Regulated transcription of human matrix metalloproteinase 13 (MMP13) and interleukin-1β (IL1B) genes in chondrocytes depends on methylation of specific proximal promoter CpG sites

The role of DNA methylation in the regulation of catabolic genes such as MMP13 and IL1B, which have sparse CpG islands, is poorly understood in the context of musculoskeletal diseases. We report that demethylation of specific CpG sites at -110 bp and -299 bp of the proximal MMP13 and IL1B promoters, respectively, detected by in situ methylation analysis of chondrocytes obtained directly from human cartilage, strongly correlated with higher levels of gene expression. The methylation status of these sites had a significant impact on promoter activities in chondrocytes, as revealed in transfection experiments with site-directed CpG mutants in a CpG-free luciferase reporter. Methylation of the -110 and -299 CpG sites, which reside within a hypoxia-inducible factor (HIF) consensus motif in the respective MMP13 and IL1B promoters, produced the most marked suppression of their transcriptional activities. Methylation of the -110 bp CpG site in the MMP13 promoter inhibited its HIF-2alpha-driven transactivation and decreased HIF-2alpha binding to the MMP13 proximal promoter in chromatin immunoprecipitation assays. In contrast to HIF-2alpha, MMP13 transcriptional regulation by other positive (RUNX2, AP-1, ELF3) and negative (Sp1, GATA1, and USF1) factors was not affected by methylation status. However, unlike the MMP13 promoter, IL1B was not susceptible to HIF-2alpha transactivation, indicating that the -299 CpG site in the IL1B promoter must interact with other transcription factors to modulate IL1B transcriptional activity. Taken together, our data reveal that the methylation of different CpG sites in the proximal promoters of the human MMP13 and IL1B genes modulates their transcription by distinct mechanisms

Evaluation of a new community-based curriculum in disaster medicine for undergraduates

BACKGROUND: Nowadays, many medical schools include training in disaster medicine in undergraduate studies. This study evaluated the efficacy of a disaster medicine curriculum recently designed for Saudi Arabian medical students. METHODS: Participants were 15 male and 14 female students in their fourth, fifth or sixth year at Jazan University Medical School, Saudi Arabia. The course was held at the Research Center in Emergency and Disaster Medicine and Computer Sciences Applied to the Medical Practice in Novara, Italy. RESULTS: The overall mean score on a test given before the course was 41.0 % and it increased to 67.7 % on the post-test (Wilcoxon test for paired samples: z = 4.71, p < 0.0001). There were no significant differences between the mean scores of males and females, or between students in their fourth, fifth or sixth year of medical school. CONCLUSIONS: These results show that this curriculum is effective for teaching disaster medicine to undergraduate medical students. Adoption of this course would help to increase the human resources available for dealing with disaster situations

'Cooperative Automated worm Response and Detection ImmuNe ALgorithm (CARDINAL) inspired by T-cell Immunity and Tolerance'

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm