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.

Introduction/objective: Alzheimer's Disease (AD) presents a significant public health challenge in the U.S., with Latina/o/x elders being disproportionately affected. This study examines the key risk factors associated with AD in this population.

Methods: We analyzed data from the National Alzheimer's Coordinating Center (2017), focusing on 9,801 Latina/o/x older adults (32.7% males and 67.3% females). Statistical analyses conducted included Chi-square tests, t-tests, and Classification and Regression Tree (CART) analysis, which was used as the main statistical tool.

Results: The CART model, trained on 70% of the sample and tested on the remaining 30% (N = 9,801), identified seven terminal nodes and selected seven key predictors from 16 candidate variables. The model demonstrated modest discriminative ability (AUC = 0.68 for both training and test sets; misclassification error ≈ 36%). Sensitivity was 75%, while specificity was 55% in the test set. The most important predictors included age, education, smoking history, BMI, hypertension, and use of antidepressant or antipsychotic medications. A critical threshold emerged at < 5.5 years of education, which, in interaction with age and smoking, was associated with notably increased AD risk.

Conclusion: This study emphasizes the crucial role of sociodemographic factors-particularly gender, age, and education-in determining AD risk among Latina/o/x elders. CART analysis identified key thresholds for age and education levels impacting AD risk. The findings suggest the need for targeted interventions and policies, with a focus on education and lifestyle factors, to mitigate AD risk in this vulnerable population.

Introduction/background: Neuropsychiatric symptoms frequently occur in patients with Alzheimer´s disease (AD); apathy, depression, delusions, optical hallucinations, anxiety, and agitation often appear as first symptoms of AD, while auditory hallucinations have never been described as the first symptom. In this case report, we describe a case of a woman who had auditory hallucinations as the first symptom of AD.

Case presentation: An 85-year-old woman was admitted to our hospital suffering from imperative auditory hallucinations without subjective and minimal objective memory complaints. Further diagnostics with an MRI scan, neuropsychological tests, and an analysis of cerebral spinal fluid were accomplished. AD was confirmed during the hospital stay, suggesting auditory hallucinations as the first symptom of AD. She was temporarily treated with risperidone, which improved the hallucinations.

Conclusion: Acoustic hallucinations in older age could be the first symptom of AD and even occur before cognitive decline.

Introduction: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by synaptic dysfunction and the accumulation of amyloid plaques. The molecular mechanisms linking gene dysregulation, pathogenic variants, and protein interaction networks to these core pathologies remain incompletely understood. This study aimed to integrate transcriptomic data with mutation and structural modeling to uncover disease mechanisms and identify therapeutic targets.

Methods: We performed differential gene expression analysis on the GSE138260 microarray dataset using GEO2R to identify DEGs in AD brain tissue. Missense mutations in DEGs were retrieved from the Alzheimer's Disease Variant Portal (ADVP). Protein-protein interaction networks were constructed using the STRING database to identify connections with the amyloid precursor protein (APP). Molecular dynamics simulations were conducted to evaluate the structural consequences of the BDNF V66M mutation.

Results: A total of 1,588 DEGs were identified, including upregulation of immune-related genes and downregulation of neuroplasticity-associated genes (e.g., BDNF, GRIN2B, GRM8). PPI analysis revealed a core network centered on APP, including BDNF as a direct interactor. The V66M variant in BDNF, confirmed to be downregulated in AD brains, showed increased rigidity and localized flexibility in structural models.

Discussion: The integration of transcriptomics and protein modeling revealed a critical link between BDNF dysfunction and APP interaction in AD. The V66M mutation was found to structurally alter BDNF, potentially disrupting its neuroprotective roles. The findings suggested that impaired BDNF signaling, driven by transcriptional repression and structural mutation, contributes to amyloid pathology and synaptic failure.

Conclusion: This multi-omics investigation has identified BDNF as a converging point of gene dysregulation and pathogenic mutation within an APP-centric network. Structural alterations induced by the V66M mutation may exacerbate amyloid accumulation and neuronal dysfunction, supporting therapeutic strategies aimed at enhancing BDNF signaling in AD.

Alzheimer's disease (AD) is a formidable and complex neurodegenerative disorder driven by multifactorial interactions, including amyloid-beta (Aβ) aggregation, neurofibrillary tangles, and neuroinflammation etc. Current therapies mainly consist of cholinesterase inhibitors and NMDA receptor antagonists, which can alleviate symptoms but fail to reverse disease progression. In recent years, emerging approaches such as immunotherapy and gene therapy have shown potential but remain in clinical exploration. Phthalocyanine (Pc) compounds, with their ability to inhibit Aβ fibril formation, favorable biocompatibility, and optical properties, have demonstrated potential in AD diagnosis and treatment. This review discusses the pathogenesis, therapeutic strategies, and research progress of Pc compounds in AD. Furthermore, the elucidation of their mechanisms of action, the optimization of blood-brain barrier penetration, and the promotion of clinical translation are needed to provide new directions for AD therapy.

Introduction: Vascular Cognitive Impairment (VCI) is a common type of dementia that affects the quality of life and lacks effective treatments. The Dengzhan Shengmian capsule (DZSM), a traditional Chinese medicine, is clinically used to alleviate VCI symptoms, but its therapeutic mechanisms are not fully understood. This study aimed to evaluate the neuroprotective effects of DZSM in VCI patients by investigating its impact on cognitive function and brain structure, thereby providing neuroimaging evidence for its clinical application.

Methods: A randomized, double-masked, 6-month trial was conducted with 100 VCI patients, assigned to either the experimental group receiving DZSM (n = 50) or the placebo group (n = 50). The efficacy of DZSM in VCI patients was assessed through cognitive behavioral assessments and neuroimaging data collected at baseline and after 6 months. A comparison was made across groups to determine cognitive and neural changes associated with the intervention.

Results: Participants receiving DZSM exhibited significant improvements across multiple cognitive domains compared to the placebo, including global cognition (MMSE, p = 0.019; ADASCog, p < 0.001), episodic memory (AVLT-N1N5, p < 0.001), visuospatial ability (CDT, p = 0.034), and working memory (DST, p = 0.015). For brain structure, the gray matter volume in the right postcentral and precentral gyrus, bilateral cuneus, left supplementary motor area, superior occipital gyrus, right hippocampus, right thalamus, bilateral lingual gyrus, left precuneus, right inferior frontal gyrus (triangular part), left inferior parietal gyrus, left superior medial frontal gyrus, right superior temporal gyrus, left middle temporal gyrus, and right parahippocampal gyrus increased in the DZSM group (FDR-corrected, p<0.05), with no significant changes in white matter microstructure. Moreover, gray matter volume increases positively correlated with improvements in global cognition and visuospatial function.

Discussion: DZSM capsules significantly improved multiple cognitive domains in VCI patients, particularly memory, visuospatial, and executive functions. The observed increases in gray matter volume suggest that DZSM may exert neuroprotective effects through structural brain remodeling, which is closely associated with cognitive enhancement.

Conclusion: This study identifies brain structural abnormalities in VCI patients that correlate with cognitive deficits. DZSM capsule treatment significantly improved cognitive function. While the underlying mechanisms remain to be fully elucidated, these effects may be related to structural changes in the brain.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by neuroinflammation, tau hyperphosphorylation, Aβ (Amyloid beta) accumulation, and synaptic dysfunction. New research indicates that the gut-brain axis, a network of two-way communication that involves immunological signals, neural pathways, and microbial metabolites, makes dysbiosis of the gut microbiota essential to the pathogenesis of AD. Alterations in the gut microbiota's composition hinder the production of crucial metabolites, such as short-chain fatty acids, trimethylamine- N-oxide, and secondary bile acids, which affect neuroinflammatory cascades, mitochondrial bioenergetics, and synaptic plasticity. Furthermore, Toll-like receptor 4 -4-mediated microglial responses are triggered by Gram-negative bacterial lipopolysaccharides. This cascade promotes oxidative stress, chronic neuroinflammation, and disruption of the (BBB) blood-brain barrier, all of which encourage the accumulation of neurotoxic proteins. Microbiome-modulating therapies, such as probiotics, prebiotics, and synbiotics, have been shown to have neuroprotective properties. They work by restoring microbial diversity, increasing (Short-chain fatty acids) SCFA-mediated anti-inflammatory pathways, and reducing glial activation. In addition to promoting gut microbiota equilibrium, dietary approaches like the Mediterranean and ketogenic diets, which are enhanced with polyphenols and omega-3 fatty acids, also lower systemic inflammation and increase neural resilience. Furthermore, the potential of postbiotics and fecal microbiota transplantation to attenuate AD-related neurodegeneration and restore gut-derived metabolic balance is being investigated. Translating these methods into standardized clinical applications is difficult, though, because individual microbiome composition varies. It will be essential to address these complications through mechanistic research and extensive clinical trials to establish gut microbiota as a promising therapeutic target in AD.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that currently affects over 55 million individuals worldwide. Conventional diagnostic approaches often rely on subjective clinical assessments and isolated biomarkers, limiting their accuracy and early-stage effectiveness. With the rising global burden of AD, there is an urgent need for objective, automated tools that enhance diagnostic precision using neuroimaging data.

Methods: This study proposes a novel diagnostic framework combining a fine-tuned VGG19 deep convolutional neural network with an eXtreme Gradient Boosting (XGBoost) classifier. The model was trained and validated on the OASIS MRI dataset (Dataset 2), which was manually balanced to ensure equitable class representation across the four AD stages. The VGG19 model was pre-trained on ImageNet and fine-tuned by unfreezing its last ten layers. Data augmentation strategies, including random rotation and zoom, were applied to improve generalization. Extracted features were classified using XGBoost, incorporating class weighting, early stopping, and adaptive learning. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC.

Results: The proposed VGG19-XGBoost model achieved a test accuracy of 99.6%, with an average precision of 1.00, a recall of 0.99, and an F1-score of 0.99 on the balanced OASIS dataset. ROC curves indicated high separability across AD stages, confirming strong discriminatory power and robustness in classification.

Discussion: The integration of deep feature extraction with ensemble learning demonstrated substantial improvement over conventional single-model approaches. The hybrid model effectively mitigated issues of class imbalance and overfitting, offering stable performance across all dementia stages. These findings suggest the method's practical viability for clinical decision support in early AD diagnosis.

Conclusion: This study presents a high-performing, automated diagnostic tool for Alzheimer's disease based on neuroimaging. The VGG19-XGBoost hybrid architecture demonstrates exceptional accuracy and robustness, underscoring its potential for real-world applications. Future work will focus on integrating multimodal data and validating the model on larger and more diverse populations to enhance clinical utility and generalizability.