Impact numbers: measures of risk factor impact on the whole population from case-control and cohort studies

Objective: To describe new measures of risk from case-control and cohort studies, which are simple to understand and relate to numbers of the population at risk. Design: Theoretical development of new measures of risk. Setting: Review of literature and previously described measures. Main results: The new measures are: (1) the population impact number (PIN), the number of those in the whole population among whom one case is attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable risk),- (2) the case impact number (CIN) the number of people with the disease or outcome for whom one case will be attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable fraction); (3) the exposure impact number (EIN) the number of people with the exposure among whom one excess case is attributable to the exposure (this is equivalent to the reciprocal of the attributable risk); (4) the exposed cases impact number (ECIN) the number of exposed cases among whom one case is attributable to the exposure (this is equivalent to the reciprocal of the aetiological fraction). The impact number reflects the number of people in each population (the whole population, the cases, all those exposed, and the exposed cases) among whom one case is attributable to the particular risk factor. Conclusions: These new measures should help communicate the impact on a population, of estimates of risk derived from cohort or case-control studies.

D2 dopamine receptor gene polymorphism discriminates two kinds of novelty seeking

Cloninger's psychobiological model of personality as applied to substance misuse has received mixed support. Contrary to the model, recent data suggest that a combination of high novelty seeking (NS) and high harm avoidance (HA) represents a significant risk for the development of severe substance misuse. A genetic polymorphism previously implicated in severe substance dependence, the A1 allele of the D2 dopamine receptor (DRD2) gene, was examined in relation to NS and HA amongst 203 adolescent boys. Specifically, we hypothesized that subjects with the A1 + allele (A1/A1 and A1/A2 genotypes) would report stronger NS and would exhibit a more positive relationship between NS and HA than those with the A1-allele (A2/A2 genotypes). These predictions were supported. The correlation between NS and HA in 81 A1 + allelic boys (r = 0.27, P = 0.02), and that in the 122 A1- allelic boys (r = -0.15, P = 0.09), indicated that this relationship differed according to allelic status (F = 8.52, P < 0:004). Among those with the A1-allele, the present results are consistent with the traditional view that novelty seeking provides positive reinforcement, or the fulfillment of appetitive drives. In contrast, novelty seeking in those with the A1 + allele appears to include a negative reinforcement or self-medicating function. (C) 2002 Elsevier Science Ltd. All rights reserved.

Keratinocyte Growth factor (KGF) in hematology and oncology

Keratinocyte Growth factor (KGF) is an epithelial cell growth factor of the fibroblast growth factor family and is produced by fibroblasts and microvascular endothelium in response to proinflammatory cytokines and steroid hormones. KGF is a heparin binding growth factor that exerts effects on epithelial cells in a paracrine fashion through interaction with KGF receptors. Preclinical data has demonstrated that KGF can prevent lung and gastrointestinal toxicity following chemotherapy and radiation and preliminary clinical data in the later setting supports these findings. In the experimental allogeneic bone marrow transplant scenario KGF has shown significant ability to prevent graft-versus-host disease by maintaining gastrointestinal tract integrity and acting as a cytokine shield to prevent subsequent proinflammatory cytokine generation. Within this setting KGF has also shown an ability to prevent experimental idiopathic pneumonia syndrome by stimulating production of surfactant protein A, promoting alveolar epithelialization and attenuating immune-mediated injury. Perhaps most unexpectantly, KGF appears able to maintain thymic function during allogeneic stern cell transplantation and so promote T cell engraftment and reconstitution. These data suggest that KGF will find a therapeutic role in the prevention of epithelial toxicity following intensive chemotherapy and radiotherapy protocols and in allogeneic stem cell transplantation.

Placental growth hormone is not suppressed by oral glucose loading in normal human pregnancy

Placental growth hormone (PGH) progressively replaces pituitary growth hormone in the maternal circulation from mid-gestation onwards in human pregnancy. Our previous investigations have shown that placental growth hormone concentrations correlate well with foetal growth. Despite the apparent correlation between PGH and birthweight, the physiology of its secretion during pregnancy has not been well defined. We investigated the response of maternal serum PCH to oral glucose loading in pregnant women (n = 24) who demonstrated normal glucose tolerance at a mean gestation of 29 weeks. Mean (SEM) fasting PGH concentrations were high (36.9 [6.4] ng/ml). No suppression of PGH was noted at one, two or three hours after a 75 g oral glucose load. Similarly, no changes were noted in growth hormone binding protein or in calculated free PGH over the course of the glucose tolerance test. As expected, insulin concentrations rose sixfold and insulin like growth factor binding protein 1 concentrations fell by 20% with glucose loading. Cot-relation analysis showed maternal weight, BMI, fasting serum glucose serum insulin to be significantly correlated with the babies' birthweight. Our results support the proposition that PGH concentrations in maternal serum are not Suppressed by oral glucose loading in non-diabetic mothers.

Stress correlation function evolution in lattice solid elasto-dynamic models of shear and fracture zones and earthquake prediction

It has been argued that power-law time-to-failure fits for cumulative Benioff strain and an evolution in size-frequency statistics in the lead-up to large earthquakes are evidence that the crust behaves as a Critical Point (CP) system. If so, intermediate-term earthquake prediction is possible. However, this hypothesis has not been proven. If the crust does behave as a CP system, stress correlation lengths should grow in the lead-up to large events through the action of small to moderate ruptures and drop sharply once a large event occurs. However this evolution in stress correlation lengths cannot be observed directly. Here we show, using the lattice solid model to describe discontinuous elasto-dynamic systems subjected to shear and compression, that it is for possible correlation lengths to exhibit CP-type evolution. In the case of a granular system subjected to shear, this evolution occurs in the lead-up to the largest event and is accompanied by an increasing rate of moderate-sized events and power-law acceleration of Benioff strain release. In the case of an intact sample system subjected to compression, the evolution occurs only after a mature fracture system has developed. The results support the existence of a physical mechanism for intermediate-term earthquake forecasting and suggest this mechanism is fault-system dependent. This offers an explanation of why accelerating Benioff strain release is not observed prior to all large earthquakes. The results prove the existence of an underlying evolution in discontinuous elasto-dynamic, systems which is capable of providing a basis for forecasting catastrophic failure and earthquakes.

Long-range automaton models of earthquakes: Power-law accelerations, correlation evolution, and mode-switching

We introduce a conceptual model for the in-plane physics of an earthquake fault. The model employs cellular automaton techniques to simulate tectonic loading, earthquake rupture, and strain redistribution. The impact of a hypothetical crustal elastodynamic Green's function is approximated by a long-range strain redistribution law with a r(-p) dependance. We investigate the influence of the effective elastodynamic interaction range upon the dynamical behaviour of the model by conducting experiments with different values of the exponent (p). The results indicate that this model has two distinct, stable modes of behaviour. The first mode produces a characteristic earthquake distribution with moderate to large events preceeded by an interval of time in which the rate of energy release accelerates. A correlation function analysis reveals that accelerating sequences are associated with a systematic, global evolution of strain energy correlations within the system. The second stable mode produces Gutenberg-Richter statistics, with near-linear energy release and no significant global correlation evolution. A model with effectively short-range interactions preferentially displays Gutenberg-Richter behaviour. However, models with long-range interactions appear to switch between the characteristic and GR modes. As the range of elastodynamic interactions is increased, characteristic behaviour begins to dominate GR behaviour. These models demonstrate that evolution of strain energy correlations may occur within systems with a fixed elastodynamic interaction range. Supposing that similar mode-switching dynamical behaviour occurs within earthquake faults then intermediate-term forecasting of large earthquakes may be feasible for some earthquakes but not for others, in alignment with certain empirical seismological observations. Further numerical investigation of dynamical models of this type may lead to advances in earthquake forecasting research and theoretical seismology.

Immunological response mounted by Aboriginal Australians living in the Northern Territory of Australia against Streptococcus pyogenes serum opacity factor

Streptococcus pyogenes (Group A streptococcus) interacts with host fibronectin via a number of distinct surface components. The streptococcal serum opacity factor (SOF) is a cell-surface protein of S. pyogenes which opalescence of human serum and mediates bacterial binding to fibronectin. In this study, hexahistidyl-tagged fusion proteins encompassing full-length SOF, and domains of SOF encompassing opacity factor activity and fibronectin-binding regions, were used in the characterization of the Aboriginal immune response to SOF. Anti-SOF serum IgG responses were found to be significantly higher (P

Colony-stimulating factor-1 suppresses responses to CpG DNA and expression of toll-like receptor 9 but enhances responses to lipopolysaccharide in murine macrophages

During bacterial infections, the balance between resolution of infection and development of sepsis is dependent upon the macrophage response to bacterial products. We show that priming of murine bone marrow-derived macrophages (BMMs) with CSF-1 differentially regulates the response to two such stimuli, LPS and immunostimulatory (CpG) DNA. CSF-1 pretreatment enhanced IL-6, IL-12, and TNF-alpha production in response to LPS but suppressed the same response to CpG DNA. CSF-1 also regulated cytokine gene expression in response to CpG DNA and LPS; CpG DNA-induced IL-12 p40, IL-12 p35, and TNF-alpha mRNAs were all suppressed by CSF-1 pretreatment. CSF-1 pretreatment enhanced LPS-induced IL-12 p40 mRNA but not TNF-alpha and IL-12 p35 mRNAs, suggesting that part of the priming effect is posttranscriptional. CSF-1 pretreatment also suppressed CpG DNA-induced nuclear translocation of NF-kappaB and phosphorylation of the mitogen-activated protein kinases p38 and extracellular signal-related kinases-1/2 in BMMs, indicating that early events in CpG DNA signaling were regulated by CSF-1. Expression of Toll-like receptor (TLR)9, which is necessary for responses to CpG DNA, was markedly suppressed by CSF-1 in both BMMs and thioglycolate-elicited peritoneal macrophages. CSF-1 also down-regulated expression of TLR1, TLR2, and TLR6, but not the LPS receptor, TLR4, or TLR5. Hence, CSF-1 may regulate host responses to pathogens through modulation of TLR expression. Furthermore, these results suggest that CSF-1 and CSF-1R antagonists may enhance the efficacy of CpG DNA in vivo.

Temporal and spatial expression of fibroblast growth factor receptor 4 isoforms in murine tissues

Fibroblast growth factor receptors (FGFRs) undergo highly regulated spatial and temporal changes of expression during development. This study describes the use of quantitative reverse transcriptase-polymerase chain reaction and immunochemistry to assess the changes in expression of FGFR4 as compared to its FGFR4-17a and -17b isoforms in mouse tissues, from early embryogenesis through to adulthood. Compared to FGFR4, the expression of the isoforms is more restricted at all developmental stages tested. The reverse transcriptase-polymerase chain reaction demonstrated that FGFR4 is expressed in more tissue types than either of its isoforms: it was found predominantly in lung, liver, brain, skeletal muscle and kidney, whereas the FGFR4-17a form was detected in lung and skeletal muscle, and the FGFR4-17b form only in lung, liver, skeletal muscle and kidney. Immunohistochemistry confirmed strong FGFR4-17b expression in the postnatal lung. When combined, the results suggest that FGFR4 variants play important roles particularly in lung and skeletal muscle development.

Modelling High-Dimensional Data by Mixtures of Factor Analyzers

We focus on mixtures of factor analyzers from the perspective of a method for model-based density estimation from high-dimensional data, and hence for the clustering of such data. This approach enables a normal mixture model to be fitted to a sample of n data points of dimension p, where p is large relative to n. The number of free parameters is controlled through the dimension of the latent factor space. By working in this reduced space, it allows a model for each component-covariance matrix with complexity lying between that of the isotropic and full covariance structure models. We shall illustrate the use of mixtures of factor analyzers in a practical example that considers the clustering of cell lines on the basis of gene expressions from microarray experiments. (C) 2002 Elsevier Science B.V. All rights reserved.