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Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.
Learn More >We conducted a systematic review to determine measurement properties and minimal important change (MIC) of World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) short (12 questions) and full (36 questions) versions in persons with non-specific LBP.
Learn More >Connectivity between the nucleus accumbens (NAc) and ventromedial prefrontal cortex (vmPFC) and reward learning independently predict the transition from acute to chronic back pain (CBP). However, how these predictors are related remains unclear. Using functional magnetic resonance imaging, we investigate NAc- and vmPFC-dependent reward learning in 50 patients with subacute back pain (SABP) and follow them over 6 months. Additionally, we compare 29 patients with CBP and 29 pain-free controls to characterize mechanisms of reward learning in the chronic stage. We find that the learning-related updating of the value of reinforcement (prediction error) in the NAc predicts the transition to chronicity. In CBP, compared with controls, vmPFC responses to this prediction error signal are decreased, but increased during a discriminative stimulus. Distinct processes of reward learning in the vmPFC and NAc characterize the development and maintenance of CBP. These could be targeted for the prevention and treatment of chronic pain.
Learn More >Severe pain is prevalent in cardiac surgery patients and can increase cardiac complications, morbidity and mortality. The objectives of the study were to assess perioperative pain intensity and to assess predictors of pain post-cardiac surgery, including clinical characteristics and depression.
Learn More >Telemedicine is defined as video-based consultations with synchronous video and sound. This systematic review investigated the use of telemedicine for headache patients. The primary outcomes of interest were treatment efficacy, feasibility, safety, convenience, compliance, and patient satisfaction.
Learn More >Run-In (RI) periods can be used to improve the validity of randomized controlled trials (RCTs), but their utility in Chronic Pain (CP) RCTs is debated. Cost-effectiveness analysis (CEA) methods are commonly used in evaluating the results of RCTs, but they are seldom used for designing RCTs. We present a step-by-step overview to objectively design RCTs via CEA methods and specifically determine the cost effectiveness of a RI period in a CP RCT.
Learn More >Piezo1 and Piezo2 are recently discovered mechanosensory ion channels. Piezo channels transduce mechanical stimulation into cellular signaling in a variety of tissues and organ systems. The functional roles of Piezo1 and Piezo2 have been revealed in both developmental and physiological scenarios by using mouse genetic models. Mechanotransduction by Piezo1 channels regulates osteoblast/osteocyte activity and, thus, strengthens the skeleton enabling it to adapt to a wide range of mechanical loadings. Deletion of the Piezo1 gene in the developing skeleton causes bone malformations that lead to spontaneous bone fractures, while inactivity of Piezo1 in adulthood results in osteoporosis. Furthermore, Piezo2 channels in sensory neurons might provide another route of skeletal regulation. Piezo channels also regulate the proliferation and differentiation of various types of stem cells. PIEZO1 and PIEZO2 mutations and channel malfunctions have been implicated in an increasing number of human diseases, and PIEZO channels are currently emerging as potential targets for disease treatment. This review summarizes the important findings of Piezo channels for skeletal development and homeostasis using the mouse genetic model system.
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