Amino-terminal CNP (NTproCNP), measurable in plasma, correlates with growth-plate activity and can be used GSI-IX manufacturer as a biomarker of growth velocity in children. Because severe inflammation in adults increases CNP, we studied CNP peptides and inflammatory markers in children with acute illness.\n\nMETHODS: Forty-two children aged 2 mo to 5 y with acute illness warranting admission to an acute assessment unit were studied. Fifteen age-matched healthy children attending an outpatient clinic served as controls. Venous CNP concentrations were measured at admission,
along with markers of acute inflammation (body temperature, C-reactive protein (CRP), and white blood cell count) in children with acute illness.\n\nRESULTS: NTproCNP and CNP SD scores (SDSs) in the acutely ill group were significantly suppressed (P < 0.001) as compared with those of healthy children or healthy population norms. NTproCNP SDS was significantly inversely related to body temperature (r = -0.42, P < 0.01) and CRP (r = -0.56, P < 0.001).\n\nCONCLUSION: Acute inflammation in young children potently reduces CNP production,
which needs to be considered when screening for growth disorders. Our data raise the possibility that the adverse effects of inflammatory cytokines on skeletal growth may be mediated in part by reduced CNP.”
“Background: HSP inhibitor Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test). The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets). Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first
null hypothesis) is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such STAT inhibitor methods may erroneously associate the biology of a particular geneset with cancer prognosis.\n\nResults: To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study.