Immunohistochemical Analysis of Proliferating Cell Nuclear Antigen (PCNA) in Dental Follicles of Impacted Third Molars

This study investigated the immunodetection of PCNA in epithelial components of dental follicles associated with impacted third molars without radiographical and morphological signs of pathosis. A total of 105 specimens of dental follicles associated with impacted third molars with incomplete rhizogenesis (between Nolla`s stage 6 and 9) were surgically removed from 56 patients. Epithelial cell proliferating was determined by using immunohistochemical labeling. Statistical analysis was performed using the Fisher exact test. Of the 105 dental follicles collected, 6 were PCNA-positive (approximate to 6%). The specimens with squamous metaplasia and epithelial hyperplasia had higher rates of positivity for PCNA, as well as those with proliferative remnants of odontogenic epithelium. In conclusion, this study shows that dental follicles at this stage of development have low proliferative potential, but suggests that squamous metaplasia, hyperplasia of the epithelial lining and presence of proliferative odontogenic epithelial rests in the connective tissue may be early signs of developing lesions of odontogenic origin.

Flood Risk Assessment in Coastal Drainage Basins through a Multivariate Analysis within a GIS-Based Model

This paper presents a GIS-based multicriteria flood risk assessment and mapping approach applied to coastal drainage basins where hydrological data are not available. It involves risk to different types of possible processes: coastal inundation (storm surge), river, estuarine and flash flood, either at urban or natural areas, and fords. Based on the causes of these processes, several environmental indicators were taken to build-up the risk assessment. Geoindicators include geological-geomorphologic proprieties of Quaternary sedimentary units, water table, drainage basin morphometry, coastal dynamics, beach morphodynamics and microclimatic characteristics. Bioindicators involve coastal plain and low slope native vegetation categories and two alteration states. Anthropogenic indicators encompass land use categories properties such as: type, occupation density, urban structure type and occupation consolidation degree. The selected indicators were stored within an expert Geoenvironmental Information System developed for the State of Sao Paulo Coastal Zone (SIIGAL), which attributes were mathematically classified through deterministic approaches, in order to estimate natural susceptibilities (Sn), human-induced susceptibilities (Sa), return period of rain events (Ri), potential damages (Dp) and the risk classification (R), according to the equation R=(Sn.Sa.Ri).Dp. Thematic maps were automatically processed within the SIIGAL, in which automata cells (""geoenvironmental management units"") aggregating geological-geomorphologic and land use/native vegetation categories were the units of classification. The method has been applied to the Northern Littoral of the State of Sao Paulo (Brazil) in 32 small drainage basins, demonstrating to be very useful for coastal zone public politics, civil defense programs and flood management.

Peptidomic Analysis of Human Cell Lines

Peptides have been proposed to function in intracellular signaling within the cytosol. Although cytosolic peptides are considered to be highly unstable, a large number of peptides have been detected in mouse brain and other biological samples. In the present study, we evaluated the peptidome of three diverse cell lines: SH-SY5Y, MCF7, and HEIC293 cells. A comparison of the peptidomes revealed considerable overlap in the identity of the peptides found in each cell line. The majority of the observed peptides are not derived from the most abundant or least stable proteins in the cell, and approximately half of the cellular peptides correspond to the N- or C- termini of the precursor proteins. Cleavage site analysis revealed a preference for hydrophobic residues in the PI position. Quantitative peptidomic analysis indicated that the levels of most cellular peptides are not altered in response to elevated intracellular calcium, suggesting that calpain is not responsible for their production. The similarity of the peptidomes of the three cell lines and the lack of correlation with the predicted cellular degradome implies the selective formation or retention of these peptides, consistent with the hypothesis that they are functional in the cells.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

Missing data mechanisms and their implications on the analysis of categorical data

We review some issues related to the implications of different missing data mechanisms on statistical inference for contingency tables and consider simulation studies to compare the results obtained under such models to those where the units with missing data are disregarded. We confirm that although, in general, analyses under the correct missing at random and missing completely at random models are more efficient even for small sample sizes, there are exceptions where they may not improve the results obtained by ignoring the partially classified data. We show that under the missing not at random (MNAR) model, estimates on the boundary of the parameter space as well as lack of identifiability of the parameters of saturated models may be associated with undesirable asymptotic properties of maximum likelihood estimators and likelihood ratio tests; even in standard cases the bias of the estimators may be low only for very large samples. We also show that the probability of a boundary solution obtained under the correct MNAR model may be large even for large samples and that, consequently, we may not always conclude that a MNAR model is misspecified because the estimate is on the boundary of the parameter space.