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Find video protocols related to scientific articles indexed in Pubmed.
The Clinical Utilisation of Respiratory Elastance Software (CURE Soft): a bedside software for real-time respiratory mechanics monitoring and mechanical ventilation management.
Biomed Eng Online
PUBLISHED: 07-09-2014
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Real-time patient respiratory mechanics estimation can be used to guide mechanical ventilation settings, particularly, positive end-expiratory pressure (PEEP). This work presents a software, Clinical Utilisation of Respiratory Elastance (CURE Soft), using a time-varying respiratory elastance model to offer this ability to aid in mechanical ventilation treatment.
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When the value of gold is zero.
BMC Res Notes
PUBLISHED: 06-24-2014
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This manuscript presents the concerns around the increasingly common problem of not having readily available or useful "gold standard" measurements. This issue is particularly important in critical care where many measurements used in decision making are surrogates of what we would truly wish to use. However, the question is broad, important and applicable in many other areas.In particular, a gold standard measurement often exists, but is not clinically (or ethically in some cases) feasible. The question is how does one even begin to develop new measurements or surrogates if one has no gold standard to compare with?We raise this issue concisely with a specific example from mechanical ventilation, a core bread and butter therapy in critical care that is also a leading cause of length of stay and cost of care. Our proposed solution centers around a hierarchical validation approach that we believe would ameliorate ethics issues around radiation exposure that make current gold standard measures clinically infeasible, and thus provide a pathway to create a (new) gold standard.
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AC electric field induced dipole-based on-chip 3D cell rotation.
Lab Chip
PUBLISHED: 06-16-2014
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The precise rotation of suspended cells is one of the many fundamental manipulations used in a wide range of biotechnological applications such as cell injection and enucleation in nuclear transfer (NT) cloning. Noticeably scarce among the existing rotation techniques is the three-dimensional (3D) rotation of cells on a single chip. Here we present an alternating current (ac) induced electric field-based biochip platform, which has an open-top sub-mm square chamber enclosed by four sidewall electrodes and two bottom electrodes, to achieve rotation about the two axes, thus 3D cell rotation. By applying an ac potential to the four sidewall electrodes, an in-plane (yaw) rotating electric field is generated and in-plane rotation is achieved. Similarly, by applying an ac potential to two opposite sidewall electrodes and the two bottom electrodes, an out-of-plane (pitch) rotating electric field is generated and rolling rotation is achieved. As a prompt proof-of-concept, bottom electrodes were constructed with transparent indium tin oxide (ITO) using the standard lift-off process and the sidewall electrodes were constructed using a low-cost micro-milling process and then assembled to form the chip. Through experiments, we demonstrate rotation of bovine oocytes of ~120 ?m diameter about two axes, with the capability of controlling the rotation direction and the rate for each axis through control of the ac potential amplitude, frequency, and phase shift, and cell medium conductivity. The maximum observed rotation rate reached nearly 140° s(-1), while a consistent rotation rate reached up to 40° s(-1). Rotation rate spectra for zona pellucida-intact and zona pellucida-free oocytes were further compared and found to have no effective difference. This simple, transparent, cheap-to-manufacture, and open-top platform allows additional functional modules to be integrated to become a more powerful cell manipulation system.
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A Novel Hierarchal-Based Approach to Measure Insulin Sensitivity and Secretion in At-Risk Populations.
J Diabetes Sci Technol
PUBLISHED: 05-31-2014
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The pathogenesis of type 2 diabetes is characterized by insulin resistance and insulin secretory dysfunction. Few existing metabolic tests measure both characteristics, and no such tests are inexpensive enough to enable widespread use. A hierarchical approach uses 2 down-sampled tests in the dynamic insulin sensitivity and secretion test (DISST) family to first determine insulin sensitivity (SI) using 4 glucose measurements. Second the insulin secretion is determined for only participants with reduced SI using 3 C-peptide measurements from the original test. The hierarchical approach is assessed via its ability to classify 214 individual test responses of 71 females with an elevated risk of type 2 diabetes into 5 bins with equivalence to the fully sampled DISST. Using an arbitrary SI cut-off, 102 test responses were reassayed for C-peptide and unique insulin secretion characteristics estimated. The hierarchical approach correctly classified 84.5% of the test responses and 94.4% of the responses of individuals with increased fasting glucose. The hierarchical approach is a low-cost methodology for measuring key characteristics of type 2 diabetes. Thus the approach could provide an economical approach to studying the pathogenesis of type 2 diabetes, or in early risk screening. As the higher cost test uses the same clinical protocol as the low-cost test, the cost of the additional information is limited to the assay cost of C-peptide, and no additional procedures or callbacks are required.
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Use of the DISST Model to Estimate the HOMA and Matsuda Indexes Using Only a Basal Insulin Assay.
J Diabetes Sci Technol
PUBLISHED: 05-31-2014
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It is hypothesized that early detection of reduced insulin sensitivity (SI) could prompt intervention that may reduce the considerable financial strain type 2 diabetes mellitus (T2DM) places on global health care. Reduction of the cost of already inexpensive SI metrics such as the Matsuda and HOMA indexes would enable more widespread, economically feasible use of these metrics for screening. The goal of this research was to determine a means of reducing the number of insulin samples and therefore the cost required to provide an accurate Matsuda Index value. The Dynamic Insulin Sensitivity and Secretion Test (DISST) model was used with the glucose and basal insulin measurements from an Oral Glucose Tolerance Test (OGTT) to predict patient insulin responses. The insulin response to the OGTT was determined via population based regression analysis that incorporated the 60-minute glucose and basal insulin values. The proposed method derived accurate and precise Matsuda Indices as compared to the fully sampled Matsuda (R = .95) using only the basal assay insulin-level data and 4 glucose measurements. Using a model employing the basal insulin also allows for determination of the 1-day HOMA value. The DISST model was successfully modified to allow for the accurate prediction an individual's insulin response to the OGTT. In turn, this enabled highly accurate and precise estimation of a Matsuda Index using only the glucose and basal insulin assays. As insulin assays account for the majority of the cost of the Matsuda Index, this model offers a significant reduction in assay cost.
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Parametric-based brain Magnetic Resonance Elastography using a Rayleigh damping material model.
Comput Methods Programs Biomed
PUBLISHED: 05-28-2014
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The three-parameter Rayleigh damping (RD) model applied to time-harmonic Magnetic Resonance Elastography (MRE) has potential to better characterise fluid-saturated tissue systems. However, it is not uniquely identifiable at a single frequency. One solution to this problem involves simultaneous inverse problem solution of multiple input frequencies over a broad range. As data is often limited, an alternative elegant solution is a parametric RD reconstruction, where one of the RD parameters (?I or ?I) is globally constrained allowing accurate identification of the remaining two RD parameters. This research examines this parametric inversion approach as applied to in vivo brain imaging. Overall, success was achieved in reconstruction of the real shear modulus (?R) that showed good correlation with brain anatomical structures. The mean and standard deviation shear stiffness values of the white and gray matter were found to be 3±0.11kPa and 2.2±0.11kPa, respectively, which are in good agreement with values established in the literature or measured by mechanical testing. Parametric results with globally constrained ?I indicate that selecting a reasonable value for the ?I distribution has a major effect on the reconstructed ?I image and concomitant damping ratio (?d). More specifically, the reconstructed ?I image using a realistic ?I=333Pa value representative of a greater portion of the brain tissue showed more accurate differentiation of the ventricles within the intracranial matter compared to ?I=1000Pa, and ?d reconstruction with ?I=333Pa accurately captured the higher damping levels expected within the vicinity of the ventricles. Parametric RD reconstruction shows potential for accurate recovery of the stiffness characteristics and overall damping profile of the in vivo living brain despite its underlying limitations. Hence, a parametric approach could be valuable with RD models for diagnostic MRE imaging with single frequency data.
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Continuous glucose monitoring and trend accuracy: news about a trend compass.
J Diabetes Sci Technol
PUBLISHED: 05-16-2014
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Continuous glucose monitoring (CGM) devices are being increasingly used to monitor glycemia in people with diabetes. One advantage with CGM is the ability to monitor the trend of sensor glucose (SG) over time. However, there are few metrics available for assessing the trend accuracy of CGM devices. The aim of this study was to develop an easy to interpret tool for assessing trend accuracy of CGM data. SG data from CGM were compared to hourly blood glucose (BG) measurements and trend accuracy was quantified using the dot product. Trend accuracy results are displayed on the Trend Compass, which depicts trend accuracy as a function of BG. A trend performance table and Trend Index (TI) metric are also proposed. The Trend Compass was tested using simulated CGM data with varying levels of error and variability, as well as real clinical CGM data. The results show that the Trend Compass is an effective tool for differentiating good trend accuracy from poor trend accuracy, independent of glycemic variability. Furthermore, the real clinical data show that the Trend Compass assesses trend accuracy independent of point bias error. Finally, the importance of assessing trend accuracy as a function of BG level is highlighted in a case example of low and falling BG data, with corresponding rising SG data. This study developed a simple to use tool for quantifying trend accuracy. The resulting trend accuracy is easily interpreted on the Trend Compass plot, and if required, performance table and TI metric.
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Early detection of abnormal left ventricular relaxation in acute myocardial ischemia with a quadratic model.
Med Eng Phys
PUBLISHED: 05-14-2014
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The time constant of left ventricular (LV) relaxation derived from a monoexponential model is widely used as an index of LV relaxation rate, although this model does not reflect the non-uniformity of ventricular relaxation. This study investigates whether the relaxation curve can be better fitted with a "quadratic" model than with the "conventional" monoexponential model and if changes in the LV relaxation waveform due to acute myocardial ischemia could be better detected with the quadratic model.
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Reconstructing 3-D skin surface motion for the DIET breast cancer screening system.
IEEE Trans Med Imaging
PUBLISHED: 04-29-2014
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Digital image-based elasto-tomography (DIET) is a prototype system for breast cancer screening. A breast is imaged while being vibrated, and the observed surface motion is used to infer the internal stiffness of the breast, hence identifying tumors. This paper describes a computer vision system for accurately measuring 3-D surface motion. A model-based segmentation is used to identify the profile of the breast in each image, and the 3-D surface is reconstructed by fitting a model to the profiles. The surface motion is measured using a modern optical flow implementation customized to the application, then trajectories of points on the 3-D surface are given by fusing the optical flow with the reconstructed surfaces. On data from human trials, the system is shown to exceed the performance of an earlier marker-based system at tracking skin surface motion. We demonstrate that the system can detect a 10 mm tumor in a silicone phantom breast.
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A patient-specific airway branching model for mechanically ventilated patients.
Comput Math Methods Med
PUBLISHED: 03-14-2014
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Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models of in vivo behaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps).
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Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control.
Comput Methods Programs Biomed
PUBLISHED: 03-05-2014
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Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal.
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Continuous Glucose Monitoring in Newborn Infants: How Do Errors in Calibration Measurements Affect Detected Hypoglycemia?
J Diabetes Sci Technol
PUBLISHED: 02-27-2014
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Neonatal hypoglycemia is common and can cause serious brain injury. Continuous glucose monitoring (CGM) could improve hypoglycemia detection, while reducing blood glucose (BG) measurements. Calibration algorithms use BG measurements to convert sensor signals into CGM data. Thus, inaccuracies in calibration BG measurements directly affect CGM values and any metrics calculated from them. The aim was to quantify the effect of timing delays and calibration BG measurement errors on hypoglycemia metrics in newborn infants. Data from 155 babies were used. Two timing and 3 BG meter error models (Abbott Optium Xceed, Roche Accu-Chek Inform II, Nova Statstrip) were created using empirical data. Monte-Carlo methods were employed, and each simulation was run 1000 times. Each set of patient data in each simulation had randomly selected timing and/or measurement error added to BG measurements before CGM data were calibrated. The number of hypoglycemic events, duration of hypoglycemia, and hypoglycemic index were then calculated using the CGM data and compared to baseline values. Timing error alone had little effect on hypoglycemia metrics, but measurement error caused substantial variation. Abbott results underreported the number of hypoglycemic events by up to 8 and Roche overreported by up to 4 where the original number reported was 2. Nova results were closest to baseline. Similar trends were observed in the other hypoglycemia metrics. Errors in blood glucose concentration measurements used for calibration of CGM devices can have a clinically important impact on detection of hypoglycemia. If CGM devices are going to be used for assessing hypoglycemia it is important to understand of the impact of these errors on CGM data.
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Visualisation of time-varying respiratory system elastance in experimental ARDS animal models.
BMC Pulm Med
PUBLISHED: 02-19-2014
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Patients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.
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Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol.
Biomed Eng Online
PUBLISHED: 01-29-2014
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The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12-48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol.
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Does the achievement of an intermediate glycemic target reduce organ failure and mortality? A post hoc analysis of the Glucontrol trial.
J Crit Care
PUBLISHED: 01-17-2014
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This research evaluates the impact of the achievement of an intermediate target glycemic band on the severity of organ failure and mortality.
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Interstitial insulin kinetic parameters for a 2-compartment insulin model with saturable clearance.
Comput Methods Programs Biomed
PUBLISHED: 01-10-2014
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Glucose-insulin system models are commonly used for identifying insulin sensitivity. With physiological, 2-compartment insulin kinetics models, accurate kinetic parameter values are required for reliable estimates of insulin sensitivity. This study uses data from 6 published microdialysis studies to determine the most appropriate parameter values for the transcapillary diffusion rate (n(I)) and cellular insulin clearance rate (n(C)). The 6 studies (12 data sets) used microdialysis techniques to simultaneously obtain interstitial and plasma insulin concentrations. The reported plasma insulin concentrations were used as input and interstitial insulin concentrations were simulated with the interstitial insulin kinetics sub-model. These simulated results were then compared to the reported interstitial measurements and the most appropriate set of parameter values was determined across the 12 data sets by combining the results. Interstitial insulin kinetic parameters values n(I)=n(C)=0.0060 min?¹ were shown to be the most appropriate. These parameter values are associated with an effective, interstitial insulin half-life, t(½)=58 min, within the range of 25-130 min reported by others.
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Continuous stroke volume estimation from aortic pressure using zero dimensional cardiovascular model: proof of concept study from porcine experiments.
PLoS ONE
PUBLISHED: 01-01-2014
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Accurate, continuous, left ventricular stroke volume (SV) measurements can convey large amounts of information about patient hemodynamic status and response to therapy. However, direct measurements are highly invasive in clinical practice, and current procedures for estimating SV require specialized devices and significant approximation.
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Complexity of continuous glucose monitoring data in critically ill patients: continuous glucose monitoring devices, sensor locations, and detrended fluctuation analysis methods.
J Diabetes Sci Technol
PUBLISHED: 12-20-2013
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Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of negative outcomes remains inconclusive. Continuous glucose monitoring (CGM) devices allow researchers to investigate glucose complexity, using detrended fluctuation analysis (DFA), to determine whether it is associated with negative outcomes. The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA.
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Multiplicative surrogate standard deviation: a group metric for the glycemic variability of individual hospitalized patients.
J Diabetes Sci Technol
PUBLISHED: 10-16-2013
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Group metrics are described to quantify blood glucose (BG) variability of hospitalized patients.
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Model-based respiratory mechanics to titrate PEEP and monitor disease state for experimental ARDS subjects.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 10-11-2013
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Modelling the respiratory mechanics of mechanically ventilated (MV) patients can provide useful information to guide MV therapy. Two model-based methods were evaluated based on data from three experimental acute respiratory distress syndrome (ARDS) induced piglets and validated against values available from ventilators. A single compartment lung model with integral-based parameter identification was found to be effective in capturing fundamental respiratory mechanics during inspiration. The trends matched clinical expectation and provided better resolution than clinically derived linear model metrics. An expiration time constant model also captured the same trend in respiratory elastance. However, the assumption of constant resistance and a slightly higher fitting error results in less insight than the single compartment model. Further research is required to confirm its application in titrating to optimal MV settings.
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Impact of haemodialysis on insulin sensitivity of acute renal failure (ARF) patients with sepsis in critical care.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 10-11-2013
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Critically ill patients often develop renal failure in addition to their primary diagnosis. However, the effect and impact of haemodialysis (HD) on insulin sensitivity n critically ill patients remains unclear. Specifically, this study investigates insulin sensitivity of acute renal failure (ARF) patients with sepsis who underwent HD and glycaemic control. Model-based insulin sensitivity (SI) profiles were identified for 20 critically ill ARF patients on Specialized Relative Insulin Nutrition Titration (SPRINT) glycaemic control during intervals onto HD (OFF/ON), and after HD (ON/OFF). Patients exhibited a median -18% (IQR -36% to -5% p<0.05) reduction in measured SI after the OFF/ON dialysis transition, and a median 9% (IQR -5% to 37%, p<0.05) rise after the ON/OFF transition. Almost 80% of patients exhibited decreased SI at the OFF/ON interval, and 60% exhibited increased SI at the ON/OFF transition. Results indicate that HD commencement has significant effect on insulin pharmacokinetics at a cohort and per-patient level. These results provide the data to design conclusive studies of HD effects on SI, and to inform glycaemic control protocol development and implementation for this specific group of critically ill patients with ARF-sepsis.
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A model-based control protocol for transition from ICU to HDU: Robustness analysis.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 10-11-2013
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The robustness of a model-based control protocol as a less intensive TGC protocol using insulin Glargine for provision of basal insulin is simulated in this study. To quantify the performance and robustness of the protocol to errors, namely physiological variability and sensor errors, an in-silico Monte Carlo analysis is performed. Actual patient data from Christchurch Hospital, New Zealand were used as virtual trial patients.
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Dextrose gel for neonatal hypoglycaemia (the Sugar Babies Study): a randomised, double-blind, placebo-controlled trial.
Lancet
PUBLISHED: 09-25-2013
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Neonatal hypoglycaemia is common, and a preventable cause of brain damage. Dextrose gel is used to reverse hypoglycaemia in individuals with diabetes; however, little evidence exists for its use in babies. We aimed to assess whether treatment with dextrose gel was more effective than feeding alone for reversal of neonatal hypoglycaemia in at-risk babies.
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On the problem of patient-specific endogenous glucose production in neonates on stochastic targeted glycemic control.
J Diabetes Sci Technol
PUBLISHED: 08-06-2013
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Both stress and prematurity can induce hyperglycemia in the neonatal intensive care unit, which, in turn, is associated with worsened outcomes. Endogenous glucose production (EGP) is the formation of glucose by the body from substrates and contributes to blood glucose (BG) levels. Due to the inherent fragility of the extremely low birth weight (ELBW) neonates, true fasting EGP cannot be explicitly determined, introducing uncertainty into glycemic models that rely on quantifying glucose sources. Stochastic targeting, or STAR, is one such glycemic control framework.
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Impact of sensor and measurement timing errors on model-based insulin sensitivity.
Comput Methods Programs Biomed
PUBLISHED: 06-14-2013
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A model-based insulin sensitivity parameter (SI) is often used in glucose-insulin system models to define the glycaemic response to insulin. As a parameter identified from clinical data, insulin sensitivity can be affected by blood glucose (BG) sensor error and measurement timing error, which can subsequently impact analyses or glycaemic variability during control. This study assessed the impact of both measurement timing and BG sensor errors on identified values of SI and its hour-to-hour variability within a common type of glucose-insulin system model. Retrospective clinical data were used from 270 patients admitted to the Christchurch Hospital ICU between 2005 and 2007 to identify insulin sensitivity profiles. We developed error models for the Abbott Optium Xceed glucometer and measurement timing from clinical data. The effect of these errors on the re-identified insulin sensitivity was investigated by Monte-Carlo analysis. The results of the study show that timing errors in isolation have little clinically significant impact on identified SI level or variability. The clinical impact of changes to SI level induced by combined sensor and timing errors is likely to be significant during glycaemic control. Identified values of SI were mostly (90th percentile) within 29% of the true value when influenced by both sources of error. However, these effects may be overshadowed by physiological factors arising from the critical condition of the patients or other under-modelled or un-modelled dynamics. Thus, glycaemic control protocols that are designed to work with data from glucometers need to be robust to these errors and not be too aggressive in dosing insulin.
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Nasogastric aspiration as an indicator for feed absorption in model-based glycemic control in neonatal intensive care.
J Diabetes Sci Technol
PUBLISHED: 06-14-2013
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STAR (stochastic targeted) is a glycemic control model-based framework for critically ill neonates that has shown benefits in reducing hypoglycemia and hyperglycemia. STAR uses a stochastic matrix method to forecast future changes in a patients insulin sensitivity and then applies this result to a physiological model to select an optimal insulin treatment. Nasogastric aspiration may be used as an indicator to suggest periods of care when enteral feed absorption is compromised, improving the performance of glycemic control. An analysis has been carried out to investigate the effect of poorly absorbed feeds on glycemic control.
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Non-identifiability of the Rayleigh damping material model in Magnetic Resonance Elastography.
Math Biosci
PUBLISHED: 05-07-2013
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Magnetic Resonance Elastography (MRE) is an emerging imaging modality for quantifying soft tissue elasticity deduced from displacement measurements within the tissue obtained by phase sensitive Magnetic Resonance Imaging (MRI) techniques. MRE has potential to detect a range of pathologies, diseases and cancer formations, especially tumors. The mechanical model commonly used in MRE is linear viscoelasticity (VE). An alternative Rayleigh damping (RD) model for soft tissue attenuation is used with a subspace-based nonlinear inversion (SNLI) algorithm to reconstruct viscoelastic properties, energy attenuation mechanisms and concomitant damping behavior of the tissue-simulating phantoms. This research performs a thorough evaluation of the RD model in MRE focusing on unique identification of RD parameters, ?I and ?I. Results show the non-identifiability of the RD model at a single input frequency based on a structural analysis with a series of supporting experimental phantom results. The estimated real shear modulus values (?R) were substantially correct in characterising various material types and correlated well with the expected stiffness contrast of the physical phantoms. However, estimated RD parameters displayed consistent poor reconstruction accuracy leading to unpredictable trends in parameter behaviour. To overcome this issue, two alternative approaches were developed: (1) simultaneous multi-frequency inversion; and (2) parametric-based reconstruction. Overall, the RD model estimates the real shear shear modulus (?R) well, but identifying damping parameters (?I and ?I) is not possible without an alternative approach.
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Utility of a novel error-stepping method to improve gradient-based parameter identification by increasing the smoothness of the local objective surface: A case-study of pulmonary mechanics.
Comput Methods Programs Biomed
PUBLISHED: 04-26-2013
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Accurate model parameter identification relies on accurate forward model simulations to guide convergence. However, some forward simulation methodologies lack the precision required to properly define the local objective surface and can cause failed parameter identification. The role of objective surface smoothness in identification of a pulmonary mechanics model was assessed using forward simulation from a novel error-stepping method and a proprietary Runge-Kutta method. The objective surfaces were compared via the identified parameter discrepancy generated in a Monte Carlo simulation and the local smoothness of the objective surfaces they generate. The error-stepping method generated significantly smoother error surfaces in each of the cases tested (p<0.0001) and more accurate model parameter estimates than the Runge-Kutta method in three of the four cases tested (p<0.0001) despite a 75% reduction in computational cost. Of note, parameter discrepancy in most cases was limited to a particular oblique plane, indicating a non-intuitive multi-parameter trade-off was occurring. The error-stepping method consistently improved or equalled the outcomes of the Runge-Kutta time-integration method for forward simulations of the pulmonary mechanics model. This study indicates that accurate parameter identification relies on accurate definition of the local objective function, and that parameter trade-off can occur on oblique planes resulting prematurely halted parameter convergence.
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Effects of Neurally Adjusted Ventilatory Assist (NAVA) levels in non-invasive ventilated patients: titrating NAVA levels with electric diaphragmatic activity and tidal volume matching.
Biomed Eng Online
PUBLISHED: 04-25-2013
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Neurally adjusted ventilatory assist (NAVA) delivers pressure in proportion to diaphragm electrical activity (Eadi). However, each patient responds differently to NAVA levels. This study aims to examine the matching between tidal volume (Vt) and patients inspiratory demand (Eadi), and to investigate patient-specific response to various NAVA levels in non-invasively ventilated patients.
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Expiratory model-based method to monitor ARDS disease state.
Biomed Eng Online
PUBLISHED: 03-25-2013
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Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection.
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Analysis of different model-based approaches for estimating dFRC for real-time application.
Biomed Eng Online
PUBLISHED: 01-25-2013
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Acute Respiratory Distress Syndrome (ARDS) is characterized by inflammation, filling of the lung with fluid and the collapse of lung units. Mechanical ventilation (MV) is used to treat ARDS using positive end expiratory pressure (PEEP) to recruit and retain lung units, thus increasing pulmonary volume and dynamic functional residual capacity (dFRC) at the end of expiration. However, simple, non-invasive methods to estimate dFRC do not exist.
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Evaluation of a model-based hemodynamic monitoring method in a porcine study of septic shock.
Comput Math Methods Med
PUBLISHED: 01-22-2013
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The accuracy and clinical applicability of an improved model-based system for tracking hemodynamic changes is assessed in an animal study on septic shock.
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Daily evolution of insulin sensitivity variability with respect to diagnosis in the critically ill.
PLoS ONE
PUBLISHED: 01-17-2013
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This study examines the likelihood and evolution of overall and hypoglycemia-inducing variability of insulin sensitivity in ICU patients based on diagnosis and day of stay.
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Simulation of left atrial function using a multi-scale model of the cardiovascular system.
PLoS ONE
PUBLISHED: 01-01-2013
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During a full cardiac cycle, the left atrium successively behaves as a reservoir, a conduit and a pump. This complex behavior makes it unrealistic to apply the time-varying elastance theory to characterize the left atrium, first, because this theory has known limitations, and second, because it is still uncertain whether the load independence hypothesis holds. In this study, we aim to bypass this uncertainty by relying on another kind of mathematical model of the cardiac chambers. In the present work, we describe both the left atrium and the left ventricle with a multi-scale model. The multi-scale property of this model comes from the fact that pressure inside a cardiac chamber is derived from a model of the sarcomere behavior. Macroscopic model parameters are identified from reference dog hemodynamic data. The multi-scale model of the cardiovascular system including the left atrium is then simulated to show that the physiological roles of the left atrium are correctly reproduced. This include a biphasic pressure wave and an eight-shaped pressure-volume loop. We also test the validity of our model in non basal conditions by reproducing a preload reduction experiment by inferior vena cava occlusion with the model. We compute the variation of eight indices before and after this experiment and obtain the same variation as experimentally observed for seven out of the eight indices. In summary, the multi-scale mathematical model presented in this work is able to correctly account for the three roles of the left atrium and also exhibits a realistic left atrial pressure-volume loop. Furthermore, the model has been previously presented and validated for the left ventricle. This makes it a proper alternative to the time-varying elastance theory if the focus is set on precisely representing the left atrial and left ventricular behaviors.
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Model-based PEEP optimisation in mechanical ventilation.
Biomed Eng Online
PUBLISHED: 11-07-2011
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Acute Respiratory Distress Syndrome (ARDS) patients require mechanical ventilation (MV) for breathing support. Patient-specific PEEP is encouraged for treating different patients but there is no well established method in optimal PEEP selection.
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Characterisation of the iterative integral parameter identification method.
Med Biol Eng Comput
PUBLISHED: 07-13-2011
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Parameter identification methods are used to find optimal parameter values to fit models to measured data. The single integral method was defined as a simple and robust parameter identification method. However, the method did not necessarily converge to optimum parameter values. Thus, the iterative integral method (IIM) was developed. IIM will be compared to a proprietary nonlinear-least-squares-based Levenberg-Marquardt parameter identification algorithm using a range of reasonable starting values. Performance is assessed by the rate and accuracy of convergence for an exemplar two parameters insulin pharmacokinetic model, where true values are known a priori. IIM successfully converged to within 1% of the true values in all cases with a median time of 1.23 s (IQR 0.82-1.55 s; range 0.61-3.91 s). The nonlinear-least-squares method failed to converge in 22% of the cases and had a median (successful) convergence time of 3.29 s (IQR 2.04-4.89 s; range 0.42-44.9 s). IIM is a stable and relatively quick parameter identification method that can be applied in a broad variety of model configurations. In contrast to most established methods, IIM is not susceptible to local minima and is thus, starting point and operator independent.
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Identification of influenza virus inhibitors which disrupt of viral polymerase protein-protein interactions.
Methods
PUBLISHED: 06-09-2011
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Due to their ability to rapidly mutate, influenza viruses quickly develop resistance against many antiviral substances, leading to an urgent need for new compounds. The trimeric viral polymerase complex, a major target for the development of new inhibitors, must be assembled from the PB1, PB2, and PA subunits for successful infection. Here, we describe ELISA-based assays which allow the identification of peptides which impair polymerase complex formation. Since the protein-protein interaction domains of the viral polymerase are highly conserved, these inhibitors are also predicted to be active against a broad range of influenza strains. Using this method, identification of small molecules and lead compounds against influenza A and B viruses should be feasible.
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Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control.
Ann Intensive Care
PUBLISHED: 05-18-2011
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Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials.
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Clinical detection and monitoring of acute pulmonary embolism: proof of concept of a computer-based method.
Ann Intensive Care
PUBLISHED: 05-13-2011
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The diagnostic ability of computer-based methods for cardiovascular system (CVS) monitoring offers significant clinical potential. This research tests the clinical applicability of a newly improved computer-based method for the proof of concept case of tracking changes in important hemodynamic indices due to the influence acute pulmonary embolism (APE).
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Mathematical multi-scale model of the cardiovascular system including mitral valve dynamics. Application to ischemic mitral insufficiency.
Biomed Eng Online
PUBLISHED: 05-11-2011
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Valve dysfunction is a common cardiovascular pathology. Despite significant clinical research, there is little formal study of how valve dysfunction affects overall circulatory dynamics. Validated models would offer the ability to better understand these dynamics and thus optimize diagnosis, as well as surgical and other interventions.
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Model-based optimal PEEP in mechanically ventilated ARDS patients in the intensive care unit.
Biomed Eng Online
PUBLISHED: 05-04-2011
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The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time.
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Influenza virus ribonucleoprotein complexes gain preferential access to cellular export machinery through chromatin targeting.
PLoS Pathog.
PUBLISHED: 04-21-2011
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In contrast to most RNA viruses, influenza viruses replicate their genome in the nucleus of infected cells. As a result, newly-synthesized vRNA genomes, in the form of viral ribonucleoprotein complexes (vRNPs), must be exported to the cytoplasm for productive infection. To characterize the composition of vRNP export complexes and their interplay with the nucleus of infected cells, we affinity-purified tagged vRNPs from biochemically fractionated infected nuclei. After treatment of infected cells with leptomycin B, a potent inhibitor of Crm1-mediated export, we isolated vRNP export complexes which, unexpectedly, were tethered to the host-cell chromatin with very high affinity. At late time points of infection, the cellular export receptor Crm1 also accumulated at the same regions of the chromatin as vRNPs, which led to a decrease in the export of other nuclear Crm1 substrates from the nucleus. Interestingly, chromatin targeting of vRNP export complexes brought them into association with Rcc1, the Ran guanine exchange factor responsible for generating RanGTP and driving Crm1-dependent nuclear export. Thus, influenza viruses gain preferential access to newly-generated host cell export machinery by targeting vRNP export complexes at the sites of Ran regeneration.
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Impact of variation in patient response on model-based control of glycaemia in critically ill patients.
Comput Methods Programs Biomed
PUBLISHED: 04-15-2011
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Critically ill patients commonly experience stress-induced hyperglycaemia, and several studies have shown tight glycaemic control (TGC) can reduce patient mortality. However, tight control is often difficult to achieve due to conflicting drug therapies and evolving patient condition. Thus, a number of studies have failed to achieve consistently safe and effective TGC possibly due to the use of fixed insulin dosing protocols over adaptive patient-specific methods. Model-based targeted glucose control can adapt insulin and dextrose interventions to match identified patient insulin sensitivity. This study explores the impact on glycaemic control of assuming patient response to insulin is constant, as many protocols do, versus time-varying. Validated virtual trial simulations of glucose control were performed on adult and neonatal virtual patient cohorts. Results indicate assumptions of constant insulin sensitivity can lead to six-fold increases in incidence of hypoglycaemia, similar to literature reports and a commonly cited issue preventing increased adoption of TGC in critical care. It is clear that adaptive, patient-specific, approaches are better able to manage inter- and intra-patient variability than typical, fixed protocols.
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A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity.
Biomed Eng Online
PUBLISHED: 03-29-2011
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Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error.
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Validation of subject-specific cardiovascular system models from porcine measurements.
Comput Methods Programs Biomed
PUBLISHED: 03-17-2011
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A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE). Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures (P(ao), P(pa)) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), P(ao), and P(pa) measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models. The inputs (GEDV, SV, P(ao), P(pa)) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU (?5-10%). These results validate the potential for implementing this approach in the intensive care unit.
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The influenza A virus NS1 protein interacts with the nucleoprotein of viral ribonucleoprotein complexes.
J. Virol.
PUBLISHED: 03-16-2011
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The influenza A virus genome consists of eight RNA segments that associate with the viral polymerase proteins (PB1, PB2, and PA) and nucleoprotein (NP) to form ribonucleoprotein complexes (RNPs). The viral NS1 protein was previously shown to associate with these complexes, although it was not clear which RNP component mediated the interaction. Using individual TAP (tandem affinity purification)-tagged PB1, PB2, PA, and NP, we demonstrated that the NS1 protein interacts specifically with NP and not the polymerase subunits. The region of NS1 that binds NP was mapped to the RNA-binding domain.
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Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?
Ann Intensive Care
PUBLISHED: 03-02-2011
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Critically ill patients are highly variable in their response to care and treatment. This variability and the search for improved outcomes have led to a significant increase in the use of protocolized care to reduce variability in care. However, protocolized care does not address the variability of outcome due to inter- and intra-patient variability, both in physiological state, and the response to disease and treatment. This lack of patient-specificity defines the opportunity for patient-specific approaches to diagnosis, care, and patient management, which are complementary to, and fit within, protocolized approaches.Computational models of human physiology offer the potential, with clinical data, to create patient-specific models that capture a patients physiological status. Such models can provide new insights into patient condition by turning a series of sometimes confusing clinical data into a clear physiological picture. More directly, they can track patient-specific conditions and thus provide new means of diagnosis and opportunities for optimising therapy.This article presents the concept of model-based therapeutics, the use of computational models in clinical medicine and critical care in specific, as well as its potential clinical advantages, in a format designed for the clinical perspective. The review is presented in terms of a series of questions and answers. These aspects directly address questions concerning what makes a model, how it is made patient-specific, what it can be used for, its limitations and, importantly, what constitutes sufficient validation.To provide a concrete foundation, the concepts are presented broadly, but the details are given in terms of a specific case example. Specifically, tight glycemic control (TGC) is an area where inter- and intra-patient variability can dominate the quality of care control and care received from any given protocol. The overall review clearly shows the concept and significant clinical potential of using computational models in critical care medicine.
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A spectrum of dynamic insulin sensitivity test protocols.
J Diabetes Sci Technol
PUBLISHED: 02-14-2011
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Numerous tests have been developed to estimate insulin sensitivity (SI). However, most of the established tests are either too expensive for widespread application or do not yield reliable results. The dynamic insulin sensitivity and secretion test (DISST) uses assays of glucose, insulin, and C-peptide from nine samples to quantify SI and endogenous insulin secretion (UN) at a comparatively low cost. The quick dynamic insulin sensitivity test has shown that the DISST SI values are robust to significant assay omissions.
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Influenza virus infection induces the nuclear relocalization of the Hsp90 co-chaperone p23 and inhibits the glucocorticoid receptor response.
PLoS ONE
PUBLISHED: 02-12-2011
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The genomic RNAs of influenza A viruses are associated with the viral polymerase subunits (PB1, PB2, PA) and nucleoprotein (NP), forming ribonucleoprotein complexes (RNPs). Transcription/replication of the viral genome occurs in the nucleus of infected cells. A role for Hsp90 in nuclear import and assembly of newly synthetized RNA-polymerase subunits has been proposed. Here we report that the p23 cochaperone of Hsp90, which plays a major role in glucocorticoid receptor folding and function, associates with influenza virus polymerase. We show that p23 is not essential for viral multiplication in cultured cells but relocalizes to the nucleus in influenza virus-infected cells, which may alter some functions of p23 and Hsp90. Moreover, we show that influenza virus infection inhibits glucocorticoid receptor-mediated gene transactivation, and that this negative effect can occur through a p23-independent pathway. Viral-induced inhibition of the glucocorticoid receptor response might be of significant importance regarding the physiopathology of influenza infections in vivo.
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Phantom elasticity reconstruction with Digital Image Elasto-Tomography.
J Mech Behav Biomed Mater
PUBLISHED: 02-11-2011
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Results from the application of a novel nonlinear hybrid reconstruction algorithm within a Digital Image Elasto-Tomography (DIET) system are presented. A hybrid reconstruction algorithm was optimized to solve for the elasticity distribution of two heterogeneous silicone phantoms using a shape-based parameterization. The hybrid algorithm achieved comparable performance to Combinatorial Optimization methods with significantly less computational expense. The specificity of three-parameter reconstruction was confirmed by successful reconstruction of a homogeneous silicone phantom, indicating the potential suitability of the DIET system for application to inclusion imaging in elastography.
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First pilot trial of the STAR-Liege protocol for tight glycemic control in critically ill patients.
Comput Methods Programs Biomed
PUBLISHED: 02-08-2011
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Tight glycemic control (TGC) has shown benefits in ICU patients, but been difficult to achieve consistently due to inter- and intra- patient variability that requires more adaptive, patient-specific solutions. STAR (Stochastic TARgeted) is a flexible model-based TGC framework accounting for patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) below 72 mg/dL. This research describes the first clinical pilot trial of the STAR approach and the post-trial analysis of the models and methods that underpin the protocol. The STAR framework works with clinically specified targets and intervention guidelines. The clinically specified glycemic target was 125 mg/dL. Each trial was 24 h with BG measured 1-2 hourly. Two-hourly measurement was used when BG was between 110-135 mg/dL for 3 h. In the STAR approach, each intervention leads to a predicted BG level and outcome range (5-95th percentile) based on a stochastic model of metabolic patient variability. Carbohydrate intake (all sources) was monitored, but not changed from clinical settings except to prevent BG<100 mg/dL when no insulin was given. Insulin infusion rates were limited (6 U/h maximum), with limited increases based on current infusion rate (0.5-2.0 U/h), making this use of the STAR framework an insulin-only TGC approach. Approval was granted by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium). Nine patient trials were undertaken after obtaining informed consent. There were 205 measurements over all 9 trials. Median [IQR] per-patient results were: BG: 138.5 [130.6-146.0]mg/dL; carbohydrate administered: 2-11 g/h; median insulin:1.3 [0.9-2.4]U/h with a maximum of 6.0 [4.7-6.0]U/h. Median [IQR] time in the desired 110-140 mg/dL band was: 50.0 [31.2-54.2]%. Median model prediction errors ranged: 10-18%, with larger errors due to small meals and other clinical events. The minimum BG was 63 mg/dL and no other measurement was below 72 mg/dL, so only 1 measurement (0.5%) was below the 5% guaranteed minimum risk level. Post-trial analysis showed that patients were more variable than predicted by the stochastic model used for control, resulting in some of the prediction errors seen. Analysis and (validated) virtual trial re-simulating the clinical trial using stochastic models relevant to the patients particular day of ICU stay were seen to be more accurate in capturing the observed variability. This analysis indicated that equivalent control and safety could be obtained with similar or lower glycemic variability in control using more specific stochastic models. STAR effectively controlled all patients to target. Observed patient variability in response to insulin and thus prediction errors were higher than expected, likely due to the recent insult of cardiac surgery or a major cardiac event, and their immediate recovery. STAR effectively managed this variability with no hypoglycemia. Improved stochastic models will be used to prospectively test these outcomes in further ongoing clinical pilot trials in this and other units.
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A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients.
Comput Methods Programs Biomed
PUBLISHED: 02-01-2011
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Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ? 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.
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The dynamic insulin sensitivity and secretion test--a novel measure of insulin sensitivity.
Metab. Clin. Exp.
PUBLISHED: 01-19-2011
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The objective was to validate the methodology for the dynamic insulin sensitivity and secretion test (DISST) and to demonstrate its potential in clinical and research settings. One hundred twenty-three men and women had routine clinical and biochemical measurements, an oral glucose tolerance test, and a DISST. For the DISST, participants were cannulated for blood sampling and bolus administration. Blood samples were drawn at t = 0, 10, 15, 25, and 35 minutes for measurement of glucose, insulin, and C-peptide. A 10-g bolus of intravenous glucose at t = 5 minutes and 1 U of intravenous insulin immediately after the t = 15 minute sample were given. Fifty participants also had a hyperinsulinemic-euglycemic clamp. Relationships between DISST insulin sensitivity (SI) and the clamp, and both DISST SI and secretion and other metabolic variables were measured. A Bland-Altman plot showed little bias in the comparison of DISST with the clamp, with DISST underestimating the glucose clamp by 0.1·10(-2)·mg·L·kg(-1)·min(-1)·pmol(-1) (90% confidence interval, -0.2 to 0). The correlation between SI as measured by DISST and the clamp was 0.82; the c unit for the receiver operating characteristic curve analysis for the 2 tests was 0.96. Metabolic variables showed significant correlations with DISST SI and the second phase of insulin release. The DISST also appears able to distinguish different insulin secretion patterns in individuals with identical SI values. The DISST is a simple, dynamic test that compares favorably with the clamp in assessing SI and allows simultaneous assessment of insulin secretion. The DISST has the potential to provide even more information about the pathophysiology of diabetes than more complicated tests.
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Development of blood glucose control for extremely premature infants.
Comput Methods Programs Biomed
PUBLISHED: 01-17-2011
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Extremely premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels and promoting needed growth. This study presents the development of a model-based stochastic targeted controller designed to adapt insulin infusion rates to match the unique and changing metabolic state and control parameters of the neonate. Long-term usage of targeted BG control requires successfully forecasting variations in neonatal metabolic state, accounting for differences in clinical practices between units, and demonstrating robustness to errors that can occur in everyday clinical usage. Simulation studies were used to evaluate controller ability to target several common BG ranges and evaluate controller sensitivity to missed BG measurements and delays in control interventions on a virtual patient cohort of 25 infants developed from retrospective data. Initial clinical pilot trials indicated model performance matched expected performance from simulations. Stochastic targeted glucose control developed using validated patient-specific virtual trials can yield effective protocols for this cohort. Long-term trials show fundamental success, however clinical interface design appears as a critical factor to ensuring good compliance and thus good control.
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Host- and strain-specific regulation of influenza virus polymerase activity by interacting cellular proteins.
MBio
PUBLISHED: 01-01-2011
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Highly pathogenic avian influenza A (HPAI) viruses of the H5N1 subtype have recently emerged from avian zoonotic reservoirs to cause fatal human disease. Adaptation of HPAI virus RNA-dependent RNA polymerase (PB1, PB2, and PA proteins) and nucleoprotein (NP) to interactions with mammalian host proteins is thought to contribute to the efficiency of viral RNA synthesis and to disease severity. While proteomics experiments have identified a number of human proteins that associate with H1N1 polymerases and/or viral ribonucleoprotein (vRNP), how these host interactions might regulate influenza virus polymerase functions and host adaptation has been largely unexplored. We took a functional genomics (RNA interference [RNAi]) approach to assess the roles of a network of human proteins interacting with influenza virus polymerase proteins in viral polymerase activity from prototype H1N1 and H5N1 viruses. A majority (18 of 31) of the cellular proteins tested, including RNA-binding (DDX17, DDX5, NPM1, and hnRNPM), stress (PARP1, DDB1, and Ku70/86), and intracellular transport proteins, were required for efficient activity of both H1N1 and H5N1 polymerases. NXP2 and NF90 antagonized both polymerases, and six more RNA-associated proteins exhibited strain-specific phenotypes. Remarkably, 12 proteins differentially regulated H5N1 polymerase according to PB2 genotype at mammalian-adaptive residue 627. Among these, DEAD box RNA helicase DDX17/p72 facilitated efficient human-adapted (627K) H5N1 virus mRNA and viral RNA (vRNA) synthesis in human cells. Likewise, the chicken DDX17 homologue was required for efficient avian (627E) H5N1 infection in chicken DF-1 fibroblasts, suggesting that this conserved virus-host interaction contributes to PB2-dependent host species specificity of influenza virus and ultimately to the outcome of human HPAI infections.
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Disruption of the viral polymerase complex assembly as a novel approach to attenuate influenza A virus.
J. Biol. Chem.
PUBLISHED: 12-23-2010
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To develop a novel attenuation strategy applicable to all influenza A viruses, we targeted the highly conserved protein-protein interaction of the viral polymerase subunits PA and PB1. We postulated that impaired binding between PA and PB1 would negatively affect trimeric polymerase complex formation, leading to reduced viral replication efficiency in vivo. As proof of concept, we introduced single or multiple amino acid substitutions into the protein-protein-binding domains of either PB1 or PA, or both, to decrease binding affinity and polymerase activity substantially. As expected, upon generation of recombinant influenza A viruses (SC35M strain) containing these mutations, many pseudo-revertants appeared that partially restored PA-PB1 binding and polymerase activity. These polymerase assembly mutants displayed drastic attenuation in cell culture and mice. The attenuation of the polymerase assembly mutants was maintained in IFN?/? receptor knock-out mice. As exemplified using a H5N1 polymerase assembly mutant, this attenuation strategy can be also applied to other highly pathogenic influenza A virus strains. Thus, we provide proof of principle that targeted mutation of the highly conserved interaction domains of PA and PB1 represents a novel strategy to attenuate influenza A viruses.
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Design and clinical pilot testing of the model-based dynamic insulin sensitivity and secretion test (DISST).
J Diabetes Sci Technol
PUBLISHED: 12-07-2010
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Insulin resistance is a significant risk factor in the pathogenesis of type 2 diabetes. This article presents pilot study results of the dynamic insulin sensitivity and secretion test (DISST), a high-resolution, low-intensity test to diagnose insulin sensitivity (IS) and characterize pancreatic insulin secretion in response to a (small) glucose challenge. This pilot study examines the effect of glucose and insulin dose on the DISST, and tests its repeatability.
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In vitro evaluation of surface based non-invasive breast cancer screening with digital image based Elasto tomography (DIET).
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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Digital Image-based Elasto Tomography (DIET) is a non-invasive breast cancer screening modality that induces vibrations into a breast and images its surface motion with digital cameras. Disturbances in the motion are caused by areas of higher stiffness within the breast, potentially cancerous tumors. A concept is presented to detect the angular location of a tumor by analyzing the phase delay of the vibrations on the surface. The approach is verified experimentally on silicone phantom breasts with stiffer inclusions ranging from 0-32 mm. A strong signal differentiating healthy and cancerous phantoms can be seen at the second modal frequency of the breast, clearly detecting a 10 mm tumor. This approach offers great potential for this low cost and accessible breast cancer screening, as an adjunct to existing modalities.
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An image based vibration sensor for soft tissue modal analysis in a Digital Image Elasto Tomography (DIET) system.
Conf Proc IEEE Eng Med Biol Soc
PUBLISHED: 11-25-2010
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Digital Image Elasto Tomography (DIET) is a non-invasive elastographic breast cancer screening technology, based on image-based measurement of surface vibrations induced on a breast by mechanical actuation. Knowledge of frequency response characteristics of a breast prior to imaging is critical to maximize the imaging signal and diagnostic capability of the system. A feasibility analysis for a non-invasive image based modal analysis system is presented that is able to robustly and rapidly identify resonant frequencies in soft tissue. Three images per oscillation cycle are enough to capture the behavior at a given frequency. Thus, a sweep over critical frequency ranges can be performed prior to imaging to determine critical imaging settings of the DIET system to optimize its tumor detection performance.
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Subject-specific cardiovascular system model-based identification and diagnosis of septic shock with a minimally invasive data set: animal experiments and proof of concept.
Physiol Meas
PUBLISHED: 11-22-2010
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A cardiovascular system (CVS) model and parameter identification method have previously been validated for identifying different cardiac and circulatory dysfunctions in simulation and using porcine models of pulmonary embolism, hypovolemia with PEEP titrations and induced endotoxic shock. However, these studies required both left and right heart catheters to collect the data required for subject-specific monitoring and diagnosis-a maximally invasive data set in a critical care setting although it does occur in practice. Hence, use of this model-based diagnostic would require significant additional invasive sensors for some subjects, which is unacceptable in some, if not all, cases. The main goal of this study is to prove the concept of using only measurements from one side of the heart (right) in a minimal data set to identify an effective patient-specific model that can capture key clinical trends in endotoxic shock. This research extends existing methods to a reduced and minimal data set requiring only a single catheter and reducing the risk of infection and other complications-a very common, typical situation in critical care patients, particularly after cardiac surgery. The extended methods and assumptions that found it are developed and presented in a case study for the patient-specific parameter identification of pig-specific parameters in an animal model of induced endotoxic shock. This case study is used to define the impact of this minimal data set on the quality and accuracy of the model application for monitoring, detecting and diagnosing septic shock. Six anesthetized healthy pigs weighing 20-30 kg received a 0.5 mg kg(-1) endotoxin infusion over a period of 30 min from T0 to T30. For this research, only right heart measurements were obtained. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the experimentally measured data, including measurements not used in the identification process for validation. Importantly, all identified parameter trends match physiologically and clinically and experimentally expected changes, indicating that no diagnostic power is lost. This work represents a further with human subjects validation for this model-based approach to cardiovascular diagnosis and therapy guidance in monitoring endotoxic disease states. The results and methods obtained can be readily extended from this case study to the other animal model results presented previously. Overall, these results provide further support for prospective, proof of concept clinical testing with humans.
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Tight glycemic control in critical care--the leading role of insulin sensitivity and patient variability: a review and model-based analysis.
Comput Methods Programs Biomed
PUBLISHED: 10-27-2010
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Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.
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A three-compartment model of the C-peptide-insulin dynamic during the DIST test.
Math Biosci
PUBLISHED: 08-24-2010
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Dynamic insulin sensitivity (SI) tests often utilise model-based parameter estimation. This research analyses the impact of expanding the typically used two-compartment model of insulin and C-peptide kinetics to incorporate a hepatic third compartment. The proposed model requires only four C-peptide assays to simulate endogenous insulin production (uen), greatly reducing the cost and clinical burden. Sixteen subjects participated in 46 dynamic insulin sensitivity tests (DIST). Population kinetic parameters are identified for the new compartment. Results are assessed by model error versus measured data and repeatability of the identified SI. The median C-peptide error was 0% (IQR: -7.3, 6.7)%. Median insulin error was 7% (IQR: -28.7, 6.3)%. Strong correlation (r=0.92) existed between the SI values of the new model and those from the original two-compartment model. Repeatability in SI was similar between models (new model inter/intra-dose variability 3.6/12.3% original model -8.5/11.3%). When frequent C-peptide samples may be available, the added hepatic compartment does not offer significant diagnostic, repeatability improvement over the two-compartment model. However, a novel and successful three-compartment modelling strategy was developed which provided accurate estimation of endogenous insulin production and the subsequent SI identification from sparse C-peptide data.
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Validation of a model-based virtual trials method for tight glycemic control in intensive care.
Biomed Eng Online
PUBLISHED: 08-20-2010
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In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods.
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Impact of glucocorticoids on insulin resistance in the critically ill.
Comput Methods Programs Biomed
PUBLISHED: 08-04-2010
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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 in insulin sensitivity from GCs in critically ill patients, and thus their impact on glycaemic control. A model-based measure of insulin sensitivity (S(I)) was used to quantify changes between two matched cohorts of 40 intensive care unit (ICU) patients. Patients in one cohort received GC treatment, while patients in the control cohort did not. All patients were admitted to the Christchurch hospital ICU between 2005 and 2007 and spent at least 24h 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/d 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, respectively. These cohort and percentile 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 1.3-4.0 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.
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Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients.
Biomed Eng Online
PUBLISHED: 07-27-2010
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The application of positive end expiratory pressure (PEEP) in mechanically ventilated (MV) patients with acute respiratory distress syndrome (ARDS) decreases cardiac output (CO). Accurate measurement of CO is highly invasive and is not ideal for all MV critically ill patients. However, the link between the PEEP used in MV, and CO provides an opportunity to assess CO via MV therapy and other existing measurements, creating a CO measure without further invasiveness.This paper examines combining models of diffusion resistance and lung mechanics, to help predict CO changes due to PEEP. The CO estimator uses an initial measurement of pulmonary shunt, and estimations of shunt changes due to PEEP to predict CO at different levels of PEEP. Inputs to the cardiac model are the PV loops from the ventilator, as well as the oxygen saturation values using known respiratory inspired oxygen content. The outputs are estimates of pulmonary shunt and CO changes due to changes in applied PEEP. Data from two published studies are used to assess and initially validate this model.The model shows the effect on oxygenation due to decreased CO and decreased shunt, resulting from increased PEEP. It concludes that there is a trade off on oxygenation parameters. More clinically importantly, the model also examines how the rate of CO drop with increased PEEP can be used as a method to determine optimal PEEP, which may be used to optimise MV therapy with respect to the gas exchange achieved, as well as accounting for the impact on the cardiovascular system and its management.
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Modelling acute renal failure using blood and breath biomarkers in rats.
Comput Methods Programs Biomed
PUBLISHED: 07-19-2010
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This paper compares three methods for estimating renal function, as tested in rats. Acute renal failure (ARF) was induced via a 60-min bilateral renal artery clamp in 8 Sprague-Dawley rats and renal function was monitored for 1 week post-surgery. A two-compartment model was developed for estimating glomerular filtration via a bolus injection of a radio-labelled inulin tracer, and was compared with an estimated creatinine clearance method, modified using the Cockcroft-Gault equation for rats. These two methods were compared with selected ion flow tube-mass spectrometry (SIFT-MS) monitoring of breath analytes. Determination of renal function via SIFT-MS is desirable since results are available non-invasively and in real time. Relative decreases in renal function show very good correlation between all 3 methods (R²=0.84, 0.91 and 0.72 for breath-inulin, inulin-creatinine, and breath-creatinine correlations, respectively), and indicate good promise for fast, non-invasive determination of renal function via breath testing.
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Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity.
Comput Methods Programs Biomed
PUBLISHED: 06-28-2010
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Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification. Mathematical functions for the a posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R=0.890 and R=0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively. These results show accurate SI estimation is possible in the absence of insulin or C-peptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohorts bias towards low SI insulin resistant subjects, limiting the data sets ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications.
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Continuous glucose monitors and the burden of tight glycemic control in critical care: can they cure the time cost?
J Diabetes Sci Technol
PUBLISHED: 06-02-2010
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Tight glycemic control (TGC) in critical care has shown distinct benefits but has also proven to be difficult to obtain. The risk of severe hypoglycemia (<40 mg/dl) raises significant concerns for safety. Added clinical burden has also been an issue. Continuous glucose monitors (CGMs) offer frequent automated measurement and thus the possibility of using them for early detection and intervention of hypoglycemic events. Additionally, regular measurement by CGM may also be able to reduce clinical burden.
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Modeling the glucose regulatory system in extreme preterm infants.
Comput Methods Programs Biomed
PUBLISHED: 04-23-2010
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Premature infants represent a significant proportion of the neonatal intensive care population. Blood glucose homeostasis in this group is often disturbed by immaturity of endogenous regulatory systems and the stress of their condition. Hypo- and hyperglycemia are frequently reported in very low birth weight infants, and more mature infants often experience low levels of glycemia. A model capturing the unique fundamental dynamics of the neonatal glucose regulatory system could be used to develop better blood glucose control methods.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.