College of Science, Engineering and Food Science - Masters by Research Theses
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Item Optimisation of a GFP-RAB7 hiPSC model to investigate phenotypic differences of point mutations in Charcot-Marie-Tooth Disease type 2B(University College Cork, 2025) O'Mahony, Katie; Burk, Katja; Lindsay, AndrewCharcot-Marie-Tooth disease type 2B (CMT2B) is a debilitating inherited peripheral neuropathy caused by mutations in the RAB7 gene. Despite many clinical trials, effective treatments targeting the underlying mechanisms of CMT2B remain elusive. One potential limitation is the reliance on non-physiological models, such as HeLa cells and mouse models, which fail to capture the complexity of human disease. To address this, the Burk Lab developed isogenic human induced pluripotent stem cell (hiPSC) lines with CRISPR-engineered GFP-RAB7 carrying the CMT2B-associated point mutations. These lines provide a physiologically relevant system to study the functional impact of RAB7 mutations, reducing the variability and enabling systematic comparison across all major missense mutations associated with CMT2B. This thesis focuses on characterising the Burk Lab’s CMT2B hiPSC lines, optimising their differentiation into induced sensory neurons (iSNs), and validating their use for studying RAB7-associated pathways, including late endosomal trafficking and autophagy. Building on the development of these hiPSC models, initial experiments aimed to validate that the GFP-tag does not affect RAB7 and its known interactors (ORP1L, RILP, WASH1, VPS26, VPS29, and VPS35). Co-immunoprecipitation (co-IP) was performed using lysates from undifferentiated hiPSCs. While the protocol requires further optimisation, initial results are promising as RAB7 can indeed be pulled down with GFP, confirming successful tagging. To further investigate functional outcomes of RAB7 mutations, neurite length, late endosomal trafficking dynamics, and autophagic flux were assessed. No significant differences were observed between the length of young neurites (DIV1 and DIV2) of wild type (WT) controls and mutant iSNs, This may suggest that neurite length may not be affected in CMT2B. This warrants further investigation at later stages of outgrowth. Building on these findings, preliminary live cell imaging of RAB7-positive vesicle in V162M mutant iSNs demonstrated increased vesicle movement along neurites compared to WT controls, suggesting altered intracellular trafficking as a potential mechanism underlying CMT2B pathology. Autophagic flux was assessed in undifferentiated GFP-RAB7 hiPSCs using starvation conditions and bafilomycin A1 treatment, followed by immunoblotting for p62 and LC3. While further optimisation is needed before this protocol can be reliably used, these initial results provide a foundational workflow for future autophagy assessments in this model. For all experiments, additional differentiations are necessary to conclusively confirm the preliminary results obtained thus far. The continued validation of these models will facilitate the identification of therapeutic targets and pave the way for more effective treatments for CMT2B.Item Hypoxic-ischemic encephalopathy grading using novel EEG signal processing and machine learning techniques(University College Cork, 2025) Twomey, Leah; Popovici, Emanuel; Temko, Andriy; Qualcomm\IEEEPARstart{H}{ypoxic-Ischemic Encephalopathy} (HIE) is a critical brain injury in newborns resulting from oxygen or blood supply deprivation during birth. Traditional diagnosis methods for HIE require specialized expertise from a trained medical professional, and immediate intervention of treatment is required within the first 6 hours post primary hypoxic-ischemic insult. Electroencephalogram (EEG) is the gold standard for analysing brain pathologies for neonates and large windows of this complex signal must be assessed for accurate HIE diagnosis. Using an openly available dataset, this thesis proposes an automated approach for HIE grading, leveraging signal processing techniques and Artificial Intelligence (AI) to provide accurate and timely assessments, with an implementation enabling edge-device deployment for real-world clinical utility. Representing the 1 hour epoch of the EEG signal in the amplitude and frequency domain through Mel Spectorgram representation, the HIE grading task becomes an image recognition problem, where Convolutional Neural Networks have shown high accuracy and efficiency. An initial test accuracy of 84.85\% is achieved. Further enhancement of the signals rhythmic behaviour is necessary to increase the grading potential of the signal, thus the FM/AM sonification algorithm was implemented, transforming the EEG signal into an amplitude and frequency modulated audio signal. This pre-processing is designed to enhance the background rhythmic pattern of the signal, an essential feature used by the clinician for visual inspection. The two dimensional (2D) CNN is designed as a regressor to map the input image to a value on the HIE grading scale to enhance the model's ability to leverage the monotonic relationship between the grades. An optimised rounding function is implemented to define the final clinical grade as a novel post-processing technique. An overall test accuracy of 89.97\% based on a rigourous nested cross-validation evaluation framework is achieved, surpassing the current state-of-the-art by 6\%. The robustness and generalisability of the proposed method is obvious from the weighted F1-score of 0.8985 and a Kappa score of 0.8219. While enhancing the accuracy and speed of HIE diagnosis with this AI-driven approach, the goal is to make it readily available at the point of care. An inference time of 62.7 milliseconds is achieved for the quantized model on the Snapdragon processor highlighting its suitability for real-time, on-device HIE grading.Item Interactive EEG visualisation in Virtual Reality: design and implementation(University College Cork, 2025) Creed, Adam; Popovici, Emanuel; Factor, Andreea; Murphy, David; QualcommElectroencephalography (EEG) has long been a cornerstone in both neuroscience research and clinical diagnostics, providing invaluable insights into the electrical activity of the brain. EEG is the gold standard to detect and analyse brain pathologies such as seizures. An early detection and diagnosis of seizures is key in an effective treatment and decrease in morbidity and mortality. However, neonatal seizures are difficult to detect through clinical signs and even through EEG. The standard method of analysis of neonatal EEG is through visualisation on conventional two dimensional (2D) displays. Despite its importance, interpreting the complex and often nuanced signals produced by EEG remains a significant challenge, particularly when viewed on conventional 2D displays. This limitation can be particularly evident for clinicians who may lack extensive experience in EEG analysis. As the demand for more accurate and accessible diagnostic tools grows, the need for more intuitive methods of data visualisation becomes increasingly apparent. This thesis addresses these challenges by presenting the design, development, and comprehensive evaluation of an innovative neonatal EEG visualisation platform within a Virtual Reality (VR) environment, developed using Unity. The platform reimagines how EEG data can be presented, leveraging the immersive capabilities of VR to offer a fully three-dimensional (3D) space where users can interact with and explore brain activity data in real time. By moving beyond the constraints of 2D screens, this approach provides a more natural, immersive, and intuitive framework for both novice and expert clinicians alike. Recently, another method of analysis through sonification of EEG was proposed to speed up analysis of EEG. This allows users to hear the brain’s electrical activity as a dynamic auditory experience. This novel sonification technique not only provides an additional sensory modality for interpreting EEG data when used complementary with visualisation but also enhances the users’ ability to detect patterns and anomalies in the brain’s electrical signals that may be overlooked visually. Therefore, it was added to this platform. Furthermore, the platform integrates an AI-driven seizure detection algorithm. This feature maps detected seizure events onto a 3D brain model, offering clinicians a more spatially accurate representation of potential seizure zones. The ability to visualise these detections in a 3D context is expected to improve clinical decision-making. To validate the effectiveness of this VR-based EEG platform, a series of user evaluations was conducted. Participants, including both clinicians with EEG knowledge and those without prior experience, interacted with the system and provided feedback on its usability and functionality. The results were overwhelmingly positive, with the platform achieving a high System Usability Scale (SUS) score of 83, indicating strong user satisfaction. Participants also completed the NASA Task Load Index (NASA-TLX), with an average score of 36.65, reflecting a low perceived cognitive workload during the interaction. This suggests that the platform not only offers a rich and engaging user experience but also minimises the mental effort required to interpret complex EEG data. In addition to its intuitive user interface, the platform was rigorously tested across a range of hardware configurations, from high-end VR systems to more affordable, lowerend devices. It performed consistently well, demonstrating its potential for widespread adoption in diverse clinical environments, from cutting-edge research facilities to more resource-constrained healthcare settings.Item Assessing the role of biostimulant AdvanceFG+ in regulating Arabidopsis thaliana growth and flowering development(University College Cork, 2024) Tolak, Sanata; Henriques, Rossana; Wingler, Astrid; CosmocelPlant growth responses depend on photoperiod (hours of light), water availability, and nutrient levels. These signals are then integrated by various signaling pathways, one of the most relevant of which is the Target of Rapamycin (TOR). Therefore, specific biostimulants may affect plant growth and development by modulating these signaling pathways. To address this hypothesis, we characterized specific growth responses (e.g. rosette area, fresh and dry weight, yield) and developmental transformations (e.g. flowering time) of Arabidopsis thaliana plants grown in soil, maintained under control conditions or treated with biostimulants. In parallel, we investigated the accumulation of different components of the TOR pathway, such as its direct target, ribosomal protein kinase 6 40S (S6K). Our findings show that biostimulant treatments promote early flowering without detrimental effects on the overall rosette area, which is also comparable to the accumulation of total S6K protein. However, the treated plants also increased total biomass and yield. These findings suggest that the use of biostimulants can promote specific developmental changes such as flowering time, a critical agricultural trait with additional economic value.Item Impact of fungal fermentation on plant-based protein ingredients and pasta production waste(University College Cork, 2024) Gautheron, Ophélie; Sahin, Aylin; Arendt, Elke K.; Horizon 2020Plant proteins are an expanding market within the food industry, offering a promising solution to meet the nutritional demands of the world’s increasing population while also supporting the shift toward more sustainable food production. However, their protein quality and techno-functional characteristics are generally inferior to that of animal proteins, and the presence of antinutritional factors and off-flavours limits their use in food products. This thesis reveals the great potential of solid-state fermentation (SSF) to modulate composition, techno-functionality, antinutrients, and sensory properties of quinoa and fava bean flour. Moreover, pasta production side-stream from industry was upcycled using liquid-state fermentation (LSF) to transform it into food ingredients. Quinoa flour and fava bean flour were characterised regarding nutritional composition, techno-functional properties, and aromatic profiles, after undergoing SSF with Aspergillus oryzae and Rhizopus oligosporus. As a result, the techno-functional properties were altered, with an overall reduction in protein solubility, and foaming properties, while an increase in water-holding capacity and particle size was observed. The SSF showed an enrichment in protein content, and an improvement in protein quality through an increase in essential amino acids. Additionally, most antinutritional factors were reduced, with a few exceptions. Olfactometry showed changes in the aromatic profile of the fermented ingredients, with predominantly “fruity” characteristics present in the fermented quinoa flour, while fermented fava bean flour contained “savoury” sensory traits. Overall, SSF of quinoa flour showed more significant changes compared to the fava bean flour. Furthermore, LSF was applied to industrial pasta waste using Mycetinis scorodonius. This fungus showed some similar metabolic behaviour to the other genera, but also caused additional changes during fermentation, such as an improvement in oil-holding capacity. In addition to a significant increase in protein, a considerable quantification of dietary fibre was observed. The changes achieved by the fermentation process led to a successful application in meat alternatives. Overall, this study revealed the great potential of fungal fermentation for modulating the undesirable attributes of plant-based ingredients and food production waste, and highlights the importance of selecting the appropriate fungi and substrates according to the food application.