Prevalence and risk factors for common respiratory pathogens within a cohort of pet cats in the UK.

OBJECTIVES: Feline herpesvirus (FHV), feline calicivirus (FCV) and Chlamydia felis are common causes of upper respiratory tract disease (URTD) in cats. Their prevalence in the UK pet cat population has not been reported and little is known regarding the risk factors for their oral carriage. METHODS: Total nucleic acid was extracted from owner-collected buccal swabs (n=600) from cats enrolled in a self-selected longitudinal cohort study. Duplex quantitative PCRs for the detection of FHV and C. felis genomic DNA and reverse-transcriptase quantitative PCRs for the detection of FCV genomic RNA were performed. Duplicates, swabs with insufficient host DNA/RNA, and cats with missing data were excluded. Selected epidemiological data were interrogated using univariable and multi-variable logistic regression modelling to identify risk factors. RESULTS: Data from 430 cats were included in the final statistical model. Of these, 2.1% (n=9/430; 95% CI 1.0% to 3.9%) were positive for FHV, 13.3% (n=57/430; 95% CI 10.2% to 16.8%) positive for FCV and 1.2% (n=5/430; 95% CI 0.4% to 2.7%) positive for C. felis. FCV co-infection was present in five (44%) FHV-positive cats and three (60%) C. felis-positive cats. FCV carriage was more frequent in purebred cats (odds ratio 2.48; 95% CI 1.37 to 4.49) and in cats with current or historical clinical signs compatible with URTD (odds ratio 2.98; 95% CI 1.22 to 7.27). CLINICAL SIGNIFICANCE: FCV was the most frequently encountered URTD pathogen in this sample of cats; this should be noted for disinfectant choice. In cats suspected of having FHV or C. felis infection, assessment for co-infection with FCV is recommended.

Network analysis: a new approach to study endocrine disorders.

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

The effect of dynamic vessel motion on haemodynamic parameters in the right coronary artery: a combined MR and CFD study.

Human right coronary artery (RCA) haemodynamics is investigated using computational fluid dynamics (CFD) based on subject-specific information from magnetic resonance (MR) acquisitions. The dynamically varying vascular geometry is reconstructed from MR images, incorporated in CFD in conjunction with pulsatile flow conditions obtained from MR velocity mapping performed on the same subject. The effects of dynamic vessel motion on instantaneous and cycle-averaged haemodynamic parameters, such as wall shear stress (WSS), time-averaged WSS (TAWSS) and oscillatory shear index (OSI), are examined by comparing an RCA model with a time-varying geometry and those with a static geometry, corresponding to nine different time-points in the cardiac cycle. The results show that the TAWSS is similar for the dynamic and static wall models, both qualitatively and quantitatively (correlation coefficient 0.89-0.95). Conversely, the OSI shows much poorer correlations (correlation coefficient 0.38-0.60), with the best correspondence being observed with the static models constructed from images acquired in late diastole (at t = 0 and 800 ms, the cardiac cycle is 900 ms). These findings suggest that neglecting dynamic motion of the RCA is acceptable if TAWSS is the primary focus but may result in underestimation of haemodynamic parameters related to the oscillatory nature of the blood flow.