Comparison of Identification Methods of a Time-varying Insulin Sensitivity Parameter in a Simulation Model of Glucose Metabolism in the Critically Ill

6-pages (invited)Models of glucose metabolism can help to simulate and predict the blood glucose response in hyperglycaemic, critically ill patients. Model prediction performance depends on a su ciently accurate estimation of the patient's time-varying insulin sensitivity. The work presents three least squares approaches, the integral method and a Bayesian method that have been compared by prediction accuracy on an absolute and on a relative scale. Clinical data yields 1491 blood glucose predictions based on 10 critically ill patients were processed. The Bayesian approach proved to be best with small errors (9:7% absolute percent error, 14:7 root mean square of logarithmic error for prediction times 2h), and fewer and smaller outliers compared to the other methods. Computationally, the Bayesian method took 1.5 times longer per prediction compared to the fastest method. It can be concluded that a Bayesian parameter estimation gives safe and e ective results for the insulin sensitivity estimation for this model

Prediction Validation of Two Glycaemic Control Models in Critical Care

Invited paperMetabolic models can substantially improve control of hyperglycaemia in critically ill patients. Control efficacy depends on how accurately a model-based system is able to predict future blood glucose (BG) concentrations after a glycaemic control intervention. This research compares two metabolic models in terms of their predictive power. 30 minutes to 10 hour forward predictions are made using the Glucosafe model (GS) and a clinically tested model (CC) from Christchurch in a retrospective study of 11 hyperglycemic patients, 6 from New Zealand and 5 from Denmark. Median and ranges of prediction errors are similar for predictions up to 360 minutes. Both models make better predictions on the Danish patients. At long prediction times of more than 5 hours, GS predictions tend to be more accurate in the cohort from New Zealand whereas the CC model tends to predict better in the cohort from Denmark. However, differences in root mean square (RMS) of prediction errors are not greater than 4–5% in both cohorts. For both models, outlying prediction errors are dominated by single patients, particularly type 1 diabetic patients. GS predicted BG values are generally higher compared to CC predicted values. As expected, the RMS prediction error increases with prediction interval for both models and cohorts. Results show the potential of both models for use in prospective clinical trials with longer than 120 min sampling intervals, though predictive power is probably related to the type of cohort in terms of admission type, degree of illness and glycaemic stability

Bacillus subtilis as a bioindicator to estimate pentachlorophenol toxicity and concentration

Pentachlorophenol (PCP) and its sodium salt (Na-PCP) are extremely toxic chemicals responsible for important soil and groundwater pollution, mainly caused by wastes from wood-treatment plants, because chlorinated phenols are widely used as wood preservatives. The methods most commonly used for routine analysis of pesticides such as PCP and Na-PCP are high-performance liquid chromatography (HPLC) and gas chromatography– mass spectroscopy (GC–MS). A variety of rapid biological screening tests using marine organisms, bioluminescent bacteria, and enzymes have also been reported. In this study, rapid biological screening analysis using Bacillus subtilis was developed, to assess the biodegradation of PCP and its by-products in liquid samples. An empirical model is proposed for spectrophotometric analysis of Na-PCP concentration after growth of Bacillus subtilis

Regulation of Blood Sugar in Intensive Care Patients

High blood sugar levels are frequent in intensive care patients, resulting in higher mortality and morbidity, and longer stay. GlucoSafe, a computer decision support system, is developed to assist clinicians in regulating blood sugar. The system uses a physiological model of sugar metabolism, including insulin production and action, and intestinal uptake of nutrients. However, efficacy will depend on how accurately it can predict future blood glucose levels (BG) after a glycemic control intervention, based on previously measured BG values. 1-10 hour forward predictions were made using GlucoSafe (GS) and a clinically tested model (CC) from New Zealand for 11 hyperglycemic patients, 6 from New Zealand and 5 from Denmark. As expected, relative RMS prediction error increases with prediction interval for both models and cohorts. Fig. 1 shows similar predictive power for GS and CC up to 3-5 hours. GS outperforms CC for predictions beyond 5 hours. A CC-based protocol has been successfully applied for glycemic control in Christchurch. Therefore, GlucoSafe is expected to be a safe, effective tool for blood sugar regulation in intensive care

Plasticity in resource allocation based life history traits in the Pacific oyster, Crassostrea gigas. I. Spatial variation in food abundance

We investigated the quantitative genetics of plasticity in resource allocation between survival, growth and reproductive effort in Crassostrea gigas when food abundance varies spatially. Resource allocation shifted from survival to growth and reproductive effort as food abundance increased. An optimality model suggests that this plastic shift may be adaptive. Reproductive effort plasticity and mean survival were highly heritable, whereas for growth, both mean and plasticity had low heritability. The genetic correlations between reproductive effort and both survival and growth were negative in poor treatments, suggesting trade-offs, but positive in rich ones. These sign reversals may reflect genetic variability in resource acquisition, which would only be expressed when food is abundant. Finally, we found positive genetic correlations between reproductive effort plasticity and both growth and survival means. The latter may reflect adaptation of C. gigas to differential sensitivity of fitness to survival, such that genetic variability in survival mean might support genetic variability in reproductive effort plasticity

Natural diatomites: Efficient green catalyst for Fenton-like oxidation of Orange II

doi: 10.1016/j.apcatb.2015.08.022The Fenton-like oxidation of the anionic azo-dye Orange II (100-500 mg/L) was batchwise performed using commercial grade diatomites (3.5% Fe content) thermally treated. Solid samples were thoroughly characterized by several techniques. Peroxidation experiments were performed varying the diatomite calcination temperature (500, 700, 1000 degrees C), reaction temperature (50, 60, 70, 80 degrees C), catalyst load (0.47, 0.94, 1.89 and 3.78g), H2O2 concentration (11.0, 13.7, 20.6 mmol/L) and dosing, pH (2-3.5) and initial dye concentration (0.28, 0.57, 1.43 mmol/L). The influence of NaCl and oxalic acid on the catalytic performance and stability was also addressed. The best results were obtained with samples calcined at 700 degrees C, with initial pH 3, at 70 degrees C and using the stoichiometric amount of H2O2, since complete decoloration, TOC reduction close to 67% and negligible Fe leaching were achieved. The stability of the catalyst maintains after 20 h of usage, with a final Fe loss of 2.25%. An average of 0.88 mg/L of iron leached after each run, which is below the discharge limit

A simulation model of insulin saturation and glucose balance for glycaemic control in ICU patients

Hyperglycaemia due to reduced insulin sensitivity is prevalent in critically ill patients and increases mortality and complications. However, consistent tight control has proven elusive. In particular, properly accounting for the saturation of insulin action is important in intensive insulin therapy. This paper introduces a composite metabolic model of insulin kinetics and blood glucose balance. Saturation of insulin action at high insulin concentrations is modelled as a non-linearity and reduced insulin sensitivity is modelled as either a scaling of peripheral insulin (before the non-linearity) or as a scaling of insulin effect (after the non-linearity). Retrospective clinical data from 10 intensive care patients are used to evaluate these approaches based on the resulting accuracy in predicting glycaemic response to intervention. For predictions of blood glucose longer than 1/2 hour ahead scaling of insulin effect gave a 1.6 fold smaller RMS error. Results for short-term (1-hour) and long-term (8-hour) predictions were 16% and 34% RMS error for scaling of insulin effect compared to 22% and 59% for scaling of peripheral insulin, respectively (P< 0.01). It can be concluded that scaling the insulin effect is a more suitable approach in this model structure