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Background: The adequacy of haemodialysis in patients of type 2 diabetes mellitus with chronic kidney disease stage 5 depends on several clinical as well as laboratory parameters. Previous studies from Western literature have identified several clinical and laboratory markers for predicting adequacy of dialysis. There is a dearth of literature regarding the same in Indian patient populace. Authors aimed to find correlation, if any, between glycemic control and adequacy of dialysis in this cohort of patients.Methods: A set of 200 patients of type 2 diabetes mellitus who have undergone hemodialysis at a tertiary care hospital were included in the study. Random blood sugar (RBS), Glycated hemoglobin (HbA1c) were measured at admission. After 4 hours of dialysis, the urea reduction ratio (URR) and Kt/V was measured for each patient. The correlation coefficient as well as linear equation of the association between these variables were calculated. Standard statistical method and software were used in the process.Results: The study revealed a linear negative correlation between the variables RBS, HbA1c and URR as well as Kt/V. This suggests the importance of pre dialysis glycemic control in patients undergoing hemodialysis.Conclusions: Authors formulate the hypothesis that glycated hemoglobin and random blood sugar at admission correlate well with the outcome and adequacy of dialysis in patients of stage 5 chronic kidney disease undergoing haemodialysis. Good glycemic control (HbA1c <6.5 % and RBS <120 mg/dL) have shown to be important predictive markers of adequate dialysis. The hypothesis needs to be tested with a larger study.
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Background: Morphometric studies based on image analysis are a useful adjunct for quantitative analysis of microscopic images. However, effective separation of overlapping objects if often the bottleneck in image analysis techniques. We employ the watershed transform for counting reticulocytes from images of supravitally stained smears.Methods: The algorithm was developed with the Python programming platform, using the Numpy, Scipy and OpenCV libraries. The initial development and testing of the software were carried out with images from the American Society of Hematology Image Library. Then a pilot study with 30 samples was then taken up. The samples were incubated with supravital stain immediately after collection, and smears prepared. The smears were microphotographed at 100X objective, with no more than 150 RBCs per field. Reticulocyte count was carried out manually as well as by image analysis.Results: 600 out of 663 reticulocytes (90.49%) were correctly identified, with a specificity of 98%. The major difficulty faced was the slight bluish tinge seen in polychromatic RBCs, which were inconsistently detected by the software.Conclusions: The watershed transform can be used successfully to separate overlapping objects usually encountered in pathological smears. The algorithm has the potential to develop into a generalized cell classifier for cytopathology and hematology.