The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
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The PROximal Fracture of the Humerus: Evaluation by Randomisation (PROFHER) trial has recently demonstrated that surgery is non-superior to non-operative treatment in the management of displaced proximal humeral fractures. The objective of this study was to assess current surgical practice in the context of the PROFHER trial in terms of patient demographics, injury characteristics and the nature of the surgical treatment. A total of ten consecutive patients undergoing surgery for the treatment of a proximal humeral fracture from each of 11 United Kingdom hospitals were retrospectively identified over a 15 month period between January 2014 and March 2015. Data gathered for the 110 patients included patient demographics, injury characteristics, mode of surgical fixation, the grade of operating surgeon and the cost of the surgical implants.Objectives
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