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Abstract：
Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.
摘要：
骨关节炎是马跛行和关节疾病的主要原因。一个简单的，经济,和准确的诊断测试是需要常规筛查OA。这项研究旨在评估基于红外（IR）的滑液生物标志物概况，以检测与创伤诱发的马腕骨关节炎（OA）模型相关的早期变化。17只健康纯种雌马中有9只通过关节镜诱发了单侧腕骨OA；其余的用作假手术对照。两组的中位年龄为2岁。在手术诱导OA(第0天)之前每周获得滑液(SF),直到第63天。从干燥的SF膜获得IR吸收光谱。在光谱预处理之后,使用随机森林的预测模型来区分OA，Sham,和对照样品。区分OA和任何其他关节组的准确性为80％。采样日的分类准确率为87%。对于成对分类任务，OA与OA的关节精度为75%。OA控制和70%的OA与Sham.按组分离马匹的准确性(OA与Sham）为68%。总之,SFIR光谱法可准确区分外伤引起的OA关节与对照。
