Relationship between abnormal maternal inflammation and insulin resistance during pregnancy

Abnormal maternal inflammation during pregnancy is linked to complications such as preeclampsia and fetal growth restriction. There is growing evidence that insulin resistance is also associated with a heightened inflammatory state, and is linked to pregnancy complications such as gestational diabetes. This study tested the hypothesis that abnormal inflammation during pregnancy is causally linked to elevations in blood glucose and insulin resistance. To induce a state of abnormal systemic inflammation, bacterial lipopolysaccharide (LPS) was administered to pregnant rats on gestational days (GD) 13.5-16.5. Dams treated with LPS exhibited an abnormal immune response characterized by an elevation in white blood cells, which was linked to reduced fetal weight and increased glucose levels over pregnancy. Abnormal inflammation is characterized by increased levels of circulating pro-inflammatory cytokines such as tumour necrosis factor alpha (TNF) and interleukin-6, which contribute to insulin resistance by inhibiting the insulin signalling pathway. TNF in particular induces a serine phosphorylation (pSer307) of insulin receptor substrate 1 (IRS-1). In our model, insulin resistance was assessed by measuring the extent of pSer307 of IRS-1 and total IRS-1 expression in skeletal muscle, as well as changes in metabolic parameters and pancreas tissue morphology associated with insulin resistance. LPS-treated dams exhibited a significant reduction in IRS-1 expression, elevation in fasting glucose levels, and reduction in insulin sensitivity indices. There were also biologically relevant increases in fasting plasma insulin levels and insulin resistance indices, but not pSer307 of IRS-1 and pancreatic islet size. To determine whether inflammation plays a role in reducing insulin signalling and the other changes associated with LPS administration, etanercept, a TNF antagonist, was administered on GDs 13.5 and 15.5 prior to LPS injections. With the exception of IRS-1 expression, in rats treated with etanercept all of the measured parameters remained at the levels observed in saline controls, indicating a link between abnormal inflammation and insulin resistance. The results of this study support the practice of monitoring the inflammatory conditions of the mother prior to and during pregnancy, and support further investigation into the potential use of anti-inflammatory agents during pregnancy in women at risk of insulin resistance and gestational diabetes.

Monitoring treatment fidelity in a randomised controlled trial of a complex intervention.

Aim. This paper is a report of a study to describe how treatment fidelity is being enhanced and monitored, using a model from the National Institutes of Health Behavior Change Consortium. Background. The objective of treatment fidelity is to minimize errors in interpreting research trial outcomes, and to ascribe those outcomes directly to the intervention at hand. Treatment fidelity procedures are included in trials of complex interventions to account for inferences made from study outcomes. Monitoring treatment fidelity can help improve study design, maximize reliability of results, increase statistical power, determine whether theory-based interventions are responsible for observed changes, and inform the research dissemination process. Methods. Treatment fidelity recommendations from the Behavior Change Consortium were applied to the SPHERE study (Secondary Prevention of Heart DiseasE in GeneRal PracticE), a randomized controlled trial of a complex intervention. Procedures to enhance and monitor intervention implementation included standardizing training sessions, observing intervention consultations, structuring patient recall systems, and using written practice and patient care plans. The research nurse plays an important role in monitoring intervention implementation. Findings. Several methods of applying treatment fidelity procedures to monitoring interventions are possible. The procedure used may be determined by availability of appropriate personnel, fiscal constraints, or time limits. Complex interventions are not straightforward and necessitate a monitoring process at trial stage. Conclusion. The Behavior Change Consortium’s model of treatment fidelity is useful for structuring a system to monitor the implementation of a complex intervention, and helps to increase the reliability and validity of evaluation findings.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

The design of a navigation guidance and control system for an unmanned surface vehicle for environmental monitoring

Maintaining the ecosystem is one of the main concerns in this modern age. With the fear of ever-increasing global warming, the UK is one of the key players to participate actively in taking measures to slow down at least its phenomenal rate. As an ingredient to this process, the Springer vehicle was designed and developed for environmental monitoring and pollutant tracking. This special issue paper highlighted the Springer hardware and software architecture including various navigational sensors, a speed controller, and an environmental monitoring unit. In addition, details regarding the modelling of the vessel were outlined based mainly on experimental data. The formulation of a fault tolerant multi-sensor data fusion technique was also presented. Moreover, control strategy based on a linear quadratic Gaussian controller was developed and simulated on the Springer model.
Gaussian controller is developed and simulated on the Springer model.

An overview of wireless networks in control and monitoring

This paper provides an overview of the current field in wireless networks for monitoring and control. Alternative wireless technologies are introduced, together with current typical industrial applications. The focus then shifts to wireless Ethernet and the specialised requirements for wireless networked control systems (WNCS) are discussed. This is followed by a brief look at some current WNCS research, including reduced communication control.