Changxiu Cao | PubMed Health

PubMed2006

Identification of effective siRNA blocking the expression of SARS viral envelope E and RDRP genes.

A cell-based assay was developed to screen small interference RNA (siRNA) to block the expression of two genes of the severe acute respiratory syndrome (SARS) virus. These two genes encode RNA-dependent RNA polymerase (RDRP) and envelope E protein. The RDRP plays an essential role in viral RNA replication where envelope E protein is involved in envelope formation and virus assembly. The RDRP and envelope E genes, based on published sequences, have been synthesized and cloned into mammalian expression vectors. In addition, four siRNA sites for the RDRP gene and two siRNA sites for envelope E gene were designed and tested. The siRNA or short hairpin RNA (shRNA) expression cassettes were co-transfected with the SARS viral RDRP or envelope E expression vectors into NIH 3T3 cells. The expression levels of RDRP and envelope E genes were examined by reverse transcription followed by quantitative real-time polymerase chain reaction (PCR). Two of the siRNA expression cassettes for RDRP successfully inhibited the expression of the gene, whereas both of the siRNA expression cassettes for envelope E decreased approx 90% of the envelope E gene expression. The siRNA and shRNA for one of the siRNA sites of the RDRP gene were also tested, and it was found that both inhibited exogenous RDRP expression in a dose-dependent manner. These siRNA molecules could be used to examine the function of these genes in SARS virus replication and assembly. Furthermore, these molecules could potentially be developed into therapeutic agents for the treatment of patients with SARS.

Animals↗

PubMed2003

[Introduction to medical data mining].

Modern medicine generates a great deal of information stored in the medical database. Extracting useful knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the database increasingly becomes necessary. Data mining in medicine can deal with this problem. It can also improve the management level of hospital information and promote the development of telemedicine and community medicine. Because the medical information is characteristic of redundancy, multi-attribution, incompletion and closely related with time, medical data mining differs from other one. In this paper we have discussed the key techniques of medical data mining involving pretreatment of medical data, fusion of different pattern and resource, fast and robust mining algorithms and reliability of mining results. The methods and applications of medical data mining based on computation intelligence such as artificial neural network, fuzzy system, evolutionary algorithms, rough set, and support vector machine have been introduced. The features and problems in data mining are summarized in the last section.

Algorithms↗