Optimizing Multiple Analyte Injections in Surface Plasmon Resonance Biosensors with Analytes having Different Refractive Index Increments.

Surface plasmon resonance-based biosensors have been successfully applied to the study of the interactions between macromolecules and small molecular weight compounds. In an effort to increase the throughput of these SPR-based experiments, we have already proposed to inject multiple compounds simultaneously over the same surface. When specifically applied to small molecular weight compounds, such a strategy would however require prior knowledge of the refractive index increment of each compound in order to correctly interpret the recorded signal. An additional experiment is typically required to obtain this information. In this manuscript, we show that through the introduction of an additional global parameter corresponding to the ratio of the saturating signals associated with each molecule, the kinetic parameters could be identified with similar confidence intervals without any other experimentation.

On-line kinetic model discrimination for optimized surface plasmon resonance experiments.

In order to improve the throughput of surface plasmon resonance-based biosensors, an on-line iterative optimization algorithm has been presented aiming at reducing experimental time and material consumption without any loss of confidence on kinetic parameters [De Crescenzo (2008) J. Mol Recognit., 21, 256-66.]. This algorithm was based on a simple Langmuirian model to compute the confidence and predict optimal injections. However, this kinetic model is not suitable for all interactions, as it does not include mass transfer limitation that may occur for fast interaction kinetics. If a simple model was to be used when this phenomenon influenced the interactions, kinetic parameters would be biased. On the other hand, we show in this paper that data analysis with a kinetic model including a mass transfer limitation step would lead to longer experiments and poorer confidence if the interactions were simple. So, in this manuscript, we present an on-line model discrimination and optimization approach to increase the throughput of surface plasmon resonance biosensors.

Increasing throughput of surface plasmon resonance-based biosensors by multiple analyte injections.

Surface plasmon resonance-based biosensors are now acknowledged as robust and reliable instruments to determine the kinetic parameters related to the interactions between biomolecules. These kinetic parameters are used in screening campaigns: there is a considerable interest in reducing the experimental time, thus improving the throughput of the surface plasmon resonance assays. Kinetic parameters are typically obtained by analyzing data from several injections of a given analyte at different concentrations over a surface where its binding partner has been immobilized. It has been already proven that an iterative optimization approach aiming at determining optimal analyte injections to be performed online can significantly reduce the experimentation time devoted to kinetic parameter determination, without any detrimental effect on their standard errors. In this study, we explore the potential of this iterative optimization approach to further reduce experiment duration by combining it with the simultaneous injection of two analytes.

Self-reported sitting time and markers of inflammation, insulin resistance, and adiposity.

BACKGROUND: Sedentary behavior is emerging as an independent risk factor for chronic disease; however, potential mechanisms underpinning these observations are not well understood. PURPOSE: This study aimed to investigate the association of self-reported weekday sitting time with biomarkers linked to chronic low-grade inflammation, insulin resistance, and adiposity. METHODS: This study reports data from individuals attending a diabetes screening program, United Kingdom, 2004-2007; analysis was conducted in 2010. Sitting time and physical activity were measured using the International Physical Activity Questionnaire; biochemical outcomes included fasting and 2-hour postchallenge glucose, fasting insulin, C-reactive protein (CRP), leptin, adiponectin, and interleukin-6 (IL-6). RESULTS: This study included 505 (female=46%; South-Asian ethnicity=19%, aged 59±10 years, BMI=29.5±4.7) individuals with valid sitting data. Increased sitting time was positively associated with fasting insulin, leptin, leptin/adiponectin ratio, CRP, and IL-6 in women, but not men, after adjustment for age, ethnicity, social deprivation, and smoking and medication status; interaction analysis revealed that the gender-specific differences were significant. The associations for women remained significant after additional adjustment for total moderate- to vigorous-intensity physical activity; however all associations were attenuated when further adjusted for BMI. There was no association between sitting time and glycemic status. CONCLUSIONS: Total self-reported weekday sitting time was associated with biomarkers linked to chronic low-grade inflammation and poor metabolic health in women, but not men, independent of physical activity.

Estimation of analyte concentration by surface plasmon resonance-based biosensing using parameter identification techniques.

Surface plasmon resonance-based biosensors have been applied to the determination of macromolecule concentration. Up to now, the proposed experimental approaches have relied either on the generation of a calibration curve that exploits only a few data points from each sensorgram or on multiple injections of the unknown sample at various flow rates. In this article, we show that prior knowledge of the kinetic parameters related to the interaction of the species with a given partner could advantageously reduce the number of injections required by both aforementioned methods, thereby reducing experimental time while maintaining a good level of confidence on the determined concentrations.

Automatic tumour volume delineation in respiratory-gated PET images.

INTRODUCTION: Positron emission tomography (PET) is a state-of-the-art functional imaging technique used in the accurate detection of cancer. The main problem with the tumours present in the lungs is that they are non-stationary during each respiratory cycle. Tumours in the lungs can get displaced up to 2.5 cm during respiration. Accurate detection of the tumour enables avoiding the addition of extra margin around the tumour that is usually used during radiotherapy treatment planning. METHODS: This paper presents a novel method to detect and track tumour in respiratory-gated PET images. The approach followed to achieve this task is to automatically delineate the tumour from the first frame using support vector machines. The resulting volume and position information from the first frame is used in tracking its motion in the subsequent frames with the help of level set (LS) deformable model. RESULTS: An excellent accuracy of 97% is obtained using wavelets and support vector machines. The volume calculated as a result of the machine learning (ML) stage is used as a constraint for deformable models and the tumour is tracked in the remaining seven phases of the respiratory cycle. As a result, the complete information about tumour movement during each respiratory cycle is available in relatively short time. CONCLUSIONS: The combination of the LS and ML approach accurately delineated the tumour volume from all frames, thereby providing a scope of using PET images towards planning an accurate and effective radiotherapy treatment for lung cancer.

Maximizing power production in a stack of microbial fuel cells using multiunit optimization method.

This study demonstrates real-time maximization of power production in a stack of two continuous flow microbial fuel cells (MFCs). To maximize power output, external resistances of two air-cathode membraneless MFCs were controlled by a multiunit optimization algorithm. Multiunit optimization is a recently proposed method that uses multiple similar units to optimize process performance. The experiment demonstrated fast convergence toward optimal external resistance and algorithm stability during external perturbations (e.g., temperature variations). Rate of the algorithm convergence was much faster than in traditional maximum power point tracking algorithms (MPPT), which are based on temporal perturbations. A power output of 81-84 mW/L(A) (A = anode volume) was achieved in each MFC.

Online optimization of surface plasmon resonance-based biosensor experiments for improved throughput and confidence.

The emergence of surface plasmon resonance-based optical biosensors has facilitated the identification of kinetic parameters for various macromolecular interactions. Normally, these parameters are determined from experiments with arbitrarily chosen periods of macromolecule and buffer injections, and varying macromolecule concentrations. Since the choice of these variables is arbitrary, such experiments may not provide the required confidence in identified kinetic parameters expressed in terms of standard errors. In this work, an iterative optimization approach is used to determine the above-mentioned variables so as to reduce the experimentation time, while treating the required standard errors as constraints. It is shown using multiple experimental and simulated data that the desired confidence can be reached with much shorter experiments than those generally performed by biosensor users.

Diabetes: glycaemic control in type 1.

INTRODUCTION: Type 1 diabetes occurs when destruction of the pancreatic islet beta cells, usually attributable to an autoimmune process, causes the pancreas to produces too little insulin or none at all. METHODS AND OUTCOMES: We conducted a systematic review and aimed to answer the following clinical questions: What are the effects of intensive treatment programmes and educational interventions in adults and adolescents with type 1 diabetes? What are the effects of different insulin regimens on glycaemic control in adults and adolescents with type 1 diabetes? We searched: Medline, Embase, The Cochrane Library, and other important databases up to December 2006 (BMJ Clinical Evidence reviews are updated periodically; please check our website for the most up-to-date version of this review). We included harms alerts from relevant organisations such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA). RESULTS: We found 16 systematic reviews, RCTs, or observational studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions. CONCLUSIONS: In this systematic review, we present information relating to the effectiveness and safety of the following interventions: different frequencies of insulin administration (continuous subcutaneous insulin infusion compared with multiple daily subcutaneous insulin injections); different frequencies of blood glucose self-monitoring; educational interventions; and intensive treatment programmes.

Diabetes: glycaemic control in type 2.

INTRODUCTION: Diabetes mellitus is now seen as a progressive disorder of glucose metabolism, affecting about 5% of the population worldwide, over 85% of whom have type 2 diabetes. Type 2 diabetes may occur with obesity, hypertension and dyslipidaemia (the metabolic syndrome), which are powerful predictors of CVD. Blood glucose levels rise progressively over time in people with type 2 diabetes regardless of treatment, causing microvascular and macrovascular complications. METHODS AND OUTCOMES: We conducted a systematic review and aimed to answer the following clinical question: What are the effects of interventions in adults with type 2 diabetes? We searched: Medline, Embase, The Cochrane Library and other important databases up to October 2006 (Clinical Evidence reviews are updated periodically, please check our website for the most up-to-date version of this review). We included harms alerts from relevant organisations such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA). RESULTS: We found 69 systematic reviews, RCTs, or observational studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions. CONCLUSIONS: In this systematic review we present information relating to the effectiveness and safety of the following interventions: combined oral drug treatment, diet, education, insulin (continuous subcutaneous infusion), insulin, intensive treatment programmes, meglitinides (nateglinide, repaglinide), metformin, monotherapy, blood glucose self-monitoring (different frequencies), and sulphonylureas (newer or older).

Pulmonary tumor volume delineation in PET images using deformable models.

Lung cancer is one of the most lethal form of cancer worldwide. The tumor present in the lungs is not static and changes its shape and position during each breathing cycle. In order to segment the tumor, the physicians manually outline the tumor on each slice. Slice by slice manual segmentation is prone to errors and causes physician fatigue. A semi-automatic method to segment and track the tumor in all the frames of PET data is proposed in this paper. The tumor is segmented from each slice of the first frame using wavelet features and support vector machine classifier. This segmented tumor, after validated by the experts is used in initialization of the contour for segmentation of the tumor in subsequent frames by the level set method. Another important contribution of this paper is setting up tumor volume obtained from the first frame as the termination condition for the level set method. The results obtained from the proposed methodology are very promising and eliminates the need for manual tumor segmentation. Our proposed technique also maintains consistent segmentation and the results obtained are not dependent on the operator as is the case in manual segmentation.

Run-to-run control of blood glucose concentrations for people with Type 1 diabetes mellitus.

Run-to-run control has been applied to several traditional batch processes in the chemical industry. The 24-h cycle of eating meals, measuring blood glucose concentrations, and delivering the correct insulin bolus, with the goal of achieving the optimal blood glucose profile, can be viewed in the same spirit as traditional batch processes such as emulsion polymerization. In this paper, we aim to exploit the "repetitive" nature of the insulin therapy of people with Type 1 diabetes. A run-to-run algorithm is used on a virtual diabetic patient model to control blood glucose concentrations. The insulin input is parameterized into the timing and amount of the dose while the glucose output is parameterized into the maximum and minimum glucose concentrations. Robustness of the algorithm to variations in the meal amount, meal timing, and insulin sensitivity parameter is addressed. In general, the algorithm is able to converge when the meal timing is varied within +/- 40 min. If the meal size is underestimated by approximately 10 grams (g), the algorithm is able to converge within a reasonable time frame for breakfast, lunch, and dinner. If the meal size is overestimated by 20-25 g, the algorithm is able to converge. When random variations in the meal timing and the meal amount are introduced, the variation on the output variables, Gmax and Gmin, scales according to the amount of variation allowed. Along with this, the insulin sensitivity of the virtual patient model is varied. The algorithm is robust for differences in insulin sensitivity less than +/- 50% of the nominal value.

Optimal operation of batch processes via the tracking of active constraints.

This paper presents a new measurement-based optimization framework for batch processes whereby optimal operation can be achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus the optimal input trajectories can be adjusted using measurements without the use of a model of the process. When only batch-end measurements are available, the proposed method leads itself to an efficient batch-to-batch optimization scheme. The approach is illustrated via the simulation of a semibatch reactor under uncertainty.