Effect of pre-cardiac and adult stages of Dirofilaria immitis in pulmonary disease of cats: CBC, bronchial lavage cytology, serology, radiographs, CT images, bronchial reactivity, and histopathology

AbstractA controlled, blind study was conducted to define the initial inflammatory response and lung damage associated with the death of precardiac stages of Dirofilaria immitis in cats as compared to adult heartworm infections and normal cats. Three groups of six cats each were used: UU: uninfected untreated controls; PreS I: infected with 100 D. immitis L3 by subcutaneous injection and treated topically with selamectin 32 and 2 days pre-infection and once monthly for 8 months); IU: infected with 100 D. immitis L3 and left untreated. Peripheral blood, serum, bronchial lavage, and thoracic radiographic images were collected from all cats on Days 0, 70, 110, 168, and 240. CT images were acquired on Days 0, 110, and 240. Cats were euthanized, and necropsies were conducted on Day 240 to determine the presence of heartworms. Bronchial rings were collected for in vitro reactivity. Lung, heart, brain, kidney, and liver tissues were collected for histopathology. Results were compared for changes within each group. Pearson and Spearman correlations were performed for association between histologic, radiographic, serologic, hematologic and bronchoalveolar lavage (BAL) results. Infected cats treated with selamectin did not develop radiographically evident changes throughout the study, were heartworm antibody negative, and were free of adult heartworms and worm fragments at necropsy. Histologic lung scores and CT analysis were not significantly different between PreS I cats and UU controls. Subtle alveolar myofibrosis was noted in isolated areas of several PreS I cats and an eosinophilic BAL cytology was noted on Days 75 and 120. Bronchial ring reactivity was blunted in IU cats but was normal in PreS I and UU cats. The IU cats became antibody positive, and five cats developed adult heartworms. All cats with heartworms were antigen positive at one time point; but one cat was antibody positive, antigen negative, with viable adult females at necropsy. The CT revealed early involvement of all pulmonary arteries and a random pattern of parenchymal disease with severe lesions immediately adjacent to normal areas. Analysis of CT 3D reconstruction and Hounsfield units demonstrated lung disease consistent with restrictive pulmonary fibrosis with an interstitial infiltrate, absence of air trapping, and decrease in total lung volume in Group IU as compared to Groups UU and PreS I. The clinical implications of this study are that cats pretreated with selamectin 1 month before D. immitis L3 infection did not become serologically positive and did not develop pulmonary arterial hypertrophy and myofibrosis

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−(13)C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown