Objective Biomarkers for preterm labor and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low molecular weight compounds which constitute the “metabolic network” of cells (metabolome) is now possible by using a “metabolomics” approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous preterm labor (PTL) at risk for preterm delivery, regardless of the presence or absence of intra-amniotic infection/inflammation (IAI). Study Design Two retrospective cross-sectional studies were conducted, including 3 groups of pregnant women with spontaneous PTL and intact membranes: 1) PTL who delivered at term; 2) PTL without IAI who delivered preterm; and 3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19 and 20 patients in groups 1, 2 and 3, respectively. The second study included 40, 33 and 40 patients in groups 1, 2 and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified by using authentic standards. The data were analyzed using Discriminant Analysis for the first study and Random Forest for the second. Results 1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. 2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells as well as xenobiotic compounds (e.g. bacterial products and Salicylamide). Conclusion Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.

No abstract

No abstract

The problem of determining a consensus value and its uncertainty from the results of multiple methods or laboratories is discussed. Desirable criteria of a solution are presented. A solution motivated by the ISO Guide to the Expression of Uncertainty in Measurement (ISO GUM) is introduced and applied in a detailed worked example. A Bayesian hierarchical model motivated by the proposed solution is presented and compared to the solution.

Random networks, Random graphs, Digraphs,

No abstract

No abstract

Social networks, In/Out Seeker/Providers (IOSP), Social network analysis, Disaster research, Katrina,