Serum level of IP-10 increases predictive value of IL28B polymorphisms for spontaneous clearance of acute HCV infection.
ABSTRACT Single nucleotide polymorphisms (SNPs) in IL28B and serum levels of interferon γ inducible protein 10 (IP-10) predict outcomes of antiviral therapy in patients with chronic hepatitis C. We associated IL28B SNPs rs12979860 and rs8099917, along with serum levels of IP-10, with outcomes of patients with acute hepatitis C (AHC).
We studied 120 patients with AHC (64 male; 37 ± 16 years old) and 96 healthy individuals (controls). The IL28B SNPs rs12979860 and rs8099917 were detected using real-time polymerase chain reaction; serum concentrations of IP-10 were measured by enzyme-linked immunosorbent assays of 62 patients with AHC.
Hepatitis C virus was cleared spontaneously from 59 patients (49.2%). The IL28B rs12979860 C/C genotype was more frequent among patients with AHC than controls (62.5% vs 39.6%; P < .001) and among patients with spontaneous clearance than those without (74.6% vs 51.7%; P = .02) (positive predictive value, 60.3%). Patients with IL28B rs12979860 C/C more frequently developed jaundice (53.2% vs 27.6%; P = .022) than carriers of the T allele. The median level of IP-10 was lower among patients with AHC and spontaneous clearance (764 [113-2470] pg/mL) than those without spontaneous clearance (1481 [141-4412] pg/mL; P = .006). Based on receiver operating characteristic analysis, 540 pg/mL IP-10 was set as the cutoff for patients most likely to have spontaneous clearance (positive predictive value, 71.4%; negative predictive value, 65.9%). Including data on IP-10 levels increased the ability of the IL28B rs12979860 C/C to identify patients most likely to have spontaneous clearance (83% of those who had an IP-10 level <540 pg/mL and 32% who had an IP-10 level >540 pg/mL) (P < .01).
The combination of serum level of IP-10 and SNPs in IL28B can identify patients with AHC who are most likely to undergo spontaneous clearance and those in need of early antiviral therapy.
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ABSTRACT: Invariant regions' are self-adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions can be extracted directly from a single image. They are then described by a set of invariant features, which makes it relatively easy to match them between views, even under wide baseline conditions. In this contribution, two methods to extract invariant regions are presented. The first one starts from corners and uses the nearby edges, while the second one is purely intensity-based. As a matter of fact, the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit. This yields more correspondences and a system that can deal with a wider range of images. To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric). They allow to test the consistency of correspondences and hence to reject falsely matched regions. Experiments on images of real-world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach.International Journal of Computer Vision 01/2004; 59:61-85. · 3.62 Impact Factor
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ABSTRACT: The paper gives a snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions. Six types of detectors are included: detectors based on affine normalization around Harris (Mikolajczyk and Schmid, 2002; Schaffalitzky and Zisserman, 2002) and Hessian points (Mikolajczyk and Schmid, 2002), a detector of ‘maximally stable extremal regions', proposed by Matas et al.(2002); an edge-based region detector (Tuytelaars and VanGool, 1999) and a detector based on intensity extrema (Tuytelaars and VanGool, 2000), and a detector of ‘salient regions', proposed by Kadir, Zisserman and Brady(2004). The performance is measured against changes in viewpoint, scale, illumination, defocus and image compression. The objective of this paper is also to establish a reference test set of images and performance software, so that future detectors can be evaluated in the same framework.International Journal of Computer Vision 01/2005; 65:43-72. · 3.62 Impact Factor
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ABSTRACT: We present a novel Object Recognition approach based on affine invariant regions. It actively counters the prob- lems related to the limited repeatability of the region de- tectors, and the difficulty of matching, in the presence of large amounts of background clutter and particularly chal- lenging viewing conditions. After producing an initial set of matches, the method gradually explores the surround- ing image areas, recursively constructing more and more matching regions, increasingly farther from the initial on es. This process covers the object with matches, and simulta- neously separates the correct matches from the wrong ones. Hence, recognition and segmentation are achieved at the same time. The approach includes a mechanism for captur- ing the relationships between multiple model views and ex- ploiting these for integrating the contributions of the vie ws at recognition time. This is based on an efficient algorithm for partitioning a set of region matches into groups lying on smooth surfaces. Integration is achieved by measuring the consistency of configurations of groups arising from dif - ferent model views. Experimental results demonstrate the stronger power of the approach in dealing with extensive clutter, dominant occlusion, and large scale and viewpoint changes. Non-rigid deformations are explicitly taken into account, and the approximative contours of the object are produced. All presented techniques can extend any view- point invariant feature extractor.International Journal of Computer Vision 01/2006; 67:159-188. · 3.62 Impact Factor