This analysis of secondary data collected from family members of nursing home residents in North Texas (n = 422) used a mixed methods approach to determine if there is a difference in perspectives on quality care among family members of Alzheimer’s/Dementia Special Care Unit (ADSCU) residents compared to those of non-ADSCU residents. Descriptive content analysis was used identify and condense responses to an open-ended question into four meaningful categories of qualities of care. An independent t-test was employed to determine if there was a difference between family members of ADSCU residents and family members of non-ADSCU residents regarding their rating of their loved-ones’ nursing home on the important qualities of care they identified from the open-ended question. Closed-ended questions were organized into indices of these qualities of care, and ordinary least square regression was employed to determine if there were significant differences between perceptions of family members of ADSCU residents and those of non-ADSCU residents regarding care their loved-ones are receiving on these qualities of care, controlling for frequency of visit.

>Magister Scientiae - MSc / Alzheimer’s Disease (AD) is a neurodegenerative disease which results from the irreversible loss of neurons in the brain. The disease is characterized by progressive cognitive impairment with recurrent short-term memory loss. AD is the leading cause of dementia and 4th leading cause of death in the elderly. Success in the treatment of AD has been limited, with drugs only treating it at a symptomatic level due to its pathology being complex and poorly understood. However, it is known that the cholinesterase and MAO-B enzymes play an important role in the disease through their association with production of amyloid plaques and oxidative stress respectively, two mechanisms associated with cell death and the symptoms seen in AD.

Background: Primary progressive aphasia (PPA) is a clinical dementia syndrome characterized by impairments in language. The presence of Alzheimer disease (AD) neuropathology has been observed in approximately 40% of PPA cases. Cross-sectional and longitudinal features of cortical atrophy in PPA are emerging but less is known about the integrity of subcortical structures, particularly the thalamus. As a major relay station in the brain, the thalamus is implicated in language functioning given its reciprocal connections with perisylvian regions in the cortex. High-dimensional brain mapping was used to characterize thalamic morphology in individuals with and without non-semantic PPA. Further, shape differences were compared between PPA participants with suspected AD pathology (PPAAβ +) and those without suspected AD pathology (PPAAβ -) as determined by amyloid PET scans. The relationship between shape and specific language deficits were also investigated. Method: Thalamic integrity was examined in 57 PPA participants relative to cognitively healthy controls (N=44) with similar demographics. MR scans were acquired using high-resolution T1-weighted MPRAGE volumes following the ADNI protocol. Thalamic shape features were estimated using Large Deformation Diffeomorphic Metric Mapping. Thalamic nuclei of interest included mediodorsal, pulvinar, and anterior regions. General linear models compared differences in thalamic shape between groups. Pearson models characterized relationships between thalamic nuclei and language function. Results: After controlling for whole brain volume, thalamic volume did not differ between groups [F(1, 99)=0.80, p=0.80]. However, PPA participants exhibited significant bilateral inward shape deformation in dorsal and ventral regions that extended in an anterior to posterior fashion, and unilateral outward deformation in medial and lateral regions only in the left thalamus relative to controls [F(9, 91)=5.75, p<0.001, Wilk's Λ=0.64]. There were no shape differences between PPAAβ + and PPAAβ – groups. Pearson models revealed significant correlations between confrontation naming and shape deformation in the left pulvinar (r=0.59, p<0.01) and left anterior (r=0.55, p<0.01) thalamic nuclei for the PPAAβ + group only, such that lower language scores reflected greater localized volume loss. Conclusions: In the absence of volumetric differences, shape measures were able to capture unique aspects of localized morphologic differences in PPA that corresponded to worse naming performance only in those with suspected AD pathology. Thalamic changes appear to be a contributing and unrecognized component to the presentation and language characterization of PPA.

Neurodegenerative diseases occur as a result of the breakdown and deterioration of the neurons of the central nervous system (CNS). They are commonly found in elderly people and are a major cause of morbidity and mortality, thereby imposing severe strains on the social welfare systems. Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder. Cholinergic deficit, senile plaque/amyloid-β peptide deposition and oxidative stress have been identified as three main pathogenic pathways which contribute to the progression of AD. The current therapeutic options cause several side-effects and have problems associated with bioavailability. Therefore, the need arises to search for new compounds from natural products with potential to treat AD. Seventeen plants were selected for this study based on their documented ethno-medicinal use in improving memory, to treat insomnia, calm agitated people, and other neurological disorders. The plants were screened for inhibition of acetylcholinesterase (AChE) using the TLC and microtiter plate method. A dose-dependent inhibition of the enzyme was observed and 4.5% of all the plants showed low (<30% inhibition) AChE inhibition. The ethyl acetate extracts of the roots of Crinum bulbispermum, Xysmalobium undulatum, Lannea schweinfurthii, Scadoxus puniceus and bulbs of Boophane disticha had the best AChE inhibition. Although the IC50 of these plant extracts were higher than that of the positive control, galanthamine (0.00053 mg/ml), they showed good AChE inhibitory activity considering they are still mixtures containing various compounds. The antioxidant activity of the plant extracts was determined by their ability to scavenge ABTS (2,2´-azinobis-3-ethylbenzothiazoline-6-sulfonic acid) and DPPH (1,1-diphenyl-2-picryl- hydrazyl) radicals. The dichloromethane/methanol (1:1) extracts of Chamaecrista mimosoides (root), Buddleja salviifolia (whole plant), Schotia brachypetala (root and bark), water extracts of Chamaecrista mimosoides (root), Buddleja salviifolia (whole plant), Schotia brachypetala (root and bark) and methanol extracts of the roots of Crinum bulbispermum, Piper capense, Terminalia sericea, Lannea schweinfurthii and Ziziphus mucronata all showed good antioxidant activity (>50%), in both assays. B. disticha contained very promising AChE inhibition and was subjected to isolation of active compounds using thin layer chromatography, column chromatography and preparative thin layer chromatography. Two compounds, 6-hydroxycrinamine (a crinine-type alkaloid) and cycloeucalenol (a cycloartane triterpene), were isolated for the first time from the bulbs of this plant. 6-Hydroxycrinamine, and two fractions, EAM 17-21 21,22 and EAE 11 (which could not be purified further due to low yield), were found to inhibit AChE with IC50 values of 0.445 ± 0.030 mM, 0.067 ± 0.005 mg/ml and 0.122 ± 0.013 mg/ml, respectively. Cytotoxicity of the isolated compounds and two active fractions was determined on human neuroblastoma (SH-SY5Y) cells using the MTT and neutral red uptake assays. 6- hydroxycrinamine and fraction EAM 17-21 21,22 were found to be toxic with IC50 values of 54.5 μM and 21.5 μg/ml as determined by the MTT assay. The isolated compounds and fractions did not show any protective effect against cell death induced by Aβ25-35 possibly due to the poor antioxidant activity of B. disticha bulbs. Cytotoxicity was also determined for the methanol extracts of the roots of C. bulbispermum, T. sericea, L. schweinfurthii and Z. mucronata, as they contained promising antioxidant activity. C. bulbispermum was the most toxic, reducing cell viability by <40% at the highest concentration tested. Z. mucronata and L. schweinfurthii were the least toxic with IC50 values exceeding 100 μg/ml, the highest concentration tested. Three concentrations of the plant extracts that were not toxic, or presented low toxicity, were selected to evaluate their possible protective effect against cell death induced by Aβ25-35. Pretreatment with Z. mucronata and T. sericea roots showed a dose dependent inhibition of cell death caused by Aβ25-35. Pre-treatment with L. schweinfurthii roots resulted in an optimum dose for inhibition of Aβ25-35 induced cell death at 25 μg/ml, while still maintaining 80% viability. The roots of C. bulbispermum at non-toxic dose still maintained >50% viability. This study confirms the neuroprotective potential of some of the plants which had AChE inhibitory and antioxidant activity. In addition, four of the plants were shown to prevent cell death caused by Aβ25-35. These plants can serve as potential leads in developing drugs relevant to treatment of AD. Furthermore, two new compounds present in the bulbs of B. disticha were identified. Additional investigations need to be carried out by applying QSAR studies to modify the structure of the alkaloid with the aim of reducing its observed toxicity. / Thesis (PhD)--University of Pretoria, 2012. / Pharmacology / unrestricted

Magister Pharmaceuticae - MPharm / Neurodegenerative diseases (ND) are chronic and progressive in nature, and characterized by the gradual loss of neurons in various regions of the central nervous system (CNS). ND include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and cerebral ischemia/reperfusion (CIR). They have various progressive neurodegenerative pathologies that can result in several severe functional impairments for patients, and ultimately lead to serious health-related issues. According to more recent data, AD accounts for the most common cause of dementia and is believed to contribute to approximately 60–70% of cases. AD is thus seen as the most common form of dementia.

Doctor Pharmaceuticae - DPharm / The central nervous system (CNS), consisting of the brain and the spinal cord, is responsible for integrating sensory information and influencing most bodily functions . The CNS is protected from toxic and pathogenic agents in the blood by permeability barrier mechanisms. These barrier mechanisms, specifically the blood brain barrier (BBB) presents a challenge for the discovery of CNS active drugs as it is requirement for these drugs to permeate the BBB to reach their target site in the CNS. The conventional processes of drug design and discovery from natural products are time consuming, tedious, expensive and have a high failure rate. It has been reported from various studies that the use of computational modelling and simulations in drug design and discovery is less costly and less time-consuming with a greater chance of success than the conventional processes. The process of drug discovery and design can, therefore, be easily carried out using proven computer models, software, and web-based tools . / 2023

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20619号 / 医博第4268号 / 新制||医||1023(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 宮本 享, 教授 渡邉 大, 教授 松原 和夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Alzheimer's disease is the most common dementia disease and the main cause of death. The hallmark is neurofibrillary tangles (abnormal aggregates of tau protein) and beta-amyloid (Aβ) neuritic plaques that leads to impaired cognitive function such as memory loss and learning difficulties. Researchers have discovered that intermittent fasting improves these cognitive abilities, even though eating regularly is recommended for good cognition. This systematic review aims to investigate further if intermittent fasting improves cognitive function in Alzheimer’s disease and if levels of Aβ and tau pathology explain these changes in cognitive function. The research question is: does intermittent fasting improve cognitive abilities in Alzheimer’s disease and does the levels of Aβ and tau pathology explain these cognitive changes? A literature search for articles was performed on three electronic databases: Pubmed, Web of Science, and WorldCat which gave n=744 articles. The cognitive tests showed a trend toward improved memory, learning, and exploratory behavior in Alzheimer’s disease from intermittent fasting. However, the effects on the levels of Aβ and tau pathology were inconsistent, which invites the possibility of a more prominent, underlying issue of Alzheimer's disease.

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第24540号 / 人健博第111号 / 新制||人健||8(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 澤本 伸克, 教授 稲富 宏之, 教授 髙橋 良輔 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

Alzheimer’s disease (AD) is among the top 10 causes of global mortality, and dementia imposes a yearly $1 trillion USD economic burden. Of particular importance, women and minoritized groups are disproportionately affected by AD, with females having higher risk of developing AD compared to male cohorts. Differentiating mild cognitive impairment (MCIstable) from early stage Alzheimer’s disease (MCIAD) is vital worldwide. Despite genetic markers, such as apo-lipoprotein-E (APOE), identification of patients before they develop early stages of MCIAD, a critical period for possible pharmaceutical intervention, is not yet possible. Based on review of the literature three key limitations in existing AD-specific prediction models are apparent: 1) models developed by traditional statistics which overlook nonlinear relationships and complex interactions between features, 2) machine learning models are based on difficult to acquire, occasionally invasive, manually selected, and costly data, and 3) machine learning models often lack interpretability. Rapid, accurate, low-cost, easily accessible, non-invasive, interpretable and early clinical evaluation of AD is critical if an intervention is to have any hope at success. To support healthcare decision making and planning, and potentially reduce the burden of AD, this research leverages the Alzheimer’s Disease Neuroimaging Initiative (ADNI1/GO/2/3) database and a mathematical modelling approach based on supervised machine learning to identify 1) predictive markers of AD, and 2) patients at the highest risk of AD. Specifically we implemented a supervised XGBoost classifier with diagnostic (Exp 1) and prognostic (Exp 2) objectives. In experiment 1 (n=441) classification of AD (n=72) was performed in comparison to healthy controls (n= 369), while experiment 2 (n=738) involved classification of MCIstable (n = 444) compared to MCIAD(n = 294). In Experiment 1, machine learning tools identified three features (i.e., Everyday Cognition Questionnaire (Study partner) - Total, Alzheimer’s Disease Assessment Scale (13 items) and Delayed Total Recall) with ROC AUC scores consistently above 97%. Low performance on delayed recall alone appears to distinguish most AD patients. This finding is consistent with the pathophysiology of AD with individuals having problems storing new information into long-term memory. In experiment 2, the algorithm identified the major indicators of MCI-to-AD progression by integrating genetic, cognitive assessment, demographic and brain imaging to achieve ROC AUC scores consistently above 87%. This speaks to the multi-faceted nature of MCI progression and the utility of comprehensive feature selection. These features are important because they are non-invasive and easily collected. As an important focus of this research, the interpretability of the ML models and their predictions were investigated. The interpretable model for both experiments maintained performance with their complex counterparts while improving their interpretability. The interpretable models provide an intuitive explanation of the decision process which are vital steps towards the clinical adoption of machine learning tools for AD evaluation. The models can reliably predict patient diagnosis (Exp 1) and prognosis (Exp 2). In summary, our work extends beyond the identification of high-risk factors for developing AD. We identified accessible clinical features, together with clinically operable decision routes, to reliably and rapidly predict patients at the highest risk of developing Alzheimer’s disease. We addressed the aforementioned limitations by providing an intuitive explanation of the decision process among the high-risk non-invasive and accessible clinical features that lead to the patient’s risk. / Thesis / Master of Science in Biomedical Engineering / Early identification of patients at the highest risk of Alzheimer’s disease (AD) is crucial for possible pharmaceutical intervention. Existing prediction models have limitations, including inaccessible data and lack of interpretability. This research used a machine learning approach to identify patients at the highest risk of Alzheimer’s disease and found that certain clinical features, such as specific executive function- related cognitive testing (i.e., task switching), combined with genetic predisposition, brain imaging, and demographics, were important contributors to AD risk. The models were able to reliably predict patient diagnosis and prognosis and were designed to be low-cost, non-invasive, clinically operable and easily accessible. The interpretable models provided an intuitive explanation of the decision process, making it a valuable tool for healthcare decision-making and planning.