No abstract

Dengue (DEN) is a mosquito-borne disease caused by four DENV serotypes (DENV-1, -2, -3, and -4) that have no treatments or vaccines. Primary infection with one DENV usually leads to acute illness followed by lifelong homotypic immunity, but susceptibility to infection by the other three DENVs remains. Therefore, a vaccine needs to protect from all four DENVs simultaneously. To date a suitable animal model to mimic systemic human illness exists only for DENV-2 in immunocompromised mice using passaged viruses; however, models are still needed for the remaining serotypes. This study describes establishment of a lethal systemic DENV-3 infection model with a human isolate in immunocompromised mice and is the first report of lethal infection by a nonadapted clinical DENV isolate without evidence of neurological disease. Our DENV-3 model provides a relevant platform to test DEN vaccines and antivirals.

Mosquitoes infect human beings with arboviruses while taking a blood meal, inoculating virus with their saliva. Mosquito saliva contains compounds that counter host hemostatic, inflammatory, and immune responses. Modulation of these crucial defensive responses may facilitate virus infection. Using a murine model we explored the potential for mosquitoes to impact the course of West Nile virus (WNV) disease by determining whether differences in pathogenesis occurred in the presence or absence of mosquito saliva. Mice inoculated intradermally with 10(4) pfu of WNV subsequent to the feeding of mosquitoes developed more progressive infection, higher viremia, and accelerated neuroinvasion than the mice inoculated with WNV alone. At a lower dose of WNV (10(2) pfu), mice fed upon by mosquitoes had a lower survival rate. This study suggests that mosquito feeding and factors in mosquito saliva can potentiate WNV infection, and offers a possible mechanism for this effect via accelerated infection of the brain.

We describe a computationally straightforward post-hoc statistical method of correcting spatially dependent image pixel intensity nonuniformity based on differences in local tissue intensity distributions. Pixel intensity domains for the various tissues of the composite image are identified and compared to the distributions of local samples. The nonuniformity correction is calculated as the difference of the local sample median from the composite sample median for the tissue class most represented by the sample. The median was chosen to reduce the effecters on determining the sample statistic and to allow a sample size small enough to accurately estimate the spatial variance of the image intensity nonuniformity. The method was designed for application to two-dimensional images. Simulations were used to estimate optimal conditions of local histogram kernel size and to test the accuracy of the method under known spatially dependent nonuniformities. The method was also applied to correct a phantom image and cerebral MRIs from 15 healthy subjects. Results show that the method accurately models simulated spatially dependent image intensity differences. Further analysis of clinical MR data showed that the variance of pixel intensities within the cerebral MRI slices and the variance of slice volumes within individuals were significantly reduced after nonuniformity correction. Improved brain-cerebrospinal fluid segmentation was also obtained. The method significantly reduced the variance of slice volumes within individuals, whether it was applied to the native images or images edited to remove nonbrain tissues. This statistical method was well behaved under the assumptions and the images tested. The general utility of the method was not determined, but conditions for testing the method under a variety of imaging sequences is discussed. We believe that this algorithm can serve as a method for improving MR image segmentation for clinical and research applications.