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BackgroundFamilial Mediterranean fever (FMF) is the most common monogenic autoinflammatory disease characterised with fever, recurrent episodes of self-limiting polyserositis and arthritis. FMF arthritis is generally acute monoarthritis especially in the larger joints of the lower extremities, healing without a sequelae. However some of the patients develop different type of chronic arthritis, predominantly oligoarticular juvenile idiopathic arthritis (JIA) and juvenile pondyloarthropathies (JSpA). Studies on JSpA among childhood FMF patients are spare.ObjectivesTo evaluate frequency of JSpA in a large childhood FMF cohort. Furthermore, we aimed to define main characteristics of JSpA among childhood FMF patients.MethodsA total of 320 juvenile FMF patients were blindly questioned according to recently proposed criteria for JSpA by 3 researchers (EO, DS, ET) that were previously educated for FMF and JSpA. A standardised case report form was prepared and completed for each patient. This form was including demographic data, clinical features, MEFV mutation and treatment. Patients fulfilled the JSpA criteria and were classified as probable JSpA. Afterwards, an expert in paediatric rheumatology (OK) reevaluated the classified patients and some of them were confirmed to be a definite while some of them were accepted as potential JSpA patients.ResultsAs a result, 37 patients (11.5%) were initially classified as potential JSpA. Furthermore, 32 (10%) of them were accepted as definite and 5 (1.5%) patients as probable JSpA in childhood FMF. Demographic, clinical and treatment data of definitive JSPA patients are shown in Table I. The most frequent MEFV mutation among JSPA patients was M694V (63.33%).Table I. Demographic, clinical and genetic features of childhood FMF patients.FMF +Definite JSPAFMF +Probable JSPAFMF patients without JIA and JSpAFMF+JIA (except ERA or JSpA) Patients, n32526815Female, n (%)10 (31.25%)1 (20%)148 (55.22%)10 (66.66%)Age of disease onset, mean±SD years7.19±3.685.60±4.934.91±3.404.93±3.32Age at study, mean±SD years14.84±3.7013.40±1.6712.51±4.4310.73±3.57Family History of FMF, n (%)15 (46.87%)1 (20%)132 (49.25%)6 (40%)Colchicine resistance in FMF patients, n(%)2 (6.25%)014 (5.22%)1 (6.66%)M694V mutation n(%)Homozygote, n(%)Heterozygote, n(%)Compound heterozygote, n(%)NA, n(%)19/30 (63.33%)7 (36.84%)5 (26.31%)7 (36.84%)2 (6.25%)3 (60%)2 (66.66%)1 (33.33%)00148/245 (60.40%)51 (34.45%)54 (36.48%)43 (29.05%)23 (8.58%)11 (73.33%)8 (72.72%)2 (18.18%)1 (9.09%)0Disease onset over 6 years,n (%) years26 (81.25%)5 (100%)6 (40%)Oligorthritis, n(%)21 (65.62%)1 (20%)14 (93.33%)Inflammatory back pain, n(%)17/32 (53.12%)3/5 (60%)0Enthesopathy22/32 (68.75%)3/5 (60%)0Sacroiliitis14/21 (66.66%)0/1 (0)0/5 (0)ConclusionsArticular involvement compatible with JSpA could be seen in childhood FMF patients. Spondyloarthropathy was detected in 10% of childhood FMF cases. The M694V mutation is the most common MEFV mutation among JSpA patients with FMF. JSpA should be considered in childhood FMF patients, especially in those chr...

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The modeling of photovoltaic (PV) systems is substantial for the estimation of energy production and efficiency analysis in the PV systems under the changing environmental conditions. A PV model mathematically expresses the electrical characteristic of the PV modules according to temperature and irradiance. The most popular electrical circuit models are the single-diode model (SDM) and the double-diode model (DDM). Considering accuracy and complexity, SDM was used in this paper. In the equivalent circuit model used to estimate the electrical behavior of the PV modules, the parameter estimating has become an optimization problem. In recent studies, it is seen that metaheuristic algorithms are often employed in solving this optimization problem. In this paper, a new six-parameter PV model is proposed to improve the accuracy of the five-parameter SDM, taking into account the temperature dependence of the series resistance. Particle swarm optimization (PSO) and a couple of metaheuristic algorithms has been executed to estimate six unknown parameters of the proposed model under standard test conditions (STC: 25 °C, 1000 W/m2, AM1.5) using current-voltage (I-V) data provided by the PV module manufacturers. In order to evaluate the performance of the proposed method under the changing environmental conditions, it was compared with the three methods commonly used in the literature. Accuracy of the proposed model has been indicated by the root-mean-square error (RMSE) within the range of current data and the model current values. Simulation results demonstrate that the proposed model can predict the I-V curve for the PV modules with high accuracy.