Gestational diabetes mellitus (GDM) and large for gestational age (LGA) offspring are two common pregnancy complications. Connections also exist between the two conditions, including mutual maternal risk factors for the conditions and an increased prevalence of LGA offspring amongst pregnancies affected by GDM. Thus, it is important to elucidate potential shared underlying mechanisms of both LGA and GDM. One potential mechanistic link relates to macronutrient metabolism. Indeed, derangement of carbohydrate and lipid metabolism is present in GDM, and maternal biomarkers of glucose and lipid control are associated with LGA neonates in such pregnancies. The aim of this paper is therefore to reflect on the existing nutritional guidelines for GDM in light of our understanding of the pathophysiological mechanisms of GDM and LGA offspring. Lifestyle modification is first line treatment for GDM, and while there is some promise that nutritional interventions may favourably impact outcomes, there is a lack of definitive evidence that changing the macronutrient composition of the diet reduces the incidence of either GDM or LGA offspring. The quality of the available evidence is a major issue, and rigorous trials are needed to inform evidence-based treatment guidelines.

Large birthweight, or macrosomia, is one of the commonest complications for pregnancies affected by diabetes. As macrosomia is associated with an increased risk of a number of adverse outcomes for both the mother and offspring, accurate antenatal prediction of fetal macrosomia could be beneficial in guiding appropriate models of care and interventions that may avoid or reduce these associated risks. However, current prediction strategies which include physical examination and ultrasound assessment, are imprecise. Biomarkers are proving useful in various specialties and may offer a new avenue for improved prediction of macrosomia. Prime biomarker candidates in pregnancies with diabetes include maternal glycaemic markers (glucose, 1,5-anhydroglucitol, glycosylated hemoglobin) and hormones proposed implicated in placental nutrient transfer (adiponectin and insulin-like growth factor-1). There is some support for an association of these biomarkers with birthweight and/or macrosomia, although current evidence in this emerging field is still limited. Thus, although biomarkers hold promise, further investigation is needed to elucidate the potential clinical utility of biomarkers for macrosomia prediction for pregnancies affected by diabetes.

The localized motion of cells within a cluster is an important feature of living organisms and has been found to play roles in cell signaling, communication, and migration, thus affecting processes such as proliferation, transcription, and organogenesis. Current approaches for inducing dynamic movement into cells, however, focus predominantly on mechanical stimulation of single cells, affect cell integrity, and, more importantly, need a complementary mechanism to pattern cells. In this article, we demonstrate a new strategy for the mechanical stimulation of large cell clusters, taking advantage of dielectrophoresis. This strategy is based on the cellular spin resonance mechanism, but it utilizes coating agents, such as bovine serum albumin, to create consistent rotation and vibration of individual cells. The treatment of cells with coating agents intensifies the torque induced on the cells while reducing the friction at the cell-cell and cell-substrate interfaces, resulting in the consistent motion of the cells. Such localized motion can be modulated by varying the frequency and voltage of the applied sinusoidal AC signal and can be achieved in the absence and presence of flow. This strategy enables the survival and functioning of moving cells within large-scale clusters to be investigated.

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

Budding yeast cells are quick and easy to grow and represent a versatile model of eukaryotic cells for a variety of cellular studies, largely because their genome has been widely studied and links can be drawn with higher eukaryotes. Therefore, the efficient separation, immobilization, and conversion of budding yeasts into spheroplast or protoplast can provide valuable insight for many fundamentals investigations in cell biology at a single cell level. Dielectrophoresis, the induced motion of particles in non-uniform electric fields, possesses a great versatility for manipulation of cells in microfluidic platforms. Despite this, dielectrophoresis has been largely utilized for studying of non-budding yeast cells and has rarely been used for manipulation of budding cells. Here, we utilize dielectrophoresis for studying the dynamic response of budding cells to different concentrations of Lyticase. This involves separation of the budding yeasts from a background of non-budding cells and their subsequent immobilization onto the microelectrodes at desired densities down to single cell level. The immobilized yeasts are then stimulated with Lyticase to remove the cell wall and convert them into spheroplasts, in a highly dynamic process that depends on the concentration of Lyticase. We also introduce a novel method for immobilization of the cell organelles released from the lysed cells by patterning multi-walled carbon nanotubes (MWCNTs) between the microelectrodes.

Biomarkers have been described as characteristics, most often molecular, that provide information about biological states, whether normal, pathological, or therapeutically modified. They hold great potential to assist diagnosis and prognosis, monitor disease, and assess therapeutic effectiveness. While a few biomarkers are routinely utilised clinically, these only reflect a very small percentage of all biomarkers discovered. Numerous factors contribute to the slow uptake of these new biomarkers, with challenges faced throughout the biomarker development pipeline. Microfluidics offers two important opportunities to the field of biomarkers: firstly, it can address some of these developmental obstacles, and secondly, it can provide the precise and complex platform required to bridge the gap between biomarker research and the biomarker-based analytical device market. Indeed, adoption of microfluidics has provided a new avenue for advancement, promoting clinical utilisation of both biomarkers and their analytical platforms. This review will discuss biomarkers and outline microfluidic platforms developed for biomarker analysis.

Ultrastructural analysis of cells can reveal valuable information about their morphological, physiological, and biochemical characteristics. Scanning electron microscopy (SEM) has been widely used to provide high-resolution images from the surface of biological samples. However, samples need to be dehydrated and coated with conductive materials for SEM imaging. Besides, immobilizing non-adherent cells during processing and analysis is challenging and requires complex fixation protocols. In this work, we developed a novel dielectrophoresis based microfluidic platform for interfacing non-adherent cells with high-resolution SEM at low vacuum mode. The system enables rapid immobilization and dehydration of samples without deposition of chemical residues over the cell surface. Moreover, it enables the on-chip chemical stimulation and fixation of immobilized cells with minimum dislodgement. These advantages were demonstrated for comparing the morphological changes of non-budding and budding yeast cells following Lyticase treatment.

Intercellular signalling has been identified as a highly complex process, responsible for orchestrating many physiological functions. While conventional methods of investigation have been useful, their limitations are impeding further development. Microfluidics offers an opportunity to overcome some of these limitations. Most notably, microfluidic systems can emulate the in-vivo environments. Further, they enable exceptionally precise control of the microenvironment, allowing complex mechanisms to be selectively isolated and studied in detail. There has thus been a growing adoption of microfluidic platforms for investigation of cell signalling mechanisms. This review provides an overview of the different signalling mechanisms and discusses the methods used to study them, with a focus on the microfluidic devices developed for this purpose.