Prevalence of oral lichen planus in Iranian children and adolescents: a 12-year retrospective study.

AIM: To investigate the prevalence of oral lichen planus in patients younger than 18 years, referred to a dermatology centre in Iran during 2002-2014. Lichen planus is a chronic inflammatory, immune-mediated disease that could affect the oral mucosa and is a pre-cancerous condition. The disease usually develops in middle age with female predominance and is rare in children. METHODS: In this retrospective study, cases with definitive histopathologic diagnosis of lichen planus, over a 12-year period from 2002 to 2014 from a dermatologic hospital archive were evaluated. The prevalence of both cutaneous and oral lichen planus, the male:female ratio and site of involvement were calculated using SPSS version 21. RESULTS: Thirty-six of 564 patients younger than 18 years old diagnosed with lichen planus. Two females (0.4%) had oral lichen planus. One patient had erosive, and one had bullous, oral lichen planus. CONCLUSION: Oral lichen planus had a very low frequency in Iranian population younger than 18 years old, identifying these patients is recommended for long-term follow-up.

DomNet: protein domain boundary prediction using enhanced general regression network and new profiles.

The accurate and stable prediction of protein domain boundaries is an important avenue for the prediction of protein structure, function, evolution, and design. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques. In this paper, we propose a new machine learning based domain predictor namely, DomNet that can show a more accurate and stable predictive performance than the existing state-of-the-art models. The DomNet is trained using a novel compact domain profile, secondary structure, solvent accessibility information, and interdomain linker index to detect possible domain boundaries for a target sequence. The performance of the proposed model was compared to nine different machine learning models on the Benchmark_2 dataset in terms of accuracy, sensitivity, specificity, and correlation coefficient. The DomNet achieved the best performance with 71% accuracy for domain boundary identification in multidomains proteins. With the CASP7 benchmark dataset, it again demonstrated superior performance to contemporary domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut, and DomainDiscovery.