Long non-coding RNAs (lncRNAs) have been demonstrated to in the pathophysiology of multiple sclerosis (MS). In order to appraise the role of T cell-related lncRNAs in this disorder, we assessed expressions of NEST, RMRP, TH2-LCR, MAFTRR and FLICR in MS patients and healthy individuals. We detected significant difference in the expression of RMRP and FLICR between cases and controls. There were substantial correlations between expressions of NEST, RMRP, TH2-LCR, MAFTRR and FLICR lncRNAs among patients, but not controls. The strongest correlations were found between RMRP and TH2-LCR, and between MAFTRR and RMRP with correlation coefficients of 0.69 and 0.59, respectively. ROC curve analysis revealed appropriate power of FLICR in differentiating between MS patients and healthy controls (AUC value = 0.84). Expression of NEST lncRNA was positively correlated with disease duration in MS patients, but negatively correlated with age at onset. In brief, we reported dysregulation of two T cell-related lncRNAs in MS patients and proposed FLICR as a putative marker for this disorder.
Background: Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease.Material and Methods: Two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients’ FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures.Results: To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist’s work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively.Conclusion: An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.
Background: Headache is one of the most common types of pain which is considered among the most disabling of diseases. However, the severity of disability in some headache patients is more than others. Objectives: The aim of the present study was to compare different types of headache regarding pain-related variables and psychological factors. We also examined the predicting factors of disability in patients with headache. Methods: This cross-sectional study was performed in 320 patients with various types of headache based on the International Headache Society criteria, which was assessed by neurologists. Data was collected using migraine disability assessment for disability, frequency of headache and for pain intensity, patient health questionnaire for depression, and pain anxiety symptoms scale for pain-related anxiety. ANOVAs and post hoc Tukey's tests were used for comparing various types of headaches regarding pain-related and psychological factors. Regression analyses assessed the relation of pain-related and psychological variables with disability. Results: Patients with different types of headache revealed no significant differences regarding pain intensity (P = 0.27). Migraine patients showed the most pain frequency and chronicity compared to the other patients (P = 0.000). The levels of disability (P = 0.000), anxiety (P = 0.000) and depressive symptoms (P = 0.000) were also higher in patients with migraine compared to patients suffering from cluster or tension type headache. Pain chronicity (P = 0.01), anxiety (P = 0.007) and depression (P = 0.002) made significant contributions to the explanation of variance in 'disability'. Conclusions: The findings add further evidence to the relevance of cognitive-behavioral models of pain suggesting an important role for pain-related emotions regarding the consequences of pain (e.g. disability). The current data could help clinicians to decide which factors should be considered for a successful treatment of disability in headache patients.
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