ß(2)-Adrenoreceptor blockade improves early posttrauma hyperglycemia and pulmonary injury in obese rats.

Early hyperglycemia after trauma increases morbidity and mortality. Insulin is widely used to control posttrauma glucose, but this treatment increases the risk of hypoglycemia. We tested a novel method for early posttrauma hyperglycemia control by suppressing hepatic glycogenolysis via ß2-adrenoreceptor blockade [ICI-118551 (ICI)]. We have shown that, after severe trauma, obese Zucker (OZ) rats, similar to obese patients, exhibit increased acute lung injury compared with lean Zucker (LZ) rats. We hypothesized that OZ rats exhibit a greater increase in early posttrauma glucose compared with LZ rats, with the increased posttrauma hyperglycemia suppressed by ICI treatment. Orthopedic trauma was applied to both hindlimbs in LZ and OZ rats. Fasting plasma glucose was then monitored for 6 h with or without ICI (0.2 mg·kg(-1)·h(-1) iv.) treatment. One day after trauma, plasma IL-6 levels, lung neutrophil numbers, myeloperoxidase (MPO) activity, and wet-to-dry weight ratios were measured. Trauma induced rapid hepatic glycogenolysis, as evidenced by decreased liver glycogen levels, and this was inhibited by ICI treatment. Compared with LZ rats, OZ rats exhibited higher posttrauma glucose, IL-6, lung neutrophil infiltration, and MPO activity. Lung wet-to-dry weight ratios were increased in OZ rats but not in LZ rats. ICI treatment reduced the early hyperglycemia, lung neutrophil retention, MPO activity, and wet-to-dry weight ratio in OZ rats to levels comparable with those seen in LZ rats, with no effect on blood pressure or heart rate. These results demonstrate that ß2-adrenoreceptor blockade effectively reduces the early posttrauma hyperglycemia, which is associated with decreased lung injury in OZ rats.

A population model of integrative cardiovascular physiology.

We present a small integrative model of human cardiovascular physiology. The model is population-based; rather than using best fit parameter values, we used a variant of the Metropolis algorithm to produce distributions for the parameters most associated with model sensitivity. The population is built by sampling from these distributions to create the model coefficients. The resulting models were then subjected to a hemorrhage. The population was separated into those that lost less than 15 mmHg arterial pressure (compensators), and those that lost more (decompensators). The populations were parametrically analyzed to determine baseline conditions correlating with compensation and decompensation. Analysis included single variable correlation, graphical time series analysis, and support vector machine (SVM) classification. Most variables were seen to correlate with propensity for circulatory collapse, but not sufficiently to effect reasonable classification by any single variable. Time series analysis indicated a single significant measure, the stressed blood volume, as predicting collapse in situ, but measurement of this quantity is clinically impossible. SVM uncovered a collection of variables and parameters that, when taken together, provided useful rubrics for classification. Due to the probabilistic origins of the method, multiple classifications were attempted, resulting in an average of 3.5 variables necessary to construct classification. The most common variables used were systemic compliance, baseline baroreceptor signal strength and total peripheral resistance, providing predictive ability exceeding 90%. The methods presented are suitable for use in any deterministic mathematical model.