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Background: Even in severe states of Alzheimer's disease and related dementias (ADRD), accounts of an unexpected or paradoxical return of awareness and lucidity have been reported in some patients, documented formally, and studied. Objective: A scoping review was undertaken to survey the literature on the topic. Methods: Five databases were searched using pertinent search terms. Results were deduplicated and subsequently screened by title and abstract for relevance. Remaining reports were read and included or excluded using specific inclusion criteria. 30 results consisted of a mix of perspective papers, case reports, qualitative surveys of caregivers, law journal comments, and mechanistic speculation. Results: An equal mix of primary and secondary research was identified. Conclusions: The papers collected in this review provide a valuable methodological outline for researching the topic of lucid episodes in ADRD. The verified legitimacy and simultaneous inexplicability of these events promote philosophical discussion, mechanistic investigations, and sorely needed research in the field of ADRD. [ABSTRACT FROM AUTHOR]

Background: Dementia occurring before age 65 is known as young-onset dementia (YOD), with Alzheimer's disease being the most common type. YOD poses unique challenges for persons and families, impacting their working-age years and family responsibilities. Person-centered interventions and services are essential to improve their quality of life and social engagement. Objective: This study aims to synthesize non-pharmacological interventions for persons with YOD and their families to inform future targeted interventions. Methods: We conducted a systematic literature search across four databases: PubMed, PsycINFO, Scopus, and CINAHL. The included articles were carefully screened, categorized, and synthesized by following Arksey and O'Malley's five stages framework. Results: We included 20 studies reported in 24 papers, with 11 studies (14 papers) on persons with YOD and nine studies (10 papers) on families. Quantitative intervention results vary, but qualitative interviews show positive feedback. Stakeholders provided positive evaluations, stating these interventions provided a sense of normalcy, facilitated communication among families, enhanced the independence of persons with YOD, and improved the families' caregiving self-efficacy, thereby reducing care burden and psychological distress. The heterogeneity among the studies posed integration challenges. Conclusions: Interventions for YOD can improve the quality of life for both persons with YOD and their families. More extensive intervention studies are urgently needed, especially in developing countries, with a focus on family-centered and life course perspectives. In future intervention research design, a more extensive incorporation of stakeholder involvement is essential for successful implementation. Moreover, the integration of new technologies shows promise as a potential avenue for intervention advancement. [ABSTRACT FROM AUTHOR]

Recent research aimed at the discovery, integration, and communication of health outcome measures (or "biomarkers") in Alzheimer's disease has raised challenging questions related to whether, how and when results from these investigations should be disclosed to research participants. Reflecting the apparent heterogeneity of many neurodegenerative diseases, biomarker or other risk factor results are often probabilistic, interactive, multi-modal, and selective. Such characteristics make it very complex to summarize and communicate to clinicians, researchers, and research participants. Whereas the format and content of academic literature is well-managed by the peer-review process, reporting individualized results to participants involves complex, sensitive, and ethical considerations. This paper describes three key factors to consider in decisions about the return of results to research participants: complexity, precision, and responsibility. The paper also presents six practical recommendations for implementing meaningful and ethical communication with research participants. [ABSTRACT FROM AUTHOR]

Background: Currently, no evidence exists on the expression of apoptosis (CASP3), autophagy (BECN1), and mitophagy (BNIP3) genes in the CA3 area after ischemia with long-term survival. Objective: The goal of the paper was to study changes in above genes expression in CA3 area after ischemia in the period of 6–24 months. Methods: In this study, using quantitative RT-PCR, we present the expression of genes associated with neuronal death in a rat ischemic model of Alzheimer's disease. Results: First time, we demonstrated overexpression of the CASP3 gene in CA3 area after ischemia with survival ranging from 0.5 to 2 years. Overexpression of the CASP3 gene was accompanied by a decrease in the activity level of the BECN1 and BNIP3 genes over a period of 0.5 year. Then, during 1-2 years, BNIP3 gene expression increased significantly and coincided with an increase in CASP3 gene expression. However, BECN1 gene expression was variable, increased significantly at 1 and 2 years and was below control values 1.5 years post-ischemia. Conclusions: Our observations suggest that ischemia with long-term survival induces neuronal death in CA3 through activation of caspase 3 in cooperation with the pro-apoptotic gene BNIP3. This study also suggests that the BNIP3 gene regulates caspase-independent pyramidal neuronal death post-ischemia. Thus, caspase-dependent and -independent death of neuronal cells occur post-ischemia in the CA3 area. Our data suggest new role of the BNIP3 gene in the regulation of post-ischemic neuronal death in CA3. This suggests the involvement of the BNIP3 together with the CASP3 in the CA3 in neuronal death post-ischemia. [ABSTRACT FROM AUTHOR]

Dementia is a syndrome characterized by cognitive and neuropsychiatric symptoms associated with progressive functional decline (FD). FD is a core diagnostic criterion for dementia, setting the threshold between its prodromal stages and the full-blown disease. The operationalization of FD continues to generate a great deal of controversy. For instance, the threshold of FD for the diagnosis of dementia varies across diagnostic criteria, supporting the need for standardization of this construct. Moreover, there is a need to reconsider how we are measuring FD to set boundaries between normal aging, mild cognitive impairment, and dementia. In this paper, we propose a multidimensional framework that addresses outstanding issues in the assessment of FD: i) What activities of daily living (ADLs) are necessary to sustain an independent living in aging? ii) How to assess FD in individuals with suspected neurocognitive disorders? iii) To whom is the assessment directed? and iv) How much does FD differentiate healthy aging from mild and major neurocognitive disorders? Importantly, the To Whom Question introduces a person-centered approach that regards patients and caregivers as active agents in the assessment process of FD. Thus, once impaired ADLs have been identified, patients can indicate how significant such impairments are for them in daily life. We envisage that this new framework will guide future strategies to enhance functional assessment and treatment of patients with dementia and their caregivers. [ABSTRACT FROM AUTHOR]

Alzheimer's disease and related dementias (ADRD) present significant challenges including cognitive and functional loss, behavioral disruption, emotional distress, and significant financial burden. These stressors are amplified in minority groups, who experience higher rates of ADRD but less frequent and later diagnosis. There is therefore a critical need to identify tangible approaches to culturally informed dementia assessment and care for patients from diverse communities. Muslim patients and particularly Muslim women are among the populations most understudied in the ADRD space. Muslim patients may hold unique religious, spiritual, and cultural beliefs and practices that can impact care-seeking for dementia symptoms, diagnostic accuracy, and treatment uptake. This paper outlines culturally informed approaches to assessing and treating Muslim women and families at each stage of ADRD care, though many recommendations extend to the broader Muslim community and others of diverse racial-ethnic backgrounds. We provide concrete suggestions for building rapport within and leveraging common family structures, respecting principles of modesty and privacy for all women including those who observe hijab or niqab, and communicating dementia diagnosis and care in the context of spiritual and ethical beliefs. While not intended as a comprehensive and prescriptive guide, this review provides important points of consideration and discussion with patients of Muslim backgrounds. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative disease with limited therapeutic strategies. NB-02 is a novel botanical drug that has shown promise as a protective and therapeutic treatment for AD in an APP/PS1 preclinical mouse model. In this paper, we investigate the underlying mechanisms by which NB-02 provides these therapeutic advantages using in vitro neuron-astrocyte co-cultures. Pretreatment with NB-02 prevented pathological calcium elevations in neurons and astrocytes after application of toxic soluble amyloid-β (Aβ) oligomers. NB-02 also prevented cell death associated with the addition of soluble Aβ oligomers suggesting NB-02 is effective at protecting both neurons and astrocytes from Aβ-mediated damage. [ABSTRACT FROM AUTHOR]

Accurate and reliable prediction of Alzheimer's disease (AD) progression is crucial for effective interventions and treatment to delay its onset. Recently, deep learning models for AD progression achieve excellent predictive accuracy. However, their predictions lack reliability due to the non-calibration defects, that affects their recognition and acceptance. To address this issue, this paper proposes a temporal attention-aware evidential recurrent network for trustworthy prediction of AD progression. Specifically, evidential recurrent network explicitly models uncertainty of the output and converts it into a reliability measure for trustworthy AD progression prediction. Furthermore, considering that the actual scenario of AD progression prediction frequently relies on historical longitudinal data, we introduce temporal attention into evidential recurrent network, which improves predictive performance. We demonstrate the proposed model on the TADPOLE dataset. For predictive performance, the proposed model achieves mAUC of 0.943 and BCA of 0.881, which is comparable to the SOTA model MinimalRNN. More importantly, the proposed model provides reliability measures of the predicted results through uncertainty estimation and the ECE of the method on the TADPOLE dataset is 0.101, which is much lower than the SOTA model at 0.147, indicating that the proposed model can provide important decision-making support for risk-sensitive prediction of AD progression. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is the leading cause of dementia in the world. It affects 6 million people in the United States and 50 million people worldwide. Alzheimer's disease is characterized by the accumulation of amyloid-β plaques (Aβ), an increase in tau protein neurofibrillary tangles, and a loss of synapses. Since the 1990s, removing and reducing Aβ has been the focus of Alzheimer's treatment and prevention research. The accumulation of Aβ can lead to oxidative stress, inflammation, neurotoxicity, and eventually apoptosis. These insults impair signaling systems in the brain, potentially leading to memory loss and cognitive decline. Aniracetam is a safe, effective, cognitive-enhancing drug that improves memory in both human and animal studies. Aniracetam may prevent the production and accumulation of Aβ by increasing α-secretase activity through two distinct pathways: 1) increasing brain derived neurotrophic factor expression and 2) positively modulating metabotropic glutamate receptors. This is the first paper to propose an evidence-based model for aniracetam reducing the accumulation and production of Aβ. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer's disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD. [ABSTRACT FROM AUTHOR]

Background: Adult day services (ADS) are an important and often underutilized support resource for older adults. For persons living with dementia (PLWD), ADS is an optimal access point to not only receive therapeutic and rehabilitative activities, but as a vehicle for respite/relief for dementia caregivers. Yet, there is currently a lack of research on integrating caregiver interventions into home and community-based services such as ADS. Objective: This paper reports on qualitative findings from the Improving Outcomes for Family Caregivers of Older Adults with Complex Conditions: The Adult Day Plus (ADS Plus) Program Trial. Methods: Drawing from semi-structured interviews conducted with family caregivers and ADS site staff, we conducted a thematic analysis to examine the implementation process of ADS Plus. Results: Themes address the relational nature of the intervention, learning, influence of the administrative infrastructure, and receptivity of ADS Plus. Conclusions: Our analysis determined that implementation of ADS Plus was feasible and accepted by site staff and dementia caregivers but also calls for additional evaluation of embedded caregiver support interventions across different contexts (e.g., staff size, limited technology environments) to further identify and test implementation mechanisms across settings. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most prevalent brain disorder which affects millions of people worldwide. Early detection is crucial for possible treatment. In this regard, machine learning (ML) approaches are widely utilized for AD detection. In this paper, we propose an ML-based method that drastically reduces the dimensionality of features while maintaining the relevant features and boosting the overall performance. To remove irrelevant features, first statistical feature extraction method is applied, and then further reduction among remaining features is applied by utilizing the harmony search method (HSM). The selected features are the most informative features that are fed to the different classifiers. To test the effectiveness of the proposed method, we deployed three classification techniques including support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (DT). The experimental results show that the proposed method has a higher performance while decreasing the dimensionality of feature space. To guarantee that the performance of the proposed method is accurate, we applied an ensemble of three classifiers (SVM, KNN, and DT) for classification. The results of the proposed method verify that this method can be successfully deployed for AD detection, due to its high performance and low dimensional features, and can help improve the accuracy and efficiency of Alzheimer's disease diagnosis. The proposed method demonstrated a significant improvement, achieving high performance in AD/HC classification, with accuracy, sensitivity, specificity, F1-score, MCC, and Cohen's Kappa rates reaching 95.5%, 97%, 94%, 95.56%, 0.9104, and 0.9109, respectively. AD/HC classification displayed the highest performance. Additionally, in the more challenging pMCI/sMCI classification, the method achieved an accuracy of 78.50%, sensitivity of 84.00%, specificity of 73.00%, F1-score of 79.62%, MCC of 0.57, and Cohen's Kappa of 0.59. [ABSTRACT FROM AUTHOR]

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and −3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy. [ABSTRACT FROM AUTHOR]

As a non-classical post-translational modification, O-linked β-N-acetylglucosamine (O-GlcNAc) modification (O-GlcNAcylation) is widely found in human organ systems, particularly in our brains, and is indispensable for healthy cell biology. With the increasing age of the global population, the incidence of neurodegenerative diseases is increasing, too. The common characteristic of these disorders is the aggregation of abnormal proteins in the brain. Current research has found that O-GlcNAcylation dysregulation is involved in misfolding or aggregation of these abnormal proteins to mediate disease progression, but the specific mechanism has not been defined. This paper reviews recent studies on O-GlcNAcylation's roles in several neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, Machado-Joseph's disease, and giant axonal neuropathy, and shows that O-GlcNAcylation, as glucose metabolism sensor, mediating synaptic function, participating in oxidative stress response and signaling pathway conduction, directly or indirectly regulates characteristic pathological protein toxicity and affects disease progression. The existing results suggest that targeting O-GlcNAcylation will provide new ideas for clinical diagnosis, prevention, and treatment of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The term ‘dementia’ covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency. This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer’s disease (AD) – a common dementia condition. The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection. A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository. The models showed good power in predicting AD levels, notably from specified cognitive tests’ scores and tauopathy related features. [ABSTRACT FROM AUTHOR]

BACKGROUND: Alzheimer's disease (AD) endangers the physical and mental health of the elderly, constituting one of the most crucial social challenges. Due to lack of effective AD intervention drugs, it is very important to diagnose AD in the early stage, especially in the Mild Cognitive Impairment (MCI) phase. OBJECTIVE: At present, an automatic classification technology is urgently needed to assist doctors in analyzing the status of the candidate patient. The artificial intelligence enhanced Alzheimer's disease detection can reduce costs to detect Alzheimer's disease. METHODS: In this paper, a novel pre-trained ensemble-based AD detection (PEADD) framework with three base learners (i.e., ResNet, VGG, and EfficientNet) for both the audio-based and PET (Positron Emission Tomography)-based AD detection is proposed under a unified image modality. Specifically, the effectiveness of context-enriched image modalities instead of the traditional speech modality (i.e., context-free audio matrix) for the audio-based AD detection, along with simple and efficient image denoising strategy has been inspected comprehensively. Meanwhile, the PET-based AD detection based on the denoised PET image has been described. Furthermore, different voting methods for applying an ensemble strategy (i.e., hard voting and soft voting) has been investigated in detail. RESULTS: The results showed that the classification accuracy was 92% and 99% on the audio-based and PET-based AD datasets, respectively. Our extensive experimental results demonstrate that our PEADD outperforms the state-of-the-art methods on both audio-based and PET-based AD datasets simultaneously. CONCLUSIONS: The network model can provide an objective basis for doctors to detect Alzheimer's Disease. [ABSTRACT FROM AUTHOR]

This correction notice is for an article titled "Loss in PKC Epsilon Causes Downregulation of MnSOD and BDNF Expression in Neurons of Alzheimer's Disease Hippocampus" published in the Journal of Alzheimer's Disease. The correction addresses errors in Figure 8 of the article, where parts of the images were mistakenly copied and pasted, resulting in overlapping and incorrect panels. However, the correction does not affect the overall findings and conclusions of the paper. The corrected figure and caption are provided in the notice. [Extracted from the article]

The Journal of Alzheimer's Disease has retracted a publication titled "Potential Mechanisms Linking Cholesterol to Alzheimer's Disease-like Pathology in Rabbit Brain, Hippocampal Organotypic Slices, and Skeletal Muscle." The retraction was made due to an ethical concern regarding Figure 1 and the first author's inability to access the original data. The validity of some representative images has also been called into question. As a result, the Editorial Office has decided to retract the paper. [Extracted from the article]