Search Results (9,387)

Autophagy is a vital cellular pathway in eukaryotic cells, including neurons, where it plays significant roles in neurodevelopment and maintenance. A crucial step in autophagy is the formation of the class III phosphatidylinositol 3-kinase complex 1 (PI3KC3-C1), which is essential for initiating autophagosome biogenesis. Beclin 1 is the key component of PI3KC3-C1, and its interactors have been reported to affect autophagy. The brain-enriched adaptor protein FE65 has been shown to interact with Alzheimer’s disease amyloid precursor protein (APP) to alter the processing of APP. Additionally, FE65 has been implicated in various cellular pathways, including autophagy. We demonstrate here that FE65 positively regulates autophagy. FE65, through its C-terminus, has been shown to interact with Beclin 1. Notably, the overexpression of FE65 enhances Beclin 1-mediated autophagy, whereas this process is attenuated in FE65 knockout cells. Moreover, the stimulatory effect of FE65 on Beclin 1-mediated autophagy is diminished by an FE65 C-terminus deletion mutant that disrupts the FE65–Beclin 1 interaction. Lastly, we have found that the FE65-Beclin 1 interaction modulates the kinase activity of the PI3KC3-C1 complex. Together, we have identified FE65 as a novel Beclin 1 interactor, and this interaction potentiates autophagy. Full article

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and Alzheimer’s disease. Differentiating early MCI (EMCI) from late MCI (LMCI) is crucial for early diagnosis and intervention. This study used free-water diffusion tensor imaging (fw-DTI) to investigate white matter differences and voxel-based correlations with Mini–Mental State Examination (MMSE) scores. Data from the Alzheimer’s Disease Neuroimaging Initiative included 476 healthy controls (CN), 137 EMCI participants, and 62 LMCI participants. Significant MMSE differences were found between the CN and MCI groups, but not between EMCI and LMCI. However, distinct white matter changes were observed: LMCI showed a higher f-index and lower fw-fractional anisotropy (fw-FA) compared to EMCI in several white matter regions. These findings indicate specific white matter tracts involved in MCI progression. Voxel-based correlations between fw-DTI metrics and MMSE scores further supported these results. In conclusion, this study provides crucial insights into white matter changes associated with EMCI and LMCI, offering significant implications for future research and clinical practice. Full article

Thyroid dysfunction is associated with a number of neuropsychiatric manifestations. Cognitive decline is a common feature of hypothyroidism and clinical or subclinical hyperthyroidism. In addition, there is a significant association between thyroid hormone (TH) levels and the degree of cognitive impairment in Parkinson’s disease (PD). The pathophysiology of TH-related neurodegeneration include changes in the blood–brain barrier, increased cellular stress, altered processing of β-amyloid precursor protein and the effect of TH on neuronal cell viability. The neurotoxicity of TH is partially mediated by the thyroid hormone responsive protein (THRP). This protein is 83% homologous to mouse c-Abl-interacting protein-2 (Abi2), a c-Abl-modulating protein with tumor suppressor activity. In cell cultures, increasing THRP expression either with TH treatment or exogenously through transfecting neuronal or PC 12 cells causes cell necrosis. The expression of exogenous THRP in other cells such as the colonic epithelial cell line Caco-2 and the glial cell line U251 has no effect on cell viability. The effect of THRP on cell viability is not modulated by c-Abl tyrosine kinase. The causal relationship between specific biochemical perturbations in cerebral tissue and thyroid dysfunction remains to be elucidated. Full article

Background/Objectives: Dementia is not a single disease but an umbrella term that encompasses a range of symptoms, such as memory loss and cognitive impairments, which are severe enough to disrupt daily life. One of the most common forms of dementia is Alzheimer’s Disease (AD), a complex neurodegenerative condition influenced by both genetic and environmental factors. Recent research has highlighted diet as a potential modifiable risk factor for AD. Decades of research have explored the role of dietary patterns, including the Mediterranean Diet (MD) and its components, in neuroprotection and cognitive health. Systematic review examines studies investigating the impact of the Mediterranean Diet, Mediterranean-like diets, the Nordic Diet (ND), dietary intake patterns, and specific components such as extra virgin olive oil and rapeseed oil on cognitive function, disease onset, and progression in AD and dementia. Methods: A comprehensive search of PubMed, the Directory of Open Access Journals, and the Social Science Research Network was conducted independently by two reviewers using predefined search terms. The search period included studies from 2006 to 2024. Eligible studies meeting the inclusion criteria were systematically reviewed, yielding 88 studies: 85 focused on the MD and its relationship to AD and dementia, while only 3 investigated the ND. Results: The findings suggest that adherence to the Mediterranean and Nordic diets is generally associated with improved cognitive function and delayed cognitive decline and that adherence to both these diets can improve cognitive function. Some studies identified that higher legume consumption decreased dementia incidence, while fruits and vegetables, carbohydrates, and eggs lowered dementia prevalence. Most studies demonstrated that high MD or ND adherence was associated with better cognitive function and a lower risk of poor cognition in comparison to individuals with lower MD or ND adherence. However, some studies reported no significant benefits of the MD on cognitive outcomes, while two studies indicated that higher red meat consumption was linked to better cognitive function. Conclusion: Despite promising trends, the evidence remains varying across studies, underscoring the need for further research to establish definitive associations between diet and cognitive function. These findings highlight the essential role of dietary interventions in the prevention and management of dementia and AD, therefore offering critical insights into the underlying mechanisms by which the diet may impact brain health. Full article

Background/Objectives: This study presents a comparative analysis of the multistage diagnosis of Alzheimer’s disease (AD), including mild cognitive impairment (MCI), utilizing two distinct types of biomarkers: blood gene expression and clinical biomarker samples. Both of these samples, obtained from participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), were independently analyzed utilizing machine learning (ML)-based multiclassifiers. This study applied novel machine learning-based data augmentation techniques to gene expression profile data that are high-dimensional, low-sample-size (HDLSS) and inherently highly imbalanced. The investigation obtained the highest multiclassification performance to date in the multistage diagnosis of Alzheimer’s disease utilizing the blood gene expression profiles of Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants. Based on the performance results obtained, and other factors such as early prediction capabilities, this study compares the efficacies of the two types of biomarkers for multistage diagnosis. This study presents the sole investigation in which multiclassification-based AD stage diagnosis was conducted utilizing blood gene expression data. We obtained the best multiclassification result in both modalities of the ADNI data in terms of F1-score and were able to identify new genetic biomarkers. Methods: The combination of the XGBoost and SFBS (Sequential Floating Backward Selection) methods was used to select the features. We were able to select the 95 most effective gene probe sets out of 49,386. For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages—cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD)/dementia. DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. Because of the high data imbalance in genomic data, borderline oversampling/data augmentation was applied in the model training and original samples for validation. Results: Utilizing clinical data, the highest ROC AUC scores attained were 0.989, 0.927, and 0.907 for the identification of the CN, MCI, and dementia stages, respectively. The highest F1 scores achieved were 0.971, 0.939, and 0.886. Employing gene expression data, we obtained ROC AUC scores of 0.763, 0.761, and 0.706 for the CN, MCI, and dementia stages, respectively, and F1 scores of 0.71, 0.77, and 0.53 for CN, MCI, and dementia, respectively. Conclusions: This represents the best outcome to date for AD stage diagnosis from ADNI blood gene expression profile data utilizing multiclassification techniques. The results indicated that our multiclassification model effectively manages the imbalanced data of a high-dimension, low-sample-size (HDLSS) nature to identify samples of the minority class. MAPK14, PLG, FZD2, FXYD6, and TEP1 are among the novel genes identified as being associated with AD risk. Full article

Alzheimer’s disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management. Full article

Alzheimer’s disease (AD) represents an escalating global health crisis, constituting the leading cause of dementia among the elderly and profoundly impairing their quality of life. Current FDA-approved drugs, such as rivastigmine, donepezil, galantamine, and memantine, offer only modest symptomatic relief and are frequently associated with significant adverse effects. Faced with this challenge and in line with advances in the understanding of the pathophysiology of this neurodegenerative condition, various innovative therapeutic strategies have been explored. Here, we review novel approaches inspired by advanced knowledge of the underlying pathophysiological mechanisms of the disease. Among the therapeutic alternatives, immunotherapy stands out, employing monoclonal antibodies to specifically target and eliminate toxic proteins implicated in AD. Additionally, the use of medicinal plants is examined, as their synergistic effects among components may confer neuroprotective properties. The modulation of the gut microbiota is also addressed as a peripheral strategy that could influence neuroinflammatory and degenerative processes in the brain. Furthermore, the therapeutic potential of emerging approaches, such as the use of microRNAs to regulate key cellular processes and nanotherapy, which enables precise drug delivery to the central nervous system, is analyzed. Despite promising advances in these strategies, the incidence of Alzheimer’s disease continues to rise. Therefore, it is proposed that achieving effective treatment in the future may require the integration of combined approaches, maximizing the synergistic effects of different therapeutic interventions. Full article

Background/Objectives: Mental representation of spatial information relies on egocentric (body-based) and allocentric (environment-based) frames of reference. Research showed that spatial memory deteriorates as Alzheimer’s disease (AD) progresses and that allocentric spatial memory is among the earliest impaired areas. Most studies have been conducted in static situations despite the dynamic nature of real-world spatial processing. Thus, this raises the question: Does temporal order affect spatial memory? The present study, by adopting a dynamic spatial memory task, explored how the temporal order of item presentation influences egocentric and allocentric spatial judgments in individuals with early-stage Alzheimer’s disease (eAD) and healthy elderly individuals (normal controls—NC). Method: Participants were required to memorize dyads of simple 3D geometrical objects presented one at a time on a desk along with a bar. Afterwards, they had to choose what stimulus appeared either closest to them (egocentric judgment) or closest to the bar (allocentric judgment). Results: Results revealed that the temporal order significantly affected spatial judgments in eAD patients but not in NC participants. While eAD patients remain anchored to the item presented first, which is more accurate regardless of the frame used, NC are equally accurate with the item that appears first or second. This is presumably because eAD patients struggle to flexibly shift attention and update spatial representations in dynamic situations, which leads to reliance on initial information and difficulties with information presented later. Conclusions: This highlights the importance of further understanding the cognitive strategies employed by AD patients. Full article

As the leading cause of dementia worldwide, Alzheimer’s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases. Full article

Origanum majorana L., also known as sweet marjoram, is a plant with multiple uses, both in the culinary field and traditional medicine, because of its major antioxidant, anti-inflammatory, antimicrobial, and digestive properties. In this research, we focused on the effects of O. majorana essential oil (OmEO, at concentrations of 25, 150, and 300 μL/L), evaluating chemical structure as well as its impact on cognitive performance and oxidative stress, in both naive zebrafish (Danio rerio), as well as in a scopolamine-induced amnesic model (SCOP, 100 μM). The fish behavior was analyzed in a novel tank-diving test (NTT), a Y-maze test, and a novel object recognition (NOR) test. We also investigated acetylcholinesterase (AChE) activity and the brain’s oxidative stress status. In parallel, we performed in silico predictions (research conducted using computational models) of the pharmacokinetic properties of the main compounds identified in OmEO, using platforms such as SwissADME, pKCSM, ADMETlab 2.0, and ProTox-II. The results revealed that the major compounds were trans-sabinene hydrate (36.11%), terpinen-4-ol (17.97%), linalyl acetate (9.18%), caryophyllene oxide (8.25%), and α-terpineol (6.17%). OmEO can enhance memory through AChE inhibition, reduce SCOP-induced anxiety by increasing the time spent in the top zone in the NTT, and significantly reduce oxidative stress markers. These findings underscore the potential of using O. majorana to improve memory impairment and reduce oxidative stress associated with cognitive disorders, including Alzheimer’s disease (AD). Full article

Arginase (ARG) is a binuclear manganese-containing metalloenzyme that can convert L-arginine to L-ornithine and urea and plays a key role in the urea cycle. It also mediates different cellular functions and processes such as proliferation, senescence, apoptosis, autophagy, and inflammatory responses in various cell types. In mammals, there are two isoenzymes, ARG-1 and ARG-2; they are functionally similar, but their coding genes, tissue distribution, subcellular localization, and molecular regulation are distinct. In recent decades, the abnormal expression of ARG-1 or ARG-2 has been reported to be increasingly linked to a variety of diseases, including cardiovascular disease, inflammatory bowel disease, Alzheimer’s disease, and cancer. Therefore, considering the current relevance of this topic and the need to address the growing demand for new and more potent ARG inhibitors in the context of various diseases, this review was conceived. We will provide an overview of all classes of ARG inhibitors developed so far including compounds of synthetic, natural, and semisynthetic origin. For the first time, the synthesis protocol and optimized reaction conditions of each molecule, including those reported in patent applications, will be described. For each molecule, its inhibitory activity in terms of IC₅₀ towards ARG-1 and ARG-2 will be reported specifying the type of assay conducted. Full article

Background: There is a paucity of evidence on the association between genetic propensity for hippocampal atrophy with cognitive outcomes. Therefore, we examined the relationship of the polygenic risk score for hippocampal atrophy (PRShp) with the incidence of amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) as well as the rates of cognitive decline. Methods: Participants were drawn from the population-based HELIAD cohort. Comprehensive neuropsychological assessments were performed at baseline and at follow-up. PRShp was derived from the summary statistics of a large genome-wide association study for hippocampal volume. Cox proportional hazards models as well as generalized estimating equations (GEEs) were used to evaluate the association of PRShp with the combined incidence of aMCI/AD and cognitive changes over time, respectively. All models were adjusted for age, sex, education, and apolipoprotein E (APOE) genotype. Results: Our analysis included 618 older adults, among whom 73 developed aMCI/AD after an average follow-up of 2.96 ± 0.8 years. Each additional SD of PRShp elevated the relative hazard for incident aMCI/AD by 46%. Participants at the top quartile of PRShp had an almost three times higher risk of converting to aMCI/AD compared to the lowest quartile group. Higher PRShp scores were also linked to steeper global cognitive and memory decline. The impact of PRShp was greater among women and younger adults. Conclusions: Our findings support the association of PRShp with aMCI/AD incidence and with global cognitive and memory decline over time. The PRS association was sex- and age-dependent, suggesting that these factors should be considered in genetic modelling for AD. Full article

Background/Objective: Neurotoxic soluble amyloid-β (Aβ) oligomers are key drivers of Alzheimer’s pathology, with evidence suggesting that early targeting of these soluble forms may slow disease progression. Traditional intravenous (IV) monoclonal antibodies (mAbs) face challenges, including limited brain penetration and risks such as amyloid-related imaging abnormalities (ARIA). This hypothetical study aimed to model amyloid dynamics in early-to-moderate Alzheimer’s disease (AD) and compare the efficacy of IV mAn with intrathecal pseudodelivery, a novel method that confines mAbs in a subcutaneous reservoir for selective amyloid clearance in cerebrospinal fluid (CSF) without systemic exposure. Methods: A mathematical framework was employed to simulate Aβ dynamics in patients with early-to-moderate AD. Two therapeutic approaches were compared: IV mAb and intrathecal pseudodelivery of mAb. The model incorporated amyloid kinetics, mAb affinity, protofibril size, and therapy-induced clearance rates to evaluate the impact of both methods on amyloid reduction, PET negativity timelines, and the risk of ARIA. Results: Intrathecal pseudodelivery significantly accelerated Aβ clearance compared to IV administration, achieving amyloid PET scan negativity by month 132, as opposed to month 150 with IV mAb. This method demonstrated no ARIA risk and reduced amyloid reaccumulation. By targeting soluble Aβ species more effectively, intrathecal pseudodelivery emerged as a safer and more efficient strategy for early AD intervention. Conclusions: Intrathecal pseudodelivery offers a promising alternative to IV mAbs, overcoming challenges associated with blood–brain barrier penetration and systemic side effects. Further research should focus on optimizing this approach and exploring combination therapies to enhance clinical outcomes in AD. Full article

Background: Poor oral health and periodontitis have been epidemiologically linked to cognitive decline and mild cognitive impairment (MCI) in older adults. However, specific metrics directly linking these clinical signs are exceedingly limited. Methods: To address this gap and develop novel tools to help clinicians identify individuals at risk of cognitive decline, we established the PerioMind Colombia Cohort, comprising elderly Colombian subjects who underwent comprehensive neurocognitive and periodontal evaluations. Results: The results revealed that subjects diagnosed with MCI exhibited significantly higher scores in specific periodontal indices, including gingival erythema and pocket depth parameters. The predictive model identified positive associations with MCI, with gingival erythema showing the strongest correlation, followed by the presence of periodontitis and variations in pocket depth measurements. Additionally, lower educational attainment was associated with a higher likelihood of being classified in the periodontitis-MCI group. Conclusions: Here, we show that specific altered periodontal metrics are associated with MCI diagnosis, and the generated results provide defined metric ranges for identifying individuals at risk. Upon validation in larger cohorts, the findings reported here could offer dental practitioners and clinicians innovative tools to identify individuals at risk of MCI and age-related dementias through routine oral health assessments, thereby enabling more accessible and highly sought-after early intervention strategies in both developing and developed countries. Full article