Computer-mediated reality technologies have the potential to improve the imageguided surgery (IGS) workflow; specifically, pre-surgical planning, intra-operative guidance, post-surgical assessment, and rehabilitation. Augmented reality (AR), a form of computer-mediated reality, uses an electronic display or projection module to add a hologram in the user's field of view (FOV). For intra-operative guidance, AR could aid in reducing the cognitive overload experienced by clinicians due to integrating multi-modal imaging data from several sources while performing the intervention on the patient. Three AR HMD systems have been developed to explore the capabilities of the Microsoft HoloLens as an AR HMD to be used in developing an AR HMD medical system. The three AR HMD systems required different software and hardware system architectures, however, each of the AR HMD system's software applications has been developed in Unity combined with the Mixed Reality Toolkit (MRTK). Each of the AR HMD systems implemented different registration techniques to localize the virtual object in the real-world coordinate system. The registration techniques were user calibration alignment to identified anatomical landmarks, fiducial marker tracking, and markerless tracking. For user calibration with anatomical landmarks, the MRTK was manipulated to allow alignment of the virtual object. For fiducial registration, the Vuforia Software Development Kit (SDK) was added to assess the alignment and spatial anchoring of the virtual object as specified. Finally, the Leap Motion Controller (LMC) and Leap's Orion SDK was used for exploring markerless tracking. The AR HMD systems developed enabled performance assessments, and alignment errors were identified during trials of the three systems. Most notably the location drift of the 3D virtual object in the spatial space due to the clinician moving around the registered location. This project entailed preliminary development towards the AR HMD medical system to create an in-vivo view of 3D patient-specific bone geometries as a hologram in the clinician's FOV.

The shoulder joint is the most mobile and unstable joint in the human body. This makes it vulnerable to soft tissue pathologies and dislocation. Insight into the kinematics of the joint may enable improved diagnosis and treatment of different shoulder pathologies. Shoulder joint kinematics can be influenced by the articular geometry of the joint. The aim of this project was to develop an analysis framework for shoulder joint kinematics via the use of articulated statistical shape models (ASSMs). Articulated statistical shape models extend conventional statistical shape models by combining the shape variability of anatomical objects collected from different subjects (statistical shape models), with the physical variation of pose between the same objects (articulation). The developed pipeline involved manual annotation of anatomical landmarks selected on 3D surface meshes of scapulae and humeri and establishing dense surface correspondence across these data through a registration process. The registration was performed using a Gaussian process morphable model fitting approach. In order to register two objects separately, while keeping their shape and kinematics relationship intact, one of the objects (scapula) was fixed leaving the other (humerus) to be mobile. All the pairs of registered humeri and scapulae were brought back to their native imaged position using the inverse of the associated registration transformation. The glenohumeral rotational center and local anatomic coordinate system of the humeri and scapulae were determined using the definitions suggested by the International Society of Biomechanics. Three motions (flexion, abduction, and internal rotation) were generated using Euler angle sequences. The ASSM of the model was built using principal component analysis and validated. The validation results show that the model adequately estimated the shape and pose encoded in the training data. Developing ASSM of the shoulder joint helps to define the statistical shape and pose parameters of the gleno humeral articulating surfaces. An ASSM of the shoulder joint has potential applications in the analysis and investigation of population-wide joint posture variation and kinematics. Such analyses may include determining and quantifying abnormal articulation of the joint based on the range of motion; understanding of detailed glenohumeral joint function and internal joint measurement; and diagnosis of shoulder pathologies. Future work will involve developing a protocol for encoding the shoulder ASSM with real, rather than handcrafted, pose variation.

Background: The anterior cruciate ligament (ACL) of the knee is one of the most frequently injured ligaments in the body. 70% of ACL injuries are sustained without any direct contact to the knee, during the early stance phase of a rapid deceleration movement. Females have a significantly greater risk of injury than males participating in the same activities. In the years following injury, ACL deficient individuals are likely to experience lasting joint pain, functional instabilities and the onset of osteoarthritis. The best practice model for management of ACL injuries is a continued emphasis on prevention, which is currently limited by an incomplete understanding of how the injuries occur. Hypothesis: Body biomechanics occurring during the terminal swing phase of a dynamic deceleration movement can predict the resulting weight acceptance phase ACL loading in both ligament bundles. This will further the understanding of the sequence of events that result in non - con tact ACL injuries. Methods: For a preliminary feasibility study, a musculoskeletal model was developed in OpenSim incorporating both anteromedial (AMB) and posterolateral (PLB) bundles of the ACL. Motion capture data of female soccer players (n = 10, mean age = 19.60 ± 1.49 years) performing unanticipated side - step cutting movements were recorded. Instantaneous, three dimensional joint angles and angular velocities at the mid - swing stage of the side - step were selected as the independent variables. The dependent variables were the maximum stance - phase AMB and PLB strains. Multiple pairwise correlation analyses were used to quantify linear relationships between these variables. To evaluate the overall potential to predict ACL strain, a best subsets linear regression model was implemented using only the significantly correlated independent variables. Each ligament bundle was analysed independently. Results: Hip internal rotation at the mid - swing stage explained 79.1% (95% CI: 59.9% - 98.2%) of the variance in maximum stance - phase anteromedial bundle strain (p = 0.0006). Mid - swing knee varus position and knee valgus velocity combined explained 83.3% (95% CI: 69.2% - 97.3%) of the variance in maximum stance - phase posterolateral bundle strain (p = 0.0019). Conclusions: Swing - phase body kinematics during a side - step movement can provide meaningful predictive information as to the future strain in both bundles of the ACL. They are thus useful components in understanding and exploring elements of the inciting e vent, particularly a kinematic "sequence of no return" that directly precedes the injury. The results validate continued research in this area, where the iv relationships identified in this preliminary investigation can guide the development of a priori hypotheses for future studies to be completed at higher levels of evidence. Clinical Relevance: A more comprehensive understanding of the variables that result in non - contact ACL injuries will allow for the design and implementation of more effective preventative measures. For example, knowledge of the "sequence of no return" could be used in sophisticated statistical systems to predict ACL injury events in real - time. This could be used to trigger an active knee brace to apply external support to the knee, pre venting damage to the ligament. The long - term outcome of this project is to move towards reducing the risk and incidence of ACL injuries and the associated negative effects, preserving knee - vitality and ensuring quality of life for athletes and active individuals.

The neuromuscular control comparison between flexion and extension of the upper extremities has been conducted in a number of studies. It has been speculated that differences in the corticospinal pathway between flexion and extension may play a role in the cortical difference detected between flexion and extension, resulting in higher cortical activation for extension. However, it is still unclear as to what roles these pathways play, and to what degree other factors (muscle force activation, sensory feedback, frequency of movement, structural and/or functional differences) might influence the cortical activation in the brain. It has been speculated that the difference in cortical muscular pathways is due to flexion movements being used more often in day to day activities, therefore requiring less cortical activation for that movement. Through the investigation of the cortical differences present during different movement types, a deeper understanding into the differences between flexion and extension may be obtained. No previous study has compared the cortical differences between flexion and extension of the upper extremities during different movement types. In this study, an offline investigation is conducted between wrist flexion and extension, during real, passive and motor imaginary movement with the help of a servo controlled hand device. Simultaneous recording of EEG, EMG and wrist dynamics (velocity, angle, strain) were made on fifteen healthy right handed subjects performing 60 randomized repartitions of right wrist flexion and extension, for kinaesthetic motor imaginary, passively moved, and voluntary real active movements. Real movements were conducted at 10% relative subject maximum voluntary contraction (MVC). A servo controlled hand device was used to regulate dynamic force applied for real movements, and provide motion during passive movements. The use of different movement types with the aid of a servo controlled hand device, may give a deeper understanding into the effects of muscle force activation, rate of movement and corticospinal pathway on flexion and extension. In order to investigate the cortical differences between flexion and extension, subjects perceived difficulty, movement dynamics, movement related cortical potential (MRCP), event related desynchronization and synchronization (ERD/ERS), and phase locking value (PLV) were measured. Each measurement examines a different aspect of the cortical activation present in the brain, during the different movement types. Although relative muscle force activation between wrist real flexion and extension was similar, the motor cortex activation during extension was higher than during flexion, by MRCP and mu-band ERD, with subjects also perceiving real wrist extension to be more difficult to perform. Passive movements found higher motor cortex activation for flexion (MRCP, beta-band ERD), however higher somatosensory cortical activation was present during extension, by mu-band ERS and PLV. Motor imagined wrist flexion showed higher cortical activation during wrist flexion, by MRCP and beta-band ERD. Although numerous variables were tested (each in difference frequency bands), with some being significant and others being non-significant, overall it can be suggested that there was higher cortical activation for extension. The higher cortical activation during wrist extension movements may be due to corticospinal and somatosensory motor control pathways to motor neuron and from sensory neuron pools for extensor/flexor muscle and muscle spindle of the upper extremities. This investigation contributes to the current literature relating to cortical differences between flexion and extension of the upper extremities, by including the real, passive and motor imaginary differences between flexion and extension.

Statistical shape models (SSMs) and statistical appearance models (SAMs) have been applied in medical analysis such as in surgical planning, finite element analysis, model-based segmentation, and in the fields of anthropometry and forensics. Similar applications can make use of SSMs and SAMs of the skull. A combination of the SSM and SAM of the skull can also be used in model-based segmentation. This document presents the development of a SSM and a SAM of the human skull from a South African population, using the Scalismo software package. The SSM development pipeline was composed of three steps: 1) Image data segmentation and processing; 2) Development of a free-form deformation (FFD) model for establishing correspondence across the training dataset; and 3) Development and validation of a SSM from the corresponding dataset. The SSM was validated using the leave one-out cross-validation method. The first eight principal components of the SSM represented 92.13% of the variation in the model. The generality of the model in terms of the Hausdorff distance between a new shape generated by the SSM and instances of the SSM had a steady state value of 1.48mm. The specificity of the model (in terms of Hausdorff distance) had a steady state value of 2.04mm. The SAM development pipeline involved four steps: 1) Volumetric mesh generation of the reference mesh to be used in establishing volumetric correspondence; 2) Sampling of intensity values from original computed tomography (CT) images using the in-correspondence volumetric meshes; and 3) Development of a SAM from the in-correspondence intensity values. A complete validation of the SAM was not possible due to limitations of the Scalismo software. As a result, only the shapes of the incomplete skulls were reconstructed and thereby validated. The amount of missing detail, as represented by absent landmarks, affected the registration results. Complete validation of the SAM is recommended as future work, via the use of a combined shape and intensity model (SSIM).

Includes bibliographical references (leaves 95-101). / An automated microscope is being developed in the MRC/UCT Medical Imaging Research Unit at the University of Cape Town in an effort to ease the workload of laboratory technicians screening sputum smears for tuberculosis (TB), in order to improve screening in countries with a heavy burden of TB. As a step in the development of such a microscope, the project described here was concerned with the extraction and identification of TB bacilli in digital images of sputum smears obtained with a microscope. The investigations were carried out on Ziehl-Neelsen (ZN) stained sputum smears. Different image segmentation methods were compared and object classification was implemented using various two-class classifiers, for images obtained using a microscope with 100x objective lens magnification. The bacillus identification route established for the 100x images, was applied to images obtained using a microscope with 20x objective lens magnification. In addition, one-class classification was applied the 100x images. A combination of pixel classifiers performed best in image segmentation to extract objects of interest. For 100x images, the product of the Bayes’, quadratic and logistic linear classifiers resulted in a percentage of correctly classified bacillus pixels of 89.38%; 39.52% of pixels were incorrectly classified. The segmentation method did not miss any bacillus objects with their length in the focal plane of an image. The biggest source of error for the segmentation method was staining inconsistencies. The pixel segmentation method performed poorly on images with 20x magnification. Geometric change invariant features were extracted to describe segmented objects; Fourier coefficients, moment invariant features and colour features were used. All two-class object classifiers had balanced performance for 100x images, with sensitivity and specificity above 95% for the detection of an individual bacillus after Fisher mapping of the feature set. Object classification on images with 20x magnification performed similarly. One-class object classification using the mixture of Gaussians classifier, without Fisher mapping of features, produced sensitivity and specificity above 90% when applied to 100x images.

Three-dimensional (3D) reconstruction from X-ray images using statistical shape models (SSM) provides a cost-effective way of increasing the diagnostic utility of two-dimensional (2D) X-ray images, especially in low-resource settings. The landmark-constrained model fitting approach is one way to obtain patient-specific models from a statistical model. This approach requires an accurate selection of corresponding features, usually landmarks, from the bi-planar X-ray images. However, X-ray images are 2D representations of 3D anatomy with super-positioned structures, which confounds this approach. The literature shows that detection and use of contours to locate corresponding landmarks within biplanar X-ray images can address this limitation. The aim of this research project was to train and validate a deep learning algorithm for detection the contour of a scapula in synthetic 2D bi-planar Xray images. Synthetic bi-planar X-ray images were obtained from scapula mesh samples with annotated landmarks generated from a validated SSM obtained from the Division of Biomedical Engineering, University of Cape Town. This was followed by the training of two convolutional neural network models as the first objective of the project; the first model was trained to predict the lateral (LAT) scapula image given the anterior-posterior (AP) image. The second model was trained to predict the AP image given the LAT image. The trained models had an average Dice coefficient value of 0.926 and 0.964 for the predicted LAT and AP images, respectively. However, the trained models did not generalise to the segmented real X-ray images of the scapula. The second objective was to perform landmark-constrained model fitting using the corresponding landmarks embedded in the predicted images. To achieve this objective, the 2D landmark locations were transformed into 3D coordinates using the direct linear transformation. The 3D point localization yielded average errors of (0.35, 0.64, 0.72) mm in the X, Y and Z directions, respectively, and a combined coordinate error of 1.16 mm. The reconstructed landmarks were used to reconstruct meshes that had average surface-to-surface distances of 3.22 mm and 1.72 mm for 3 and 6 landmarks, respectively. The third objective was to reconstruct the scapula mesh using matching points on the scapula contour in the bi-planar images. The average surface-to-surface distances of the reconstructed meshes with 8 matching contour points and 6 corresponding landmarks of the same meshes were 1.40 and 1.91 mm, respectively. In summary, the deep learning models were able to learn the mapping between the bi-planar images of the scapula. Increasing the number of corresponding landmarks from the bi-planar images resulted into better 3D reconstructions. However, obtaining these corresponding landmarks was non-trivial, necessitating the use of matching points selected from the scapulae contours. The results from the latter approach signal a need to explore contour matching methods to obtain more corresponding points in order to improve the scapula 3D reconstruction using landmark-constrained model fitting.

An enduring goal of synthetic biology is to engineer cells to perform increasingly sophisticated therapeutic functions. Inspired by natural gene expression programs that elegantly govern diverse cellular behaviors, numerous technological advances have employed synthetic transcriptional programs to coordinate therapeutic cellular activities. These programs are mediated by heterologous or engineered transcription factors that orthogonally regulate the expression of therapeutic agents. However, key features of existing transcriptional components and programs render them unsuitable for therapeutic applications, including high potential for immunogenic or off-target effects and challenging delivery of large genetic payloads. There remains a need for well-designed elements that overcome these fundamental barriers to translation and ultimately enable customizable, tunable, and effective therapeutic responses. Here, we describe an advanced platform for synthetic transcriptional control poised for diverse therapeutic applications. We first engineered a class of programmable transcriptional regulators based upon zinc finger DNA-binding domains, which are highly advantageous due to their compact size and derivation from native mammalian transcriptional systems. We constructed a library of humanized synthetic transcription factors with binding motifs that were unique and putatively orthogonal to the human genome. Our evaluation of cellular transcriptome response when these synthetic components were expressed in cell lines revealed highly specific on-target and low off-target regulation. Furthermore, we developed two highly useful classes of regulatable gene expression programs, by connecting our synthetic transcription factors to domains responsive to favorable exogenous or endogenous signals. We first generated synthetic transcription programs responsive to safe and FDA-approved small molecules, and demonstrated how these programs could tunably and dynamically regulate the expression of therapeutic agents in relevant in vitro and in vivo contexts. Moreover, we constructed synthetic transcription programs responsive to desirable endogenous signals using customizable synthetic Notch receptors. Taken together, we envision that our advanced platform for synthetic transcriptional regulation will facilitate the design of sophisticated therapeutic programs for a wide range of gene and cell therapy applications. / 2022-01-18T00:00:00Z

Light is a ubiquitous form of energy used in many disciplines and industry sectors. Biomedical applications that harness the distinctive properties of light are rapidly growing in many areas, such as medical imaging, radiation therapy, and pathogen identification. This dissertation investigates two unique systems, in which the specific characteristics of light enable the single-molecule detection of fluorescently labeled polypeptides and the 3D printing of hydrogel-based cellular scaffolds and nasopharyngeal swabs. The first half of the dissertation presents a novel solid-state nanopore sensor that employs multicolor fluorescence detection to facilitate the discrimination of two polypeptides at a single-molecule level. Solid-state nanopore devices drilled in locally supported, free-standing TiO2 membranes exhibit exceptionally low photoluminescence in the visible spectral range under simultaneous excitation of multiple lasers. The significant reduction of the optical signal-to-background ratio enables the differentiation of a single fluorophore between two polypeptide populations, thus introducing future possibilities for optical based identification of more complex peptides and proteins in nanopores. The second half of the dissertation focuses on an emerging micro- and nano-fabrication technique based on direct laser writing (DLW) via two-photon polymerization. An innovative two-photon DLW-patterned hydrogel system to modulate cell alignment and adhesion is reported. Variations in the laser writing speed in the fabrication process lead to polymerized structures with distinctive stiff and soft components, without changing the photoresist. On cell-adhesive hydrogels, the width of the alternating stiff and soft patterns dictates the degree of F-actin alignment in hMSCs. The addition of a second hydrogel with cell-repellent properties enables the selective adhesion and alignment of hMSCs on microstructures with both flat and curved features. Lastly, the development of a novel 3D-printed nasopharyngeal test swab during the COVID-19 pandemic is presented. The optimized swab designs demonstrate non-inferior mechanical stability and testing accuracy compared to existing commercial test swabs. In summary, significant progress has been made in both nanopore-based optical sensing and DLW of microscale cellular scaffolds. Future work will enhance existing technologies in the detection of complex peptides and proteins and the fabrication of functional, biocompatible, and dissolvable 3D-printed scaffolds to enable their clinical applications in protein molecular biomarker diagnostics and stem-cell-derived regenerative tissues for the diagnosis and treatment of a wide range of diseases. / 2023-01-18T00:00:00Z

Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) continue to be a significant global burden, disproportionately affecting low- and middle-income countries (LMICs). While much progress has been made in treating these epidemics, this has led to a rise in liver complications, as patients on anti-retroviral therapies (to treat HIV) and anti-TBs (to treat TB) are at an increased risk of drug-induced liver injury (DILI). Therefore, patients on these medicines require consistent screening of liver function. But, due to logistical barriers, gold standard DILI screening fails to be executed at the point-of-care (POC) in LMICs. This thesis aims to fill a current and critical void in diagnosis and management of liver diseases in patients with HIV/AIDS and TB in these settings where conventional diagnostic approaches are prohibitively expensive. To address this gap in technology and patient care, we have developed and optimized a robust, novel assay for on-site POC monitoring of liver health. We take an electrochemical approach to quantify the levels of alanine aminotransferase, a key biomarker of liver function, from whole blood samples. Additionally, we build a patient- and provider-centric platform for detection, aiming to minimize sample preparation steps and simplify the user experience. Furthermore, we use a computational approach to explore the impact of our technology at the POC in LMICs, quantifying both the efficacy and cost-effectiveness. Using this technology, health care providers can assess patient liver health at the POC and make clinical decisions in real time. In the field this technology has the potential to impact HIV and TB patient treatment and improve patient quality of life.