Greg Constantine

IN SILICO MODELS OF ACUTE INFLAMMATION IN ANIMALS

ABSTRACT Trauma and hemorrhagic shock elicit an acute inflammatory response, predisposing patients to sepsis, organ dysfunction, and death. Few approved therapies exist for these acute inflammatory states, mainly due to the complex interplay of interacting inflammatory and physiological elements working at multiple levels. Various animal models have been used to simulate these phenomena, but these models often do not replicate the clinical setting of multiple overlapping insults. Mathematical modeling of complex systems is an approach for understanding the interplay among biological interactions. We constructed a mathematical model using ordinary differential equations that encompass the dynamics of cells and cytokines of the acute inflammatory response, as well as global tissue dysfunction. The model was calibrated in C57Bl/6 mice subjected to (1) various doses of lipopolysaccharide (LPS) alone, (2) surgical trauma, and (3) surgery + hemorrhagic shock. We tested the models predictive ability in scenarios on which it had not been trained, namely, (1) surgery ± hemorrhagic shock + LPS given at times after the beginning of surgical instrumentation, and (2) surgery + hemorrhagic shock + bilateral femoral fracture. Software was created that facilitated fitting of the mathematical model to experimental data, as well as for simulation of experiments with various inflammatory challenges and associated variations (gene knockouts, inhibition of specific cytokines, etc.). Using this software, the C57Bl/6-specific model was recalibrated for inflammatory analyte data in CD14−/− mice and was used to elucidate altered features of inflammation in these animals. In other experiments, rats were subjected to surgical trauma ± LPS or to bacterial infection via fibrin clots impregnated with various inocula of Escherichia coli. Mathematical modeling may provide insights into the complex dynamics of acute inflammation in a manner that can be tested in vivo using many fewer animals than has been possible previously.

Hypoxia-induced overexpression of BNIP3 is not dependent on hypoxia-inducible factor 1α in mouse hepatocytes.

We sought to investigate the expression of the cell death protein BNIP3 in hypoxic hepatocytes, as well as the role that hypoxia-inducible factor 1 (HIF-1&agr;) plays in the upregulation of BNIP3 in hypoxic primary mouse hepatocytes and in the livers of mice subjected to ischemia-reperfusion. Freshly isolated mouse hepatocytes were exposed to 1% hypoxia for 1, 3, 6, 24, and 48 h, and the RNA and protein were isolated for reverse transcriptase-polymerase chain reaction and Western blot analysis. Similarly, livers from mice subjected to segmental (70%) hepatic warm ischemia for 30 min or 1 h, or to 1-h ischemia followed by 0.5- to 4-h reperfusion, were collected and subjected to Western blot analysis for HIF-1&agr; protein. We showed that hypoxic stress increases the formation of the BNIP3 homodimer while decreasing the amount of the monomeric form of BNIP3 in primary mouse hepatocytes. In contrast to RAW264.7 macrophages, there is a basal expression of HIF-&agr; protein in normoxic primary mouse hepatocytes that does not change significantly upon exposure to hypoxia. Using siRNA technology, we demonstrated that reduced HIF-1&agr; protein levels did not block the hypoxia-induced overexpression of BNIP3. In contrast to the effect on BNIP3 expression reported previously, livers from ischemic animals demonstrated only a modest increase in HIF-1&agr; protein as compared with resting livers from control animals; and this expression was not statistically different from sham controls. These results suggest that HIF-1&agr; does not mediate the hypoxia-induced upregulation of BNIP3 in mouse hepatocytes in vitro and possibly in the liver in vivo.

Statistical Inference and Modelling of Momentum In Stock Prices

The following results are obtained, (i) It is possible to obtain a time series of market data {y(t)} in which the fluctuations in fundamental value have been compensated for. An objective test of the efficient market hypothesis (EMH), which would predict random correlations about a constant value, is thereby possible, (ii) A time series procedure can be used to determine the extent to which the differences in the data and the moving averages are significant. This provides a model of the form y(t)-y(t-l)=0.5{y(t- l)-y(t-2)}+e(t)+0.8e(r-1) where e(t) is the error at time t, and the coefficients 0.5 and 0.8 are determined from the data. One concludes that todays price is not a random perturbation from yesterdays; rather, yesterdays rate of change is a significant predictor of todays rate of change. This confirms the concept of momentum that is crucial to market participants. (iii) The model provides out-of-sample predictions that can be tested statistically. (iv) The model and coefficients obtained in this way can be used to make predictions on laboratory experiments to establish an objective and quantitative link between the experiments and the market data. These methods circumvent the central difficulty in testing market data, namely, that changes in fundamentals obscure intrinsic trends and autocorrelations. This procedure is implemented by considering the ratio of two similar funds (Germany and Future Germany) with the same manager and performing a set of statistical tests that have excluded fluctuations in fundamental factors. For the entire data of the first 1149 days beginning with the introduction of the latter fund, a standard runs test indicates that the data is 29 standard deviations away from that which would be expected under a hypothesis of random fluctuations about the fundamental value. This and other tests provide strong evidence against the efficient market hypothesis and in favour of autocorrelations in the data. An ARIMA time series finds strong evidence (9.6 and 21.6 standard deviations in the two coefficients) that the data is described by a model that involves the first difference, indicating that momentum is the significant factor. The first quarters data is used to make out-of-sample predictions for the second quarter with results that are significant to 3 standard deviations. Finally, the ARIMA model and coefficients are used to make predictions on laboratory experiments of Porter and Smith in which the intrinsic value is clear. The models forecasts are decidedly more accurate than that of the null hypothesis of random fluctuations about the fundamental value.

Early Detection of Pressure Ulcer Development Following Traumatic Spinal Cord Injury Using Inflammatory Mediators.

OBJECTIVE To identify changes in concentrations of inflammatory mediators in plasma and urine after traumatic spinal cord injury (SCI) and before the occurrence of a first pressure ulcer. DESIGN Retrospective; secondary analysis of existing data. SETTING Acute hospitalization and inpatient rehabilitation sites at a university medical center. PARTICIPANTS Individuals with a pressure ulcer and plasma samples (n=17) and individuals with a pressure ulcer and urine samples (n=15) were matched by age and plasma/urine sample days to individuals with SCI and no pressure ulcer (N=35). INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Plasma and urine samples were assayed in patients with SCI, capturing samples within 4 days after the SCI to a week before the formation of the first pressure ulcer. The Wilcoxon signed-rank test was performed to identify changes in the inflammatory mediators between the 2 time points. RESULTS An increase in concentration of the chemokine interferon-γ-induced protein of 10kd/CXCL10 in plasma (P<.01) and a decrease in concentration of the cytokine interferon-α in urine (P=.01) were observed before occurrence of a first pressure ulcer (∼4d) compared with matched controls. CONCLUSIONS Altered levels of inflammatory mediators in plasma and urine may be associated with pressure ulcer development after traumatic SCI. These inflammatory mediators should be explored as possible biomarkers for identifying individuals at risk for pressure ulcer formation.