Calculation of a Reference Interval for Rectal Temperature in Adult Dogs Presenting for Veterinary Care Using an Algorithm for Mixed Data.

Veterinarians rely on the measurement of canine body temperature to define the health status of dogs, but no studies exist defining a reference range for rectal temperature on a large group of dogs. The aim of this study was to define the rectal body temperature of dogs based on a large data set of diseased and healthy animals and to evaluate the capability of the employed algorithm to calculate reference intervals of numerical clinical data. Out of 24,013 recorded measurements, statistical analysis was applied to data from 9782 adult dogs that underwent clinical examination at a university clinic between 2008 and 2017. The reference interval was calculated using an algorithm developed by the Deutsche Gesellschaft für Klinische Chemie und Laboratoriumsmedizin e.V. as part of its Reference Limit Estimator software (version 1.40.36.07). The following values were excluded: multiple measurements in a given dog, samples without assigned age or dogs younger than one year, and values <30.0 °C and >43.0 °C. Out of 9782 adult dogs, 665 temperature measurements were identified as outliers, and 9117 were used for further statistical analysis. The mean rectal temperature was 38.6 °C (90% CI: 38.6-38.6 °C) with a reference interval of 37.7 °C (90% CI: 37.7-37.7 °C) to 39.5 °C (90% CI: 39.5-39.5 °C). Validation according to CLSI guidelines showed the results to be valid. The determination of a reference interval for rectal temperatures in dogs using an algorithm for mixed datasets yielded results comparable to the existing reference intervals. This demonstrates that the calculation of reference intervals from mixed datasets of clinical numerical data can be used to confirm existing reference intervals or establish such de novo.

Assessment of cell cycle arrest biomarkers and neutrophil gelatinase-associated lipocalin to distinguish acute kidney injury from other diseases in dogs.

BACKGROUND: Cell cycle arrest biomarkers (tissue inhibitor of metalloproteinase-2 [uTIMP-2] and insulin-like growth factor binding protein 7 [uIGFBP7]), and neutrophil gelatinase-associated lipocalin (NGAL) variables are valuable biomarkers for early diagnosis of acute kidney injury (AKI) in people. OBJECTIVES: To evaluate uTIMP-2, uIGFBP7, fractional excretion of NGAL (FeNGAL), and urinary to serum NGAL ratio (u/sNGAL) in healthy dogs, dogs with AKI, dogs with chronic kidney disease (CKD), and critically ill (CI) dogs. ANIMALS: Forty-two client-owned dogs (healthy, n = 10; AKI, n = 11; CKD, n = 11; CI, n = 10). METHODS: Prospective, observational study. After assessment of routine renal biomarkers, stress (uTIMP-2, uIGFBP7) and damage (NGAL) biomarkers were measured, using ELISA kits, and normalized to urinary creatinine (uCr). RESULTS: Normalized uTIMP-2 and [uTIMP-2] × [uIGFBP7]/uCr were significantly higher in the AKI group (median 151.9 [range, 2.2-534.2] and 62.9 [1.1-266.8] pg/mL respectively), compared to healthy dogs (0.3 [0.2-74.7]; P < .001 and 0.16 [0.1-58.1] pg/mL; P < .001), dogs with CKD (0.7 [0.3-742.5]; P = .04 and 0.37 [0.2-180.1] pg/mL; P = .03) and CI dogs (1.9 [0.2-37.0]; P = .03 and 0.8 [0.1-16.1] pg/mL; P = .02). Fractional excretion of NGAL was significantly higher in dogs with AKI (54.17 [7.93-155.32] %), than in healthy (0.03 [0.01-0.21] %; P < .001) and CI dogs (3.05 [0.05-28.86] %; P = .02). CONCLUSIONS AND CLINICAL IMPORTANCE: Normalized uTIMP-2, [uTIMP-2] × [uIGFBP7]/uCr, and FeNGAL can be valuable renal biomarkers for early diagnosis of AKI in dogs.

Bacterial microbiome in the nose of healthy cats and in cats with nasal disease.

BACKGROUND: Traditionally, changes in the microbial population of the nose have been assessed using conventional culture techniques. Sequencing of bacterial 16S rRNA genes demonstrated that the human nose is inhabited by a rich and diverse bacterial microbiome that cannot be detected using culture-based methods. The goal of this study was to describe the nasal microbiome of healthy cats, cats with nasal neoplasia, and cats with feline upper respiratory tract disease (FURTD). METHODOLOGY/PRINCIPAL FINDINGS: DNA was extracted from nasal swabs of healthy cats (n = 28), cats with nasal neoplasia (n = 16), and cats with FURTD (n = 15), and 16S rRNA genes were sequenced. High species richness was observed in all samples. Rarefaction analysis revealed that healthy cats living indoors had greater species richness (observed species p = 0.042) and Shannon diversity (p = 0.003) compared with healthy cats living outdoors. Higher species richness (observed species p = 0.001) and Shannon diversity (p<0.001) were found in middle-aged cats in comparison to healthy cats in different age groups. Principal coordinate analysis revealed separate clustering based on similarities in bacterial molecular phylogenetic trees of 16S rRNA genes for indoor and outdoor cats. In all groups examined, the most abundant phyla identified were Proteobacteria, Firmicutes, and Bacteroidetes. At the genus level, 375 operational taxonomic units (OTUs) were identified. In healthy cats and cats with FURTD, Moraxella spp. was the most common genus, while it was unclassified Bradyrhizobiaceae in cats with nasal neoplasia. High individual variability was observed. CONCLUSION: This study demonstrates that the nose of cats is inhabited by much more variable and diverse microbial communities than previously shown. Future research in this field might help to develop new diagnostic tools to easily identify nasal microbial changes, relate them to certain disease processes, and help clinicians in the decision process of antibiotic selection for individual patients.

Bacterial microbiome of the nose of healthy dogs and dogs with nasal disease.

The role of bacterial communities in canine nasal disease has not been studied so far using next generation sequencing methods. Sequencing of bacterial 16S rRNA genes has revealed that the canine upper respiratory tract harbors a diverse microbial community; however, changes in the composition of nasal bacterial communities in dogs with nasal disease have not been described so far. Aim of the study was to characterize the nasal microbiome of healthy dogs and compare it to that of dogs with histologically confirmed nasal neoplasia and chronic rhinitis. Nasal swabs were collected from healthy dogs (n = 23), dogs with malignant nasal neoplasia (n = 16), and dogs with chronic rhinitis (n = 8). Bacterial DNA was extracted and sequencing of the bacterial 16S rRNA gene was performed. Data were analyzed using Quantitative Insights Into Microbial Ecology (QIIME). A total of 376 Operational Taxonomic Units out of 26 bacterial phyla were detected. In healthy dogs, Moraxella spp. was the most common species, followed by Phyllobacterium spp., Cardiobacteriaceae, and Staphylococcus spp. While Moraxella spp. were significantly decreased in diseased compared to healthy dogs (p = 0.005), Pasteurellaceae were significantly increased (p = 0.001). Analysis of similarities used on the unweighted UniFrac distance metric (p = 0.027) was significantly different when nasal microbial communities of healthy dogs were compared to those of dogs with nasal disease. The study showed that the canine nasal cavity is inhabited by a highly species-rich bacterial community, and suggests significant differences between the nasal microbiome of healthy dogs and dogs with nasal disease.