This thesis describes the development of an objective motor score (OMS) of Parkinson's disease that utilizes the Quantitative Motor Assessment Tool (QMAT) developed through efforts by the Intel Corporation and the Kinetics Foundation. Parkinson's disease (PD) is a movement disorder which is a member of a group of neurodegenerative diseases marked by the depletion or impairment of dopamine-producing cells in the brain. Since PD is chronic and degenerative, treatments are intended to either improve the quality of life for sufferers by superficially treating symptoms or slow and ultimately reverse the progression of the disease. No blood test or biomarker exists, so current assessment of the disease relies on a subjective tool called the Unified Parkinson's disease rating scale (UPDRS) which is a coarse scale that requires costly clinical administration and is subject to rater bias. The objective motor score described in this thesis exhibits excellent clinimetric properties, having demonstrated usability, validity, reliability, and responsiveness. It was calibrated to the motor section of the UPDRS, but in addition to high correlation with the motor UPDRS, it demonstrated an excellent ability to track deep brain stimulation treatment levels and to detect improvement in motor function of subjects due to dopaminergic treatment. With an excellent intraclass correlation coefficient, the OMS is a reliable measure and due to the objective nature of the test, it does not suffer from rater bias. Though these results come from the development phase, they suggest that confirmatory studies will firmly establish the excellent properties of the OMS. While further studies are in motion to improve upon the sensitivity of the OMS by exploring metrics of voice recordings and paced tapping tests, the OMS presented here is a complete and usable tool for assessing the severity of PD-related symptoms. In conjunction with the QMAT, it is ready to be used in clinical trials, clinical practice, and even in the homes of patients who suffer from PD. This makes it an invaluable tool that could begin to replace the UPDRS for use in PD research, reducing costs and confounding factors in studies as well as extending their capabilities into the home.

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published_or_final_version / Community Medicine / Master / Master of Public Health

published_or_final_version / Pathology / Master / Master of Medical Sciences

No description available.

No description available.

The purpose of this study was to determine if distinct subgroups of patients with Senile Dementia of the Alzheimer Type (SDAT) could be identified using seven scales of the Cognitive Behavior Rating Scale (CBRS). Ward's method of cluster analysis was used to group 104 patients with a probable diagnosis of SDAT into subtypes.The following three clusters were identified: (a) Moderately Impaired, (b) Severely Impaired, and (c) Emotionally Intact which displayed differences in symptom severity. Clusters could be partially defined by the amount of time they had been diagnosed with the disease. Differences in the cluster's configuration of scores had little/no descriptive utility. Subsequent discrimination analyses indicated that patient demographics were not as useful as the CBRS in classification of patients.This study provided evidence for the CBRS's ability to differentially portray SDAT patients' profiles. Results provide partial support for a stage model of SDAT. Implications of existing subgroups in SDAT are discussed as they pertain to patient management issues. / Department of Counseling Psychology and Guidance Services

Background: There has been a rise in dementia awareness, with policy changes leading to increased diagnosis rates. However, the stigma of dementia is likely to cause challenges in diagnostic communication. This is complicated by the effect of dementia on cognitive functioning. The aims of this study were to (1) identify how diagnoses of dementia are communicated, (2) identify how people with dementia respond to the diagnosis, and (3) explore doctors’ perspectives on dementia diagnosis delivery. Methodology: A systematic literature review was conducted. Twenty doctors from 9 memory clinics across 4 NHS trusts participated. Eighty-one dementia diagnosis feedback meetings were video-recorded. Conversation analysis was used to identify patterns in diagnosis delivery. Four focus groups with the participating doctors were analysed using thematic analysis (inter-rater reliability 0.89). Findings: The literature review highlighted the dilemma of communicating both sensitively and honestly with people with dementia, as well as challenges stemming from cognitive impairment. This was also evident in diagnostic communication. Prior to diagnosis doctors elicited patient orientation to the meeting purpose (“do you know why you’re here?) and perspective into symptoms (“how is your memory?”). The majority of patients displayed some confusion as to the meeting purpose and offered non-medicalised explanations for their symptoms. Doctors attempted to address this through repeated explication of test results and statements of the clinic purpose. Dementia was always explicitly named. Diagnoses were often delivered indirectly (“that is dementia”), a practice to manage patient resistance and negative responses. However, over 40% were delivered directly (“you have dementia”), especially when patients were more cognitively impaired. Doctors pursued non-minimal responses to diagnosis, apparently to obtain perspective before progressing to treatment. However, resistance was not always addressed and prognosis was often avoided. Doctors highlighted pressure to make diagnoses and an aim to emphasise “living well” rather than discussing prognosis. Conclusion: The findings of this study highlighted the delicate balance between minimising likely resistance and distress and maximising understanding in the context of cognitive impairment. Instilling hope is evidently a priority for doctors. The diagnosis meeting is just one part of the journey of the person with dementia, and sufficient pre- and post-diagnosis support is integral.

Alzheimer's disease has become one of the most common diseases jeopardizing the health of the human being. The main pathological feature of AD is the accumulation of Aβ in the brain to form senile plaques. Therefore, it is of great significance to develop new and efficient drugs targeting at amyloid-β for the detection, diagnosis and therapeutics for Alzheimer's disease. Xanthohumol (Xn) naturally presents in hops (Humulus lupulus L). Studies have shown that it has anti-lipoperoxidative, anti-inflammatory, anti-proliferative activities, antiangiogenic and antioxidant effects, which further illustrates its potential therapeutic for AD. However, the bio-incompatibility and blood-brain barrier impermeability of Xanthohumol hindered it in vivo efficacy potential for treating Alzheimer's disease. Thus, we designed and prepared a series of Xanthohumol derivatives, namely, Xn-n, (n = 1-9) and its chalcone derivatives C-n, (n = 1-10) to enhance the desirable physical, biological and pharmacological properties, especially the blood-brain barrier permeability for intervention of AD. As an effective technique for in vivo visualization, Near-infrared fluorescence imaging based on organic small molecule probes has a promising application in the diagnosis of Alzheimer's disease. However, most of the reported imaging probes can only visualize Aβ-plaques but do not have therapeutic potential such as neuroprotection against Aβ induced toxicity. Herein, we designed and synthesized a series of oligomeric Aβ targeted near infrared (NIR) fluorescent probes for the diagnosis and therapeutics of Alzheimer's disease, namely DBAN-SLM, DBAN-SLOH, DBAN-OSLM which showed remarkably effective inhibitory effect on Aβ aggregation, significant neuroprotection effect against the Aβ-induced toxicities, and suppression on Aβ-induced ROS generation. indicating its great promise as a useful theragnostic agent for the early diagnosis and therapy of AD. Dual-modal imaging is an important approach to overcome the limitations of single imaging technology in the diagnosis of AD disease. Therefore, based on the dual-modal, we designed and synthesized the NIR/MR dual-modal detection and theragnostic probes namely Dyad-1, Dyad-2, Dyad-3 and NP@SiO2@F-SLOH. More surprising is that the two NIR/MR dual-modal probes show excellent biological properties, including the ability to inhibit Aβ aggregation to a certain extent, neuroprotective effects on cytotoxicity caused by different forms of Aβ species, blood-brain barrier (BBB) permeability, and high stability. All of these newly designed and synthesized molecules were characterized with 1H NMR, 13C NMR, and HRMS and found to show good agreement with the desired structures. The photophysical properties and biological properties of these novel designed and synthesized fluorescent probe such as UV-vis absorption, fluorescence emission, dissociation constant determined by fluorescence titration, cytotoxicity assay, neuroprotection, and inhibition of Aβ aggregation were investigated

Indiana University-Purdue University Indianapolis (IUPUI) / The ultimate goal of this study is to build a decision support system to aid the pathologists in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) in both adults and children. The disease is caused by accumulation of excess fat in liver cells. It is prevalent in approximately 30% of the general population in United States, Europe and Asian countries. The growing prevalence of the disease is directly related to the obesity epidemic in developed countries. We built computational methods to detect and quantify the histological features of a liver biopsy which aid in staging and phenotyping NAFLD. Image processing and supervised machine learning techniques are predominantly used to develop a robust and reliable system. The contributions of this study include development of a rich web interface for acquiring annotated data from expert pathologists, identifying and quantifying macrosteatosis in rodent liver biopsies as well as lobular inflammation and portal inflammation in human liver biopsies. Our work on detection of macrosteatosis in mouse liver shows 94.2% precision and 95% sensitivity. The model developed for lobular inflammation detection performs with precision and sensitivity of 79.3% and 81.3% respectively. We also present the first study on portal inflammation identification with 82.1% precision and 88.3% sensitivity. The thesis also presents results obtained for correlation between model computed scores for each of these lesions and expert pathologists' grades.