Mycosis fungoides two decades after exposure to sulphur mustard: a follow-up of 1100 victims.

BACKGROUND: Sulphur mustard (SM) is an alkylating chemical warfare agent which causes acute and chronic injuries to the eyes, skin, lung and respiratory tract. OBJECTIVE: We aimed to investigate the relationship between SM poisoning and Mycosis fungoides (MF) as a late consequence. MATERIAL AND METHODS: In this retrospective study, the medical files of 1100 Iranian veterans confirmed to have exposure to SM agent during the Iraq-Iran war of the 1980s were reviewed. RESULTS: All 10 cases with MF were confirmed by clinical and histopathological examinations. The mean age of the studied subjects was 43.3 ± 9.8 (years). In comparison to MF incidence rate in Iranian general population (0.39/100 000 person-years), we found an incidence rate of 0.799/100 000 person-years for MF among those who had short-term exposure to SM. The most common sites for SM lesions were flexural and thin skin areas. The main limitation was the retrospective design. CONCLUSION: This study indicates that the risk of MF in those exposed to SM may increase over time. Therefore, their follow-up is recommended.

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle.