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In our January issue: articles on immune-modulatory cytokines in rheumatoid arthritis, gene therapy for cartilage repair, management of systemic lupus erythematosus and machine learning prediction models in knee osteoarthritis. Image of skin from a patient with dermatomyositis.
Image supplied by Majid Zeidi, Kristen Chen and Victoria P. Werth, University of Pennsylvania School of Medicine.
‘Patient-centered’ research has traditionally meant that researchers and clinicians design trials for the benefit of patients. By contrast, patients today are central to study design and reporting outcomes, and new research agendas recognize that patients can point the way to research questions and how to address them.
Vitamin D is important for skeletal metabolism and calcium homeostasis, but conflicting evidence exists as to whether vitamin D supplementation has a protective effect on musculoskeletal outcomes. Do the results of a new meta-analysis bring clarity or increase confusion?
The ability to resolve, rather than suppress, inflammation could enable new possibilities for the treatment of chronic diseases such as rheumatoid arthritis. Knowing more about the function of immune-regulatory cytokines is the first step towards realizing their therapeutic potential.
Gene therapy and tissue engineering strategies for the treatment of cartilage repair each pose unique challenges to clinical translation. Could combining the two approaches open new avenues for the treatment of articular cartilage defects in patients with osteoarthritis?
The treatment of systemic lupus erythematosus involves a balance between control of disease activity and prevention of therapeutic harm that requires careful optimization. In this Review, the authors discuss available and emerging therapeutic strategies that exploit the current drug armamentarium.
In osteoarthritis, identifying those patients at most risk of disease progression and/or who might benefit the most from therapy is an important step. Incorporating machine-learning into the development of prediction models has great potential for moving towards precision medicine