Serological responses to prednisolone treatment in leprosy reactions: study of TNF-α, antibodies to phenolic glycolipid-1, lipoarabinomanan, ceramide and S100-B.

BACKGROUND: Corticosteroids have been extensively used in the treatment of immunological reactions and neuritis in leprosy. The present study evaluates the serological response to steroid treatment in leprosy reactions and neuritis. METHODS: Seven serological markers [TNF-α, antibodies to Phenolic glycolipid-1 (PGL-1 IgM and IgG), Lipoarabinomannan (LAM IgG1 and IgG3), C2-Ceramide and S100 B] were analyzed longitudinally in 72 leprosy patients before, during and after the reaction. At the onset of reaction these patients received a standard course of prednisolone. The levels of the above markers were measured by Enzyme linked immunosorbent assay (ELISA) and compared with the individuals own value in the month prior to the reaction and presented as percentage increase. RESULTS: One month before the reaction individuals showed a varying increase in the level of different markers such as TNF-α (53%) and antibodies to Ceramide (53%), followed by to PGL-1 (51%), S100B (50%) and LAM (26%). The increase was significantly associated with clinical finding of nerve pain, tenderness and new nerve function impairment. After one month prednisolone therapy, there was a fall in the levels [TNF-α (60%), C2-Ceramide (54%), S100B (67%), PGL-1(47%) and LAM (52%)] with each marker responding differently to steroid. CONCLUSION: Reactions in leprosy are inflammatory processes wherein a rise in set of serological markers can be detected a month before the clinical onset of reaction, some of which remain elevated during their action and steroid treatment induces a variable fall in the levels, and this forms the basis for a variable individual response to steroid therapy

An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques

Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification