Current Alzheimer research最新文献

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.

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.

A serine/threonine kinase with a wide variety of substrates, Glycogen Synthase Kinase-3 (GSK-3) is widely expressed. GSK-3 is a key player in cell metabolism and signaling, modulating numerous cellular functions and playing significant roles in both healthy and diseased states. The two histopathological features of Alzheimer's disease, the intracellular neurofibrillary tangles composed of hyperphosphorylated tau, and the extracellular senile plaques composed of beta-amyloid, have been linked to GSK-3. It alters multiple tau protein locations found in neurofibrillary tangles. Additionally, GSK-3 can react to this peptide and regulate the production of beta-amyloid. The overexpression of GSK-3 in several transgenic models has been linked to tau hyperphosphorylation, neuronal death, and a reduction in cognitive function. It has been shown that lithium, a medication commonly used to treat affective disorders, inhibits GSK-3 at therapeutically relevant concentrations and stops tau phosphorylation. In this review, we provide an overview of the most recent research on the potential of GSK-3 inhibitors for treating Alzheimer's disease.

Introduction: The complement receptor 1 (CR1) gene is identified as the one closely associated with Alzheimer's disease (AD). However, there has been no exploration of the imaging alterations associated with the CR1 gene in AD patients of the Han population. The purpose of this study is to investigate the association between the rs6656401 mutation and neuroimaging variations in Han AD patients.

Methods: We collected nuclear magnetic resonance images from 101 patients with AD and 98 healthy controls (HC). The subjects in this study, based on the different genotypes of rs6656401, were divided into three groups, with the number of AA, AG, and GG genotypes in the AD group being 1, 17, and 83, and 1, 8, and 89 in the HC group. Data were analyzed using the dominant model. Structural differences in the brain tissue between genotypes at the rs6656401 polymorphic locus were compared using voxel-based morphological analysis, cortical thickness, and graph-theoretic analysis to construct structural networks.

Results: Seven regions (namely, right precuneus, right caudal middle frontal cortical, right rostral middle frontal, right superior frontal, right bankssts, right superior parietal, and right paracentral) were significantly different across CR1 rs6656401 genotypes. The voxel-based morphometry analysis revealed that voxel cluster sizes in the left cerebellum, left superior temporal gyrus, right superior frontal gyrus orbital, right precuneus, and right superior parietal were significantly different in the AA, AG, and GG groups. The degree centrality (Dc) of the left inferior frontal gyrus was significantly greater in the GG group than in the AG group after false discovery rate correction in the structural network analysis.

Discussion: This study demonstrates that the rs6656401 AA genotype primarily induces structural alterations in the frontal, temporal, and parietal lobes of AD patients, with significant changes in the right middle frontal gyrus, precuneus, and superior parietal gyrus, along with Dc index alterations in the left inferior frontal gyrus affecting brain network function. Our findings confirm the association between the rs6656401 polymorphism and AD-related brain structural changes, providing the first evidence of these regional alterations in Han Chinese AD cohorts. Future studies will elucidate the locus's pathological mechanism to inform early diagnosis and targeted therapies.

Conclusion: Our study first indicated that CR1 rs6656401 genotypes significantly influenced the morphological and structural covariate networks in Han AD patients.

Introduction: Alzheimer's disease is expressed as chronic neuroinflammation in the brain, which results in neuronal dysfunction, aberrant protein folding, and declining cognitive abilities. miR-146a-5p is a potent anti-inflammatory agent that can be used to treat several inflammatory diseases, as well as promote wound healing. Our research aimed to utilize network pharmacology to elucidate the therapeutic potential of miR-146a-5p in treating Alzheimer's disease using a biocomputational approach.

Method: Alzheimer's disease genes were extracted from DisGeNET, OMIM, and GeneCards databases. At the same time, miR-146a-5p candidate genes were sourced from four prediction databases: miRDB, miRWalk, miRNet, and TargetScan.

Results: The overlap between miR-146a-5p and Alzheimer's disease genes was established using STRING, with a score greater than 0.9, revealing a total of 157 nodes in the compound-target disease network.

Discussions: Pathway enrichment analysis further revealed key candidate genes associated with Alzheimer's, including those involved in neuronal death, leukocyte migration, and axon development. EGFR, IL6, NFKB1, TLR4, CXCL8, FN1, CXCR4, and BCL2 were pinpointed as the top 8 key candidate genes of miR-146a-5p. Between these key candidate genes, the miR-146a-5p Regulatory Network also demonstrated that miR-146a-5p downregulates EGFR and CXCR4. Furthermore, this research revealed the regulatory network of miR-146a-5p, which modulates the transcriptional activities of IL6, NFKB1, TLR4, CXCL8, FN1, and BCL2.

Conclusion: Therefore, the current network pharmacology study explored the principal mechanism behind the anti-inflammatory effects of miR-146a-5p in treating Alzheimer's disease, and potentially to be applied to other neurodegenerative diseases.

Introduction: This study aimed to explore the value of diffusion tensor imaging (DTI)- based radiomics in the early diagnosis of Alzheimer's disease (AD) and predicting the progression of mild cognitive impairment (MCI) to AD.

Methods: A cohort of 186 patients with MCI was obtained from the publicly accessible Alzheimer's. Disease Neuroimaging Initiative (ADNI) database, and 49 of these individuals developed AD over a 5-year observation period. The subjects were divided into a training set and a test set in a ratio of 7 to 3. Radiomic features were extracted from the corpus callosum within the DTI post-processed images. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm was employed to develop radiomic signatures. The performance of the radiomic signature was assessed using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).

Results: In the training set, 35 patients were converted, and in the test set, 14 patients were converted. Among all the patients, notable differences were observed in age, CDR-SB, ADAS, MMSE, FAQ, and MOCA between the stable group and the transformed group (p < 0.05). In the test set, the AUCs of the radiomics signatures constructed based on fractional anisotropy, axial diffusivity, mean diffusivity, and radial diffusivity were 0.824, 0.852, 0.833, and 0.862, respectively. The AUC of the clinical model was 0.868, and that of the combined model was 0.936. DCA demonstrated that the combined model had the best performance.

Discussion conclusion: The combined radiomics and clinical model, utilizing DTI data, can relatively accurately forecast which patients with MCI are likely to progress to AD. This approach offers potential for early AD prevention in MCI patients.

Circadian rhythms are crucial for essential physiological functions such as metabolism, sleep-wake cycles, hormone balance, and cognitive abilities, which are regulated by the central Suprachiasmatic Nucleus (SCN) and peripheral clocks. Disruptions to circadian rhythms, which may be caused by aging, lifestyle factors, and environmental influences, are linked to metabolic disorders and Neurodegenerative Diseases (NDs). This review examines the reciprocal relationship between circadian control and metabolism, highlighting the molecular processes that maintain circadian rhythms and how these processes change with age. Aging diminishes SCN efficiency and disrupts peripheral clock alignment, leading to impaired physiological functions, increased oxidative stress, and neuroinflammation, all of which contribute to the progression of NDs such as Alzheimer's (AD), Parkinson's disease (PD), Huntington's disease (HD), etc. Emerging therapeutic strategies aim to restore circadian function through interventions, including bright light therapy, melatonin supplementation, and pharmacological agents targeting clock gene regulators and neuropeptides. Furthermore, lifestyle modifications, such as Structured Physical Activity (SPA) and Time-Restricted Feeding (TRF), can enhance circadian health by synchronizing metabolic and hormonal rhythms. Future directions include chrono-pharmacology, gene editing, and Artificial Intelligence (AI)-driven personalized medicine, all of which emphasize the development of tailored circadian therapies. Advancing circadian research holds the potential to facilitate better health outcomes and improve quality of life, while also addressing the growing concerns of the aging population and NDs.

Introduction: Alzheimer's disease (AD) is a leading cause of death, making early detection critical to improve survival rates. Conventional manual techniques struggle with early diagnosis due to the brain's complex structure, necessitating the use of dependable deep learning (DL) methods. This research proposes a novel RESIGN model is a combination of Res-InceptionSeg for detecting AD utilizing MRI images.

Methods: The input MRI images were pre-processed using a Non-Local Means (NLM) filter to reduce noise artifacts. A ResNet-LSTM model was used for feature extraction, targeting White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). The extracted features were concatenated and classified into Normal, MCI, and AD categories using an Inception V3-based classifier. Additionally, SegNet was employed for abnormal brain region segmentation.

Results: The RESIGN model achieved an accuracy of 99.46%, specificity of 98.68%, precision of 95.63%, recall of 97.10%, and an F1 score of 95.42%. It outperformed ResNet, AlexNet, Dense- Net, and LSTM by 7.87%, 5.65%, 3.92%, and 1.53%, respectively, and further improved accuracy by 25.69%, 5.29%, 2.03%, and 1.71% over ResNet18, CLSTM, VGG19, and CNN, respectively.

Discussion: The integration of spatial-temporal feature extraction, hybrid classification, and deep segmentation makes RESIGN highly reliable in detecting AD. A 5-fold cross-validation proved its robustness, and its performance exceeded that of existing models on the ADNI dataset. However, there are potential limitations related to dataset bias and limited generalizability due to uniform imaging conditions.

Conclusion: The proposed RESIGN model demonstrates significant improvement in early AD detection through robust feature extraction and classification by offering a reliable tool for clinical diagnosis.

Alzheimer's disease (AD) is a neurodegenerative disorder associated with age, marked by progressive memory loss linked to the decline of cholinergic neurons, accumulation of amyloid plaques, and the presence of Neurofibrillary Tangles (NFTs). Neuropil threads in the brain contribute to amyloidosis and dementia. Despite extensive research, AD's etiology remains unclear, and currently, no promising therapy exists. This review examines the role of natural, semi-synthetic, and synthetic drugs in AD treatment. Natural drugs demonstrate safety and efficacy with minimal adverse effects, while most agents, whether natural or synthetic, target multiple steps or directly counteract amyloidogenesis, tau protein pathology, oxidative stress, NMDA receptor activity, inflammation, acetylcholine (AChE) function, or α, β, γ secretase activity. In pursuit of improved treatment outcomes, we explore the effectiveness and challenges of various therapeutic interventions. Our hypothesis underscores the importance of an integrated approach combining these drug types for tailored symptom relief, suggesting combined therapies may offer greater therapeutic benefits compared to single-drug approaches. The drugs discussed show potential in regulating AD, thereby presenting viable options for its management. However, to obtain more favorable results, additional studies are needed by combining these drugs.

Objective: At present, there is limited research on the association between dietary intake of anthocyanidins and Alzheimer's disease (AD). More epidemiological studies are needed to better understand this relationship.

Methods: We explored the relationship between dietary Anthocyanidins intake and AD among 3806 American adults in the National Health and Nutrition Examination Survey (NHANES) and the United States Department of Agriculture's Food and Nutrient Database for Dietary Studies (FNDDS) from 2007 to 2010, and 2017 to 2018. We use weighted logistic regression model, restricted cubic spline (RCS) and weighted quantile sum (WQS) regression analysis to analyze the relationship between anthocyanidins monomer and AD.

Results: The weighted logistic regression model showed that the total intake of anthocyanidins was the fourth (OR:0.979; 95% CI: 0.966-0.992) quantile (relative to the lowest quantile) is related to the reduction of AD risk. RCS analysis showed that the total intake of anthocyanidins was negatively linearly correlated with AD (nonlinear P value was 0.002). The WQS regression analysis shows that cyanidin and malvidin are the main contributors to the comprehensive effects of six anthocyanidins.

Conclusion: Our results show that a higher dietary intake of anthocyanidins is associated with a lower risk of AD.