Objective: Scoliosis may impact the mechanical loading and cause secondary changes of the sacroiliac joints and lumbar spine. Our goal was to look how lumbar scoliosis modify the clinical and... Show moreObjective: Scoliosis may impact the mechanical loading and cause secondary changes of the sacroiliac joints and lumbar spine. Our goal was to look how lumbar scoliosis modify the clinical and imaging-study in patients with recent-onset inflammatory back pain (IBP) suggesting axial spondyloarthritis (axSpA).Methods: Baseline weight-bearing lumbar-spine radiographs obtained in the DESIR cohort of patients aged 18-50 years and having IBP for at least 3 months but less than 3 years suggesting axSpA were studied. After training on scoliosis detection based on Cobb's angle>10 degrees plus Nash-Moe grade >= 1, readers blinded to patient data measured spine lumbar scoliosis, sacral horizontal angle, lumbosacral angle and lumbar lordosis on the radiograph of the lumbar and scored sacroiliitis on the radiograph of the pelvis. Baseline MRIs T1 and STIR of the lumbar spine and sacroiliac joints were evaluated for respectively degenerative changes and signs of axSpA.Results: Of the 360 patients (50.8% females) 88.7% had lumbar pain and 69.3% met ASAS criteria for axSpA. Mean Cobb's angle was 3.2 degrees +/- 5.0 degrees and 28 (7.7%) patients had lumbar scoliosis. No statistical differences were observed for radiographic sacroiliitis, MRI sacroiliitis, modified Stoke Ankylosing Spondylitis Spinal Score, Pfirmmann score, high-intensity zone, protrusion, extrusion, MODIC score between patients with and without scoliosis. In both groups, degenerative changes by MRI were rare and predominated at L4-L5 and L5-S1.Conclusion: In patients with early IBP suggesting axSpA, lumbar scoliosis was not associated with inflammatory or degenerative changes. (C) 2019 Elsevier Inc. All rights reserved. Show less
Background: The pathophysiology of systemic sclerosis (SSc) is complex and elusive, however, considering the strong female preponderance and different clinical characteristics between men and women... Show moreBackground: The pathophysiology of systemic sclerosis (SSc) is complex and elusive, however, considering the strong female preponderance and different clinical characteristics between men and women, a contribution of sex hormones has been proposed.Objectives: We undertook this systematic literature review to investigate: (1) the role played by male and female sex hormones in the pathogenesis of SSc; (2) how sex hormone levels change in SSc patients and how hormonal variations modify the progression of SSc; (3) the effect of therapies targeting sex hormones on the disease course.Methods: A literature search was performed in Pubmed, Embase, Web of Science, and Cochrane library databases. Given the heterogeneity in study design, different quality assessment tools were applied where appropriate.Results: We retrieved 300 articles and 30 were included in the review. The available evidence points to a fibrogenic, but also a vasodilatory, role of estrogens in SSc. With the limitation of small sample sizes, women with SSc tend to have lower levels of androgens and non-significantly higher levels of estradiol compared to healthy controls, while in men we found increased levels of estradiol and discordant results for androgens. After menopause the skin score seems to decrease and prevalence of pulmonary artery hypertension seems to rise, which might be prevented by the use of hormone replacement therapy. No recent high-quality trial evaluated the efficacy of hormone-targeting therapies in SSc.Conclusions: Few translational studies of varying quality evaluated the role of sex hormones in SSc showing possible profibrotic and vasodilatatory effects of estrogens, but more research is needed to elucidate the extent of this contribution. Insights on the influence of sex hormones, along with the availability of new compounds acting on estrogen pathways, might provide ideas for additional studies on the application of sex hormone-targeting therapies in SSc. (C) 2019 Elsevier Inc. All rights reserved. Show less
To prevent chronicity of Rheumatoid Arthritis (RA) by early treatment, detecting inflammatory signs in an early phase is essential. Since Magnetic Resonance Imaging (MRI) of the wrist, hand and... Show moreTo prevent chronicity of Rheumatoid Arthritis (RA) by early treatment, detecting inflammatory signs in an early phase is essential. Since Magnetic Resonance Imaging (MRI) of the wrist, hand and foot can detect inflammation before it is clinically detectable, this modality may play an important role in achieving very early diagnoses. By collecting large amounts of MRI data from healthy controls and patients with arthralgia suspicious for progression to RA, patterns can be studied that are most specific for early development of RA. Furthermore, MRI can be used as outcome parameter for randomized placebo-controlled trials on early RA treatment, by detecting subtle changes in image intensities originating from natural progression or treatment effects. Very large amounts of MRI data, however, make manual quantification impractical and the coarse scale used in visual scoring systems (i.e. whole values between 0 and 3) limits its sensitivity to detect changes that are likely to be very subtle in such an early phase. In recent years, advances in artificial intelligence and especially 'deep learning' in interpreting medical images have shown that -in specific areas- a computerized analysis can outperform human observers. Therefore, research has been initiated into applying these artificial intelligence techniques to the quantification of early RA from MRI data. In this paper, an overview is given on the background and history of artificial intelligence, with a special focus on recent developments in 'deep learning', and how these techniques could be applied to detect subtle inflammatory changes in MRI data. (C) 2019 The Author(s). Published by Elsevier Inc. Show less
