Biomedical Engineering - Theses
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ItemDynamic stability and variability of perturbed walking in young adults( 2018)Falls are the third major cause of inadvertent injury in young Australian adults aged between 18-35 years. The inability of an individual to respond to external perturbations due to walking on an inclined surface, or internal perturbations such as dual-task walking, are known to be associated with significantly higher risk of balance loss. Significant factors known to increase risk of balance loss during walking include performing an additional task requiring high motor-cognitive, sensory or cognitive load (internal perturbations), and walking on uneven surfaces such as sloped terrain (external perturbations). At present, however, dynamic balance of the entire body and the risk of balance loss during walking under such perturbations is not well understood. The objective of this study was to investigate dynamic stability and variability of the human body during walking, and assess the influence of external, motor-cognitive, sensory and cognitive perturbations on dynamic balance, including surface inclination, use of a cell phone, auditory and visual stimulation, and mental calculation. Nineteen healthy young adult males were recruited. Three-dimensional joint kinematics were obtained using an optical motion capturing system as subjects walked at their self-selected speed on an instrumented treadmill. Dual-tasking was simulated by subjecting participants to motor-cognitive, visual, cognitive and auditory perturbations during walking including cell phone usage (talking, texting and reading), watching a video clip, listening to music, and performing numeric calculations mentally. External perturbations were also applied through alteration of surface inclination. Variability analysis was performed on spatiotemporal gait parameters using Detrended Fluctuation Analysis (DFA) and Standard Deviation. Dynamic stability was subsequently estimated for the entire body as well as the head, trunk and lower extremity joints using linear and nonlinear measures including Margin of Stability (MoS), Lyapunov Exponent (LyE) and Maximum Floquet Multipliers (MaxFM). A novel method was devised to assess stability using Margin of Stability at heel contact (HC) and minimum foot clearance (MFC), gait events associated with backward and forward balance loss, respectively. Slip and trip propensity estimated using Required Coefficient of Friction (RCoF) and MFC height, respectively. Finally, the most destabilizing additional task while walking was determined using deviation of MoS and trip propensity values during dual-task trials from the corresponding values during baseline walking. The results showed that dual-tasking during walking adversely affects balance in a direction specific-manner. Specifically, cell phone texting and reading while walking reduces balance in the mediolateral direction, while cell phone talking increases the risk of tripping in the anteroposterior direction. Upslope terrain increased the risk of balance loss in the anteroposterior and vertical directions and did not affect gait balance in the mediolateral direction, while walking down was associated with greater stability in the anteroposterior direction. Cognitive and sensory perturbations affected gait balance mostly in the anteroposterior and vertical directions rather than the mediolateral direction. Analysis of trip propensity showed that motor-cognitive dual-tasking due to cell phone usage while walking, cognitive and sensory perturbations due to performing additional auditory and visual tasks while walking are associated with greater risk of tripping, as measured by a lower MFC height. Particularly, talking while walking, and cognitive and sensory dual-tasking while walking may ultimately lead to an increase in risk of tripping in young adults. However, the risk of tripping in young individuals is not sensitive to external perturbations caused by sloped terrains. Participants mostly changed their step length and step time during walking under perturbations. Among the various measures used to determine the most destabilizing secondary task while walking, MFC height was more sensitive to the applied perturbations. Talking while walking was associated with the largest deviation from baseline condition. The findings of this investigation confirmed that head stabilization during ambulation has higher priority compared to other segments, and individuals try to adopt different strategies to attenuate perturbations from the lower body to the head. The current data highlighted the importance of arm swing in balance maintenance during walking under perturbations, and demonstrated that individuals try to compensate restricted arm swing during walking by modulating step width. With respect to gait adaptations, the results of this research support the idea that individual’s response to applied perturbations through dual-tasking while walking depend on the magnitude of the applied perturbation. The evidence from this study suggests that talking while walking is the most challenging secondary task during locomotion among the applied perturbations in this study, and additional sensory tasks are the least challenging one. These findings have significant implications for development of a gait training protocol for more frail people to successfully address common perturbations arising from daily living activities during everyday life. These data also suggest that MFC height analysis and local stability analysis of the lower body should be performed to gain better understanding on the effect of additional concurrent task while walking on the risk of tripping and gait stability, respectively. The analysis of MoS presented extends knowledge of step-to-step balance changes during walking at different gait events associated with common fall patterns occurring at HC and MFC. Further work needs to be done to analyse the influence of similar attention-demanding secondary tasks or ‘distractions’ in more vulnerable populations, including the elderly, fallers, and individuals with sensory or motor impairments that affect locomotor control.
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ItemAssessment of the anabolic effects of PTH drug treatment and mechanical loading on bone using high-resolution imaging and in silico modelling( 2018)Osteoporosis (OP) is a progressive bone disease characterised by significant reduction of bone mineral density (BMD) due to loss of bone matrix and changes in bone tissue properties. OP is regarded as a worldwide health issue and identifying novel treatments is of central clinical importance. Daily injections of parathyroid hormone (PTH) and exercise have been proven to have an anabolic effect on bone, i.e., are capable of restoring bone mass. In this thesis, the anabolic action of PTH drug treatment and mechanical loading was investigated using in silico modelling and high-resolution imaging techniques. Novel drugs are continuously developed to reduce the risk of bone fractures in osteoporotic patients. PTH peptides such as PTH(1-34) are the first anabolic agents approved to treat severe OP. Despite its success to restore bone mass, PTH mechanism of action on bone cells is still unclear. Recently, the understanding of OP pathophysiology has considerably improved. Biomarkers reflecting bone physiology have been identified at cellular, tissue and organ levels. Cellular biomarkers reflect the dynamics of bone remodelling on a short time scale, whereas tissue and organ scale biomarkers show changes of BMD on a larger time scale. Computational modelling is a novel approach that allows to quantitatively characterise the effect of a drug treatment on the disease progression integrating physiology, disease progression, drug treatment and biomarker data in a comprehensive mechanism-based in silico model. In this context, part of this work was focused on the development of a full time-dependent mechanistic pharmacokinetic-pharmacodynamic (PK/PD) model of the action of PTH(1-34) on bone modelling and remodelling. This model was applied to rat models of OP to shed light on the inter-cellular and tissue scale mechanisms involved in the action of PTH(1-34) on bone cells. This in silico model has the potential to predict the long-term effects of drug treatments on clinical outcomes and provide a means for patient-specific estimation of bone fracture risk. Furthermore, it is well known that bone adapts its mass and structure in response to stresses and strains induced by an external mechanical load. The most extensively used animal model to test hypotheses related to mechanical loading is the in vivo axial compression of the mouse tibia. Common outcome measures of these models are bone geometric dimensions and bone mineral density using high-resolution imaging techniques, i.e., micro-computed tomography (micro-CT). In this thesis, end-point micro-CT imaging data were analysed to quantify the local adaptation response of bone to both mechanical loading and PTH(1-34) drug treatment in the mouse tibia loading model. An innovative image post-processing algorithm was developed to quantify the cortical thickness locally along the periosteum. Furthermore, an algorithm was developed to estimate stresses, strains and strain energy density (SED) on periosteal surfaces of the tibia, combining micro- finite element analysis and beam theory to compute animal-specifi c SED. Bone adaptation to mechanical loading was variable along the periosteum. Results suggest that bone adaptation is higher in regions with higher SED. Moreover, mechanical loading and PTH induce a combined anabolic adaptation effect on bone suggesting that the association of PTH(1-34) administration and exercise may be an effective treatment for OP.
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ItemUltra-low power, low-noise and small size transceiver for wearable and implantable biomedical devices and neural prosthesis( 2018)There is high demand for research into the innovation and development of miniaturized electronics devices for biomedical applications such as implantable medical devices (IMD), neural prostheses (NP), embedded neural systems, body area network (BAN) systems and wireless biosensors systems (WBS) for the monitoring, treatment and diagnostics of diseases such as retinal degenerative diseases (bionic eye), hearing loss (bionic ear), and epilepsy (neurobionics). These electronic systems must be wireless as wires penetrating through human skin increase the risk of infections as they act as conduits for viruses and bacteria and they also limit the flexibility of movement for patients. It is critical to have the smallest size possible for implanted devices to minimize required space, and to have high quality implant grades and off-chip components. Therefore, an integrated design for transceivers in single chips without any cheap components is preferable. Another advantage of minimum size and integrated transceiver design in a single chip is that it minimizes power consumption and heating. Ultra-low power transceivers are essential because implanted batteries are undesirable due to their limited lifespan and the risk of infection they pose. Also, a limited amount of power can be transferred through the wireless power link system, and in the bionic eye, most of the transferred power will be consumed for stimulation in the electrodes array. Frequency is another important factor and limitation of transceiver designs in biomedical applications. The Medical Implant Communication Service (MICS) frequency band (402-405 MHz) is a relatively low frequency and has a small channel bandwidth. Therefore, achieving an ultra-low power design of less than one milliwatt remains challenging. There is a high amount of data transmitted and received in some implantable biomedical devices like retinal prostheses (bionic eyes) so high-speed transceiver systems are required for these applications. This work endeavours to explore the development of techniques for designing single chip ultra-low power, low noise, high speed and small sized transceivers for implantable biomedical devices for the bionic eye as an example. Ring oscillators were examined because they do not require external inductors or capacitors, and are area-efficient compared to LC oscillators. Because the VCO is the most critical part of the transceiver design in terms of power consumption and phase noise, a linear model of CMOS ring oscillator was used to derive a phase noise model. A phase noise versus power consumption of a five-stage ring oscillator was formulated, and a new model for optimizing phase noise versus power consumption for different frequencies and based on different transistor aspect ratio is presented. This thesis is predominantly devoted to the design and implementation of a MICS band transceiver with super low power consumption. A new ultra-low power, low-phase-noise and small sized ring VCO for use in PLL is introduced in this research. This VCO operates in the Medical Implant Communication Service (MICS) frequency band. This ring oscillator VCO does not need external inductors and capacitors like other LC oscillators and requires a small die area. A new control loop for low power, low noise, and quick settlement PLL is also outlined. Furthermore, a new architecture and modulation technique for the ultra-low power, low noise and high-speed transceiver for biomedical applications is presented. The proposed architecture and modulation technique can increase data transmission and receive speed as well as reduce the power consumption of the transceiver and the die size area, and minimize the complexity of the receiver and transmitter architectures.
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ItemModeling human neurogenesis in vitro using pluripotent stem cells: the evolution from 2D to 3D( 2018)Progress in stem cell technologies is based on fundamental research on exploring culture conditions that improve and optimize in vitro generation of human cell types and tissues. To date, there has been significant progress in developing protocols for in vitro neural differentiation of human pluripotent stem cells (hPSCs). Despite these advances, there are still large gaps in our understanding in how to create the ideal microenvironment in vitro to support ‘normal’ cellular processes occurring in vivo. Addressing these gaps would significantly advance the stem cell-based approach for in vitro modeling and drug screening studies as well as for regenerative medicine applications. In the last five years, a significant advancement in stem cell biology has been the shift to developing three dimensional (3D) hPSC cell culture systems. Establishing a faithful in vitro model of human neurogenesis is to date one of the main challenges of the field. To this end, in this thesis we explored the utilization of innovative 3D culture systems for deriving human neural-like tissue in vitro. Our main approach was the use of a rotary cell culture system (RCCS) to generate hPSC-derived neural organoids, which mimics microgravity conditions. Our results show that although neural organoids could be generated and maintained in microgravity conditions, there were changes in expression of rostral-caudal neural patterning genes and cortical markers compared to organoids generated in standard conditions. In particular, we showed that RCCS-derived organoids are capable of supporting otic-like specification and recapitulate some characteristics of inner ear development, including generation of hair cells displaying vestibular-like morphological and physiological phenotypes that resemble developing human fetal inner ear hair cells. Another aim of this thesis was to interrogate the application of a conductive 3D scaffold, the graphene foam, to support in vitro culture of hPSCs-derived neurons. Our data demonstrated biocompatibility of the graphene foam for human neurons and suggested it may be a suitable scaffold for developing 3D in vitro platforms for studies examining neural connectivity and circuit formation. Finally, we explored the application of hPSC culture systems for in vitro disease modeling with regard to Autism Spectrum Disorders (ASD). Establishing a valuable in vitro model of ASD would be of a great benefit for advancing our understanding of this complex condition and designing appropriate treatments. Our study provided preliminary but promising characterization data of three ASD induced pluripotent stem cell (PSC) line-derived cortical-like tissue. In conclusion, we propose alternative 3D in vitro systems for modeling several aspects of human neurogenesis. Our data provide new insights for using hPSC to study human neurodevelopment and related neurodevelopmental diseases.
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ItemMultiple compartment modelling and estimation in magnetic resonance imaging( 2018)The magnetic resonance (MR) signal encapsulates invaluable information about the structural and functional organization of an object of interest. In brain imaging applications, parametric models of the MR signal are designed to infer tissue structure by performing parameter estimation. Given a signal model, a typical parameter estimation algorithm solves an optimization problem to identify model parameter values that best describe the measured MR signal. A commonly employed modelling technique, known as multicompartment modelling, compartmentalizes the tissue into two or more discrete compartments, describing the MR signal as a composite sum of the signals arising from each compartment. This thesis is concerned with the utility and limitations of multicompartment modelling in sodium and diffusion-weighted imaging. The output of an analysis of MRI data is often spatial maps of parameter estimates, the result of having applied a model to the measured signal. Commonly employed bi-exponential models of T2*-weighted sodium data are susceptible to uncertainty in parameter estimates, resulting in noisy parameter maps with low contrast between brain tissue types. This thesis develops a continuum model of sodium T2* decay, applied to in vivo human multi-echo 7T data, which leads to high quality, high contrast parameter maps. In diffusion-weighted imaging, two component models of diffusion-weighted signal decay have been advocated for use in the estimation of axon diameter distributions. This thesis demonstrates that axon diameters are not distinguishable under the commonly assumed short pulse approximation, even at high gradient strengths available on pre-clinical MRI systems. Instead, the long pulse regime theoretically provides a stronger diffusion weighting under which axon diameters are maximally separated, as are the hindered and restricted diffusion compartments. Through experimental MRI, it is shown that even under long gradient pulses, a simplistic two-compartment model is incapable of capturing experimental decay behaviour, calling into question the utility of these models for axon diameter density estimation. Prior to performing parameter estimation, it is desirable to improve the quality of the MR signal, either by increasing the signal strength or reducing the noise level. Echo averaging is commonly employed for SNR improvement and contrast enhancement in multiecho MRI data. The number of echoes used in the averaging operation is an important factor in determining the overall SNR gain in the averaged image. This thesis studies the impact of the number of echoes on the averaging process and derives an analytical expression that predicts the optimum number of echoes for achieving maximum SNR gain. This technique is demonstrated to be applicable to the mono-exponential, bi-exponential and gamma distribution models of T2-weighted MRI signal. Experimental results demonstrate the ability to predict the optimal echo averaging conditions, both globally or locally in a voxelwise procedure. The assessment of the parameter estimation framework is a crucial step in determining the veracity of the resultant parameter estimates. The Cramer Rao lower bound (CRLB), a lower bound on the variance of parameter estimates, is frequently employed as a metric of precision and a method for experimental de- sign. CRLB is valid only under the assumptions of model correctness and has the potential to provide misleading estimates of parameter precision when such assumptions are not met. This thesis exposes the limitations of CRLB analyses of the MRI models, and instead, proposes the use of the observed Fisher Information (OFI) as an empirical metric of precision, which is not constrained by an assumption of model correctness. Further, the maximum likelihood (ML) value provides an empirical measure of accuracy. Hence, a joint ML-OFI analysis of the parameter estimates is proposed to provide a robust assessment of estimation performance, applied to the multicompartment models of diffusion as an example.
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ItemAdjusting the parameters of electrical stimulation of retinal ganglion cells to reduce neural adaptation and improve efficacy of retinal prostheses( 2018)Retinal prostheses aim to provide visual percepts through electrical stimulation of the retina to blind people affected by diseases caused by photoreceptor degeneration. Two challenges presented by current devices are a lack of selectivity in the activation of retinal ganglion cells (RGCs) and neural adaptation in the retina, which is believed to be the cause of fading—an effect where artificially produced percepts disappear over a short period of time, despite continuous stimulation of the retina. We aim to (1) understand the neural adaptation generated in RGCs during electrical stimulation, (2) obtain the preferred stimulation parameters (waveform) of each morphological class of RGCs and (3) use the preferred waveform of each morphological class to selectively activate different neurons. RGCs have been classified by morphology into 4 main groups: A, B, C and D. We performed an spike-triggered covariance (STC) analysis on the responses of 44 RGCs to extracellular electrical white noise and 43 RGCs to intracellular white noise. We then recovered their temporal electrical receptive fields (tERF), or waveform. A number of RGCs were stimulated with all the previously recovered waveforms to test the efficacy of each waveform on each other. The waveform recovered from the responses to intracellular stimulation have shown that RGCs can be classified into their respective morphological types by using a K-means clustering algorithm. Extracellular stimulation did not result in waveforms with a clear correlation between clusters and morphological classes. Cells from B and D morphological types had lower thresholds when stimulated with the waveform recovered from cells in the same morphological class. A-RGCs on the contrary, did not seem to share the same temporal features in their waveform with other A-type neurons. Further studies involving a larger data set might determine whether the waveform could preferentially stimulate cells from a specific morphological class. Current visual prostheses use electrical pulses with fixed frequencies and amplitudes modulated over hundreds of milliseconds to stimulate the retina. However, in nature, neuronal spiking occurs with stochastic timing, hence the information received naturally from other neurons by RGCs is irregularly timed. We used a single epiretinal electrode to stimulate and compare rat RGC responses to stimulus trains of biphasic pulses delivered at regular and random inter-pulse intervals (IPI), the latter taken from an exponential distribution. Our observations suggest that stimulation with random IPIs result in lower adaptation rates than stimulation with constant IPIs at frequencies of 50 Hz and 200 Hz. We also found a high proportion of lower amplitude action potentials, or spikelets. The spikelets were more prominent at high stimulation frequencies (50 Hz and 200 Hz) and were less susceptible to adaptation, but it was not clear if they propagated along the axon. Using random IPI stimulation in retinal prostheses reduces the decay of RGCs and this could potentially reduce fading of electrically induced visual perception.
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ItemNeural tissue electrical modelling at micro and macro scales( 2018)A better understanding of electrical stimulation of the retina by neural prostheses may be essential for progress to be made towards a viable mass-market design of such devices. Dividing the problem into electrode models, target neuron models, and models of the tissue filling the volume between the electrodes and neurons, we focus on the tissue models. Prior work suggests that to model the relevant tissue, the neural retina, a standard, homogeneous, volume conductor may not be an appropriately faithful choice, even one with an anisotropic conductivity and permittivity. This is due to the capacitance of neural membranes and the macroscopic dimensions of the cable-like neural processes forming the tissue. Prior work on the subject resulted in alternative models being proposed (mean-field models). However, while those prior models may be solved approximately, there had been no well-established method to estimate the amount of error in those approximate solutions. We propose an alternative approach to derive a mean-field model, on the basis of finite element discretisations of a reference microstructural model. The latter is made up of infinitely-long axons running parallel to one another. To estimate the accuracy of those finite element solutions, we adapt the Global Convergence Index (GCI) technique. Our adaptation incorporates round-off error into the GCI technique in a systematic and conservative way. Our resulting mean-field model, the quantified-uncertainty (QU) model, produces solutions together with uncertainty estimates. While there are some differences between the QU model and prior models, they produce compatible solutions, in the sense that solutions using the prior models generally fall within the uncertainty band of solutions produced using the QU model, under boundary conditions of practical relevance. We describe a detailed method for solving a simple instance of a situated application problem incorporating the QU model. The derivation of the QU model proceeds by transforming the microstructural model into an appropriate spectral domain, then solving for a point source in a large, coarsely-discretised instance, in order to establish the claim that the far-field behaviour in two lattice directions is sufficient to characterise the whole response. We then solve finely-meshed finite element models corresponding to these two directions, under far-field boundary conditions. Observing that the solutions converge exponentially (and rapidly) towards functions with useful symmetry properties, we take advantage of the latter to constrain equivalent discrete models, reduced so as to represent only the quantities relevant to the mean-field description: potential, current flow across the fibres, and current flow along the fibres ("absorption"). We find equivalent continuous-domain models to the discrete models. We were also able to express the QU model in terms of two rational interpolating functions with a small number of coefficients. The uncertainty part of the QU model is formed so as to cover the differences between the two directions mentioned above, in addition to accounting for the fitting residuals from interpolation, for the discretisation error from the finite element representation and for the round-off error from solving the finite element matrices, including their conditioning.
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ItemTechniques for signal acquisition, reconstruction and analysis in sodium magnetic resonance imaging( 2018)Over the last decades, Magnetic Resonance Imaging (MRI) has undergone a high-paced development rendering it an extremely versatile imaging technique with revolutionary impact on medical practice and research. Typically, MRI acquisitions are sensitised to hydrogen-1 nuclei which facilitates high resolution images with unprecedented soft tissue contrast but lacks specificity as it only offers an indirect link to pathologies. The study of other MR-observable nuclei (so called x-Nuclei MRI) offers the potential to extend standard MRI to the detection of pathologies without apparent structural abnormalities and enhance therapy monitoring capabilities. Sodium as the second most abundant MR-observable nucleus and as a direct link to cell integrity and vitality via the Sodium-Potassium-Pump promises means for a quantitative in- sight into pathological processes. Despite its potential and its early beginnings in the 1980s, Sodium MRI is still considered a niche of MRI and plays no significant role in clinical routine. Its progression is hampered by its relatively low in vivo signal strength and challenging NMR-signal characteristics. While the former can be mitigated through the use of stronger magnetic fields, the latter dictates the acquisition principle but also provides great analysis potential. This thesis focusses on the second challenge, the Sodium NMR-signal, and exploits its aspects across the three main stages of an MRI experiment: signal acquisition, signal reconstruction, and signal analysis. Beginning with signal acquisition, this thesis introduces zGRF-RHE, an improved sequence timing concept for a reduction of encoding time. Compared with the conventional ultra-short echo time (UTE) sequence timing approach, the presented technique mitigates T2∗-induced blurring artefacts and improves image SNR. It illustrates a general sequence timing concept with applicability to any centre-out trajectory design. With regards to signal reconstruction, this work demonstrates a comprehensive investigation of Compressed Sensing (CS) reconstructions with a focus on parameter optimisation and an evaluation of accurate image intensity reconstructions. Lastly, concerning both acquisition and signal analysis, this thesis investigates the diverse Sodium NMR-signal characteristics. It presents an optimised multi-echo acquisition scheme together with a Rician-noise corrected parameter estimation approach facilitating a differentiation of underlying motion regimes. It provides a time-efficient measurement protocol from which multiple parameters are obtainable within the course of a standard Sodium-density weighted acquisition.
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ItemEpileptic seizures: mechanisms and forecasting( 2018)Seizure forecasting, like weather forecasting, was once considered the domain of charlatans and purveyors of science fiction. However, neuroscience has now advanced to the point of translating seizure forecasting research into widely available clinical applications. Just like weather apps that report the probability of rain on a given day, it is now conceivable that devices will inform people with epilepsy about their current likelihood of having a seizure. This information could be life-changing: restoring a sense of control and the ability to participate in everyday activities. Over 65 million people around the world have epilepsy; one third cannot control their seizures with medication. The unpredictability of seizures can be devastating, leading to persistent anxiety, exclusion from day-to-day life, serious injury or death. The aim of this thesis is to develop a clinically useful framework for forecasting seizures. The presented research addresses several key questions towards this goal: What drives seizure transitions? Are there underlying rhythms governing seizure onset? If underlying rhythms exist, how can they be integrated into a single determination of an individual's seizure likelihood? By presenting answers to these questions this thesis aims to form the basis for an innovative approach to seizure forecasting.
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ItemNano-scale design of cardiovascular biomaterials( 2018)Cardiovascular disease is the leading cause of death worldwide. The development of blood-compatible biomaterials could relieve this burden by improving the performance of cardiovascular devices such assmall-diameter vascular grafts. An attractive strategy for improving blood compatibility of an interface is to generate biomaterials that foster a confluent and functioning endothelial cell layer. Although several strategies have been explored to improve endothelialisation, there are still no commercially available blood-compatible grafts that promotes endothelialization. The lack of a blood-compatible interface is one of the most pressing challenges in the biomaterials field. As such, additional research is required in order to develop new technologies to meet this need. The aim of this work is to design a biomaterial that promotes endothelialization by mimicking cellextracellular matrix interactions. In order to achieve this, we used two biomimetic approaches: (1)nanoclustering of cell adhesive ligands (ligand multivalency) to promote the clustering of cell receptors, especially integrin receptors, and (2) dual functionalization of materials with both integrin- and syndecan-binding ligands to engage both cell receptor types to utilise their synergistic effects. To accomplish this, we synthesized a random copolymer via reversible addition-fragmentation chain transfer (RAFT) polymerization. The polymer was composed of methyl methacrylate and polyethylene glycol methacrylate-containing units. The polymer was functionalized with integrin- and syndecanbinding ligands. A blending technique was used to generate interfaces with ligand multivalency. Specifically, highly peptide-functionalized polymer chains were blended with non-functionalized polymers chains. Upon film casting, these generated surfaces displaying nano-scale islands of high peptide density due to the size and shape of the polymer random coils. Endothelial cells were cultured on these surfaces and their behaviours were investigated under static and flow conditions. Our results show that the biomaterials functionalized with multivalent integrin-binding ligands promote the formation of focal adhesions, improve endothelial cell adhesion, migration, and endothelialization rate compared to surfaces functionalized with random distribution of integrin-binding ligands. Additionally, the biomaterials functionalized with mixed population of multivalent integrin- and syndecan-binding ligands show additional improvement over surfaces with just multivalent integrinbinding or syndecan-binding ligands alone. Specifically, we observed synergistic improvement of endothelial cell adhesion, improved focal adhesion formation and cytoskeletal assembly, an increased rate of endothelialization, and regulation of migration speed. These surfaces also regulate a range of endothelial cell functions when the cells were exposed to laminar flow shear stress including increased spreading, larger and more abundant actin stress fibers, elongation and alignment in the direction of flow, increased capture of endothelial cells from flow, and robust attachment of cells under flow. These results demonstrate that bioengineered materials presenting nanoclusters of both integrin- and syndecan-binding ligands could be used for the development of next-generation biomedical devices, especially small-diameter vascular grafts.