5 research outputs found
Data mining methods for the prediction of different forms of asthma
The article examines the diagnosis of bronchial asthma, cites the classification of the disease, proves the relevance of this research, and represents the result of primary data analysis by using a powerful tool for data analysis - Rapid Miner
Ex Vivo Metrics™, a preclinical tool in new drug development
Among the challenges facing translational medicine today is the need for greater productivity and safety during the drug development process. To meet this need, practitioners of translational medicine are developing new technologies that can facilitate decision making during the early stages of drug discovery and clinical development. Ex Vivo Metrics™ is an emerging technology that addresses this need by using intact human organs ethically donated for research. After hypothermic storage, the organs are reanimated by blood perfusion, providing physiologically and biochemically stable preparations. In terms of emulating human exposure to drugs, Ex Vivo Metrics is the closest biological system available for clinical trials. Early application of this tool for evaluating drug targeting, efficacy, and toxicity could result in better selection among promising drug candidates, greater drug productivity, and increased safety
Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers
<p>Abstract</p> <p>Methods</p> <p>We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.</p> <p>Results</p> <p>Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.</p> <p>Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.</p> <p>Conclusion</p> <p>The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.</p