Prevalence of Multiple Sclerosis in Vitiligo Patients and Their First-Degree Relatives: Two Diseases with Similarities in Pathogenesis and Treatment

Introduction: Vitiligo is a common pigmentation disorder manifested by white macules and patches. It is accompanied by some autoimmune and neurological diseases. Recently, it has been suggested that multiple sclerosis (MS) is more common in vitiligo patients and that they have a higher risk of developing MS during their lifetime. Objectives: In this study, we aimed to determine the prevalence of MS in patients with vitiligo and their first-degree relatives and compare it with the prevalence in the population. Methods: In this cross-sectional study, data were consecutively collected from patients referred to Razi Hospital from March 2020 to December 2021. Results: 709 patients with vitiligo participated in this study, and 15 reported a history of MS (2.12%, 95% CI: 1.06-3.17%). This rate was significantly higher than the prevalence of MS in the average population of Tehran (p < 0.001). Of the 2886 first-degree relatives of the patients, 10 had MS (0.35%, 95% CI: 0.13-0.56%), which was higher than the prevalence of MS, yet not statistically significant. Conclusions: A significant association between vitiligo and MS was observed, which should be of clinical and therapeutic importance. However, the prevalence of MS in first-degree relatives of vitiligo patients was higher than the average rate, yet not statistically significant

Serologic Biomarkers in Pemphigus Monitoring: C-reactive Protein, Macrophage Migration Inhibitory Factor, and Prolactin Levels Versus Autoantibody Assays

Evaluation and monitoring of pemphigus vulgaris (PV) typically involve autoantibody detection by enzyme-linked immunosorbent assay (ELISA) and indirect immunofluorescence (IIF). We aimed to determine the levels of antipemphigus immunoglobulin (Ig) G autoantibodies using ELISA and IIF (as standard biomarkers), and compare it to prolactin, macrophage migration inhibitory factor (MIF), and C-reactive protein (CRP) (as nonstandard biomarkers) to determine which of these non-standard biomarkers is appropriate for PV monitoring. The experiment was performed before and during therapy. Anti-Dsg immunoglobulin G autoantibodies were measured using ELISA and IIF (as standard biomarkers) versus prolactin, MIF, and CRP (nonstandard), before 1 and 3 months after the treatment. Before beginning the treatment, the severity of the disease was determined using the pemphigus disease area Index (PDAI). We enrolled 60 newly diagnosed patients with PV (32 men and 28 women; mean age=43.8±14.2 years). Before treatment, the levels of anti-Dsg1, anti-Dsg3, and IIF were high and had a significant relationship with PDAI. PDAI also had a connection with the levels of CRP and prolactin. The anti-Dsg1, anti-Dsg3, IIF, and CRP titers decreased in patients treated with conventional (prednisolone plus azathioprine) and rituximab therapy during and after treatment. In conclusion, anti-Dsg1, anti-Dsg3, and IIF autoantibody titers remain standard biomarkers for assessing disease activity, severity, and PV monitoring. The trend of CRP was similar to that of anti-Dsg1, anti-Dsg3, and IIF. Thus, CRP may be used for PV monitoring

A study on the immune response induced by a DNA vaccine encoding Mtb32C-HBHA antigen of Mycobacterium tuberculosis

Objective(s): Tuberculosis (TB) has still remained a global health issue. One third of the world's population is infected with tuberculosis and the current BCG vaccine has low efficiency; hence, it is necessary to develop a new vaccine against TB. The aim of the current study was to evaluate the efficiency of a novel DNA vaccine encoding Mtb32C-HBHA antigen in inducing specific immune responses against Mycobacterium tuberculosis. Materials and Methods: A DNA plasmid vaccine expressing Mtb32C-HBHA fusion protein was constructed and its ability in protein expression was examined by RT-PCR and Western blot methods. Female BALB/c mice were vaccinated with 100 μg of purified recombinant vector in an attempt to assess its immunogenicity and protective efficacy. Further, the cytokines, IFN-γ, IL-12, IL-4, IL-10, and TGF-β were assessed. Results: The levels of all the studied cytokines were significantly increased (

A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion

Detecting existing communities in social networks is a significant process in analyzing these networks. In recent years, the community detection problem has become popular for detecting structures of social networks. Due to high importance of this problem, various algorithms have been developed in the literature to find communities of complex networks. In this research, a hybrid meta-heuristic consisting of the genetic algorithm (GA) and the invasive weed optimization (IWO) method have been proposed which aims to find appropriate and high quality solutions for the community detection problem. In this hybrid method, the initial solutions are generated via the IWO algorithm, and thereafter the optimization process is continued by means of the genetic algorithm. The proposed algorithm is known as the GAIWO. Fitness of solutions is determined in terms of the modularity density criterion. Modularity density has a maximization essence and determines the quality of detected communities. To evaluate the efficiency of the GAIWO, four other methods have been employed and their results have been compared. Comparisons have been made on several networks with different sizes. Input parameters of all algorithms have been tuned by a design of experiments approach. The outputs indicate appropriate efficiency of the proposed algorithm. Validation of the results have been investigated by means of the Normalized Mutual Information (NMI) metric

Multi-lead ECG heartbeat classification of heart disease based on HOG local feature descriptor

Introduction: ECG data play an important role in the diagnostics of various cardiovascular diseases. Classification of multi-lead ECG signals could be challenging even for well-trained physicians. In this study we propose a new approach for multi-lead ECG classification. Method: Five-types of 15-lead ECG data namely healthy control, bundle branch block, cardiomyopathy, Dysrhythmia, and myocardial infarction patients from two types of datasets, 5319 and 6647 heartbeats from Baqiyatallah and PTB Diagnostic ECG database, were used, respectively. One-dimensional total variation regularization was used to denoising ECG data. Heartbeats were extracted by one cardiologist and saved as images with jpg format. Histogram of oriented gradients method was used to extract feature of images. for classification task support vector machine and fully connected neural network were used. Five-fold cross validation was used for validating the models. Result: For 15-lead ECG PTB Diagnostic database, the best classification models are SVM model with cubic (accuracy: 99.9%, Range: 99.77% - 100%) and quadratic (accuracy: 99.88%, Range: 99.77%-100%) kernel function, for this dataset fully connected accuracy is 99.4% with range of 99.02%- 99.70%. Regarding to the Baqyatallah dataset SVM with cubic (accuracy: 99.83%, Range:99.72%-100%) and quadratic (accuracy: 99.77%, Range: 99.62%-99.9%) were the best classification model and the accuracy for fully connected neural network was 99.1% with the range of 98.59%-99.62% based on HOG descriptors. Expected sigmodal kernel all classification method have accuracy more than 99%. Discussion: simultaneous use of HOG feature extraction method and appropriate classification algorithm such as SVM or fully connected neural network can classify 15-lead ECG heart-beat for different heart disease with high accuracy and adding other relevant patients’ information can be easily done in order to increase the method performance

Illness perception of patients with pemphigus vulgaris

Objective: Little is known about illness perception in patients with pemphigus vulgaris (PV). We designed a cross-sectional study to clarify the beliefs about PV. Methods: A total of 100 patients with PV (45 men, 55 women) completed the Illness Perception Questionnaire-Revised to assess beliefs about seven aspects of illness perception, including chronicity, recurrence, consequences, self and medicine role in controlling illness, coherence, and emotional representation. The relationship between illness perception and clinical and demographic variables was evaluated. Results: Patients viewed PV as a chronic and cyclical disease with important impression on their life and emotions. Patients had a good understanding of the disease and supposed an acceptable role for themselves and medical treatment. Interestingly, the clinical subtype and severity of the disease did not influence any aspect of illness perception, but some differences on the basis of demographic data were demonstrated. Conclusion: Our patients had a relatively good understanding of their illness and a correct perception about chronicity and the cyclical identity of illness. The patients believed that their life and emotions had been strongly influenced by the disease but were hopeful for a cure. Because correction of misconceptions about a disease may improve treatment outcomes, an assessment of patients' illness perception may be useful to try and modify perception

Distinguishing Immunohistochemical Features of Alopecia Areata From Androgenic Alopecia

Background: Distinction between alopecia areata (AA) and androgenic alopecia (AGA) can be made according to clinical presentation and biopsy findings. However, it is sometimes difficult to differentiate them, especially when the diffuse pattern of both AA and AGA is in the differential diagnosis of hair loss in androgen-dependent areas. Objectives: To evaluate the characteristics of inflammatory cell infiltration using CD3, CD4, CD8, and CD20 antigens, in AA and AGA to find some consistent histological clues for distinguishing these two entities. Methods: A retrospective analysis of patients with diagnosed AA (30 cases) and AGA (30 cases) was performed based on the clinical and histopathological criteria. We studied immunohistochemical findings for CD3, CD4, CD8, and CD20 in all selected cases. Results: Immunohistochemical stains for CD4 and CD20 were not helpful in differentiating AA from AGA, but the inflammation density for AA was significantly (P-value = .025, .001) higher than AGA in CD3 (specificity= 86.7% and sensitivity= 96.7%) and CD8 (specificity= 50% and sensitivity=86.6%). Our findings revealed that intrafollicular CD3 (P-value = .017) and CD8 (P-value = ˂.001) infiltrations were significantly higher in AA samples in comparison with AGA. Conclusion: Characterization of CD3 and CD8 in IHC samples is helpful, especially when the density of CD3 and CD8 T cells are significant in more than 50% of the infiltrated cells and are located intrafolliculary. Moreover, the most specific and sensitive test for differentiating of AA from AGA is CD3

Distinguishing immunohistochemical features of alopecia areata from androgenic alopecia

Background: Distinction between alopecia areata (AA) and androgenic alopecia (AGA) can be made according to clinical presentation and biopsy findings. However, it is sometimes difficult to differentiate them, especially when the diffuse pattern of both AA and AGA is in the differential diagnosis of hair loss in androgen-dependent areas. Objectives: To evaluate the characteristics of inflammatory cell infiltration using CD3, CD4, CD8, and CD20 antigens, in AA and AGA to find some consistent histological clues for distinguishing these two entities. Methods: A retrospective analysis of patients with diagnosed AA (30 cases) and AGA (30 cases) was performed based on the clinical and histopathological criteria. We studied immunohistochemical findings for CD3, CD4, CD8, and CD20 in all selected cases. Results: Immunohistochemical stains for CD4 and CD20 were not helpful in differentiating AA from AGA, but the inflammation density for AA was significantly (P-value = .025, .001) higher than AGA in CD3 (specificity= 86.7% and sensitivity= 96.7%) and CD8 (specificity= 50% and sensitivity=86.6%). Our findings revealed that intrafollicular CD3 (P-value = .017) and CD8 (P-value = ˂.001) infiltrations were significantly higher in AA samples in comparison with AGA. Conclusion: Characterization of CD3 and CD8 in IHC samples is helpful, especially when the density of CD3 and CD8 T cells are significant in more than 50% of the infiltrated cells and are located intrafolliculary. Moreover, the most specific and sensitive test for differentiating of AA from AGA is CD3