Each year in the United States, approximately 1.35 million people are a ected by mTBI (aka concussion) and subsequent cognitive impairment. Approximately 33% of mTBI cases results in persistent long-term cognitive de cits despite no abnormalities appearing on conventional neuroimaging scans. Therefore, an accurate and reliable imaging method is needed to determine injury location and extent of healing. The goal of this study was to characterize and quantify mTBI through DTI, an advanced MRI technique that encodes voxel-wise tissue water microstructural di usivity as a tensor, as well as QSM, which measures iron deposition within tissues. We hypothesized that personalizing the analysis of DTI and QSM will provide a better understanding of trauma-induced microstructural damage leading to improved diagnosis and prognosis accuracy. Through regression analysis, a preliminary comparison between DTI data to QSM measurements was performed to determine potential correlations between the two MRI techniques. Further, a large database of healthy pediatric brain DTI data was downloaded and each was warped into a standardized brain template to ultimately use for voxel-wise z-score analysis of individual mTBI patients (n=26). This allowed localization and quantitation of abnormal regions on a per-patient basis. Signi cant abnormalities were commonly observed in a number of regions including the longitudinal fasciculus, fronto-occipital fasciculus, and corticospinal tract, while unique abnormalities were localized in a host of other areas (due to the individuality of each childs injury). Further, through group-based Bonferroni corrected T-test analysis, the mTBI group was signi cantly di erent from controls in approximately 65% of regions analyzed. These results show that DTI is sensitive to the detection of microstructural changes caused by mTBI and has potential to be a useful tool for improving mTBI diagnosis accuracy / Thesis / Master of Applied Science (MASc) / Concussions affect over one million people in the United States each year. In a number of cases, these individuals must cope with persistent long-term cognitive impairment resulting from the injury. A current, significant problem is that concussion cannot be reliably diagnosed using conventional CT and MR imaging methods. Therefore, an accurate and reliable imaging method is needed to determine both injury location and severity, as well as to monitor healing. The goal of this study was to quantify concussion through MR imaging techniques known as Di ffusion Tensor Imaging and Quantitative Susceptibility Mapping, which accurately model the brain's mi- crostructure. Analysis utilizing these MRI methods found signifi cant abnormalities in a number of brain regions of concussed subjects relative to healthy individuals. These results suggest that DTI, in particular, is sensitive to microstructural changes caused by concussions and has the potential to be a useful tool for improving diagnosis accuracy.

Actinomyces species play an important role in the pathogenesis of oral diseases and infections. Susceptibility testing is not always routinely performed, and one may oversee a shift in resistance patterns. The aim of the study was to analyze the antimicrobial susceptibility of 100 well-identified clinical oral isolates of Actinomyces spp. against eight selected antimicrobial agents using the agar dilution (AD) and E-Test (ET) methods. We observed no to low resistance against penicillin, ampicillin-sulbactam, meropenem, clindamycin, linezolid and tigecycline (0–2% ET, 0% AD) but high levels of resistance to moxifloxacin (93% ET, 87% AD) and daptomycin (83% ET, 95% AD). The essential agreement of the two methods was very good for benzylpenicillin (EA 95%) and meropenem (EA 92%). The ET method was reliable for correctly categorizing susceptibility, in comparison with the reference method agar dilution, except for daptomycin (categorical agreement 87%). Penicillin is still the first-choice antibiotic for therapy of diseases caused by Actinomyces spp.

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