| Literature DB >> 25482211 |
Raquel S Sevilla1, Francisco Cruz2, Chi-Sung Chiu3, Dahai Xue4, Kimberly A Bettano1, Joe Zhu1, Kalyan Chakravarthy3, Robert Faltus3, Shubing Wang5, Amy Vanko1, Gain Robinson1, Mark Zielstorff3, John Miao3, Erica Leccese3, Donald Conway6, Lily Y Moy3, Belma Dogdas2, Milenko Cicmil3, Weisheng Zhang7.
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease resulting in joint inflammation, pain, and eventual bone loss. Bone loss and remodeling caused by symmetric polyarthritis, the hallmark of RA, is readily detectable by bone mineral density (BMD) measurement using micro-CT. Abnormalities in these measurements over time reflect the underlying pathophysiology of the bone. To evaluate the efficacy of anti-rheumatic agents in animal models of arthritis, we developed a high throughput knee and ankle joint imaging assay to measure BMD as a translational biomarker. A bone sample holder was custom designed for micro-CT scanning, which significantly increased assay throughput. Batch processing 3-dimensional image reconstruction, followed by automated image cropping, significantly reduced image processing time. In addition, we developed a novel, automated image analysis method to measure BMD and bone volume of knee and ankle joints. These improvements significantly increased the throughput of ex vivo bone sample analysis, reducing data turnaround from 5 days to 24 hours for a study with 200 rat hind limbs. Taken together, our data demonstrate that BMD, as quantified by micro-CT, is a robust efficacy biomarker with a high degree of sensitivity. Our innovative approach toward evaluation of BMD using optimized image acquisition and novel image processing techniques in preclinical models of RA enables high throughput assessment of anti-rheumatic agents offering a powerful tool for drug discovery.Entities:
Keywords: Automatic segmentation; Bone mineral density; CIA; Micro-CT; Polynomial fitting; Rheumatoid arthritis
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Year: 2014 PMID: 25482211 DOI: 10.1016/j.bone.2014.11.014
Source DB: PubMed Journal: Bone ISSN: 1873-2763 Impact factor: 4.398