Aseptic Meningitis in Children: Analysis of 506 Cases

BACKGROUND: Non-polio human enteroviruses are the leading cause of aseptic meningitis in children. The role of enterovirus PCR for diagnosis and management of aseptic meningitis has not been fully explored. METHODOLOGY/PRINCIPAL FINDINGS: A retrospective study was conducted to determine the epidemiological, clinical, and laboratory characteristics of aseptic meningitis and to evaluate the role of enterovirus PCR for the diagnosis and management of this clinical entity. The medical records of children who had as discharge diagnosis aseptic or viral meningitis were reviewed. A total of 506 children, median age 5 years, were identified. The annual incidence rate was estimated to be 17/100,000 children less than 14 years of age. Most of the cases occurred during summer (38%) and autumn (24%). The dominant clinical symptoms were fever (98%), headache (94%) and vomiting (67%). Neck stiffness was noted in 60%, and irritation in 46% of the patients. The median number of CSF cell count was 201/mm(3) with polymorphonuclear predominance (>50%) in 58.3% of the cases. Enterovirus RNA was detected in CSF in 47 of 96 (48.9%) children tested. Children with positive enterovirus PCR had shorter hospitalization stay as compared to children who had negative PCR or to children who were not tested (P = 0.01). There were no serious complications or deaths. CONCLUSIONS: Enteroviruses accounted for approximately one half of cases of aseptic meningitis. PCR may reduce the length of hospitalization and plays important role in the diagnosis and management of children with aseptic meningitis

Multianalyte Sensing Of Addictive Over-the-counter (otc) Drugs

A supramolecular sensor array composed of two fluorescent cucurbit[n]uril-type receptors (probe 1 and probe 2) displaying complementary selectivities was tested for its ability to detect and quantify drug-related amines. The fluorimetric titration of the individual probes showed highly variable and cross-reactive analyte-dependent changes in fluorescence. An excellent ability to recognize a variety of analytes was demonstrated in qualitative as well as quantitative assays. Importantly, a successful quantitative analysis of several analytes of interest was achieved in mixtures and in human urine. The throughput and sensitivity surpass those of the current state-of-the-art methods that usually require analyte solid-phase extraction (SPE). These results open up the opportunity for new applications of cucurbit[n]uril-type receptors in sensing and pave the way for the development of simple high-throughput assays for various drugs in the near future

Modelling the covariance structure in marginal multivariate count models: Hunting in Bioko Island.

The main goal of this article is to present a flexible statistical modelling framework to deal with multivariate count data along with longitudinal and repeated measures structures. The covariance structure for each response variable is defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. In order to specify the joint covariance matrix for the multivariate response vector, the generalized Kronecker product is employed. We take into account the count nature of the data by means of the power dispersion function associated with the Poisson–Tweedie distribution. Furthermore, the score information criterion is extended for selecting the components of the matrix linear predictor. We analyse a data set consisting of prey animals (the main hunted species, the blue duiker Philantomba monticola and other taxa) shot or snared for bushmeat by 52 commercial hunters over a 33-month period in Pico Basilé, Bioko Island, Equatorial Guinea. By taking into account the severely unbalanced repeated measures and longitudinal structures induced by the hunters and a set of potential covariates (which in turn affect the mean and covariance structures), our method can be used to indicate whether there was statistical evidence of a decline in blue duikers and other species hunted during the study period. Determining whether observed drops in the number of animals hunted are indeed true is crucial to assess whether species depletion effects are taking place in exploited areas anywhere in the world. We suggest that our method can be used to more accurately understand the trajectories of animals hunted for commercial or subsistence purposes and establish clear policies to ensure sustainable hunting practices

Model-based clustering for noisy longitudinal circular data, with application to animal movement

In this work, we introduce a model for circular data analysis to robustly estimate parameters, under a longitudinal clustering setting. A hidden Markov model for longitudinal circular data combined with a uniform conditional density on the circle to capture noise observations is proposed. A set of exogenous covariates is available; they are assumed to affect the evolution of clustering over time. Parameter estimation is carried out through a hybrid expectation–maximization algorithm, using recursions widely adopted in the hidden Markov model literature. Examples of application of the proposal on real and simulated data are performed to show the effectiveness of the proposal