Pinealectomy interferes with the circadian clock genes expression in white adipose tissue

Melatonin, the main hormone produced by the pineal gland, is secreted in a circadian manner (24‐hr period), and its oscillation influences several circadian biological rhythms, such as the regulation of clock genes expression (chronobiotic effect) and the modulation of several endocrine functions in peripheral tissues. Assuming that the circadian synchronization of clock genes can play a role in the regulation of energy metabolism and it is influenced by melatonin, our study was designed to assess possible alterations as a consequence of melatonin absence on the circadian expression of clock genes in the epididymal adipose tissue of male Wistar rats and the possible metabolic repercussions to this tissue. Our data show that pinealectomy indeed has impacts on molecular events: it abolishes the daily pattern of the expression of Clock, Per2, and Cry1 clock genes and Pparγ expression, significantly increases the amplitude of daily expression of Rev‐erbα, and affects the pattern of and impairs adipokine production, leading to a decrease in leptin levels. However, regarding some metabolic aspects of adipocyte functions, such as its ability to synthesize triacylglycerols from glucose along 24 hr, was not compromised by pinealectomy, although the daily profile of the lipogenic enzymes expression (ATP‐citrate lyase, malic enzyme, fatty acid synthase, and glucose‐6‐phosphate dehydrogenase) was abolished in pinealectomized animals.     

Spatial distribution of intestinal parasitic infections in a Kaingáng indigenous village from Southern Brazil

The spatial distribution of enteroparasitosis in an indigenous village from Paraná was evaluated to identify areas of risk for these infections. A cross-sectional study (from November 2010 to June 2011) was performed using Three Faecal Test^® and Kato &; Katz method and a questionnaire on housing and hygiene conditions was administered. Local geostatistical analyses were performed to determine the spatial distribution of intestinal parasitic infections. The overall prevalence of enteroparasites was 67.2?% (457/680), and the most prevalent taxa were Ascaris lumbricoides (48.8?%) and Trichuris trichiura (44.7?%). The prevalence of heavy infection by soil-transmitted helminths was 3.6?% and the families lived in houses with an average of 5.1 residents and < 2 bedrooms per household. The average number of species per individual present spatial heterogeneity with the highest values (≥0.8) in areas with high clustering of residences. The visualization of the spatial distribution of intestinal parasites in this indigenous village is an important contribution to determining health risk areas and planning decisions and services.     

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.