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Evaluating the impact of glycemic levels during neonatal encephalopathy on long-term neurodevelopmental outcomes, with a focus on cerebral visual function
(2025-03) Lagacé, Micheline; Tam, Emily WY; Hahn, Cecil D; Medical Science
Background: Dysglycemia during neonatal encephalopathy (NE) is a potential target to improve neurodevelopmental outcomes, including cerebral visual impairment (CVI). Objectives: Assess how neonatal dysglycemia, in the context of NE, affects neurodevelopmental outcomes, focusing on CVI.
Methodology: Neurodevelopmental outcomes of 44 NE survivors, with neonatal dysglycemia in 55%, were assessed at early school age using neuropsychological testing and magnetoencephalography.
Results: Neonatal dysglycemia was associated with intrinsic brain activity, including visual and dorsal attention resting-state networks on magnetoencephalography. The behavioural response to a visuomotor task, but not magnetoencephalography visual evoked responses, showed associations with maximal and labile neonatal glucose. Despite scoring lower than norms for executive function and visual processing, no association with neonatal dysglycemia was demonstrated on neuropsychological tests.
Conclusions: Results support worthwhile concerns for CVI and long-term associations of dysglycemia during NE with the dorsal stream vulnerability model. Discrepancies between assessment tools demonstrate the need for ongoing multimodal investigations.
Fingerpads with Adaptive Surface Properties for Robotic Gripping
(2025-03) Allison, Katherine; Hatton, Benjamin; Kelly, Jonathan; Mechanical and Industrial Engineering
Recent advances in materials science have created surfaces with controllable adhesion, friction, and compliance. These "adaptive surfaces" enable a versatile control paradigm in robotic gripping, where control over contact properties improves performance across widely varying target surfaces. Using actively adaptive grip surfaces, friction can be increased to grip securely and then decreased to gently release or manipulate in-hand. Existing adaptive surface designs show good friction control; however, most require specialized fabrication processes and are impractical for gripping. This thesis presents a practical and inexpensive multi-level adaptive fingerpad design that is easily adapted to existing grippers. Actuating the fingerpad deflects its contact surface in structured active regions, tuning friction by combining microscale effects from microtopography with mesoscale effects from surface morphology changes and macroscale effects from grip articulation. The fingerpad's friction-tuning capabilities are characterized through shear force testing and additional features are demonstrated including macroscale interlocking behaviour and pressure-based object detection.
Modelling and simulation of the retinal dopamine hypothesis of circadian dysfunction in attention-deficit/hyperactivity disorder
(2025-03) LeBlanc Doucet, Tresa; Stinchcombe, Adam R; Mathematics
Experimental evidence suggests that dysfunction of intrinsically photosensitive retinal ganglion cells (ipRGCs) may be the root cause of circadian dysfunction in attention deficit/hyperactivity disorder. We review the literature linking these topics, as well as how they relate to neural noise and the role of retinal dopamine. We propose a hypothesis that reduced dopamine increases neural noise in ipRGC signalling, thus causing circadian dysfunction. We interrogate our hypothesis using a mathematical model of the M1-type ipRGC developed by Stinchcombe et al. We create bifurcation diagrams concerning the model's parameters, and observe simulations of the model with added current noise. We conclude that this hypothesis is consistent with modelling results, and use these results to propose future directions for experimental research to validate our hypothesis.
Human and mouse unipolar brush cells in the developing cerebellum and relevance to Group 4 medulloblastoma
(2025-03) Cheong, Ian; Pai, Shraddha; Medical Biophysics
Medulloblastoma is the most common malignant pediatric brain cancer. The Group 3 and Group 4 molecular subtypes comprise over 60% of all medulloblastoma cases, yet their molecular and regulatory drivers remain poorly understood. Transcriptomically, Group 4 medulloblastoma tumours resemble unipolar brush cells (UBCs) which arise from an evolutionarily recent neural progenitor cell niche in humans. To ascertain if human-enriched neural progenitor states could give rise to Group 4 medulloblastoma, I integrated 336,598 human and mouse single-cell transcriptomes of the developing hindbrain. I identified two human-enriched UBC subpopulations, one of which predominates at 11 post-conception weeks, is driven by SOX4 and SOX11, and is enriched for axonogenesis pathways. Gene regulatory network analysis of medulloblastoma single-cell transcriptomes revealed that Group 4 medulloblastoma tumours are also predicted to be driven by SOX4 and SOX11. This work provides initial evidence of SOX4 and SOX11 as potential regulators of cerebellar development and Group 4 medulloblastoma.
Collagen Morphometry Exploration in Health and Disease
(2025-03) Huang, Xin Sophia; Bozec, Laurent LB; Dentistry
Collagen morphometry refers to the quantitative analyses of collagen fibril morphology to understand its role in health and disease. This thesis explores the critical function of collagen morphometrics in tissue architecture and cancer progression. Initially, we perform quantitative analysis on collagen morphometrics in skin aging to assess whether collagen structure and mechanics variations can serve as biomarkers of biological age. The findings suggest that relative dermal collagen structure and mechanics variations can effectively differentiate skin samples across age groups.To bridge collagen morphological analysis with computational pathology, we develop convolutional neural network (CNN) models to predict the malignant transformation of oral epithelial dysplasia (OED) into oral squamous cell carcinoma. A novel approach integrating histopathological and clinical data was employed for more accurate risk assessment. The CNN model, trained on whole-slide images of OED, effectively distinguished benign dysplastic lesions from those progressing to carcinoma and generated an attention-based heatmap to enhance model interpretability. We further utilized collagen hybridizing peptide (CHP) fluorescence signals to assess correlations between collagen morphometrics and OED progression. Despite achieving good training accuracy, validation results highlighted potential overfitting, emphasizing the need for more extensive, diverse datasets for better generalizability. Nevertheless, the study demonstrates the feasibility of using CNN models to predict malignant transformation in OED by integrating clinical outcomes and morphometrical analyses.
To better interpret the CHP staining result, we investigated the mechanism of CHP interactions with collagen fibrils in vitro. We developed high-density collagen scaffolds that emulate native tissue collagen density and found that CHP accelerates collagen fibril growth and promotes fibril alignment. Additionally, we used CNN models to identify collagen's D-banding patterns, reducing reliance on manual quantification of nanoscale collagen morphometrics.
This thesis provides a multidimensional approach to understanding collagen morphometrics in aging skin, oral cancer progression, and tissue regeneration. The development of CNN models for collagen identification and cancer prediction, coupled with using CHP to promote collagen fibrillogenesis, establishes a foundation for further exploration of collagen as a biomarker and therapeutic target in cancer and other pathologies.