Microfocal X-Ray Computed Tomography Post-Processing Operations for Optimizing Reconstruction Volumes of Stented Arteries During 3D Computational Fluid Dynamics Modeling

Restenosis caused by neointimal hyperplasia (NH) remains an important clinical problem after stent implantation. Restenosis varies with stent geometry, and idealized computational fluid dynamics (CFD) models have indicated that geometric properties of the implanted stent may differentially influence NH. However, 3D studies capturing the in vivo flow domain within stented vessels have not been conducted at a resolution sufficient to detect subtle alterations in vascular geometry caused by the stent and the subsequent temporal development of NH. We present the details and limitations of a series of post-processing operations used in conjunction with microfocal X-ray CT imaging and reconstruction to generate geometrically accurate flow domains within the localized region of a stent several weeks after implantation. Microfocal X-ray CT reconstruction volumes were subjected to an automated program to perform arterial thresholding, spatial orientation, and surface smoothing of stented and unstented rabbit iliac arteries several weeks after antegrade implantation. A transfer function was obtained for the current post-processing methodology containing reconstructed 16 mm stents implanted into rabbit iliac arteries for up to 21 days after implantation and resolved at circumferential and axial resolutions of 32 and 50 μm, respectively. The results indicate that the techniques presented are sufficient to resolve distributions of WSS with 80% accuracy in segments containing 16 surface perturbations over a 16 mm stented region. These methods will be used to test the hypothesis that reductions in normalized wall shear stress (WSS) and increases in the spatial disparity of WSS immediately after stent implantation may spatially correlate with the temporal development of NH within the stented region

Detection of Hard Exudates in Retinal Fundus Images using Deep Learning

Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an early stage. Research has been done to detect hard exudates using regular image processing techniques and Machine Learning techniques. Here, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems, Computation, Automation and Networking http://icscan.in

Keywords given by authors of scientific articles in database descriptors

This paper analyses the keywords given by authors of scientific articles and the descriptors assigned to the articles in order to ascertain the presence of the keywords in the descriptors. 640 INSPEC, CAB abstracts, ISTA and LISA database records were consulted. After detailed comparisons it was found that keywords provided by authors have an important presence in the database descriptors studied, since nearly 25% of all the keywords appeared in exactly the same form as descriptors, with another 21% while normalized, are still detected in the descriptors. This means that almost 46% of keywords appear in the descriptors, either as such or after normalization. Elsewhere, three distinct indexing policies appear, one represented by INSPEC and LISA (indexers seem to have freedom to assign the descriptors they deem necessary); another is represented by CAB (no record has fewer than four descriptors and, in general, a large number of descriptors is employed; in contrast, in ISTA, a certain institutional code towards economy in indexing, since 84% of records contain only four descriptors

Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available