Current Alzheimer research最新文献

Background: It is debatable whether demographic factors alter the relationship between serum lipid traits and cognitive function. Few data have examined the effects of non-traditional lipid metrics on the lipid-cognition relationship. We aim to test the generality of relationships between lipid traits and cognitive function in Chinese adults.

Methods: Data from 5,959 participants were obtained from the China Health and Retirement Longitudinal Study (2011-2020). The cognitive function was assessed via the Mini-Mental State Examination. Effects of traditional lipid metrics (Total Cholesterol, TC, Triglycerides, TG, Low-Density Lipoprotein, LDL, High-Density Lipoprotein, HDL) and non-traditional lipid metrics (TC/HDL, LDL/HDL) were analyzed. We employed mixed-effect models, Group-Based Trajectory Models (GBTM), and logistic regression to examine the associations between baseline serum lipid traits and cognitive function.

Results: As continuous variables, higher TG levels were correlated with higher cognitive scores (P = 0.036), and similar patterns were found in TC/HDL (P < 0.01) and LDL/HDL (P < 0.01). In contrast, higher HDL levels were associated with lower cognitive scores. Similar trends were observed when lipid traits were analyzed as categorical quartiles, and grouped by gender and age. Non-traditional lipid metrics (LDL/HDL, TC/HDL) had higher contributions to the variation of cognitive scores than traditional lipid metrics (TC, TG, LDL, HDL).

Discussion: Results of this study further supported the protective effect of TG and negative effect of HDL in elderly adults, though confounding factors like baseline cognitive heterogeneity warrant future investigation. Notably, non-traditional lipid ratios demonstrated stronger predictive value for cognitive variation than individual lipid metrics.

Conclusion: Our study provided evidence for the generality of a significant association between traditional/ non-traditional lipid metrics and cognitive function in middle-aged and elderly adults. The factors that vary with genders and age groups do not appear to significantly alter the lipid-cognition relationship.

Background: Quinolinic Acid (QA), a neurotoxic metabolite in the kynurenine pathway, contributes to neuronal damage, oxidative stress, and neuroinflammation, playing a key role in Alzheimer's Disease (AD) pathogenesis. This study investigates the neuroprotective potential of polyphenolic compounds, particularly lycopene and a Curcumin-Zinc (Cur-Zn) complex, using in- -silico and in-vitro approaches targeting the kynurenine pathway.

Methodology: This study evaluated the neuroprotective potential of lycopene and Cur-Zn complex using in-silico and in-vitro approaches. Molecular docking was performed to assess their binding affinities with the kynurenine pathway enzymes, and in-vitro neuroprotection assays on N2a cells measured their efficacy against QA-induced oxidative stress.

Results: Docking analysis revealed strong binding affinities of Cur-Zn and lycopene to IDO1 and KMO, with fitness scores of 143.11 and 126.41, respectively, indicating their potential as enzyme- specific inhibitors. Lycopene exhibited the most potent neuroprotective effect (IC50 = 0.63 μM), followed by Cur-Zn (1.59 μM). Both compounds significantly reduced QA-induced ROS levels, as confirmed by DCFDA fluorescence imaging. Additionally, they upregulated KAT and QPRT enzymes, promoting neuroprotective metabolite production.

Discussion: Lycopene and Cur-Zn effectively modulate key kynurenine pathway enzymes while mitigating oxidative stress, supporting their potential as neuroprotective agents. Although bisabolol and bromelain exhibited some efficacy, their effects were comparatively lower.

Conclusion: Lycopene and Cur-Zn are promising candidates for AD therapy, demonstrating not only anti-oxidant activity but also a capacity to minimise the neurotoxic effects of QA, offering a dual mechanism of action. Further, in-vivo studies are needed to validate their therapeutic potential in neurodegenerative diseases.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, significantly impacting the quality of life for affected individuals. This manuscript explores various innovative therapeutic strategies aimed at enhancing drug delivery to the brain, particularly through the use of nanotechnology. This paper discussed the application of Solid Lipid Nanoparticles (SLNs), dendrimers, and Polymeric Nanoparticles (PNPs) in targeting the Central Nervous System (CNS) to improve bioavailability and therapeutic efficacy. The findings indicate that these advanced delivery systems can enhance brain penetration, reduce Amyloid-Beta (Aβ) deposition, and improve cognitive functions in animal models of AD. Furthermore, the review highlights the challenges associated with these technologies, including limited scalability and potential toxicity, while suggesting future directions for research and development in the field of AD treatment.

The manuscript describes how the framework of the integrative hypothesis of Alzheimer's disease (AD) can be deciphered using existing experimental and clinical data. First, the analysis of amyloid biomarkers and stable-isotope label kinetics (SILK) studies indicate a correlation between AD diagnosis and heightened cellular uptake of beta-amyloid. Since beta-amyloid must be taken up by cells to become toxic, its uptake rate correlates with neurodegeneration. Also, aggregation seeds cannot form extracellularly due to low beta-amyloid levels in interstitial fluid but can develop inside lysosomes. Consequently, the density of extracellular aggregates correlates positively with cellular amyloid uptake rate. The model, which ties both beta-amyloid cytotoxicity and aggregation to cellular uptake, accurately predicts AD diagnosis patterns in the population. Second, beta-amyloid enters cells through endocytosis. Endocytosed beta-amyloid induces lysosomal permeabilization that occurs without plasma membrane damage and explains intracellular ion disturbances (including calcium overload) after exposure to extracellular beta-amyloid. The permeabilization is caused by channels formed in lysosomal membranes by some amyloid fragments produced by proteolysis of full-length beta-amyloid. Some membrane channels are large enough to leak cathepsins to the cytoplasm, causing necrosis or apoptosis. Also, local spikes of calcium cytosolic concentration due to calcium leakage from lysosomes can activate calpains, contributing to cell death. In surviving cells, accumulation of damaged lysosomes results in autophagy failure and slow mitochondrial recycling, promoting the production of reactive oxygen species and further cell damage. In this framework, AD's etiology is the membrane channel formation by amyloid fragments produced in lysosomes. The pathogenesis includes lysosomal permeabilization and the appearance of activated proteases in the cytoplasm. The correlation between AD diagnosis and the density of amyloid aggregates occurs because both amyloid cytotoxicity and extracellular aggregate formation stem from cellular amyloid uptake. To reflect key processes, we call this framework the Amyloid Degradation Toxicity Hypothesis of Alzheimer's Disease. It explains various phenomena and paradoxes associated with AD pathobiology across molecular, cellular, and biomarker levels. The hypothesis also highlights the limitations of current AD biomarkers and suggests new diagnostic and prognostic tools based on disease pathogenesis. Additionally, the framework identifies potential pharmacological targets for preventing disease progression.

Introduction: Alzheimer's disease (AD) is the most common form of dementia, and it is important to diagnose the disease at an early stage to help people with the condition and their families. Recently, artificial intelligence, especially deep learning approaches applied to medical imaging, has shown potential in enhancing AD diagnosis. This comprehensive review investigates the current state of the art in multimodal deep learning for the early diagnosis of Alzheimer's disease using image processing.

Methods: The research underpinning this review spanned several months. Numerous deep learning architectures are examined, including CNNs, transfer learning methods, and combined models that use different imaging modalities, such as structural MRI, functional MRI, and amyloid PET. The latest work on explainable AI (XAI) is also reviewed to improve the understandability of the models and identify the particular regions of the brain related to AD pathology.

Results: The results indicate that multimodal approaches generally outperform single-modality methods, and three-dimensional (volumetric) data provides a better form of representation compared to two-dimensional images.

Discussion: Current challenges are also discussed, including insufficient and/or poorly prepared datasets, computational expense, and the lack of integration with clinical practice. The findings highlight the potential of applying deep learning approaches for early AD diagnosis and for directing future research pathways.

Conclusion: The integration of multimodal imaging with deep learning techniques presents an exciting direction for developing improved AD diagnostic tools. However, significant challenges remain in achieving accurate, reliable, and understandable clinical applications.

Aims: This study aims, to trace the history of age-associated dementia from the earliest historical periods to the beginning of the modern age.

Background: Since the medical literature prior to the early 19th century is relatively scarce, the near absence of senile dementia has been hypothesized.

Objective: Verify the prevalence of senile dementia across different historical periods.

Methods: Beyond the medical literature, reviewed papers addressing legal and social aspects were examined to provide a comprehensive overview of the subject.

Results: While the medical literature on the subject is limited, there are a greater abundance of sources discussing social and legislative aspects. The scientific study of dementia had began only in the early 1800s.

Conclusion: In ancient times, dementia was not particularly rare, but it was often overlooked, as it was considered an inevitable consequence of aging.

Introduction: Arsenic, a metalloid, is well associated as a risk factor for the development and progression of neurodegenerative diseases, including Alzheimer's Disease (AD), which is characterized by impairment in cognition. However, specific effects of arsenic on Acetylcholinesterase (AChE) activity and inflammatory markers in different brain regions, as well as its impact on behaviour, are not yet fully understood.

Methods: Arsenic was administered (20 mg/kg by gavage for 4 weeks) to male and female mice, and its effects on behaviour were assessed by using the object recognition memory test and lightdark box test. AChE activity and neuronal Nitric Oxide (nNOS) were assessed by histoenzymology, and immunohistochemistry was employed for assessment of Glial Fibrillary Acidic Protein (GFAP).

Results: Both the behavioural tests showed significant impairment of learning and memory functions and development of psychiatric abnormalities in arsenic-fed mice. The histoenzymology and immunohistochemistry analysis of the cortex and hippocampus region of these arsenic-fed mice revealed the increment of AChE activity and inflammatory markers, viz. GFAP and nNOS.

Discussion: The observed increment in AChE activity in the cortex and hippocampus of arsenic-fed mice may contribute to the impairment of learning and memory functions, as well as to the development of psychiatric abnormalities. Furthermore, the enhancement of inflammatory processes in these brain regions may be either a consequence or a contributing factor to the elevated AChE activity, thus establishing a self-fuelling cycle of neuroinflammation and increased AChE activity.

Conclusion: Given the gender bias in neurodegenerative diseases, our findings indicate that arsenic exposure does not lead to significant differences in neuropathological and neurobehavioural outcomes between male and female mice. Moreover, current outcomes underscore the potential of arsenic to act as a neurotoxic agent in AD development.

Introduction: Alzheimer's disease (AD) is a debilitating neurodegenerative condition marked by progressive cognitive decline and memory impairment, affecting millions worldwide. Despite extensive research, no definitive cure exists, underscoring the need for innovative approaches to drug discovery and development.

Methods: This review focuses on the application of molecular docking techniques in the context of AD drug discovery. The methodology involves the use of computational modeling tools to predict and analyze the interactions between small drug-like molecules and key protein targets implicated in AD pathogenesis, particularly amyloid-beta (Aβ) and tau proteins.

Results: Molecular docking has enabled the virtual screening of large chemical libraries to identify potential inhibitors of Aβ aggregation and tau hyperphosphorylation. Numerous studies have validated docking-predicted interactions with in vitro and in vivo experiments, resulting in the discovery of novel compounds with promising pharmacological profiles. Docking has also aided in the optimization of ligand binding affinity and selectivity toward AD-relevant targets.

Discussion: The integration of molecular docking with experimental techniques enhances the reliability and efficiency of the drug discovery process. Docking allows for the early identification of bioactive molecules, reducing time and cost compared to traditional methods. However, limitations such as rigid receptor assumptions and scoring function inaccuracies require further refinement.

Conclusion: Molecular docking stands out as a powerful computational tool in the quest for effective AD therapies. Simulating protein-ligand interactions accelerates the identification of potential drug candidates and supports the rational design of targeted interventions, paving the way for future clinical applications in combating Alzheimer's disease.

Objectives: Dementia has become a major global cause of death, posing significant health and economic challenges. Alzheimer's disease (AD) is the most common type of dementia. Recent studies have shown that long noncoding RNAs (lncRNAs) play a role in AD development. In this context, the current study conducted a comprehensive meta-analysis of high-throughput Gene Expression Omnibus (GEO) datasets to identify significant lncRNAs that could play a crucial role in the pathogenesis of AD.

Methods: Three microarray expression profiles of human subjects diagnosed with AD and corresponding healthy controls were obtained from the GEO database. Afterward, the expression profiles from the chosen microarray datasets were combined. A network of differentially expressed genes (DEGs) was visualized, identifying key hub genes. Subsequently, the two significant lncRNAs, identified as LINC01003 and CHASERR, were chosen based on the number of interactions between hubs and lncRNAs. Blood samples were collected from AD patients as well as from healthy control individuals. Ultimately, the expression levels of CHASERR and LINC01003 were quantitatively assessed in the blood samples of 50 AD patients and 50 healthy controls using the quantitative Real-Time PCR (q-PCR) technique.

Results: Experimental validation showed that CHASERR was differentially expressed in Alzheimer's disease (AD) patients compared to the control group. In contrast, LINC01003 revealed no significant difference between the AD patients and the control group.

Conclusion: This study thoroughly examined the molecular landscape of AD, identifying key differentially expressed genes and highlighting candidate CHASERR as a potential molecular biomarker for AD patients.