Current Alzheimer research最新文献

Artificial intelligence (AI) refers to a system that can simulate and execute the processes of human thinking and learning, and make informed decisions. Fueled by the development of AI, the quality and effectiveness of medical work have gained momentum. AI technology plays an increasingly important role in healthcare, exhibiting substantial potential in clinical practice and decision-making processes. In Alzheimer's disease (AD), where early diagnosis and treatment remain challenging due to clinical heterogeneity and insidious progression, AI could offer excellent solutions. AI models can integrate multi-modal data to identify pre-symptomatic biomarkers and stratify high-risk cohorts, improving diagnostic accuracy, assisting with personalizing treatment and care. Furthermore, AI can accelerate drug discovery and development through drug-target identification and predictive modeling of compound efficacy. However, data quality, supervision, transparency, privacy, and ethical concerns need to be addressed. By identifying and retrieving studies for the systematic review, this article provides a comprehensive overview of current progress and related AI applications in AD.

Background: Alzheimer's disease is associated with dysfunction of the cholinergic system, making the inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) a promising therapeutic approach.

Objective: This study aimed to evaluate the neuroprotective effects and toxicity of essential oil (EO) and carlina oxide from Carthamus caeruleus in mice, assessing their potential for Alzheimer's disease treatment.

Methods: The chemical composition of the essential oil extracted from the roots of Carthamus caeruleus was analyzed using gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The main component, carlina oxide, was isolated via column chromatography. The inhibitory activities of AChE and BChE were evaluated in vitro for both the essential oil and carlina oxide. Additionally, in vivo, toxicity was assessed in laboratory mice.

Results: Chemical analysis identified carlina oxide (81.6%) as the major constituent, along with minor compounds such as 13-methoxycarlin oxide and hexadecanoic acid. Both the essential oil and its main component, carlina oxide, exhibited significant inhibitory activity against AChE and BChE, enzymes associated with Alzheimer's disease. The essential oil demonstrated promising IC₅₀ values, with stronger anti-BChE activity compared to the reference drug, galantamine. Toxicity tests in mice revealed no adverse effects at lower doses (0.2-0.5 g/kg). However, higher doses (1.0-2.0 g/kg) resulted in mild to significant toxicity, including weight loss and mortality.

Discussion: The essential oil and carlina oxide demonstrated potent BChE inhibition, particularly relevant in advanced Alzheimer's disease. While effective at low doses, signs of toxicity were observed at higher concentrations, highlighting the importance of dose optimization. These findings suggest that C. caeruleus may serve as a natural source of cholinesterase inhibitors, pending further in vivo studies and clinical validation.

Conclusion: Carthamus caeruleus essential oil and carlina oxide show promising inhibitory effects on AChE and BChE, suggesting their potential as neuroprotective agents. However, their toxicity at higher doses highlights the need for cautious use and further investigation.

Aims: This study seeks to examine the relationship between cerebrospinal fluid (CSF) biomarkers (Aβ1-42, Phospho-Tau181p, Total-Tau) and brain volumetric changes measured by Brain Shift Integral (BSI) in Alzheimer's disease (AD) spectrum. We explore the potential of BSI as a complementary, non-invasive tool for early diagnosis and progression monitoring of AD.

Background: AD is a neurodegenerative disorder marked by amyloid plaques and tau tangles, leading to cognitive decline. CSF biomarkers are key indicators of AD pathology, but their integration with imaging metrics like BSI could enhance early diagnosis. BSI quantifies brain volume changes via MRI, offering valuable insights into neurodegeneration across the AD spectrum.

Objectives: The current study explores the use of BSI and CSF biomarkers for the early detection of Alzheimer's disease.

Methods: This study utilized data from the ADNI database, including CSF biomarkers (Aβ1-42, t-tau, ptau181) and BSI measurements from baseline and month 24 visits. Spearman correlations were performed to assess associations between biomarkers and brain volumetric changes. Linear regression models were used to examine the predictive value of biomarkers on BSI, controlling for potential confounders.

Results: A total of 239 participants were included in the study, comprising 94 cognitively normal (CN) individuals, 104 with mild cognitive impairment (MCI), and 41 with AD. Significant negative correlations were observed between Aβ1-42 and both BBSI and VBSI in MCI at baseline (p=0.013) and 24 months (p=0.018), as well as between Aβ1-42 and VBSI in CN at baseline (p=0.039) and 24 months (p=0.033). In MCI, p-tau181 was positively correlated with BBSI (p=0.013) and VBSI (p=0.030) at baseline and with BBSI at 24 months (p=0.013). Linear regression analysis confirmed that Aβ1-42 and p-tau181 significantly predicted BSI measures in MCI (R²=0.141-0.173, p<0.05), while Aβ1-42 was a significant predictor of VBSI in CN (R2=0.156-0.166, p<0.01). No significant associations were found in AD.

Discussion: This study underscores the role of CSF biomarkers-particularly Aβ1-42 and p-tau181-in detecting early brain atrophy across the Alzheimer's disease spectrum, with limited utility in advanced stages. The findings highlight the importance of early intervention and support the integration of CSF biomarkers and BSI as diagnostic tools for monitoring disease progression and staging.

Conclusion: The application of the BSI is pivotal for monitoring brain volume alterations and their association with CSF biomarkers.

Introduction: Much emphasis has been given to the biological activities of Fenugreek against various diseased conditions. This study investigated the effect of fenugreek leaf extract on behavioural and cognitive function of transgenic Drosophila having human Aβ-42 expression in the neurons, herein referred as Alzheimer's disease model flies (AD flies).

Methods: AD flies were exposed to four different doses of fenugreek leaf extract (FE) containing i.e., 0.005, 0.010, 0.015 and 0.02 g/ml for 30 days. Thereafter, behavioural and cognitive assessment was done using climbing ability, activity pattern, aversive phototaxis and odour choice indexes. The life span of different groups of flies was also recorded. The effect of FE on the oxidative stress markers, acetylcholinesterase, monoamine oxidase (MAO) and caspase 3 and 9 activities were determined. The deposition of Aβ-42 aggregates in the brain tissue of the flies was studied by performing immunostaining. Also, the metabolic profile of different groups of flies was studied by performing LC-MS/MS. Compared with control flies, 22 selected metabolites were found to be upregulated and downregulated among transgenic AD flies and FE exposed AD flies compared to control.

Results: The findings of this study showed the neuroprotective role of fenugreek extract, which could be employed for the treatment of Alzheimer's disease. The AD flies exposed to FE showed a dose-dependent postponement in the decline of climbing ability, activity and cognitive impairments. A significant dose dependent increase in the life span was also noticed in the AD flies exposed to FE. A significant reduction in the oxidative stress, acetylcholinesterase, monoamine oxidase, and caspase-3&9 activities was also observed in a dose dependent manner. The results obtained from the immunostaining suggest the reduction in the deposition of Aβ-42 fibril, which was also confirmed by the docking studies showed the energetically favoured interaction useful for inhibiting the acetylcholinesterase and Aβ-42 aggregates.

Discussion: This study demonstrates the neurological potency of fenugreek leaf extract (FE) in a Drosophila model of AD due to its antioxidantive, anti-cholinesterase, and neuroprotective properties. Using a combination of behavioral, biochemical, histological, and metabolomic approaches, we evaluated the therapeutic potential of FE in mitigating AD-like symptoms in transgenic flies expressing Aβ-42.

Conclusion: Fenugreek leaf extract may serve as a potential natural remedy for slowing down or alleviating the progression of AD.

Introduction: Alzheimer's disease is a progressive neurodegenerative condition characterized by the gradual deterioration of cognitive functions. Early identification of functional brain changes is crucial for timely diagnosis and effective intervention. This study employs multiplex network analysis to examine alterations in brain connectivity topology associated with Alzheimer's Disease, to identify early biomarkers and uncover potential therapeutic targets.

Methods: This study presents a secondary cross-sectional analysis based on a publicly available EEG dataset comprising spectral coherence measurements from 25 patients with clinically diagnosed Alzheimer's Disease (AD) and 25 age- and gender-matched Healthy Controls (HC). Functional connectivity matrices were generated across seven distinct frequency bands, with each brain region modeled as a network node and inter-regional coherence values represented as weighted edges. These matrices were then used to construct multiplex brain networks, which were rigorously analyzed using graph-theoretical approaches. The analysis encompassed key metrics, including modularity, centrality measures (Betweenness and MultiRank), motif distribution, and network controllability, to characterize and compare the underlying patterns of functional brain organization in AD and healthy aging.

Results: Networks associated with AD exhibited significantly reduced modularity, disrupted centrality patterns, and a higher occurrence of 2 and 3-node motifs, indicating local reorganization of connectivity. Additionally, the spatial distribution of driver nodes was markedly altered in AD. Centrality analyses revealed a pronounced shift in network hubs toward the temporal and insular cortices, suggesting compensatory or pathological reallocation of influence. Controllability assessments demonstrated a lower energy requirement for network control in AD, accompanied by increased inter-layer fragmentation, reflecting compromised integrative function across frequency bands.

Discussion: The findings revealed specific topological alterations, including reduced modularity, altered centrality, and decreased controllability, all of which are closely linked to AD-related network degeneration. By leveraging multi-frequency EEG data, the multiplex approach shows significant clinical potential for monitoring disease progression and supporting personalized treatments, with the ability to detect subtle connectivity disruptions before cognitive symptoms manifest.

Conclusion: Multiplex network analysis reveals distinct and robust alterations in the functional brain architecture of individuals with Alzheimer's Disease. These network-level disruptions offer valuable insights into the pathophysiology of AD and highlight potential avenues for early diagnosis and targeted therapeutic strategies aimed at preserving cognitive function.

Introduction: Dementia is a set of acquired and progressive neuropsychiatric disorders. The most common types of dementia include Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD). Early intravital diagnosis of both types of dementia is difficult. Both molecular and neuroimaging markers are important for the diagnosis of different types of dementia.

Methods: This review employed freely accessible databases, including PubMed, Google Scholar, and ScienceDirect, using keywords such as molecular parameters, neuroimaging factors, dementia, FTD, Alzheimer's disease, and fMRI.

Results: Among the molecular markers of dementia, there are parameters common to its various types and enabling their differentiation. These parameters include both genetic and biochemical factors. Markers include genetic factors that help differentiate AD (APP, PSEN1, PSEN2) from FTD (e.g., TARDBP, FUS, MAPT). Simultaneously, there are important biochemical parameters differentiating AD (amyloid-beta (Aβ), neurofibrillary tangles) from FTD (TDP-43, FUS, and different forms of tau protein aggregates). Currently, there is growing interest in neuroimaging studies in the differential diagnosis of dementia. Positron Emission Tomography (PET) imaging enables the quantification and localization of Aβ deposits in the brain through the selective binding of the Pittsburgh Compound-B (PiB) ligand. This method has become the standard in AD diagnostics. In the context of magnetic resonance imaging studies, it is worth noting the search for structural differences between AD (mainly affecting the temporal lobe, including the hippocampus and entorhinal cortex, and the parietal lobe) and FTD (primarily involving the prefrontal cortex, anterior temporal lobes, and subcortical structures, as well as exhibiting an anteroposterior gradient of atrophy). However, the method of the future appears to be functional Magnetic Resonance Imaging (fMRI), especially since functional changes precede structural changes in the development of dementia.

Discussion: The review encompasses the basic diagnostic criteria for AD and FTD dementia, as well as molecular and neuroimaging parameters important for the intravital diagnosis of these dementias. It seems that the use of fMRI can contribute to both early diagnosis and early introduction of targeted treatment in developing dementia. Although it is not yet widely used clinically, its diagnostic value is increasingly recognized.

Conclusion: The benefits of fMRI studies complementing molecular markers in the diagnosis of dementia were highlighted.

Introduction/objective: Primary progressive aphasia (PPA) is a clinical syndrome characterized by progressive language impairment. Three subtypes have been identified: semantic (svPPA), nonfluent (nfPPA), and logopenic (lvPPA). Although clinical criteria exist to classify these subtypes, the specific ways in which semantic cognition is impaired across these variants have not yet been fully elucidated. This cross-sectional study aimed to analyze the effects of cognitive demand and imaginability on semantic cognition in patients with PPA.

Methods: Fifteen patients with PPA (five per variant) and 20 healthy controls completed a semantic association task comprising 20 items. The task included two levels of cognitive demand (low and high) and two types of concepts (concrete and abstract). Participants selected the word with the strongest semantic link to a probe word, based on synonymy, categorical relations, or shared features. Accuracy and reaction times were recorded and analyzed using nonparametric statistics.

Results: All PPA groups performed significantly worse than controls, showing fewer correct responses and longer reaction times. svPPA patients exhibited the greatest impairment across all conditions. nfPPA patients performed similarly to controls with concrete concepts but showed deficits with abstract words. lvPPA patients experienced greater difficulty under high cognitive demand, particularly with abstract words, indicating impaired semantic control.

Discussion: These findings suggest that svPPA is characterized by global impairment of conceptual knowledge, whereas nfPPA and lvPPA exhibit more selective deficits depending on concept type and cognitive demand.

Conclusion: The research herein highlights the importance of considering cognitive demand and imaginability when assessing semantic cognition in PPA.

Alzheimer's disease (AD) has been ranked as the most common cause of dementia worldwide, which makes it a major cause of public health concern. The development of AD has been linked to a combination of factors, among which lifestyle-related factors can be targeted to minimize the risk of AD. A balanced diet acts as a source of all essential nutrients that can facilitate the functioning of the brain, promote cognitive longevity, safeguard against neurodegeneration, and, accordingly, reduce the risk of AD. Despite the availability of conclusive evidence highlighting the role of nutrition in the prevention of AD, a range of concerns have been identified that limit dietary adherence and public health efforts. This calls for the need to adopt a multipronged approach, including interventions targeting policy-level changes, the education sector, improvement in the food systems, and behavioural modifications to encourage long-term adherence to diets that are healthy for the brain. In conclusion, diet plays a crucial role in Alzheimer's disease, and there arises the need to incorporate food items that are healthy for the brain to maintain cognitive health and reduce the overall risk. The available data suggests that food items rich in antioxidants, omega-3 fatty acids, and B vitamins are associated with a lower risk of developing Alzheimer's disease.

Lecanemab, a therapeutic antibody designed to target amyloid-beta (Aβ) clearance, has recently been approved by the FDA and introduced in multiple countries, representing a significant milestone in advancing Alzheimer's disease (AD) treatment. However, its limited clinical efficacy underscores the need for further investigation of disease pathogenesis. Emerging evidence suggests that glucose and lipid metabolism dysfunction plays a critical role in AD, with metabolic changes emerging as one of the most significantly altered pathways in the early stage of pathology. These findings highlight the therapeutic potential of targeting metabolic regulation as a strategy to address AD.

Alzheimer's disease (AD) and vascular dementia (VD) are the leading causes of dementia, presenting a significant challenge in differential diagnosis. While their clinical presentations can overlap, their underlying pathologies are distinct. AD is characterized by the accumulation of amyloid plaques and neurofibrillary tangles, leading to progressive neurodegeneration. VD, on the other hand, arises from cerebrovascular insults that disrupt blood flow to the brain, causing neuronal injury and cognitive decline. Despite distinct etiologies, AD and VD share common risk factors such as hypertension, diabetes, and hyperlipidemia. Recent research suggests a potential role for oral microbiota in both diseases, warranting further investigation. The diagnostic dilemma lies in the significant overlap of symptoms including memory loss, executive dysfunction, and personality changes. The absence of definitive biomarkers and limitations of current neuroimaging techniques necessitate a multi-modal approach integrating clinical history, cognitive assessment, and neuroimaging findings. Promising avenues for improved diagnosis include the exploration of novel biomarkers like inflammatory markers, MMPs, and circulating microRNAs. Additionally, advanced neuroimaging techniques hold promise in differentiating AD and VD by revealing characteristic cerebrovascular disease patterns and brain atrophy specific to each condition. By elucidating the complexities underlying AD and VD, we can refine diagnostic accuracy and optimize treatment strategies for this ever-growing patient population. Future research efforts should focus on identifying disease-specific biomarkers and developing more effective neuroimaging methods to achieve a definitive diagnosis and guide the development of targeted therapies.