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.

Alzheimers sjukdom är den vanligaste demenssjukdomen och är en av Sveriges största folksjukdomar. Sjukdomen medför kognitiva försämringar som under sjukdomsförloppet kan försämras påtagligt, vilket kan ställa höga krav på anhöriga som vårdar personer med Alzheimer sjukdom. Syfte: Att öka och fördjupa kunskapen om anhörigas upplevelser av att vårda personer med Alzheimers sjukdom i hemmet. Metod: En litteraturstudie där 11 kvalitativa artiklar granskades och analyserades. Resultat: Analysen resulterade i tre huvudkategorier: Anhörigas positiva upplevelser av att vårda, anhörigas negativa upplevelser av att vårda samt behovet av stöd och information. Slutsats: En person med Alzheimers kräver mycket hjälp och tillsyn. Anhöriga upplevde förändrade roller samt svårigheter i omvårdnaden samt behov av stöd. Sjuksköterskor kan ge information, utbildning och stöd för att öka möjligheterna för anhöriga att hantera omvårdnaden samt klara av det dagliga livet bättre. / Alzheimer's disease is the most common form of dementia and is one of Sweden's most common diseases. The disease causes cognitive declines during the disease progression, which can markedly get worse, this puts very high demands on caregivers who’s caring for people with Alzheimer's disease. Aim: To increase and deepen the knowledge of caregivers experiences when caring for people with Alzheimer's disease at home. Method: A literature study where 11 of qualitative articles were reviewed and analyzed. Results: The analysis resulted in three main categories: Caregivers positive experiences of caring, caregivers negative experiences of caring and the need for support and information. Conclusion: A person with Alzheimer's requires a lot of help and supervision. Caregivers experienced changing roles and difficulties in the nursing care and the support needs. Nurses can provide information, training and support to increase the opportunities for families to manage the care and cope with daily life better.

Patients with Alzheimer’s disease (AD) experience sleep disturbances, including disruption in slow-wave sleep (SWS). Slow oscillations (≤1 Hz), a brain rhythm prevalent during SWS, play a role in memory consolidation. Interestingly, patients with AD exhibit slow oscillations of low amplitude, which might contribute to their memory impairments. The mechanisms underlying slow-wave disruptions in AD remain unknown. Slow oscillations originate in the neocortex. Cortical neurons from all layers oscillate between UP and DOWN states during slow oscillations. Astrocytes are known to support neuronal circuit functions, and disruptions in astrocyte activity might contribute to slow-wave aberrations. Here, we investigated astrocytic contributions to slow-wave disruptions in an animal model of beta-amyloidosis (APP mice). First, we monitored astrocytic calcium transients to determine whether astrocytic calcium dynamics were disrupted in APP mice. Fourier transform analysis revealed that the power, but not the frequency of astrocytic calcium transients, was disrupted in young APP mice. This suggested calcium dynamic of astrocytic network was altered and might contribute to the disruption of slow waves in APP mice. Second, we used optogenetics to synchronize cortical astrocyte activity at 0.6 Hz to drive slow oscillations in APP mice. Our results showed that optogenetic activation of ChR2-expressing astrocytes at the endogenous frequency of slow waves restored slow-wave power. Furthermore, chronic optogenetic stimulation of astrocytes at 0.6Hz for 14 or 28 days reduced amyloid plaque deposition, prevented calcium overload in neurites, and improved memory performance in APP mice. These results revealed a malfunction of the astrocytic network driving slow-wave disruptions, and suggested a novel target to restore slow-wave power in APP mice, with translational potential to treat AD.

Neuropsychological testing is one method used in the diagnosis of Alzheimer’s disease and other cognitive disorders. However, the testing process may be affected by subtle external factors which if not controlled for may have the ability to affect the scores obtained. The primary aim of this thesis was to investigate the effects of some of these external factors, namely caffeine, non-oily fish consumption and time of day. A secondary aim was to evaluate the use of a novel virtual assessment as a possible tool for the early detection of AD. Healthy elderly participants over the age of sixty with no existing cognitive impairment or neurological condition were recruited to take part. For each external factor investigated participants were required to undertake a cognitive assessment. The results demonstrated that subtle external factors present during a typical testing session have the ability to significantly affect the scores obtained. Scores on one part of the virtual test correlated with existing tests used for the early detection of cognitive impairment and were significantly lower in participants classified as mildly impaired. With further modification this test has the potential to be used as an early detection tool. The results have implications for the interpretation of neuropsychological test scores which may be considered when classifying participants, determining treatment interventions, selecting participants for research and making a diagnosis. These findings have important considerations for psychological and cognitive research that investigates human brain function.

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer’s disease (AD) is typified by a robust microglial-mediated immune response. Genetic studies have demonstrated that many genes that alter AD risk are involved in the innate immune response and are primarily expressed in microglia. Among these genes is phospholipase C gamma 2 (PLCG2), a critical element for various immune receptors and a key regulatory hub for immune signaling. PLCG2 genetic variants are associated with altered AD risk. The primary objective of this thesis was to determine the role of PLCG2 in AD pathogenesis. We observed significant upregulation of PLCG2 expression in three brain regions of late-onset AD (LOAD) patients and a significant positive correlation of PLCG2 expression with amyloid plaque density. Furthermore, the differential gene expression analysis highlighted inflammatory response-related pathways. These results suggest that PLCG2 plays an important role in AD. We systematically investigated the impact of PLCG2 haploinsufficiency on the microglial response and amyloid pathology in the amyloidogenic 5xFAD mouse model. The results demonstrated that Plcg2 haploinsufficiency altered the phenotype of plaqueassociated microglia, suppressed cytokine levels, increased compact X34-positive plaque deposition, and downregulated the expression of microglial genes associated with immune cell activation and phagocytosis. Our study highlights the role of PLCG2 in immune responses; loss of function of PLCG2 exacerbates the amyloid pathology of AD. Genetic studies demonstrated that the hypermorphic P522R variant is protective and that the loss of function M28L variant confers an elevated risk for AD. Our results demonstrated that PLCG2 variants modulate disease pathologies through specific transcriptional programs. In the presence of amyloid pathology, the M28L risk variant impaired microglial response to plaques, suppressed cytokine release, downregulated disease-associated microglial genes, and increased plaque deposition. However, microglia harboring the P522R variant exhibit a transcriptional response endowing them with a protective immune response signature linked to their association with plaques and Aβ clearance, attenuating disease pathogenesis in an amyloidogenic mouse model of AD. Collectively, our study provides evidence that the M28L variant is associated with accelerated and exacerbated disease-related pathology, and conversely, the P522R variant appeared to attenuate disease severity and progression. / 2024-10-03