Browsing by Author "Wong, Alexander"

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    Disseminated Exophiala dermatitidis causing septic arthritis and osteomyelitis
    (2018-06-04) Lang, Raynell; Minion, Jessica; Skinner, Stuart; Wong, Alexander
    Abstract Background Exophiala dermatitidis is a melanized fungus isolated from many environmental sources. Infections caused by Exophiala species are typically seen in immunocompromised hosts and manifest most commonly as cutaneous or subcutaneous disease. Systemic infections are exceedingly rare and associated with significant morbidity and mortality Case presentation A 28-year-old female originally from India presented with fevers, chills, weight loss and increasing back pain. She had a recent diffuse maculopapular rash that resulted in skin biopsy and a tentative diagnosis of sarcoidosis, leading to administration of azathioprine and prednisone. An MRI of her spine revealed a large paraspinal abscess requiring surgical intervention and hardware placement. Cultures from the paraspinal abscess grew a colony of dark pigmented mold. Microscopy of the culture revealed a melanized fungus, identified as Exophiala dermatitidis. Voriconazole was initially utilized, but due to relapse of infection involving the right iliac crest and left proximal humerus, she received a prolonged course of amphotericin B and posaconazole in combination and required 7 separate surgical interventions. Prolonged disease stability following discontinuation of therapy was achieved. Conclusions Described is the first identified case of disseminated Exophiala dermatitidis causing osteomyelitis and septic arthritis in a patient on immunosuppressive therapy. A positive outcome was achieved through aggressive surgical intervention and prolonged treatment with broad-spectrum antifungal agents.
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    Hepatitis C virus infection characteristics and treatment outcomes in Canadian immigrants
    (2020-09-03) Cooper, Curtis L; Read, Daniel; Vachon, Marie-Louise; Conway, Brian; Wong, Alexander; Ramji, Alnoor; Borgia, Sergio; Tam, Ed; Barrett, Lisa; Smyth, Dan; Feld, Jordan J; Lee, Sam
    Abstract Background There are multiple obstacles encountered by immigrants attempting to engage hepatitis C virus (HCV) care and treatment. We evaluated the diversity and treatment outcomes of HCV-infected immigrants evaluated for Direct Acting Antiviral (DAA) therapy in Canada. Methods The Canadian Network Undertaking against Hepatitis C (CANUHC) Cohort contains demographic information and DAA treatment information prospectively collected at 10 Canadian sites. Information on country of origin and race are collected. Characteristics and outcomes (sustained virological response; SVR) were compared by immigration status and race. Results Between January 2016 and May 2018, 725 HCV-infected patients assessed for DAA therapy were enrolled in CANUHC (mean age: 52.66 ± 12.68 years); 65.66% male; 82.08% White, 5.28% Indigenous, 4.64% South East Asian, 4.64% East Indian, 3.36% Black). 18.48% were born outside of Canada. Mean age was similar [immigrants: 54.36 ± 13.95 years), Canadian-born: 52.27 ± 12.35 years); (p = 0.085)]. The overall baseline fibrosis score (in kPa measured by transient elastography) was similar among Canadian and foreign-born patients. Fibrosis score was not predicted by race or genotype. The proportion initiating DAA therapy was similar by immigrant status (56.72% vs 49.92%). SVR rates by intent-to-treat analysis were similar (immigrants-89.47%, Canadian-born-92.52%; p = 0.575). Conclusion A diverse immigrant population is engaging care in Canada, initiating HCV antiviral therapy in an equitable fashion and achieving SVR proportions similar to Canada-born patients. Our Canadian experience may be of value in informing HCV elimination efforts in economically developed regions.
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    Object-Subject Relationship Detection using Deep Learning and Decision Support Systems for Safety and Security Applications
    (2022-01) Truong, Thomas; Yanushkevich, Svetlana; Wong, Alexander; Murari, Kartikeya; Nielsen, John; Behjat, Laleh
    This thesis develops the theoretical foundations and practical realization of state-of-the-art computer vision methodologies for safety and security applications. Deep neural networks have shattered standard benchmarks on popular computer vision datasets in recent years, outperforming classical digital image processing methodologies by significant margins. Unfortunately, research on useful applications of these state-of-the-art computer vision models to real-world scenarios related to safety and security are currently lacking. The first portion of this thesis consists of several manuscripts presenting the development of a state-of-the-art deep neural network models which address the object and relationship detection problem. The object and relationship detection problem involves predicting the visual relationship triplet, . The subject and the object detections are localized regions on the image which contain the identified subject and object. The predicate is the description of the relationship between the subject and the object. We demonstrate that convolutional neural network-based approaches to object detection with a soft-attention mechanism for visual relationship detection is effective on several computer vision problems involving subject-object weapon relationships, face mask relationships, and workplace personal protective equipment relationships. The second portion of this thesis contains manuscripts which approach interpretation of deep neural network outputs through meta-analysis of validation results to improve testing results and real-world applicability and trustworthiness. Methodologies utilizing score-level fusion methods and Bayesian networks are demonstrated to improve model performance and interpretability in computer vision problems involving person identification and object detection. The third and final portion of this thesis covers the development of a graphical user interface and deep neural network-based backend model which combines the developments in this thesis into a stand-alone tool for researchers to use. The tool provides a foundational base for future research and development related to visual relationship detection and its applications to safety and security.