NP-complete. Here we develop an efficient heuristic for generating good transitive approximations. We evaluate the computational efficiency of the algorithm in simulations, and demonstrate its use in the context of a large genome-wide study on mature aggressive lymphomas. AVAILABILITY: The software used in our analysis is freely available from http://compdiag.uni-regensburg.de/software/transApproxs.shtml.