Descriptive study of perineuronal net in enteric nervous system of humans and mice.

Perineuronal nets (PNN) are highly specialized structures of the extracellular matrix around specific groups of neurons in the central nervous system (CNS). They play functions related to optimizing physiological processes and protection neurons against harmful stimuli. Traditionally, their existence was only described in the CNS. However, there was no description of the presence and composition of PNN in the enteric nervous system (ENS) until now. Thus, our aim was to demonstrate the presence and characterize the components of the PNN in the enteric nervous system. Samples of intestinal tissue from mice and humans were analyzed by RT-PCR and immunofluorescence assays. We used a marker (Wisteria floribunda agglutinin) considered as standard for detecting the presence of PNN in the CNS and antibodies for labeling members of the four main PNN-related protein families in the CNS. Our results demonstrated the presence of components of PNN in the ENS of both species; however its molecular composition is species-specific.

CUSUM chart to monitor autocorrelated counts using Negative Binomial GARMA model.

Cumulative sum control charts have been used for health surveillance due to its efficiency to detect soon small shifts in the monitored series. However, these charts may fail when data are autocorrelated. An alternative procedure is to build a control chart based on the residuals after fitting autoregressive moving average models, but these models usually assume Gaussian distribution for the residuals. In practical health surveillance, count series can be modeled by Poisson or Negative Binomial regression, this last to control overdispersion. To include serial correlations, generalized autoregressive moving average models are proposed. The main contribution of the current article is to measure the impact, in terms of average run length on the performance of cumulative sum charts when the serial correlation is neglected in the regression model. Different statistics based on transformations, the deviance residual, and the likelihood ratio are used to build cumulative sum control charts to monitor counts with time varying means, including trend and seasonal effects. The monitoring of the weekly number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current method.

CUSUM control charts to monitor series of Negative Binomial count data.

To detect outbreaks of diseases in public health, several control charts have been proposed in the literature. In this context, the usual generalized linear model may be fitted for counts under a Negative Binomial distribution with a logarithm link function and the population size included as offset to model hospitalization rates. Different statistics are used to build CUSUM control charts to monitor daily hospitalizations and their performances are compared in simulation studies. The main contribution of the current paper is to consider different statistics based on transformations and the deviance residual to build control charts to monitor counts with seasonality effects and evaluate all the assumptions of the monitored statistics. The monitoring of daily number of hospital admissions due to respiratory diseases for people aged over 65 years in the city São Paulo-Brazil is considered as an illustration of the current proposal.