Biomedical Engineering - Research Publications
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ItemThe "Spin-3/2 Bloch Equation": System matrix formalism of excitation, relaxation, and off-resonance effects in biological tissue(WILEY, 2022-05-24)PURPOSE: This work proposes "Spin-3/2 Bloch Equation" (SBE), a consolidated formalism for spin-3/2 dynamics in biological environments. The formalism encapsulates excitation, relaxation, and off-resonance with accessible matrix representation for a straightforward implementation with high computational efficiency. THEORY: The SBE is derived using spherical tensor operators to encapsulate the spin-3/2 dynamics in biological systems in a single system matrix, a formalism akin to the well-known Bloch Equations (BE). METHODS: Using the proposed SBE, simulations of three classical 23 Na pulse sequences were performed to demonstrate the versatility and applicability of the model, returning the evolution of the 23 Na spin system during these experiments: soft rectangular and adiabatic inversion recovery (IR) and triple-quantum filtering. IR simulations were compared with two existing spin-3/2 simulators and the adaptive BE as a first-order approximation. RESULTS: The proposed SBE is straightforward to implement and facilitates accurate and fast simulations of the underlying higher order coherence in sodium experiments of biological tissues. SBE simulations and comparison spin-3/2 simulators outperform the BE simulations as expected, with the SBE offering superior computational efficiency achieved by the single system matrix formalism. CONCLUSION: The proposed SBE enables comprehensive and accurate simulations for spin-3/2 systems in biological tissue. With a one-line call to an ordinary differential equation solver, it offers a computationally efficient and accessible method for use in 23 Na pulse sequence design.
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ItemSeizure forecasting: Bifurcations in the long and winding road(WILEY, 2022-05-23)To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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ItemCardioVinci: building blocks for virtual cardiac cells using deep learning(ROYAL SOC, 2022-11-21)Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.
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ItemEffects of altered cellular ultrastructure on energy metabolism in diabetic cardiomyopathy: an in silico study.(The Royal Society, 2022-11-21)Diabetic cardiomyopathy is a leading cause of heart failure in diabetes. At the cellular level, diabetic cardiomyopathy leads to altered mitochondrial energy metabolism and cardiomyocyte ultrastructure. We combined electron microscopy (EM) and computational modelling to understand the impact of diabetes-induced ultrastructural changes on cardiac bioenergetics. We collected transverse micrographs of multiple control and type I diabetic rat cardiomyocytes using EM. Micrographs were converted to finite-element meshes, and bioenergetics was simulated over them using a biophysical model. The simulations also incorporated depressed mitochondrial capacity for oxidative phosphorylation (OXPHOS) and creatine kinase (CK) reactions to simulate diabetes-induced mitochondrial dysfunction. Analysis of micrographs revealed a 14% decline in mitochondrial area fraction in diabetic cardiomyocytes, and an irregular arrangement of mitochondria and myofibrils. Simulations predicted that this irregular arrangement, coupled with the depressed activity of mitochondrial CK enzymes, leads to large spatial variation in adenosine diphosphate (ADP)/adenosine triphosphate (ATP) ratio profile of diabetic cardiomyocytes. However, when spatially averaged, myofibrillar ADP/ATP ratios of a cardiomyocyte do not change with diabetes. Instead, average concentration of inorganic phosphate rises by 40% owing to lower mitochondrial area fraction and dysfunction in OXPHOS. These simulations indicate that a disorganized cellular ultrastructure negatively impacts metabolite transport in diabetic cardiomyopathy. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.
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ItemElectrodetection of Small Molecules by Conformation-Mediated Signal Enhancement(AMER CHEMICAL SOC, 2022-10-17)Electrochemical biosensors allow the rapid, selective, and sensitive transduction of critical biological parameters into measurable signals. However, current electrochemical biosensors often fail to selectively and sensitively detect small molecules because of their small size and low molecular complexity. We have developed an electrochemical biosensing platform that harnesses the analyte-dependent conformational change of highly selective solute-binding proteins to amplify the redox signal generated by analyte binding. Using this platform, we constructed and characterized two biosensors that can sense leucine and glycine, respectively. We show that these biosensors can selectively and sensitively detect their targets over a wide range of concentrations-up to 7 orders of magnitude-and that the selectivity of these sensors can be readily altered by switching the bioreceptor's binding domain. Our work represents a new paradigm for the design of a family of modular electrochemical biosensors, where access to electrode surfaces can be controlled by protein conformational changes.
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ItemNumerical study of when and who will get infected by coronavirus in passenger car.(Springer Science and Business Media LLC, 2022-08)In light of the COVID-19 pandemic, it is becoming extremely necessary to assess respiratory disease transmission in passenger cars. This study numerically investigated the human respiration activities' effects, such as breathing and speaking, on the transport characteristics of respiratory-induced contaminants in passenger car. The main objective of the present study is to accurately predict when and who will get infected by coronavirus while sharing a passenger car with a patient of COVID-19 or similar viruses. To achieve this goal, transient simulations were conducted in passenger car. We conducted a 3D computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a passenger car. The Eulerian-Eulerian flow model coupled with k-ε turbulence approach was used to track respiratory contaminants with diameter ≥ 1 μm that were released by different passengers within the passenger car. The results showed that around 6.38 min, this is all that you need to get infected with COVID-19 when sharing a poorly ventilated car with a driver who got coronavirus. It also has been found that enhancing the ventilation system of the passenger car will reduce the risk of contracting Coronavirus. The predicted results could be useful for future engineering studies aimed at designing public transport and passenger cars to face the spread of droplets that may be contaminated with pathogens.
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ItemFrequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces(FRONTIERS MEDIA SA, 2022-12-21)OBJECTIVE: Multi-frequency steady-state visual evoked potential (SSVEP) stimulation and decoding methods enable the representation of a large number of visual targets in brain-computer interfaces (BCIs). However, unlike traditional single-frequency SSVEP, multi-frequency SSVEP is not yet widely used. One of the key reasons is that the redundancy in the input options requires an additional selection process to define an effective set of frequencies for the interface. This study investigates systematic frequency set selection methods. METHODS: An optimization strategy based on the analysis of the frequency components in the resulting multi-frequency SSVEP is proposed, investigated and compared to existing methods, which are constructed based on the analysis of the stimulation (input) signals. We hypothesized that minimizing the occurrence of common sums in the multi-frequency SSVEP improves the performance of the interface, and that selection by pairs further increases the accuracy compared to selection by frequencies. An experiment with 12 participants was conducted to validate the hypotheses. RESULTS: Our results demonstrated a statistically significant improvement in decoding accuracy with the proposed optimization strategy based on multi-frequency SSVEP features compared to conventional techniques. Both hypotheses were validated by the experiments. CONCLUSION: Performing selection by pairs and minimizing the number of common sums in selection by pairs are effective ways to select suitable frequency sets that improve multi-frequency SSVEP-based BCI accuracies. SIGNIFICANCE: This study provides guidance on frequency set selection in multi-frequency SSVEP. The proposed method in this study shows significant improvement in BCI performance (decoding accuracy) compared to existing methods in the literature.
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ItemSpatio-temporal feature extraction in sensory electroneurographic signals.(The Royal Society, 2022-07-25)The recording and analysis of peripheral neural signal can provide insight for various prosthetic and bioelectronics medicine applications. However, there are few studies that investigate how informative features can be extracted from population activity electroneurographic (ENG) signals. In this study, five feature extraction frameworks were implemented on sensory ENG datasets and their classification performance was compared. The datasets were collected in acute rat experiments where multi-channel nerve cuffs recorded from the sciatic nerve in response to proprioceptive stimulation of the hindlimb. A novel feature extraction framework, which incorporates spatio-temporal focus and dynamic time warping, achieved classification accuracies above 90% while keeping a low computational cost. This framework outperformed the remaining frameworks tested in this study and has improved the discrimination accuracy of the sensory signals. Thus, this study has extended the tools available to extract features from sensory population activity ENG signals. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.
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ItemEthical and regulatory issues of stem cell-derived 3-dimensional organoid and tissue therapy for personalised regenerative medicine(BMC, 2022-12-27)BACKGROUND: Regenerative medicine has the potential to treat genetic disorders and replace damaged or missing tissue. The use of donor or animal tissue raises many well-known issues, including limited tissue availability, the possibility of rejection and patient infection. Stem cell therapy raised hope of overcoming these issues, but created new risks including tumour formation and limited benefit if the desired target tissue does not form. The recent development of 3-dimensional tissues, including organoids, allows the creation of more complex tissues for personalised regenerative medicine. METHODS: This article details the potential health risks of 3-dimensional organoid and tissue therapy versus dissociated stem cell therapy. The current ethical and regulatory issues surrounding 3-dimensional organoid and tissue therapy are presented with a focus on the highly influential FDA and International Society of Stem Cell Research (ISSCR) guidelines. CONCLUSIONS: The potential use of 3-dimensional organoid and tissue therapy may deliver greater patient benefits than other regenerative medicine approaches, but raises new health and ethical risks. Preclinical testing of these therapies will not mitigate some of their risks; they may only be understood after first-in-human trials. The potential irreversibility and high risk of these therapies affects how clinical trials should be structured, including post-trial care for participants.