GOALS: The goal was to ascertain whether urine testing could be used as screening method to detect C. trachomatis infections in commercial sex workers, patients at sexually transmitted diseases clinic, and asymptomatic patients in Kuala Lumpur, Malaysia.
METHODS: First-void urine specimens from 300 men and 300 women were tested by LCR, as well as by a commercially available enzyme immunoassay. The LCR assay amplifies specific sequences within the chlamydial plasmid with ligand-labeled probes, and the resultant amplicons are detected by an automated immunoassay. Specimens with discrepant results were confirmed by another LCR of the specimen that targeted the gene for the major outer membrane protein (OMP1).
RESULTS: There were 31 LCR-positive male urine and 37 LCR-positive female urine specimens. The resolved sensitivity and specificity for the LCR of the male urine specimens were 100% and 99.6%, respectively, whereas for female urine specimens, the sensitivity and specificity were 100% and 98.5%, respectively. After resolution of discrepant test results by OMP1 LCR, the prevalence was 10% for men and 11% for women. The urine enzyme immunoassay was not useful in diagnosing C. trachomatis infections in either men or women, as the resolved sensitivities were 10% and 15.2%, respectively. The specificities were 99.6% for men and 98.9% for women.
CONCLUSIONS: Testing first-void urine specimens by LCR is a highly sensitive and specific method to diagnose C. trachomatis infections in men and women, providing health care workers and public health officials with a new molecular amplification assay that uses noninvasive urine specimens for population-based screening purposes.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.