Bose-Einstein condensation of excitons in Cu2_2O

We present a parameter-free model which estimates the density of excitons in Cu$_2$O, related to experiments that have tried to create an excitonic Bose-Einstein condensate. Our study demonstrates that the triplet-state excitons move along adiabats and obey classical statistics, while the singlet-state excitons are a possible candidate for forming a Bose-Einstein condensate. Finally we show that the results of this study do not change qualitatively in a two-dimensional exciton gas, which can be realized in a quantum well.Comment: 6 pages, RevTex, 1 ps figur

A Fast and Accurate Diagnostic Test for Severe Sepsis Using Kernel Classifiers

Severe sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however gold standard blood culture test results may return in up to 48 hours. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glycemic control protocols are able to accurately identify SI in real-time. A biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0–4 for increasing severity). Kernel density estimates were used for the development of joint probability density profiles for ss = 2 and ss < 2 data hours (213 and 5858 respectively of 6071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined for in-sample and out-of-sample estimates. A biomarker including concurrent insulin sensitivity and clinical data for the diagnosis of severe sepsis (ss = 2) achieves 69–94% sensitivity, 75–94% specificity, 0.78–0.99 AUC, 3–17 LHR+, 0.06–0.4 LHR-, 9–38% PPV, 99–100% NPV, and a diagnostic odds ratio of 7–260 for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. Thus, the clinical biomarker shows good to high accuracy and may provide useful information as a real-time diagnostic test for severe sepsis

A guanosine 5′-triphosphate-dependent protein kinase is localized in the outer envelope membrane of pea chloroplasts

A guanosine 5-triphosphate (GTP)-dependent protein kinase was detected in preparations of outer chloroplast envelope membranes of pea (Pisum sativum L.) chloroplasts. The protein-kinase activity was capable of phosphorylating several envelope-membrane proteins. The major phosphorylated products were 23- and 32.5-kilo-dalton proteins of the outer envelope membrane. Several other envelope proteins were labeled to a lesser extent. Following acid hydrolysis of the labeled proteins, most of the label was detected as phosphoserine with only minor amounts detected as phosphothreonine. Several criteria were used to distinguish the GTP-dependent protein kinase from an ATP-dependent kinase also present in the outer envelope membrane. The ATP-dependent kinase phosphorylated a very different set of envelope-membrane proteins. Heparin inhibited the GTP-dependent kinase but had little effect upon the ATP-dependent enzyme. The GTP-dependent enzyme accepted phosvitin as an external protein substrate whereas the ATP-dependent enzyme did not. The outer membrane of the chloroplast envelope also contained a phosphotransferase capable of transferring labeled phosphate from [-32P]GTP to ADP to yield (-32P]ATP. Consequently, addition of ADP to a GTP-dependent protein-kinase assay resulted in a switch in the pattern of labeled products from that seen with GTP to that typically seen with ATP

Long term verification of glucose-insulin regulatory system model dynamics

doi: 10.1109/IEMBS.2004.1403269Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. A long-term verification of a model that captures the essential glucose- and insulin-kinetics is presented, using retrospective data gathered in an Intensive Care Unit (ICU). The model uses only two patient specific parameters, for glucose clearance and insulin sensitivity. The optimization of these parameters is accomplished through a novel integration-based fitting approach, and a piecewise linearization of the parameters. This approach reduces the non-linear, non-convex optimization problem to a simple linear equation system. The method was tested on long-term blood glucose recordings from 17 ICU-patients, resulting in an average error of 7%, which is in the range of the sensor error. One-hour predictions of blood glucose data proved acceptable with an error range between 7- 11%. These results verify the model’s ability to capture longterm observed glucose-insulin dynamics in hyperglycaemic ICU patients

Impact of glucocorticoids on insulin resistance in the critically ill

Glucocorticoids (GCs) have been shown to reduce insulin sensitivity in healthy individuals. Widely used in critical care to treat a variety of inflammatory and allergic disorders, they may inadvertently exacerbate stress-hyperglycaemia. This research uses model-based methods to quantify the reduction of insulin sensitivity from GCs in critically ill patients, and thus their impact on glycaemic control. A clinically validated model-based measure of insulin sensitivity (SI) was used to quantify changes between two matched cohorts of 40 intensive care unit (ICU) patients who received GCs and a control cohort who did not. All patients were admitted to the Christchurch hospital ICU between 2005 and 2007 and spent at least 24 hours on the SPRINT glycaemic control protocol. A 31% reduction in whole-cohort median insulin sensitivity was seen between the control cohort and patients receiving glucocorticoids with a median dose equivalent to 200mg/day of hydrocortisone per patient. Comparing percentile-patients as a surrogate for matched patients, reductions in median insulin sensitivity of 20, 25, and 21% were observed for the 25th, 50th and 75th-percentile patients. All these cohort and per-patient reductions are less than or equivalent to the 30-62% reductions reported in healthy subjects especially when considering the fact that the GC doses in this study are 1.3-4 times larger than those in studies of healthy subjects. This reduced suppression of insulin sensitivity in critically ill patients could be a result of saturation due to already increased levels of catecholamines and cortisol common in critically illness. Virtual trial simulation showed that reductions in insulin sensitivity of 20-30% associated with glucocorticoid treatment in the ICU have limited impact on glycaemic control levels within the context of the SPRINT protocol

Development of a Clinical Type 1 Diabetes Metabolic System Model and in Silico Simulation Tool

Invited journal symposium paperObjectives: To develop a safe and effective protocol for the clinical control of Type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements, and multiple daily injection (MDI) with insulin analogues. To develop an in silico simulation tool of Type 1 diabetes to predict long-term glycaemic control outcomes of clinical interventions. Methods: The virtual patient method is used to develop a simulation tool for Type 1 diabetes using data from a Type 1 diabetes patient cohort (n=40). The tool is used to test the adaptive protocol (AC) and a conventional intensive insulin therapy (CC) against results from a representative control cohort. Optimal and suboptimal basal insulin replacement are evaluated as a function of self-monitoring blood glucose (SMBG) frequency in conjunction with the (AC and CC) prandial control protocols. Results: In long-term glycaemic control, the AC protocol significantly decreases HbA1c in conditions of suboptimal basal insulin replacement for SMBG frequencies =6/day, and reduced the occurrence of mild and severe hypoglycaemia by 86-100% over controls over all SMBG frequencies in conditions of optimal basal insulin. Conclusions: A simulation tool to predict long-term glycaemic control outcomes from clinical interventions is developed to test a novel, adaptive control protocol for Type 1 diabetes. The protocol is effective and safe compared to conventional intensive insulin therapy and controls. As fear of hypoglycaemia is a large psychological barrier to glycaemic control, the AC protocol may represent the next evolution of intensive insulin therapy to deliver increased glycaemic control with increased safety. Further clinical or experimental validation is needed to fully prove the concept

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the Continuous Glucose Monitoring System (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute one-hour glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalisable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies

Phosphoproteins and protein-kinase activity in isolated envelopes of pea (Pisum sativum L.) chloroplasts

A protein kinase was found in envelope membranes of purified pea (Pisum sativum L.) chloroplasts. Separation of the two envelope membranes showed that most of the enzyme activity was localized in the outer envelope. The kinase was activated by Mg2+ and inhibited by ADP and pyrophosphate. It showed no response to changes in pH in the physiological range (pH 7-8) or conventional protein substrates. Up to ten phosphorylated proteins could be detected in the envelope-membrane fraction. The molecular weights of these proteins, as determined by polyacrylamide-gel electrophoresis were: two proteins higher than 145 kDa, 97, 86, 62, 55, 46, 34 and 14 kDa. The 86-kDa band being the most pronounced. Experiments with separated inner and outer envelopes showed that most labeled proteins are also localized in the outer-envelope fraction. The results indicate a major function of the outer envelope in the communication between the chloroplast and the parent cell

Overview of Glycemic Control in Critical Care - Relating Performance and Clinical Results

Inagural review article invited for inaugural journalBackground: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. Methods: The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoc titration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95th percentile (±2 ) standard error (SE95%) to enable better comparison across cohorts. Results: Model-based control trials lower blood glucose into a 72-110mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE95% shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Summary: Model-based methods provide tighter, more adaptable “one method fits all” solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to current “one size fits all” sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or “one method fits all”, control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient

Glargine as a Basal Insulin Supplement in Recovering Critically Ill Patients - An In Silico Study

Tight glycaemic control is now benefiting medical and surgical intensive care patients by reducing complications associated with hyperglycaemia. Once patients leave this intensive care environment, less acute wards do not continue to provide the same level of glycaemic control. Main reason is that these less acute wards do not have the high levels of nursing resources to provide the same level of glycaemic control. Therefore developments in protocols that are less labour intensive are necessary. This study examines the use of insulin glargine for basal supplement in recovering critically ill patients. These patients represent a group who may benefit from such basal support therapy. In silico study results showed the potential in reducing nursing effort with the use of glargine. However, a protocol using only glargine for glucose control did not show to be effective in the simulated patients. This may be an indication that a protocol using only glargine is more suitable after discharge from critical care