Naturally occurring antisense RNAs are small, diffusible, untranslated transcripts that pair to target RNAs at specific regions of complementarity to control their biological function by regulating gene expression at the post-transcriptional level. This review focuses on known cases of antisense RNA control in prokaryotes and provides an overview of some natural RNA-based mechanisms that bacteria use to modulate gene expression, such as mRNA sensors, riboswitches and antisense RNAs. We also highlight recent advances in RNA-based technology. The review shows that studies on both natural and synthetic systems are reciprocally beneficial.

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Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. Methods: The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. Result: The RF method extracted very small sets of genes while it retained its predictive performance. About colon cancer data set DIEXF , GUCA2A , CA7 , and IGHA1 key genes with the accuracy of 87.39 and precision of 85.45 were selected. The SNCA , USP20 , and SNRPA1 genes were selected for prostate cancer with the accuracy of 73.33 and precision of 66.67. Also, key genes of leukemia data set were BAG4 , ANKHD1 - EIF4EBP3 , PLXNC1 , and PCDH9 genes, and the accuracy and precision were 100 and 95.24, respectively. Conclusion: The current study results showed most of the selected genes involved in the processes and cancerous pathways were previously reported and had an important role in shifting from normal cell to abnormal.

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Injecting drug users (IDUs) are the main at-risk population for hepatitis C virus (HCV) transmission. We studied HCV infection, risk factors, and genotype distribution in relation to the year of first injection among Iranian IDUs. Of a total of 126 specimens positive for HCV antibody, 93 (74 %) had detectible HCV RNA, and the NS5B gene was sequenced for 83, with genotype 3a (n = 48, 58 %) being predominant, followed by 1a (n = 35, 42 %). Tattooing was an independent predictor for HCV infection. No significant difference was found between HCV genotypes and IDU characteristics. Although there was no change in the distribution of prevalent genotypes before and after 1997, a slight variation in the prevalence was observed (p = 0.71). The difference in the prevalence of subtypes 1a and 3a (9.1 % in the period 1984-1996 and 18.2 % in the period 1997-2009) during 25 years was 9.1 %. These findings indicate a high prevalence of HCV infection among Iranian IDUs and highlights HCV-3a as the most prevalent subtype for the past 25 years. Harm-reduction strategies appear to be the most important measures to reduce the transmission of HCV in Iran.