Discovering Drug-Drug Interactions Using Association Rule Mining from Electronic Health Records

In this paper, we propose utilising Electronic Health Records (EHR) to discover previously unknown drug-drug interactions (DDI) that may result in high rates of hospital readmissions. We used association rule mining and categorised drug combinations as high or low risk based on the adverse events they caused. We demonstrate that the drug combinations in the high-risk group contain significantly more drug-drug interactions than those in the low-risk group. This approach is efficient for discovering potential drug interactions that lead to negative outcomes, thus should be given priority and evaluated in clinical trials. In fact, severe drug interactions can have life-threatening consequences and result in adverse clinical outcomes. Our findings were achieved using a new association rule metric, which better accounts for the adverse drug events caused by DDI

A framework for information dissemination in social networks using Hawkes processes

International audienceWe define in this paper a general Hawkes-based framework to model information diffusion in social networks. The proposed framework takes into consideration the hidden interactions between users as well as the interactions between contents and social networks, and can also accommodate dynamic social networks and various temporal effects of the diffusion, which provides a complete analysis of the hidden influences in social networks. This framework can be combined with topic modeling, for which modified collapsed Gibbs sampling and variational Bayes techniques are derived. We provide an estimation algorithm based on nonnegative tensor factorization techniques, which together with a dimensionality reduction argument are able to discover , in addition, the latent community structure of the social network. At last, we provide numerical examples from real-life networks: a Game of Thrones and a MemeTracker datasets

Composición del aceite de la semilla del pistacho (Pistacia vera): efectos de la situación geográfica y de la variedad

This study was aimed at characterizing four Tunisian pistachio cultivations (Pistacia vera) from the Mateur (North), Nabeul (North-East), Kairouan (Middle) and Sfax (Middle- East) regions and two varieties, Mateur and Ohadi, considering fatty acid composition and main lipid class contents (polar lipids, diacylglycerols, triacylglycerols and free fatty acids) in ripe seeds. Lipid classes were separated using thin layer chromatography, and fatty acid methyl esters (FAMEs) were analyzed by gas chromatography. Oleic acid (C18:1 Δ9) was the major fatty acid for all samples; those of the Mateur region (the northern region) differed significantly with the lowest content in C18:1 (54.2 % of total fatty acids) and the highest in C18:2 (24.1 %). The total amount of fatty acids (TFA) in the seeds of Ohadi variety was significantly lower than that of the Mateur variety from the Sfax region. Triacylglycerols were most abundant in pistachio seeds from Nabeul (98.5% of total glycerolipids).Este estudio tiene como objetivo caracterizar cuatro cultivos de pistacho (Pistacia vera) de Túnez, de las regiones de Mateur (Norte), Nabeul (Nordeste), Kairouan (Centro) y Sfax (Centro-Este) y dos variedades, Mateur and Ohadi, considerando la composición en ácidos grasos y de lípidos mayoritarios (lípidos polares, diglicéridos, triglicéridos y ácidos grasos libres) de semillas maduras. Los lípidos se separaron mediante cromatografía en capa fina, y los ésteres metílicos de los ácidos grasos (FAMEs) se analizaron por cromatografía de gases. El ácido oleico (C18:1 Δ9) fue el mayoritario en todas las muestras; las de la región de Mateur (Norte) fueron significativamente distintas, presentando el contenido más bajo de C18:1 (54.2 % del total de ácidos grasos) y más alto de C18:2 (24.1 %). La cantidad total de ácidos grasos (TFA) en las semillas de la variedad Ohadi fue significativamente más bajo que el de la variedad Mateur de la región de Sfax. Los triglicéridos fueron el componente más importante en la semillas de pistacho de Nabeul (98.5% del total de glicerolípidos)

Hurler disease (mucopolysaccharidosis type IH): clinical features and consanguinity in Tunisian population

Mucopolysaccharidosis type I (MPS I) was a group of rare autosomal recessive disorder caused by the deficiency of the lysosomal enzyme, alpha -L -iduronidase, and the resulting accumulation of undergraded dematan sulfate and heparan sulfate. MPS I patients have a wide range of clinical presentations, that makes it difficult to predict patient phenotype which is needed for genetic counseling and also impedes the selection and evaluation of patients undergoing therapy bone marrow transplantation

Circulating microparticles: square the circle

Background: The present review summarizes current knowledge about microparticles (MPs) and provides a systematic overview of last 20 years of research on circulating MPs, with particular focus on their clinical relevance. Results: MPs are a heterogeneous population of cell-derived vesicles, with sizes ranging between 50 and 1000 nm. MPs are capable of transferring peptides, proteins, lipid components, microRNA, mRNA, and DNA from one cell to another without direct cell-to-cell contact. Growing evidence suggests that MPs present in peripheral blood and body fluids contribute to the development and progression of cancer, and are of pathophysiological relevance for autoimmune, inflammatory, infectious, cardiovascular, hematological, and other diseases. MPs have large diagnostic potential as biomarkers; however, due to current technological limitations in purification of MPs and an absence of standardized methods of MP detection, challenges remain in validating the potential of MPs as a non-invasive and early diagnostic platform. Conclusions: Improvements in the effective deciphering of MP molecular signatures will be critical not only for diagnostics, but also for the evaluation of treatment regimens and predicting disease outcomes

Admission Control in the Downlink of WCDMA/UMTS

Abstract. In this paper, we develop a novel CAC algorithm that takes into account the mobility of users inside the cell with a focus on the downlink of third generation mobile systems. We first study the system capacity in a multiple cell setting and obtain effective bandwidth expressions for different calls as a function of both their positions in the cell as well as their classes of traffic (voice versus data). We then use this formulation to derive a mobility-based admission control algorithm which we analyze by Markov chains. We hence obtain several performance measures, namely the blocking probability, the dropping probability, both intra and inter-cell, as well as the overall cell throughput. We eventually investigate the performance of our CAC and show how to extend the Erlang capacity bounds, i.e., the set of arrival rates such that the corresponding blocking/dropping probabilities are kept below predetermined thresholds.