A randomized,triple‐blind,placebo‐controlled clinical trial,evaluating the sesamin supplement effects on proteolytic enzymes,inflammatory markers,and clinical indices in women with rheumatoid arthritis

Inflammation is one of the main characteristics of rheumatoid arthritis. Based on the antiinflammatory properties of sesame, this study was conducted to evaluate the sesamin supplement effects on serum levels of some proteolytic enzymes, inflammatory biomarkers, and clinical indices in women with rheumatoid arthritis. In this randomized, triple‐blind, placebo‐controlled clinical trial, 44 patients were randomly divided in intervention and control groups. Patients received 200‐mg/day sesamin supplement or placebo in the intervention and control group for 6 weeks. Serum levels of proteolytic enzymes (hyaluronidase, aggrecanase, and matrix metalloproteinases‐3) and inflammatory biomarkers (hs‐CRP, IL‐1β, IL‐6, TNF‐α, and cyclooxygenase‐2) were measured with enzyme‐linked immunosorbent assay method at the beginning and end of the study. After intervention, serum levels of hyaluronidase and matrix metalloproteinases‐3 decreased significantly in sesamin group. Also, serum levels of hs‐CRP, TNF‐α, and cyclooxygenase‐2 in intervention group were significantly decreased in intervention group compared with placebo group. Sesamin supplementation also caused a significant reduction in the number of tender joints and severity of pain in these patients. According to the results, it seems that the sesamin by reducing inflammatory mediators can relieve clinical symptoms and pathological changes that caused by inflammatory impairment in patients with rheumatoid arthritis.     

Comparison of different compressed sensing algorithms for low SNR 19F MRI applications—Imaging of transplanted pancreatic islets and cells labeled with perfluorocarbons

Transplantation of pancreatic islets is a possible treatment option for patients suffering from Type I diabetes. In vivo imaging of transplanted islets is important for assessment of the transplantation site and islet distribution. Thanks to its high specificity, the absence of intrinsic background signal in tissue and its potential for quantification, ¹⁹F MRI is a promising technique for monitoring the fate of transplanted islets in vivo. In order to overcome the inherent low sensitivity of ¹⁹F MRI, leading to long acquisition times with low signal‐to‐noise ratio (SNR), compressed sensing (CS) techniques are a valuable option. We have validated and compared different CS algorithms for acceleration of ¹⁹F MRI acquisition in a low SNR regime using pancreatic islets labeled with perfluorocarbons both in vitro and in vivo. Using offline simulation on both in vitro and in vivo low SNR fully sampled ¹⁹F MRI datasets of labeled islets, we have shown that CS is effective in reducing the image acquisition time by a factor of three to four without seriously affecting SNR, regardless of the particular algorithms used in this study, with the exception of CoSaMP. Using CS, signals can be detected that might have been missed by conventional ¹⁹F MRI. Among different algorithms (SPARSEMRI, OMMP, IRWL1, Two‐level and CoSAMP), the two‐level l₁ method has shown the best performance if computational time is taken into account. We have demonstrated in this study that different existing CS algorithms can be used effectively for low SNR ¹⁹F MRI. An up to fourfold gain in SNR/scan time could be used either to reduce the scan time, which is beneficial for clinical and translational applications, or to increase the number of averages, to potentially detect otherwise undetected signal when compared with conventional ¹⁹F MRI acquisitions. Potential applications in the field of cell therapy have been demonstrated.