Current Alzheimer research最新文献

The prevalence of Alzheimer's disease (AD) is increasing as the elderly population, which hurts elderly people's cognition and capacity for self-care. The process of mitophagy involves the selective clearance of ageing and impaired mitochondria, which is required to preserve intracellular homeostasis and energy metabolism. Currently, it has been discovered that mitophagy abnormalities are intimately linked to the beginning and progression of AD. This article discusses the mechanism of mitophagy, abnormal mitophagy, and therapeutic effects in AD. The purpose is to offer fresh perspectives on the causes and remedies of AD.

Acetylcholine (ACh) is ubiquitously present in the nervous system and has been involved in the regulation of various brain functions. By modulating synaptic transmission and promoting synaptic plasticity, particularly in the hippocampus and cortex, ACh plays a pivotal role in the regulation of learning and memory. These procognitive actions of ACh are mediated by the neuronal muscarinic and nicotinic cholinergic receptors. The impairment of cholinergic transmission leads to cognitive decline associated with aging and dementia. Therefore, the cholinergic system has been of prime focus when concerned with Alzheimer's disease (AD), the most common cause of dementia. In AD, the extensive destruction of cholinergic neurons occurs by amyloid-β plaques and tau protein-rich neurofibrillary tangles. Amyloid-β also blocks cholinergic receptors and obstructs neuronal signaling. This makes the central cholinergic system an important target for the development of drugs for AD. In fact, centrally acting cholinesterase inhibitors like donepezil and rivastigmine are approved for the treatment of AD, although the outcome is not satisfactory. Therefore, identification of specific subtypes of cholinergic receptors involved in the pathogenesis of AD is essential to develop future drugs. Also, the identification of endogenous rescue mechanisms to the cholinergic system can pave the way for new drug development. In this article, we discussed the neuroanatomy of the central cholinergic system. Further, various subtypes of muscarinic and nicotinic receptors involved in the cognition and pathophysiology of AD are described in detail. The article also reviewed primary neurotransmitters that regulate cognitive processes by modulating basal forebrain cholinergic projection neurons.

Background: Alzheimer's Disease (AD) represents a neurodegenerative disorder characterized by cognitive and behavioral impairments significantly hindering social and occupational functioning. Melatonin, a hormone pivotal in regulating the body's intrinsic circadian rhythm, also acts as a catalyst in the breakdown of beta-amyloid deposits, offering a promising therapeutic approach for AD. The upregulation of Brain and Muscle ARNT-Like 1 (Bmal1) gene expression, stimulated by melatonin, emerges as a potential contributor to AD intervention. Current pharmacological interventions, such as FDA-approved cholinesterase inhibitors and the recently authorized monoclonal antibody, Lecanemab, are utilized in AD management. However, the connection between these medications and Bmal1 remains insufficiently explored.

Objective: This study aims to investigate the molecular effects of FDA-endorsed drugs on the CLOCK: Bmal1 dimer. Furthermore, considering the interactions between melatonin and Bmal1, this research explores the potential synergistic efficacy of combining these pharmaceutical agents with melatonin for AD treatment.

Methods: Using molecular docking and MM/PBSA methodologies, this research determines the binding affinities of drugs within the Bmal1 binding site, constructing interaction profiles.

Results: The findings reveal that, among FDA-approved drugs, galanthamine and donepezil demonstrate notably similar binding energy values to melatonin, interacting within the Bmal1 binding site through analogous amino acid residues and functional groups.

Conclusion: A novel therapeutic approach emerges, suggesting the combination of melatonin with Lecanemab as a monoclonal antibody therapy. Importantly, prior research has not explored the effects of FDA-approved drugs on Bmal1 expression or their potential for synergistic effects.

Background: As the cholinesterase theory is a prominent hypothesis underlying our current understanding of Alzheimer's disease (AD), the goal of this study was to compose functional vegan lunchtime soups with potential health benefits in the prevention of AD (in the context of cholinesterase inhibition).

Materials and methods: The potential of 36 edible plant raw materials in terms of acetyl- and butyrylcholinesterase inhibition was investigated using a 96-well microplate reader. The most promising ingredients were combined to obtain 18 palatable vegetable soup recipes with 6 dominant flavor, appearance, and aroma variants. To shortlist candidates for in-depth analysis and potential consideration in industrial production, our team performed a sensory analysis of the soups.

Results: The white boletus soup exhibited the highest potential for cholinesterase inhibition, further bolstered by the inclusion of other ingredients known for their elevated capacity to inhibit both AChE and BChE. Ingredients such as blackthorn (Prunus spinosa), garlic, and white potato contributed significantly to this inhibitory effect (nearly 100% of AChE inhibition). Notably, intriguing results were also observed for asparagus soup, despite the fact that the inhibitory potential of asparagus itself is negligible compared to other raw materials. The success of the asparagus soup lies in the meticulous selection of various ingredients, each contributing to its overall effectiveness. It was observed that mushroom soups scored the highest in this respect, while the team members' response to nettle soup was the least favorable.

Conclusion: The outcomes of our study should serve as a catalyst for further exploration of this important research domain. Our current research focuses on deeper insights into the potential of comprehensive meal options. Furthermore, the synergy/antagonism/non-interaction between respective soup ingredients as well as elements of individual soups' chemical composition is a very interesting topic currently under our intensive scientific investigation.

Dementia, an international health issue distinguished by the impairment of daily functioning due to cognitive decline, currently affects more than 55 million people worldwide, with the majority residing in low-income and middle-income countries. Globally, dementia entails significant economic burdens in 2019, amounting to a cost of 1.3 trillion US dollars. Informal caregivers devote considerable hours to providing care for those affected. Dementia imposes a greater caregiving and disability-adjusted life-year burden on women. A recent study has established a correlation between prolonged Proton Pump Inhibitor (PPI) usage and dementia, in addition to other neurodegenerative conditions. PPIs are frequently prescribed to treat peptic ulcers and GERD (gastroesophageal reflux disease) by decreasing stomach acid secretion. They alleviate acid-related symptoms through the inhibition of acid-secreting H⁺-K⁺ ATPase. In a number of observational studies, cognitive decline and dementia in the elderly have been linked to the use of PPIs. The precise mechanism underlying this relationship is unknown. These drugs might also alter the pH of brain cells, resulting in the accumulation of amyloid-beta (Aβ) peptides and the development of Alzheimer's disease (AD). Despite the compelling evidence supporting the association of PPIs with dementia, the results of studies remain inconsistent. The absence of a correlation between PPI use and cognitive decline in some studies emphasizes the need for additional research. Chronic PPI use can conceal underlying conditions, including cancer, celiac disease, vitamin B12 deficiency, and renal injury, highlighting dementia risk and the need for further investigations on cognitive health.

Background: The pathological manifestations of Alzheimer's disease (AD) include not only brain amyloid β protein (Aβ) containing neuritic plaques and hyperphosphorylated tau (p-- tau) containing neurofibrillary tangles but also microgliosis, astrocytosis, and neurodegeneration mediated by metabolic dysregulation and neuroinflammation.

Methods: While antibody-based therapies targeting Aβ have shown clinical promise, effective therapies targeting metabolism, neuroinflammation, and p-tau are still an urgent need. Based on the observation that Ras homolog (Rho)-associated kinases (ROCK) activities are elevated in AD, ROCK inhibitors have been explored as therapies in AD models. This study determines the effects of fasudil, a ROCK inhibitor, on neuroinflammation and metabolic regulation in the P301S tau transgenic mouse line PS19 that models neurodegenerative tauopathy and AD. Using daily intraperitoneal (i.p.) delivery of fasudil in PS19 mice, we observed a significant hippocampal-specific decrease of the levels of phosphorylated tau (pTau Ser202/Thr205), a decrease of GFAP+ cells and glycolytic enzyme Pkm1 in broad regions of the brain, and a decrease in mitochondrial complex IV subunit I in the striatum and thalamic regions.

Results: Although no overt detrimental phenotype was observed, mice dosed with 100 mg/kg/day for 2 weeks exhibited significantly decreased mitochondrial outer membrane and electron transport chain (ETC) protein abundance, as well as ETC activities.

Conclusion: Our results provide insights into dose-dependent neuroinflammatory and metabolic responses to fasudil and support further refinement of ROCK inhibitors for the treatment of AD.

Background: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis.

Methods: In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors.

Results: We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes.

Conclusion: This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English- language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

Discoveries in the field of medical sciences are blooming rapidly at the cost of voluminous efforts. Presently, multidisciplinary research activities have been especially contributing to catering cutting-edge solutions to critical problems in the domain of medical sciences. The modern age computing resources have proved to be a boon in this context. Effortless solutions have become a reality, and thus, the real beneficiary patients are able to enjoy improved lives. One of the most emerging problems in this context is Alzheimer's disease, an incurable neurological disorder. For this, early diagnosis is made possible with benchmark computing tools and schemes. These benchmark schemes are the results of novel research contributions being made intermittently in the timeline. In this review, an attempt is made to explore all such contributions in the past few decades. A systematic review is made by categorizing these contributions into three folds, namely, First, Second, and Third Generations. However, priority is given to the latest ones as a handful of literature reviews are already available for the classical ones. Key contributions are discussed vividly. The objectives set for this review are to bring forth the latest discoveries in computing methodologies, especially those dedicated to the diagnosis of Alzheimer's disease. A detailed timeline of the contributions is also made available. Performance plots for certain key contributions are also presented for better graphical understanding.

Aim: The aim of the study was to investigate the factors that underpin neuropsychiatric symptoms and how they might evolve over time in people with Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) dementia.

Background: Neuropsychiatric symptoms are psychiatric and behavioural manifestations that occur in people with AD. These are highly prevalent along the continuum of the disease, including at the stage of MCI, as well as before cognitive decline. Various small- and large-scale projects have investigated the underlying factors that underpin these symptoms; however, the identification of clear clusters is still a matter of debate; furthermore, no study has investigated how the clusters might change across the development of AD pathology by comparing different time points.

Objective: Our objective was to investigate the factors that underpin neuropsychiatric symptoms in Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) and to assess how the loadings might differ based on considerations such as the disease stage of the samples.

Methods: Data was obtained from the Alzheimer's Disease Neuroimaging Initiative database (adni. loni.usc.edu), using scores from the Neuropsychiatric Inventory, followed up yearly from baseline until month 72. Participant groups included those with MCI or AD dementia, or a mixture of both, with all participants presenting with at least one neuropsychiatric symptom. A series of exploratory Principal Component and Factor (Principal Axis) Analyses were performed using Direct Oblimin rotation.

Results: The best-fitting structure was interpreted for each time point. A consistent, unique structure could not be identified, as the factors were unstable over time, both within the MCI and AD groups. However, some symptoms showed a tendency to load on the same factors across most measurements (i.e., agitation with irritability, depression with anxiety, elation with disinhibition, delusions with hallucinations).

Conclusion: Although the analyses revealed some degree of co-occurrence of neuropsychiatric symptoms across time points/samples, there was also considerable variation. In the AD group, more discrete syndromes were evident at the early time points, whereas a more complex picture of co-occurring symptoms, with differences likely reflecting disease staging, was seen at later time points. As a clear and distinctive factor structure was not consistently identified across time points/ samples, this highlights the potential importance of sample selection (e.g., disease stage and/or heterogeneity) when studying, for example, the neurobiological underpinnings of neuropsychiatric symptoms.