Biological therapy in patients with psoriasis: What we know about the effects on renal function.

Psoriasis is a chronic inflammatory dermatosis affecting 2%-3% of the general population. The link between psoriasis and renal dysfunction has been investigated, demonstrating a common pro-inflammatory pathogenesis. This study is aimed at evaluating renal function in patients with moderate-to-severe chronic plaque psoriasis treated with biological therapy. We analyzed 92 patients, correlating PASI and serum creatinine levels at baseline, after 6 months and after 1 year of continuous treatment with biological therapy. Data were analyzed using paired t-test and the linear mixed model for PASI and serum creatinine levels correlation, whereas the analysis of variances (ANOVA) was used for creatinine levels assessment between the baseline, the 6-months and, 1-year later evaluation. We observed a significant mean decrease in comparing serum creatinine levels after 1 year of biological therapy (p < 0.001). Interestingly, PASI reduction is correlated with creatinine decrease, and the renal function improvement is greater when complete psoriasis remission is attained. Our data suggest that a drop in systemic inflammation, secondary to biological therapy administration, might improve renal function. Future research is needed to confirm and expand our findings.

Bayesian inference for quantiles of the log-normal distribution.

The log-normal distribution is very popular for modeling positive right-skewed data and represents a common distributional assumption in many environmental applications. Here we consider the estimation of quantiles of this distribution from a Bayesian perspective. We show that the prior on the variance of the log of the variable is relevant for the properties of the posterior distribution of quantiles. Popular choices for this prior, such as the inverse gamma, lead to posteriors without finite moments. We propose the generalized inverse Gaussian and show that a restriction on the choice of one of its parameters guarantees the existence of posterior moments up to a prespecified order. In small samples, a careful choice of the prior parameters leads to point and interval estimators of the quantiles with good frequentist properties, outperforming those currently suggested by the frequentist literature. Finally, two real examples from environmental monitoring and occupational health frameworks highlight the improvements of our methodology, especially in a small sample situation.

Physico-chemical and microbiological characterisation of Italian fermented sausages in relation to their size.

BACKGROUND: The aim of this work was to study the physico-chemical and microbiological parameters of 10 Italian fermented sausages, produced industrially, in order to highlight the differences in relation to their size. The sausages were classified as small, medium and large and the data concerning every feature considered were analysed with some statistical explorative tools: ANOVA, principal component analysis (PCA) and linear discriminant analysis (LDA). RESULTS: Significant differences in relation to the sausage size were found regarding microbial populations (in particular, enterobacteria and staphylococci). The pH was higher in the small sausages, and consequently the presence of lactate and acetate was higher in the bigger one. Also the biogenic amine content (particularly tyramine, cadaverine and putrescine) was influenced by the size. CONCLUSIONS: In spite of the extreme variability of the sausage types at the end of ripening, this work showed that it was possible to find a strict relationship between sausages diameter and some essential physico-chemical parameters. Thus, the cross-sectional size of the product is an essential parameter, which can drive the biochemical processes during ripening, not only by affecting the kinetics of water losses, but also by influencing the microbiota enzymatic activity. © 2015 Society of Chemical Industry.

Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable.

The analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these models is gaining popularity as it allows to easily handle complex experimental designs and data dependence structures. When working on the log of the response variable, the use of standard priors for the variance parameters can create inferential problems and namely the non-existence of posterior moments of parameters and predictive distributions in the original scale of the data. The use of the generalized inverse Gaussian distributions with a careful choice of the hyper-parameters is proposed as a general purpose option for priors on variance parameters. Theoretical and simulations results motivate the proposal. A software package that implements the analysis is also discussed. As the log-transformation of the response variable is often applied when modelling response times, an empirical data analysis in this field is reported.

Modeling of yeast thermal resistance and optimization of the pasteurization treatment applied to soft drinks.

Yeast are usually responsible for spoilage of soft drinks and fruit beverages, because of the particular characteristics of these products (low pH, high C/N ratio). The microbial stability is guaranteed by thermal treatments. However, excessive heat treatments can affect food sensorial quality. In this work the thermal resistance of different yeasts strains (seven belonging to the species Saccharomyces cerevisiae and six belonging to the species Kluyveromyces marxianus, Zygosaccharomyces bisporus, Z. mellis, Z. rouxii, Schizosaccharomyces pombe and Saccharomycodes ludwigii) was assessed in a model system. The results showed non-linear death curves and a high variability also within the same species. The most resistant strain, belonging to the species S. cerevisiae, was chosen for further experiments in orange juice based industrial beverages: first, death curves were performed; then, the probability of beverage spoilage in relation to process parameters (initial inoculum, temperature, treatment time) was evaluated using a logistic regression model. Finally, a cross-validation was performed to investigate the predictive capability of the fitted model. Pasteurization in the soft drink industry is commonly applied according to parameters defined several decades ago, which does not consider the successive findings concerning microbial physiology and stress response, the process improvement and the more recent tools provided by predictive microbiology. In this perspective, this study can fill a gap in the literature on this subject, going to be a basis for optimizing thermal processes. In fact, the data obtained indicated an interesting possibility for food industry to better modulated (and even reduce) thermal treatments, with the aim to guarantee microbial stability while reducing thermal damage and energy costs.

Environmental Factors Affecting Escherichia coli Concentrations in Striped Venus Clam (Chamelea gallina L.) Harvested in the North Adriatic Sea.

The aim of this work was to evaluate the microbiological quality of striped venus clams (Chamelea gallina L.) harvested in the north Adriatic Sea during an 8-year monitoring period. A total of 387 samples were analyzed to assess the presence of Escherichia coli. Environmental parameters (salinity, pH, dissolved oxygen, seawater temperature, and freshwater outflow) were collected to find out a possible relationship between the E. coli counts and environmental factors. The results evidenced that the microbiological quality of the clams was good, with only about 5% of the samples not complying with European and Italian regulations for this product, that is, with E. coli counts higher than 230 most probable number (MPN) per 100 g of flesh and intravalvar liquid. Statistical analyses revealed a relationship between microbial contamination and the season and water temperature, probably due to the difference in the filtering activity of the mollusks. However, the main factor affecting the E. coli concentration in the clams turned out to be the flow rate of Marecchia, the major river that reaches the sea in the area of harvesting. In fact, a model fitted to evaluate the probability of finding a higher E. coli count in relation to the environmental parameters evidenced that it was an increase of the level of the Marecchia led to a higher probability of elevated E. coli contamination. This result could be explained by the higher supply of both nutrients and coliforms (including E. coli) when the river is higher and by the anthropogenic characteristics of the lands crossed by the river.

Correlation between volatile profiles of Italian fermented sausages and their size and starter culture.

The aroma profiles of 10 traditional Italian fermented sausages were evaluated. The volatile organic compounds (VOCs) obtained by solid-phase microextraction and gas chromatograph-mass spectrometry were analysed using principal component analysis (PCA) and linear discriminant analysis (LDA). PCA allowed an acceptable separation but some sausage typologies were not well separated. On the other hand, the supervised approach of LDA allowed a clear grouping of the samples in relation to sausage size and starter culture. In spite of the extreme variability of the volatile profiles of the sausage typologies, this work showed the influence of diameter on VOC profile. The differences observed can be related to the effects that some fundamental physicochemical characteristics (such as water loss kinetics and oxygen availability) have on the results of ripening processes. Differences in VOC profiles were also observed due to the lactic acid bacteria used as starter cultures, with differences mainly attributable to compounds deriving from pyruvate metabolism.