In 2010, in Osteoarthritis and Cartilage, we published a comprehensive systematic review applying the consensus BIPED criteria (Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic) criteria on serum and urinary biochemical markers for knee and hip osteoarthritis (OA) using publications that were available at that time. It appeared that none of the biochemical markers at that time were sufficiently discriminating to allow diagnosis and prognosis of OA in individual or limited numbers of patients, nor performed so consistently that they could function as primary outcome parameters in clinical trials. Also at present, almost 3 years later, this ultimate goal has not been reached (yet). Frankly, it might be questioned whether we are making the most adequate steps ahead and maybe we have to take a step back to reconsider our approaches. Some reflections are made and discussed: A critical review of molecular metabolism in OA and validation of currently investigated marker molecules in this may be vital and may lead to new and better markers. Creating cohorts in which synovial fluid (SF) is obtained in a systematic way, together with serum and urine, may also bring the field a further step ahead. Thirdly, better understanding of different phenotypes (subtypes) of OA may facilitate identification and validation of biochemical markers. Finally, the systems biology approach as discussed in the last years OA in review on biomarkers, although very complex, might provide steps forward. Looking ahead, we are optimistic but realistic in our expectations, we believe that the field can be brought forward by critically and cautiously reconsidering our approaches, and making changes forward, one step at a time.
Osteoarthritis year 2013 in review: biomarkers; reflecting before moving forward, one step at a time
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细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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