Sofia Nahavandi

Microfluidic platforms for biomarker analysis

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.

Microfluidic platforms for the investigation of intercellular signalling mechanisms

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.

Using dielectrophoresis to study the dynamic response of single budding yeast cells to Lyticase

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.

Controlled rotation and vibration of patterned cell clusters using dielectrophoresis.

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.

High Resolution Scanning Electron Microscopy of Cells Using Dielectrophoresis

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.

A morphing technique for facial image representation

Presents a method for the representation of facial images. The proposed method consists of two modules: face-image matching and face-image morphing. In the first module, the correspondence between two images are calculated for all pixel locations. A novel area-based matching method is proposed that makes use of the concept of the fractal dimension, and develops a non-parametric local transform as a basis for establishing the correspondence between two face images. In the second module, a mapping is performed for deformation of the source face image on to the target face image. This is done to map the pixels in the source face image to the location of their corresponding pixels in the target image.

Application of Extended Multivariate Modeling for Information Flow Analysis of Event Related Responses

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.

Mixing characterisation for a serpentine microchannel equipped with embedded barriers

This paper describes the design, simulation, fabrication and experimental analysis of a passive micromixer for the mixing of biological solvents. The mixer consists of a T-junction, followed by a serpentine microchannel. The serpentine has three arcs, each equipped with circular barriers that are patterned as two opposing triangles. The barriers are engineered to induce periodic perturbations in the flow field and enhance the mixing. CFD (Computational Fluid Dynamics) method is applied to optimise the geometric variables of the mixer before fabrication. The mixer is made from PDMS (Polydimethylsiloxane) using photo- and soft-lithography techniques. Experimental measurements are performed using yellow and blue food dyes as the mixing fluids. The mixing is measured by analysing the composition of the flows colour across the outlet channel. The performance of the mixer is examined in a wide range of flow rates from 0.5 to 10 μl/min. Mixing efficiencies of higher than 99.4% are obtained in the experiments confirming the results of numerical simulations. The proposed mixer can be employed as a part of lab-on-a-chip for biomedical applications.

Thermal control with image processing and fuzzy controllers

Image processing is used to identify areas of different temperatures in die thermal images for thermal control of high pressure die casting. Areas of higher and lower temperature ranges than the optimum empirical/experimental range can be identified. Using the heat index developed, the heat stored in different areas of the die can be quantitatively calculated and controlled using fuzzy neural networks. This fuzzy neural networks control system makes the decision about whether to take more (H/sub -/) or less heat (H/sub +/) away from a specific area.

Die temperature monitoring of high pressure die casting

The high pressure die casting (HPDC) process is normally referred to as the cold chamber process which solves the materials problem by separating the molten metal reservoir from the actuator for most of the process cycle. In this process, the thermal effects of molten metal flow in the die are a major factor in determining casting surface quality, die life, and many internal quality parameters such as porosity. Therefore, development of an effective technology to promptly evaluate the effect of changes in thermal process variables is vital to the quality control and improvement of productivity. Image processing technology has been applied to analyse the thermal images. The information is correlated to thermal energy stored in the die to develop a thermal control system and improve the quality of castings.