Analysis of the common genetic component of large-vessel vasculitides through a meta- Immunochip strategy

Giant cell arteritis (GCA) and Takayasu's arteritis (TAK) are major forms of large-vessel vasculitis (LVV) that share clinical features. To evaluate their genetic similarities, we analysed Immunochip genotyping data from 1,434 LVV patients and 3,814 unaffected controls. Genetic pleiotropy was also estimated. The HLA region harboured the main disease-specific associations. GCA was mostly associated with class II genes (HLA-DRB1/HLA-DQA1) whereas TAK was mostly associated with class I genes (HLA-B/MICA). Both the statistical significance and effect size of the HLA signals were considerably reduced in the cross-disease meta-analysis in comparison with the analysis of GCA and TAK separately. Consequently, no significant genetic correlation between these two diseases was observed when HLA variants were tested. Outside the HLA region, only one polymorphism located nearby the IL12B gene surpassed the study-wide significance threshold in the meta-analysis of the discovery datasets (rs755374, P?=?7.54E-07; ORGCA?=?1.19, ORTAK?=?1.50). This marker was confirmed as novel GCA risk factor using four additional cohorts (PGCA?=?5.52E-04, ORGCA?=?1.16). Taken together, our results provide evidence of strong genetic differences between GCA and TAK in the HLA. Outside this region, common susceptibility factors were suggested, especially within the IL12B locus

Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models

Fires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment

Clinical history for inflammatory back pain in ankylosing spondylitis: the sensitivity, specificity and consistency of clinical features

WOS: 000261751900021PubMed ID: 1871239

Quality of life in patients with Takayasu's arteritis is impaired and comparable with rheumatoid arthritis and ankylosing spondylitis patients

The aims of the study were to assess the health-related quality of life (QOL) in patients with Takayasu's arteritis (TA) by two different generic QOL instruments and to compare the results with those patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), and healthy controls (HC). A cross-sectional study was performed in 51 patients with TA (41 women; mean age 38.4 +/- 13.5), 43 RA (36 women; 55.2 +/- 9.6), 31 AS (12 women; 41.2 +/- 13.1), and 75 HC (53 women; 38.8 +/- 10.9). Quality of life was assessed by using Short-Form 36 (SF-36) and Nottingham Health Profile (NHP). Separate dimensions of SF-36 and NHP and physical and mental summary scores of SF-36 as well were compared between patients and control groups. Physical and mental health summary scores and all SF-36 subscales, except for social functioning, were significantly lower in patients with TA than healthy controls. No significant differences between TA, RA, and AS patients were found in all SF-36 subscales and summary scores. NHP scores for energy level, pain, emotional reactions, and physical mobility were significantly higher in TA patients than controls. All NHP subscales, except for pain, were comparable in patients with TA, RA, and AS. Pain score was worse in RA patients. The NHP scores for sleep and social isolation were not different between patients and controls. Many aspects of QOL in patients with TA are significantly impaired in comparison with local healthy controls and similar to those in patients with RA and AS