Current Alzheimer research最新文献

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: This study investigated the relationship between Life's Essential 8 (LE8), a recently updated lifestyle-related health factor, and cognition across multiple life stages.

Methods: We enrolled 1098 cognitively normal participants from the Chinese Alzheimer's Biomarker and Lifestyle (CABLE) study. We investigated the interactions between age and LE8 on cognition. Multiple linear regression models were utilized to explore the relationship between the LE8 total scores and its two subscales scores with cognition in the total sample, as well as in the mid-age (≤65 years) and the late-age (>65 years) subgroups. In addition, mediation analyses were performed to explore the biologically plausible pathways between LE8 and cognition.

Results: There was a significant interaction effect between age and LE8 total scores on MOCA score (P = 0.030). The mid-age subgroup showed a positive correlation between LE8 total scores and CM-MMSE (β = 0.110, P = 0.005) and MOCA (β = 0.112, P = 0.005) scores. However, no significant associations were found in the late-age subgroup. In the mid-age subgroup, CSF p-tau partially mediated the relationship between LE8 total scores and its two subscales and cognition, with a mediation proportion ranging from 6% to 12%.

Conclusion: Our findings revealed that the association of the LE8 total scores with MOCA and CM-MMSE scores were significant in mid-age adults rather than late-age adults, indicating that the association might be age-specific and emphasizing the importance of lifestyle interventions in mid-life.

Background: The lack of effective therapy for the treatment of Alzheimer's disease demands both the search for new drugs and the reconsideration of already known substances currently used in other areas of medicine. Drosophila melanogaster offers the potential to model features of Alzheimer's disease, study disease mechanisms, and conduct drug screening.

Objectives: The purpose of this work was to analyze the neuroprotective properties of the drug "carnicetine", which is an acetylated form of the natural low molecular weight compound L-carnitine. The drug is able to cross the blood-brain barrier and is currently used as a means of improving cellular metabolism.

Methods: Using tissue-specific drivers, direct expression of amyloid beta peptide (42 amino acids) was exhibited in certain groups of neurons in the Drosophila melanogaster brain, namely in dopaminergic and cholinergic neurons. The effect of acetyl-L-carnitine (carnicetine) on the death of these neurons and the memory of flies was analyzed.

Results: The expression of amyloid beta peptide in dopaminergic or cholinergic neurons resulted in neurodegeneration of cholinergic neurons in the Drosophila brain and memory impairment. The use of carnicetine added to animal food made it possible to treat these disorders. At the same time, no effect on dopaminergic neurons was noted.

Conclusion: The data obtained confirmed the neuroprotective properties of the drug under study, demonstrating its participation in the restoration of the cholinergic system and the feasibility of using carnicetine for the treatment of Alzheimer's disease.

Alzheimer's disease (AD), characterised by gradual memory loss and neurodegeneration, is an important risk to global health. Despite the recent advances in the field of neuroscience, the complex biological mechanisms underlying the aetiology and pathology of AD have not been elucidated yet. The development of amyloid-beta plaques, hyperphosphorylation of tau protein, oxidative stress, and neuroinflammation have been identified as important components. The genesis of AD has been illuminated by advances in molecular techniques, which have shown the contributions of environmental, genetic, and epigenetic variables. Ongoing research is focused on the potential of bioactive compounds as therapeutic agents. Quercetin, epigallocatechin gallate, huperzine A, ginsenosides, omega-3 fatty acids, vitamin E, zinc, bacosides from brahmi, and withanolide A from ashwagandha are among the compounds that have demonstrated encouraging effects in modifying disease pathways. These bioactive substances demonstrate their potential for symptomatic relief by providing neuroprotective, antioxidant, anti-inflammatory, and cognitive-enhancing properties. The present review presents the recent findings on AD pathogenesis, molecular mechanisms, and the impact of natural compounds, offering a comprehensive perspective on current and emerging strategies for managing this debilitating condition.

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.

Introduction: Alzheimer's disease is a chronic brain disease that includes memory and language disorders. This disease, which is considered the most common cause of dementia worldwide, accounts for 60-80% of all dementia cases. Recent studies suggest that the cerebellum may play a role in cognitive functions as well as motor functions.

Materials and methods: The study was conducted on 40 Alzheimer's patients and 40 healthy individuals. In our study, volumetric evaluation of the cerebellum was performed.

Results: As expected, significant differences were found in cerebellar volume reduction in AD patients compared to healthy controls. Significant volume increase was observed in some regions of the cerebellum in Alzheimer's patients compared to healthy individuals.

Conclusion: The findings supported the role of the cerebellum in cognitive functions. Volume reductions may assist clinicians in making an early diagnosis of AD.

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.

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.