We aimed to investigate the effect of rapid antigen detection test (RADT) in the diagnosis of streptococcal pharyngitis, its impact on antibiotic prescription decision of pediatricians and influence on reduction of antibiotic treatment costs in children with pharyngitis. The study group consisted of 223 patients who were diagnosed with pharyngitis by pediatricians. The sensitivity and specificity of RADT were 92.1% (95% Cl: 78.6-98.3%) and 97.3% (95% Cl: 93.8-99.1%), respectively. In the first assessment, before performing RADT, pediatricians decided to prescribe antibiotics for 178 (79.8%) patients with pharyngitis. After learning RADT results, pediatricians finally decided to prescribe antibiotics for 83 (37.2%) patients with pharyngitis, and antibiotic prescription decreased by 42.6%. Antibiotic costs in non-Group A streptococcus pharyngitis, Group A streptococcus pharyngitis and all subjects groups decreased by 80.8%, 48%, and 76.4%, respectively. Performing RADT in children with pharyngitis has an important impact on treatment decision of clinicians, reduction of unnecessary antibiotic prescriptions and antibiotic costs.

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ÖzetçeGen ifadelerini düzenleyen mekanizmaların anlaşılması, moleküler biyolojideki önemli araştırma konularından birisidir. Bu konudaki önemli problemlerden birisi, transkripsiyon (yazım) faktörleri için Deoksiribonükleik Asit'te (DNA) bulunan bağlanma konumları gibi düzenleyici elemanları (motifleri) tanıma işlemidir. Bu çalışmada insan ve fare genlerinde bulunan motiflerin tahmini için Olasılıksal Sonek Ağacı (OSA) kullanan bir yöntem önerilmiştir. Deneysel sonuçlar on üç farklı motif bulma araçları ile karşılaştırmalı olarak değerlendirilmiştir. Elde edilen sonuçlar, önerilen yöntemin karşılaştırılan diğer yöntemlerden, nükleotid seviyesinde duyarlılık (nSn) değerine göre daha iyi sonuçlar verdiğini göstermiştir. AbstractThe identification of regulatory elements (motifs) is a challenging task in mollecular biology. An important challenge in this study is to identify regulatory elements (motifs), notably the binding sites in Deocsiribonucleic Acid (DNA) for transcription factors. Based on this motivation we propose a method for motif prediction of mouse and human genes by using Probabilistic Suffix Tree (PST). Experimental results are evaluated comparatively by thirteen distinct motif prediction tools. Our results show that, the proposed method gives a better recognition rate than the compared motif prediction tools, where the recognition rate is nucleotide level sensitivity (nSn).