The polygenic nature of essential hypertension and its dependence on environmental factors pose a challenge for biomedical research. We hypothesized that the analysis of gene expression profiles from peripheral blood cells would distinguish patients with hypertension from normotensives. In order to test this, total RNA from peripheral blood cells was isolated. RNA was reversed-transcribed and labeled and gene expression analyzed using significance Analysis Microarrays (Stanford University, CA, USA). Briefly, Significance Analysis Microarrays (SAM) thresholding identified 31 up-regulated and 18 down-regulated genes with fold changes of ≥2 or≤0.5 and q-value ≤5 % in expression. Statistically significantly gene ontology (GO) function and biological process differentially expressed in essential hypertension were MHC class II receptor activity and immune response respectively. Biological pathway analysis identified several related pathways which are associated with immune/inflammatory responses. Quantitative Real- Time RT-PCR results were consistent with the microarray results. The levels of C - reactive protein were higher in hypertensive patients than normotensives and inflammation-related genes were increased as well. In conclusion, genes enriched for “immune/inflammatory responses” may be associated with essential hypertension. In addition, there is a correlation between systemic inflammation and hypertension. It is anticipated that these findings may provide accurate and efficient strategies for prevention, diagnosis and control of this disorder.

In current large databases, there is a widespread defect of poor immunity. It is mainly due to the huge amount of data which makes the traditional algorithm falling into a defect of low efficiency of local search, causing inconspicuous effect to detect potential risks and other defects. To this end, we propose an intrusion detection model of medical information diversity database under cloud computing. By using the similar risk attributes of data, an intrusion test model under cloud computing environment is constructed. The use of intrusion tolerance theory can reduce the impact of failure report phenomenon for the stability of whole system. This model can effectively resist denial of service attacks, but also through the mutual cooperation between the servers to prevent the single failure node attacking to the system, ensure the safety and smooth of the cloud computing services at the greatest degree, in order to enhance the user experience. Experiments show that the algorithm improves the accuracy of the intrusion detection of medical information diversity database, and achieves good results.

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