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Multiple sclerosis is a recurrent and progressive inflammatory autoimmune disease causing demyelination in the central nervous system. Nowadays, the number of MS patients is increasing, but the diagnostic process and disease management are still quite difficult and costly and time consuming. The combination of methods used for clinical MS diagnosis mainly relies on MRI, that cannot be used as routine analysis. Classical methods of bio-logical liquids analysis used for disease diagnosis and monitoring, include electrophoretic and labeled antibody -based techniques requiring professional personnel for analysis performing and results interpretation. In line with that, there is a need for reliable, sensitive and cost-effective methods that would be easier to take for both the staff and the patient. Biosensors application for MS biomarkers detection would provide such advantages. This review aimed to summarize studies carried out in this field available at the literature so far, evaluate current situation and emphasize possible perspectives for research and clinical application. Since this is multi-disciplinary area of research, including development of biosensors, their use in clinical practice and making diagnostic clues, this review is expected to help different specialists, medical doctors, engineers, biochemists to use the results of each other's work for common good. Possible transition to the use biosensors in clinical practice may be associated with some difficulties that must be taken into account were either considered.

Objectives: To summarize and compare the benefits and harms of biological agents used as monotherapy for rheumatoid arthritis (RA) in order to inform decisions for patients who are intolerant to conventional DMARD therapy.Methods: We searched MEDLINE, EMBASE, CENTRAL, and other sources for randomised trials that compared biological monotherapy with methotrexate, placebo, or other biological monotherapies. Primary outcomes were ACR50 and the number of patients who discontinued due to adverse events. Our network meta-analysis was based on mixed-effects logistic regression, including both direct and indirect comparisons of the treatment effects, while preserving the randomised comparisons within each trial. PROSPERO identifier: CRD42012002800.Results: The analysis comprises 28 trials (8602 patients), including all nine biological agents approved for RA. Eight trials included "DMARD-naive", and 20 "DMARD-Inadequate responder" (DMARD-IR) patients. All agents except anakinra and infliximab were superior (p 0.52). However, because rituximab was evaluated in just 40 patients, our confidence in the estimates is limited. When including only DMARD-IR trials, the same statistical pattern emerged; in addition etanercept and tocilizumab were superior to abatacept. At recommended doses, both etanercept and tocilizumab were superior to adalimumab and certolizumab. No statistically significant differences among biological agents were found with respect to discontinuation due to adverse events (p > 0.068).Conclusions: Evidence from randomised trials suggests that most biological agents are effective as mono therapy. Although our confidence in the estimates is limited, etanercept or tocilizumab may be the optimal choice for most patients who need treatment with biological monotherapy. However, given our limited confidence in the estimates including possibility of bias, it is appropriate to strongly weight patients' preferences and values in the final treatment choice. (C) 2017 Elsevier Inc. All rights reserved.

Objectives: The aim of this paper is the development of an automated method for the segmentation and quantification of inflammatory tissue of the hand in patients suffering form rheumatoid arthritis using contrast enhanced T1-weighted magnetic resonance images. Methods and materials: The proposed automatic method consists of four stages: (a) preprocessing of images, (b) identification of the number of clusters, by minimizing the appropriate validity index, (c) segmentation using the fuzzy C-means algorithm employing four features which are related to intensity and the location of pixels and (d) postprocessing, where defuzzification is performed and small objects and vessels are eliminated and quantification takes place. Results: The proposed method is evaluated using a dataset of image sequences obtained from 25 patients suffering from rheumatoid arthritis. For 17 of them we have obtained follow-up images after 1 year treatment. The obtained sensitivity and positive predictive rate is 97.71% and 83.35%, respectively. In addition, quantification of inflammation before and after treatment, as welt as, comparison with manual segmentation is carried out. Conclusions: The proposed method performs very well and results in high detection and quantification accuracy. However, the reduction of false positives and the identification of old inflammation must be addressed. (c) 2007 Elsevier B.V. All rights reserved. Artif Intell Med