Impact of long-term corticosteroid therapy on the distribution pattern of lower limb atherosclerosis

OBJECTIVE: Ectopic calcification and mediacalcinosis can be promoted by corticosteroid use. Aim of the present investigation is to describe macrovascular disease features in patients with long-term corticosteroid therapy and symptomatic lower limb peripheral arterial occlusive disease (PAD). METHODS: A consecutive series of 2783 patients undergoing clinical and angiographic work-up of PAD were screened for long-term (>5 years) corticosteroid use (group A). Comparison was performed to a randomly selected age-, sex- and risk factor-matched PAD control cohort from the same series without corticosteroid use (group B). Patients with diabetes mellitus or severe renal failure were excluded. Arterial calcification was evaluated by qualitative assessment on radiographic images. Severity of atherosclerotic lesions was analysed from angiographic images using a semi-quantitative score (Bollinger score). RESULTS: In general, 12 patients (5 males, mean age 78.5 +/- 9.0 years) with 15 ischaemic limbs qualified to be enrolled in group A and were compared to 23 matching control patients (6 2 males, mean age 79.5 +/- 6 years) with 32 ischaemic limbs. Incompressibility of ankle arteries determined by measurement of the ankle-brachial index was seen in 12 limbs (80%) in group A compared to 3 limbs (9%) in group B (p = 0.0009). No significant difference was found comparing group A and B for segmental calcification, whereas comparison of the atherosclerotic burden using the angiographic severity score showed a significantly higher score at the infragenicular arterial level in group A (p = 0.001). CONCLUSION: Findings suggest that the long-term corticosteroid therapy is associated with a distally accentuated, calcifying peripheral atherosclerosis inducing arterial incompressibility. This occlusion pattern is comparable to patients with renal failure or diabetes. Further research is required to support our observations.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

An application of combinatorial optimization in the printing industry

Offset printing is a common method to produce large amounts of printed matter. We consider a real-world offset printing process that is used to imprint customer-specific designs on napkin pouches. The print- ing technology used yields a number of specific constraints. The planning problem consists of allocating designs to printing-plate slots such that the given customer demand for each design is fulfilled, all technologi- cal and organizational constraints are met and the total overproduction and setup costs are minimized. We formulate this planning problem as a mixed-binary linear program, and we develop a multi-pass matching-based savings heuristic. We report computational results for a set of problem instances devised from real-world data.