Pulmonary anthracosis in Dhaka Zoo collections - a public health forecasting for city dwellers

Anthracosis is a lung disease associated with inhalation of coal dust and carbon particles. We tested for anthracosis symptoms in 36 captive animals in the Dhaka Zoo. Necropsy revealed 27 out of 36 animals to be affected by pulmonary anthracosis. The changes included minute black spots to blackish discoloration and congestion in the lungs. In some cases, the lungs showed yellow-white caseous nodules of variable size. Lung tissue samples were stored in 10% neutral buffered formalin. Fixed tissues were processed and stained as per standard procedure. Histopathological examination revealed deposition of carbon particles of varying sizes, shapes and amounts in the lung parenchyma, in the alveolar septa and in and around granuloma. Reptiles, newly arrived animals and animals less than one year of age were found free from anthracosis. This report confirms the occurrence of anthracosis in captive animals at Dhaka zoo indicating severe air pollution in and around the zoo

A reliable auto-robust analysis of blood smear images for classification of microcytic hypochromic anemia using gray level matrices and gabor feature bank

Accurate blood smear quantification with various blood cell samples is of great clinical importance. The conventional manual process of blood smear quantification is quite time consuming and is prone to errors. Therefore, this paper presents automatic detection of the most frequently occurring condition in human blood-microcytic hyperchromic anemia-which is the cause of various life-threatening diseases. This task has been done with segmentation of blood contents, i.e., Red Blood Cells (RBCs), White Blood Cells (WBCs), and platelets, in the first step. Then, the most influential features like geometric shape descriptors, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gabor features (mean squared energy and mean amplitude) are extracted from each of the RBCs. To discriminate the cells as hypochromic microcytes among other RBC classes, scanning is done at angles (0°, 45°, 90°, and 135°). To achieve high-level accuracy, Adaptive Synthetic (AdaSyn) sampling for imbalance learning is used to balance the datasets and locality sensitive discriminant analysis (LSDA) technique is used for feature reduction. Finally, upon using these features, classification of blood cells is done using the multilayer perceptual model and random forest learning algorithms. Performance in terms of accuracy was 96%, which is better than the performance of existing techniques. The final outcome of this work may be useful in the efforts to produce a cost-effective screening scheme that could make inexpensive screening for blood smear analysis available globally, thus providing early detection of these diseases. © 2020 by the authors