Comparative analysis of the tissue inflammatory response in human cutaneous and disseminated leishmaniasis

Cutaneous leishmaniasis (CL) is the most frequent clinical form of tegumentary leishmaniasis and is characterised by a single or a few ulcerated skin lesions that may disseminate into multiple ulcers and papules, which characterise disseminated leishmaniasis (DL). In this study, cells were quantified using immunohistochemistry and haematoxylin and eosin staining (CD4+, CD68+, CD20+, plasma cells and neutrophils) and histopathology was used to determine the level of inflammation in biopsies from patients with early CL, late CL and DL (ulcers and papules). The histopathology showed differences in the epidermis between the papules and ulcers from DL. An analysis of the cells present in the tissues showed similarities between the ulcers from localised CL (LCL) and DL. The papules had fewer CD4+ T cells than the DL ulcers. Although both CD4+ cells and macrophages contribute to inflammation in early CL, macrophages are the primary cell type associated with inflammation intensity in late ulcers. The higher frequency of CD20+ cells and plasma cells in lesions demonstrates the importance of B cells in the pathogenesis of leishmaniasis. The number of neutrophils was the same in all of the analysed groups. A comparison between the ulcers from LCL and DL and the early ulcers and papules shows that few differences between these two clinical forms can be distinguished by observing only the tissue.

Applicability of a distributed watershed pollution model in a data-poor environment in Santa Catarina State, Brazil

Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges (< 25, 25-60, > 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.

Duration-frequency relationships of heavy rainfall in Santa Catarina, Brazil

The purpose of this study was to adjust equations that establish relationships between rainfall events with different duration and data from weather stations in the state of Santa Catarina, Brazil. In this study, the relationships between different duration heavy rainfalls from 13 weather stations of Santa Catarina were analyzed. From series of maximum annual rainfalls, and using the Gumbel-Chow distribution, the maximum rainfall for durations between 5 min and 24 h were estimated considering return periods from 2 to 100 years. The data fit to the Gumbel-Chow model was verified by the Kolmogorov-Smirnov test at 5 % significance. The coefficients of Bell's equation were adjusted to estimate the relationship between rainfall duration t (min) and the return period T (y) in relation to the maximum rainfall with a duration of 1 hour and a 10 year return period. Likewise, the coefficients of Bell's equation were adjusted based on the maximum rainfall with a duration of 1 day and a 10 year return period. The results showed that these relationships are viable to estimate short-duration rainfall events at locations where there are no rainfall records.