The Serbian version of the Juvenile Arthritis Multidimensional Assessment Report (JAMAR).

The Juvenile Arthritis Multidimensional Assessment Report (JAMAR) is a new parent/patient-reported outcome measure that enables a thorough assessment of the disease status in children with juvenile idiopathic arthritis (JIA). We report the results of the cross-cultural adaptation and validation of the parent and patient versions of the JAMAR in the Serbian language. The reading comprehension of the questionnaire was tested in 10 JIA parents and patients. Each participating centre was asked to collect demographic, clinical data and the JAMAR in 100 consecutive JIA patients or all consecutive patients seen in a 6-month period and to administer the JAMAR to 100 healthy children and their parents. The statistical validation phase explored descriptive statistics and the psychometric issues of the JAMAR: the three Likert assumptions, floor/ceiling effects, internal consistency, Cronbach's alpha, interscale correlations, test-retest reliability, and construct validity (convergent and discriminant validity). A total of 248 JIA patients (5.2% systemic, 44.3% oligoarticular, 23.8% RF-negative polyarthritis, 26.7% other categories) and 100 healthy children were enrolled in three centres. The JAMAR components discriminated healthy subjects from JIA patients. All JAMAR components revealed good psychometric performances. In conclusion, the Serbian version of the JAMAR is a valid tool for the assessment of children with JIA and is suitable for use both in routine clinical practice and clinical research.

Transition readiness in adolescents with juvenile idiopathic arthritis and their parents: Our single-center experience.

Objectives: We aimed to identify characteristics of juvenile idiopathic arthritis (JIA) patients associated with good self-management skills in the transition readiness process and to investigate the readiness of JIA patients and their families for the transition into the adult healthcare system. Patients and methods: Between March 2021 and June 2021, a total of 44 JIA patients (9 males, 35 females; median age: 15.1 years; range, 12.3 to 19.3 years) admitted to the pediatric rheumatology outpatient and inpatient clinics and their parents were included. Transition Readiness Assessment Questionnaire (TRAQ) was cross-culturally adapted. The TRAQ was administered to all JIA patients and their parents at one point. Demographic and clinical data were collected. Results: Fourteen (31.8%) of 44 JIA patients had a concomitant disease, while 10 (22.7%) of them had uveitis. Eleven (25%) of them had a family history of autoimmune diseases. In total, 21 (47.7%) of JIA patients were receiving biologics. There was a strong correlation between older age and total TRAQ scores among patients (ρ=0.799, p<0.001) and a moderate correlation between older patient age and total TRAQ scores among parents (ρ=0.522, p<0.001). Patient and parent total TRAQ scores were strongly correlated (ρ=0.653, p<0.001). There was no significant association of JIA patient characteristics (JIA disease subtypes, disease duration, gender, concomitant diseases, uveitis, family history of autoimmune diseases, number of hospitalizations, and treatment with biologics) with TRAQ scores and JIA patients' and parents' readiness for transition. Conclusion: Transition readiness of JIA patients increases with advancing age. There is no significant difference between transition readiness for JIA patients and their parents.

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification.

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique "brain print", which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.