Identification Of Rheumatoid Arthritis Research Articles

A Review of Connecting Bioinformatic Techniques to Rheumatoid Arthritis and its Associated Comorbidities.

Rheumatoid Arthritis (RA) is a progressive autoimmune condition inflicting serious threats to people's life and health by causing severe pain and joint destruction. It affects not only bones and joints but also causes comorbid conditions and shortens the lifetime. The interactions and synergistic effects of comorbid disease with RA are not yet well studied. Hence, understanding how these conditions will collectively affect the progression and outcome of RA is the current area of research. Identification of RA and comorbidities associated with target genes may uncover diagnosis and treatment methodologies. This review is to provide an overview of the interlinking approach of Rheumatoid Arthritis with its comorbid conditions and its systemic complications using bioinformatic techniques which would be useful to identify the genes and pathways that are in common for both RA and comorbid diseases. It would also emphasize the significance of bioinformatics in comparing the pathological features of RA and comorbid diseases. With the help of bioinformatics, valuable insights into the mechanism underlying Rheumatoid arthritis and comorbid diseases would be better understood.

Identification of rheumatoid arthritis using bone image features with radial basis function neural networks

AbstractRheumatoid arthritis (RA) leads to the destruction, deformation, and loss of function and causes joint damages to 85% of patients. The detection of bone mineral density from traditional x‐ray images consumes more time and it is observer dependent which decreases the evaluation performance when RA is in its early stage. Therefore, it is necessary to develop an observer‐independent computer‐aided automatic analysis system for evaluating JS narrowing. An efficient RA detection system based on feed‐forward neural network is proposed in this article. Initially the dataset is preprocessed to remove blur, redundant data and nonlinearities in RA images using wiener filter in less computation time. The edge boundaries are detected using nonlinear partial differential equation as the texture features of bones has great impact on the system accuracy. The detection strategy is implemented by optimized gray‐level co‐occurrence matrix based on bone image features. The selected features are optimized by genetic algorithm to improve the classification performance. The images are then classified as inflamed and noninflamed by radial basis function neural network. The effectiveness of the proposed classification approach is verified in terms of accuracy, sensitivity, and specificity and compared with the conventional convolutional neural network, artificial neural network, and support vector machine classifiers. Among the several existing techniques, our method found to be much effective with a maximum accuracy of 98.5% as some novel approaches are proposed and also tends to possess more compatible than the present system. Our computerized rheumatoid arteries detection approach will give more precise and flawless consistency rate.

Celecoxib and Etoricoxib may reduce risk of ischemic stroke in patients with rheumatoid arthritis: A nationwide retrospective cohort study.

Previous studies reported conflicting results about the risk of ischemic stroke associated with the use of non-steroidal anti-inflammatory drugs (NSAIDs) in patients with rheumatoid arthritis (RA). We aimed to investigate two specific COX-2 inhibitors, Celecoxib and Etoricoxib, and their corresponding effects on the risk of ischemic stroke in patients with RA. 10,857 patients newly diagnosed with RA were identified and sampled from the Taiwanese National Health Insurance Research Database during the period from 2001 to 2009. The identification of RA was based on the criteria of ICD-9-CM diagnosis code 714.0. Patients diagnosed with cerebrovascular disease and those receiving RA treatment prior to the first diagnosis of RA were excluded. Study endpoint was ischemic stroke, defined by ICD-9-CM code. Cox proportional hazard models and Kaplan Meier curves were used to reveal covariates and differences by drugs in the risk of ischemic stroke. Dosages for Celecoxib were defined as ≤ 200 and >200 mg/day; those for Etoricoxib were 0 and >0 mg/day. Among 7,904 RA patients, 6,669 did not take Celecoxib and 564 (8.46%) of them experienced an ischemic stroke event. Of the 597 individuals who took ≤ 200 mg/day of Celecoxib, 58 (9.72%) had strokes. Of the 638 patients who took >200 mg/day of Celecoxib, 38 (5.96%) eventually experienced a stroke. Among the 7,681 patients who did not take Etoricoxib, 654 (8.51%) experienced an ischemic stroke, while 6 (2.69%) in 223 patients who consumed Etoricoxib had a stroke event. Consuming more than 200 mg of Celecoxib per day for <3.5 years lowered the incidence rate for strokes [hazard ratio (HR) 0.67, 95% Confidence Interval (CI) 0.48-0.93 for dosage and HR 0.22, 95% CI 0.10-0.46 for duration, both p < 0.001], while consuming any dosage of Etoricoxib significantly decreases the possibility (HR 0.35, 95% CI 0.16-0.80, p < 0.001). On the other hand, consuming Etoricoxib for 8 years might have a neutral or even a potentially protective effect compared to at 3.8 years. This population-based retrospective cohort study has shown that Celecoxib and Etoricoxib reduce the risk of ischemic stroke in patients with RA in a dose- and time-dependent manner.

The prevalence of rheumatoid arthritis in middle-aged and elderly people living in Naqu City, Tibet, Autonomous Region of China

ObjectiveTo estimate the prevalence of rheumatoid arthritis (RA) in the Tibet Autonomous Region (China).MethodsA population-based cross-sectional survey was conducted on 1458 residents of Luoma Town, Tibet Autonomous Region, who were aged ≥ 40 years old. We interviewed participants using questionnaires, and rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA), and C-reactive protein (CRP) were determined. The identification of RA in this study was on the basis of criteria issued by the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) guideline.ResultsHerein, 782 participants completed all items of RA. The overall prevalence of RA was 4.86%, and the prevalence was higher in women than that in men (7.14% vs. 2.56%, p = 0.005). The age-standardized prevalence of RA was 6.30% (95% confidence interval (CI) 4.20–8.64%), which was 2.46% (95% CI 1.04%, 4.10%) and 9.59% (95% CI 5.93%, 13.77%) in men and women, respectively.ConclusionThe prevalence of RA is relatively higher in the Tibet than that in other areas of China.

Rheumatoid arthritis in 2017: Protective dietary and hormonal factors brought to light.

Tremendous progress has been made in the identification of rheumatoid arthritis (RA) risk factors in 2017. The results of epidemiologic studies highlighted dietary factors and hormones that are associated with slowing the transition from one preclinical phase of RA to another, potentially protecting individuals from developing RA.

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation.

IntroductionDiscrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers.MethodsThree multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.ResultsThe optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways.ConclusionFirst-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

Current Concepts in Synovial Fluid Analysis

The systematic examination of synovial fluid confirms the noninflammatory nature of degenerative joint disease, is diagnostic of gout, pseudogout, and septic arthritis, and will usually allow the identification of rheumatoid arthritis, systemic lupus erythematosus and Reiter's syndrome.