Cytokine analysis in Atlantic bottlenose dolphins: Molecular characterization of IL-4, IL-6, and IL-10

The health status of wild and captive Atlantic Bottlenose dolphins ( Tersiops truncatis) is difficult to ascertain. Mass strandings of these animals have been attributed to pollutants, as well as bacterial infections. Using human Enzyme Linked Immuno-Assays (ELISA) for immunological cytokines, I measured soluble cytokine levels with respect to their health status. In a retrospective analysis of dolphin sera, there was a trend of higher cytokine levels in “sick” animals. I cultured dolphin lymphocytes in the presence of a mitogen (PHA), a super antigen (Staph-A), Lipopolysaccharide (LPS), and a calcium flux inducer (PMA). Levels of messenger RNA, from these cultured cells, were assayed with Polymerase Chain Reaction (PCR) using primers for the human cytokines IL-2, IL-4, IL-6, IL-10, Tumor Necrosis Factor, and Interferon gamma. Only IL-4, IL-6, and IL-10 messages were obtained, inferring similar nucleotide homology to the human primer sequences. The PCR products were sequenced. Sixteen IL-4 sequences, twelve IL-6 sequences and seven IL-10 sequences were obtained and analyzed. Each cytokine exhibited the same nucleotide sequence in all dolphins examined. There was no difference in the cytokine profile in response to the various stimuli. The derived amino acid composition for each of the dolphin cytokines was used for molecular modeling, which showed that dolphin IL-4, IL-6, and IL-10 were structurally similar to the corresponding proteins of Perissodactyla. ^

Automated detection of hematological abnormalities through classification of flow cytometric data patterns

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification ability than is currently available. Toward this end, the purpose of this dissertation was to establish a feature extraction and pattern classification framework capable of providing improved accuracy for detecting specific hematological abnormalities in flow cytometric blood data. ^ This involved extracting a unique and powerful set of shift-invariant statistical features from the multi-dimensional flow cytometry data and then using these features as inputs to a pattern classification engine composed of an artificial neural network (ANN). The contribution of this method consisted of developing a descriptor matrix that can be used to reliably assess if a donor’s blood pattern exhibits a clinically abnormal level of variant lymphocytes, which are blood cells that are potentially indicative of disorders such as leukemia and infectious mononucleosis. ^ This study showed that the set of shift-and-rotation-invariant statistical features extracted from the eigensystem of the flow cytometric data pattern performs better than other commonly-used features in this type of disease detection, exhibiting an accuracy of 80.7%, a sensitivity of 72.3%, and a specificity of 89.2%. This performance represents a major improvement for this type of hematological classifier, which has historically been plagued by poor performance, with accuracies as low as 60% in some cases. This research ultimately shows that an improved feature space was developed that can deliver improved performance for the detection of variant lymphocytes in human blood, thus providing significant utility in the realm of suspect flagging algorithms for the detection of blood-related diseases.^