Biomedical engineering

Supervisors: A/Prof Lining Arnold Ju; Dr Wittaya Suwakulsiri

Eligibility: The ideal candidate should demonstrate proficiency in basic molecular biology techniques, which include but are not limited to, micro-volume pipetting and centrifugation. Complementing this, familiarity with Unix-based scripts and R programming language would be advantageous. Preference is given to applicants who have a strong interest in microfluidic system, organ-on-a-chip models, Genetics, and programming. 

The prospective outcomes of this position hold great promise. The successful candidate would contribute significantly to establishing an all-in-one pipeline for investigation of gene expression profiles in organ-on-a-chip models This breakthrough could potentially form the basis for the development of new biomarkers, as well as the creation of innovative therapeutic strategies such as drug screening and personalised medicine based on gene expression profiles.

Project Description: The use of human organ-on-a-chip systems in biomedical and clinical research has surged due to the increasing need for models that accurately represent human diseases. Traditional preclinical models often fail to predict human physiological responses, underscoring the significance of organ-on-a-chip technology. In our laboratory, we leverage organ-on-a-chip models in tandem with 'vascular mechanobiology'.

to investigate the mechanobiological changes associated with cardiovascular diseases. However, our understanding of how genetic materials influence these mechanobiological alterations in organ-on-a-chip models remains limited. To address this pressing need, we are establishing a comprehensive framework for RNA isolation, RNA sequencing and bioinformatic analysis from organ-on-a-chip models.

We conjoin the realms of microfluidic system, Genetics and programming/Bioinformatics to identify unique gene targets. This innovative approach holds the potential to lay the groundwork for the identification of novel biomarkers for cardiovascular disease detection, as well as the creation of innovative therapeutic strategies such as drug screening and personalised medicine. These strategies would be based on gene expression profiles sourced from organ-on-a-chip models, providing a promising outlook for future healthcare scenarios in cardiovascular diseases.

Requirement to be on campus: *Yes dependent on government’s health advice..

Supervisors: A/Prof Lining Arnold Ju; Nicole Alexis Yap

Eligibility: Recommended but not compulsory: Familiar with MATLAB, Python, ANSYS, or any related algorithm programming software. Knowledgeable on the techniques used in clinical image processing and machine learning identification. Knows how to work around 3D printing technologies, along with associated computer software to printer input interfaces.

Preferably an individual with an interest in AI, machine learning, microfluidics, thrombosis, cardiovascular and cerebrovascular health. A passion in biomedical research and the development of novel diagnostic, testing, and treatment tools in the hematological health sector.

Project Description: Cardiovascular diseases such as stroke and heart attacks are the leading cause of mortality worldwide. The underlying cause of disease is the development of clots within the circulatory system, which either initiate or eventually find their way to the key vessels of the human body, namely the carotid and coronary vessels, where partial or full occlusion may lead to the development of ischemic dysfunction characteristic of brain and heart failure. Thrombosis is a multivariate condition, where factors such as age, sex, activity, diet, and genetics may all play a role in the susceptibility to pathology. More notably, it has been shown that vessel geometry, vascular endothelium health, and endogenous blood coagulability all play a significant role in thrombogenesis, of which the presentations of each are highly patient-specific.

In response to this, our lab has developed personalised full-lumen microvasculature-on-a-chip devices to study the effect of patient-specific hemodynamic environment on thrombosis. The microchannels are designed to recapitulate the full 3D architecture of the patient vessels, and be coated with human carotid artery cells before being perfused with whole blood to fully replicate blood flow in the patient body. Using cutting-edge AI technology and in light of the avant-garde of machine learning and automation within global society today, we aim to develop an AI algorithm to aid the automation of 3D microprinting these vessel-chips, through both MRV segmentation of patient clinical images for double-layered chip alignment for the full lumen chips, and identifying key area of thrombus development and pattern in various patient vessel geometries. The algorithm derived images would then be used in the 3D chip fabrication process and experimental data analysis.

Requirement to be on campus: Yes (special arrangements possible)

Supervisor: Dr. Ann-Na Cho

Eligibility: 

• Has Tissue culture experience
• Interested in Neuroscience
• Willing to contribute to animal replacement model (humanised and miniaturised organ model, organoid)
• Availability to conduct all 6 weeks on campus in the lab

Project Description: The recent breakthrough in developing the Brain organoid which involves cutting-edge technology of human stem cells and knowledge of nervous system development has been highlighted as a next-generation in vitro model for personalised disease modeling and medicine.

The 3 dimensional (3D) self-organising Brain organoid created from iPSCs enables the recapitulation of the endogenous cellular composition, organ-specific structure, phenotype, and functionality of human physiology and development including aging. Furthermore, to reflect the complexity of the central nervous system, organoid methodologies have ever advanced to physically assemble two or three different brain regions of organoid to be integrated into one organoid called ‘assembloid’. This novel platform will not only enable an investigation of neuronal crosstalk between diverse brain organoids in psychiatric disorders but also screening for potential drug candidates and personalised therapeutics. This project will suit students interested in stem cell engineering and laboratory-grown human brain tissue.

Requirement to be on campus: *Yes, dependent on government’s health advice.

Supervisors: Dr. Jinglei Lv, A/Prof. Mayuresh Korgaonkar, Prof. Fernando Calamante.

Eligibility: 

• Basic skills with programming. 
• Necessary knowledge about medical image and signal processing.
• Self-motivation, curiosity about research and passion to succeed. 

Project Description: We are so close to reading the mind with the modern neuroimaging technology. The electroencephalogram (EEG) records the electrical activity of billions of neurons while the functional magnetic resonance imaging (MRI) reflects the blood oxygen consumption because of neuronal firing.

Now at our lab, we have the hardware setup to record both signal modalities simultaneously. We can record the brain activity during resting state as well as with cognitive tasks, even movie watching. The concurrent activity recording from both EEG and fMRI helps us not only understand how the brain works and how the mind is generated, but also suggests potential biomarkers for psychiatric disorders, such as Depression, Bipolar and Schizophrenia. It demands smart engineering to decode faithful signals among massive noise in this advanced setting. In this project, you will work with both biomedical scientists and neuroscientists to develop a pipeline of experiment design, data collection and data processing with simultaneous EEG and fMRI. Together with T1w and Diffusion MRI imaging, you would explore the possibility of finding the signal sources and signal pathways in the human brain.

Requirement to be on campus: No

Supervisors: Dr Mariano Cabezas, Dr Jinglei Lv, Dr Tim Wang and Prof. Fernando Calamante.

Eligibility:

• Basic skills with programming. Python programming is preferable. 
• Basic knowledge about medical image. 
• Self-motivation, curiosity about research and passion to succeed.

Project Description: The human brain is a complex dynamic system with still many unknowns when it comes to its functional behaviour. However, functional MRI provides a means to record brain activity signals. The signal at a given time point can then be used to define a brain state. That begs an interesting research question, i.e., whether one brain state can predict following states.This biological question can be formulated as an auto-regression problem in machine learning. Recent deep learning techniques, specifically transformers, can learn patterns from high dimensional time series and can model the sequential relationship between states (also known as tokens). We aim to develop a transformer based method to explore this research question. Furthermore, we could use this model trained on healthy brains to model the brain states of patients with pathology. Afterwards, the measured prediction error could be employed to find anomaly regions related to a specific disease. The Human Brain Connectome dataset will be used for model training and validation while patient data from OASIS, ADNI or PPMI will be used for the disease research part and to compare to healthy controls.

Requirement to be on campus: No

Supervisors: Prof Wei Chen, Dr Gautam Anand, and Dr Jia Liu

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description: Electroencephalogram or EEG is an electrical signal recorded from the head to indicate brain’s electrical activity. It is a vital physiological measurement which provides biomarkers of several neurological disorders, cognitive workload, stress, vestibular disorders and sleep. Since the past few decades, EEG has been the most important indicator for sleep assessment, where it is used in conjunction with several other measured bio signals in a procedure called Polysomnography (PSG).

This project will aim at developing a sensor which can be worn on the forehead providing convenience and comfort with good quality signal. As such, this work will focus on developing a non-contact or capacitive EEG sensor to obtain biomarkers for different sleep stages. The work will include developing state-of-the-art electronics with optimal sensor design through a working prototype. Performance evaluation will be carried out against a reference EEG device in laboratory settings. In this project you will learn the fundamentals of designing, developing and testing biomedical electronics, along with the knowledge of acquiring bio signals from human body and consideration of environmental interferences. This will not only enrich your electronics skills, but also inform you about the process of system design for a medical wearable device.

Requirement to be on campus: Yes *dependent on government’s health advice.

Supervisors: Prof Wei Chen, Dr Gautam Anand, and Dr Jia Liu

Eligibility: WAM>75 and Undergraduate candidates must have already completed at least 96 credit points towards their undergraduate degree at the time of application.

Project Description: Electrical stimulation has emerged as a promising method for enhancing sleep quality and addressing sleep disorders. By applying controlled electrical currents to specific areas of the brain or body, this technique aims to modulate neural activity and promote restorative sleep patterns. Understanding the mechanisms and efficacy of electrical stimulation for sleep enhancement is crucial for developing effective interventions and improving overall sleep health.

This project aims to comprehensively understand the role of electrical stimulation in improving sleep disorders and enhancing sleep quality. The tasks will focus on conducting a thorough literature review to identify state-of-the-art methods and research findings related to sleep improvement through electrical stimulation. Topics of investigation will include different stimulation modalities, target brain regions or physiological pathways, and outcomes on sleep architecture and quality. In this project, you will explore a deep understanding of the principles and applications of electrical stimulation for sleep enhancement. Through comprehensive literature review and analysis, you will gain insights into the theoretical foundations, research methodologies, and current advancements in this field, which also cultivates a robust foundational skill of early-stage research work and laying the groundwork for future research and innovation.

Requirement to be on campus: Hybrid- to be arranged with the supervisors.