Congenital rubella syndrome in Iran

BACKGROUND: Congenital rubella syndrome (CRS) can be prevented with appropriate vaccination programs. The prevalence rates of rubella and CRS in Iran are unknown; therefore, the risk of exposure in pregnant women is not clear. The prevalence of CRS in the pre-vaccine period can be estimated by evaluating the proportion of children in the population with sensorineural hearing loss attributable to rubella. METHODS: This was a case-control study to estimate prevalence of CRS in Tehran (Iran) by evaluating the proportion of children with sensorineural hearing loss attributable to rubella. The study used rubella antibody titer as an indicator, and compared the prevalence of rubella antibody between children with and without sensorineural hearing loss. Using these findings, the proportion of cases of sensorineural hearing loss attributable to rubella was estimated. RESULTS: A total of 225 children aged 1 to 4 years were entered into the study (113 cases and 112 controls). There was a significant difference between cases and controls with regard to rubella antibody seropositivity (19.5% vs. 8.9%, respectively, odds ratio = 2.47, 95% CI = 1.04–5.97). The proportion of sensorineural hearing loss cases attributable to rubella was found to be 12%, corresponding to a CRS prevalence of 0.2/1000. CONCLUSION: The prevalence of CRS was approximately 0.2/1000 before rubella vaccination in Iran, Moreover; the results suggest that implementation of appropriate rubella vaccination programs could potentially prevent about 12% of cases of sensorineural hearing loss in Iranian children. This data could potentially be used as baseline data, which in conjunction with an appropriate method, to establish a surveillance system for rubella vaccination in Iran. An appropriate surveillance system is needed, because the introduction of a rubella vaccine without epidemiological data and an adequate monitoring program could result in the shifting of rubella cases to higher ages, and increasing the incidence of CRS

PROPER: global protein interaction network alignment through percolation matching

Background The alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging. Results In this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria. Conclusions We highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch