5 resultados para vector-borne disease

em Aston University Research Archive


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After a severe outbreak of West Nile virus (WNV) in Cook County, Illinois, in 2002, detections of WNV in mosquitoes were frequent across the state in the following years despite small numbers of human cases. We conducted a spatio-temporal analysis of Culex (subgenus Culex) mosquitoes collected in 2004 in three mosquito abatement districts (MAD) in Cook County by calculating monthly estimates of mosquito density, prevalence of infected mosquitoes, and exposure intensity, which in turn is a product of mosquito density and infection rates. Mosquito infections were detected early at three sites in late May and were widely detected throughout the three MADs in the summer with infection rates as high as 13 per 1000 in August. Exposure intensities were higher at sites adjacent to the Des Plaines River, especially in August and September. The aggregated pattern of WNV transmission along the river might be related to the existence of substantial forest preserves and wetlands that might produce ecological conditions favorable for mosquito proliferation and interactions between mosquitoes and birds.

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The avascular nature of the human intervertebral disc (IVD) is thought to play a major role in disc pathophysiology by limiting nutrient supply to resident IVD cells. In the human IVD, the central IVD cells at maturity are normally chondrocytic in phenotype. However, abnormal cell phenotypes have been associated with degenerative disc diseases, including cell proliferation and cluster formation, cell death, stellate morphologies, and cell senescence. Therefore, we have examined the relative influence of possible blood-borne factors on the growth characteristics of IVD cells in vitro.

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In variant Creutzfeldt-Jakob disease (vCJD), a disease linked to bovine spongiform encephalopathy (BSE), florid-type prion protein (PrP(sc)) deposits are aggregated around the larger diameter (> 10 µm) cerebral microvessels. Clustering of PrP(sc) deposits around blood vessels may result from blood-borne prions or be a consequence of the cerebral vasculature influencing the development of the florid deposits. To clarify the factors involved, the dispersion of the florid PrP(sc) deposits was studied around the larger diameter microvessels in the neocortex, hippocampus, and cerebellum of ten cases of vCJD. In the majority of brain regions, florid deposits were clustered around the larger diameter vessels with a mean cluster size of between 50 µm and 628 µm. With the exception of the molecular layer of the dentate gyrus, the density of the florid deposits declined as a negative exponential function of distance from a blood vessel profile suggesting that diffusion of molecules from blood vessels is a factor in the formation of the florid deposits. Diffusion of PrP(sc) directly into the brain via the microvasculature has been demonstrated in vCJD in a small number of cases. However, the distribution of the prion deposits in vCJD is more likely to reflect molecular 'chaperones' diffusing from vessels and promoting the aggregation of pre-existing PrP(sc) in the vicinity of the vessels to form florid deposits.

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We present an assessment of the practical value of existing traditional and non-standard measures for discriminating healthy people from people with Parkinson's disease (PD) by detecting dysphonia. We introduce a new measure of dysphonia, Pitch Period Entropy (PPE), which is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. We collected sustained phonations from 31 people, 23 with PD. We then selected 10 highly uncorrelated measures, and an exhaustive search of all possible combinations of these measures finds four that in combination lead to overall correct classification performance of 91.4%, using a kernel support vector machine. In conclusion, we find that non-standard methods in combination with traditional harmonics-to-noise ratios are best able to separate healthy from PD subjects. The selected non-standard methods are robust to many uncontrollable variations in acoustic environment and individual subjects, and are thus well-suited to telemonitoring applications.

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There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.