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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]

Background: Digital cognitive tests offer several potential advantages over established paper-pencil tests but have not yet been fully evaluated for the clinical evaluation of mild cognitive impairment.Objective: The NeuroCognitive Performance Test (NCPT) is a web-based, self-directed, modular battery intended for repeated assessments of multiple cognitive domains. Our objective was to examine its relationship with the Alzheimer's Disease Assessment Scale-Cognition Subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) as well as with established paper-pencil tests of cognition and daily functioning in mild cognitive impairment (MCI).Methods: We used Spearman correlations, regressions and principal components analysis followed by a factor analysis (varimax rotated) to examine our objectives.Results: In MCI subjects, the NCPT composite is significantly correlated with both a composite measure of established tests (r = 0.78, p < 0.0001) as well as with the ADAS-Cog (r = -0.55, p < 0.0001). Both NCPT and paper-pencil test batteries had a similar factor structure that included a large "g" component with a high eigenvalue. The correlation for the analogous tests (e.g., Trails A and B, learning memory tests) were significant (p < 0.0001). Further, both the NCPT and established tests significantly (p < 0.0001) predicted the University of California San Diego Performance-Based Skills Assessment and Functional Activities Questionnaire, measures of daily functioning.Conclusion: The NCPT, a web-based, self-directed, computerized test, shows high concurrent validity with established tests and hence offers promise for use as a research or clinical tool in MCI. Despite limitations such as a relatively small sample, absence of control group and cross-sectional nature, these findings are consistent with the growing literature on the promise of self-directed, web-based cognitive assessments for MCI. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) is a self-reported experience of declining cognitive function showing normal performance in cognitive assessments, which is a known risk factor for dementia. Recent studies highlight the importance of nonpharmacological multidomain interventions that can target multiple risk factors of dementia in older adults. Objective: This study investigated the efficacy of the Silvia program, a mobile-based multidomain intervention, to improve cognitive function and health-related outcomes of older adults with SCD. We compare its effects to a conventional paper-based multidomain program on various health indicators related to risk factors of dementia. Methods: This prospective randomized controlled trial involved 77 older adults with SCD recruited from the Dementia Prevention and Management Center in Gwangju, South Korea during May to October 2022. Participants were randomly assigned to either the mobile- or paper-based group. Interventions were administered for 12 weeks, where pre- and post-assessments were conducted. Results: The K-RBANS total score did not show significant differences between groups. The mobile group showed better improvement in K-PRMQ scores and PSS scores than the paper group. Differences within groups showed that mobile-based interventions significantly improved K-PRMQ, STAI-X-1, PSS, and EQ-5D-5 L scores, while paper-based interventions significantly improved PSS, and EQ-5D-5 L scores. Patient adherence rate was 76.6%. Conclusion: Overall, the Silvia program was effective for improving self-reported memory failures, stress, anxiety, and health-related quality of life in older adults with SCD. However, longer periods of administration for more than 12 weeks may be needed to achieve significant improvements in cognitive function by objective measures. [ABSTRACT FROM AUTHOR]

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: 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]

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: Dementia is a general term for several progressive neurodegenerative disorders including Alzheimer's disease. Timely and accurate detection is crucial for early intervention. Advancements in artificial intelligence present significant potential for using machine learning to aid in early detection. Objective: Summarize the state-of-the-art machine learning-based approaches for dementia prediction, focusing on non-invasive methods, as the burden on the patients is lower. Specifically, the analysis of gait and speech performance can offer insights into cognitive health through clinically cost-effective screening methods. Methods: A systematic literature review was conducted following the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The search was performed on three electronic databases (Scopus, Web of Science, and PubMed) to identify the relevant studies published between 2017 to 2022. A total of 40 papers were selected for review. Results: The most common machine learning methods employed were support vector machine followed by deep learning. Studies suggested the use of multimodal approaches as they can provide comprehensive and better prediction performance. Deep learning application in gait studies is still in the early stages as few studies have applied it. Moreover, including features of whole body movement contribute to better classification accuracy. Regarding speech studies, the combination of different parameters (acoustic, linguistic, cognitive testing) produced better results. Conclusions: The review highlights the potential of machine learning, particularly non-invasive approaches, in the early prediction of dementia. The comparable prediction accuracies of manual and automatic speech analysis indicate an imminent fully automated approach for dementia detection. [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]

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]

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]

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]

Alzheimer's disease (AD) prediction is a critical task in the field of healthcare, and researchers have been exploring various techniques to improve its accuracy. This research paper focuses on the major contributions of a hybrid deep convolutional neural network (CNN) with denoising using a multilayer perceptron (MLP) and pooling layers in AD prediction. The proposed hybrid model leverages the power of deep CNNs to extract meaningful features from molecular or imaging data related to AD. The model incorporates denoising techniques using MLP to enhance the quality of the input data and reduce noise interference. Additionally, pooling layers are employed to summarize the extracted features and capture their essential characteristics. Several experiments and evaluations were conducted to assess the performance of the proposed model. Comparative analyses were carried out with other techniques such as PCA, CNN, Resnet18, and DCNN. The results were presented in a comparison chart, highlighting the superiority of the hybrid deep CNN with denoising and pooling layers in AD prediction. The research paper further discusses the accuracy, precision, and recall values obtained through the proposed model. These metrics provide insights into the model's ability to accurately classify AD cases and predict disease progression. Overall, the hybrid deep CNN with denoising using MLP and pooling layers presents a promising approach for AD prediction. The combination of these techniques enables more accurate and reliable predictions, contributing to early detection and improved patient care. The findings of this research contribute to the advancement of AD prediction methodologies and provide valuable insights for future studies in this domain. [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]

In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) plagues 6.5 million Americans 65+, yet treatments are lacking. The Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet has been developed to address the expansive impact of dementias on the general public. This systematic review evaluated the impact of the MIND diet on cognition in those with pathologies across the dementia spectrum. Objective: To evaluate the application of the MIND diet for prevention and/or treatment of dementia. Methods: PubMed was used to conduct a search using the MIND diet and terms related to cognition. Articles were excluded if they were published prior to 2018, studied a population without dementia or significant risk factors, or did not include those 65 +. The overall quality of each source was analyzed based on the cognitive test(s) used, the selection of subjects, and the sample size. Results: The search generated 33 papers, which yielded 11 articles after screening. Of these studies, one was conducted on those with mild cognitive impairment, one with AD, two with general dementia, and seven with at-risk individuals. All the studies found a positive correlation between adherence and some form of cognitive functioning, but results were mixed for specific cognitive domains. Conclusions: These findings suggest that the MIND diet may be a useful long-term treatment option for those with various dementia pathologies. However, more research is needed on subjects with onset dementias. Additionally, there is a need for more research into the mechanisms behind the common comorbidities. [ABSTRACT FROM AUTHOR]

Microglia and astrocytes are regarded as active participants in the central nervous system under various neuropathological conditions, including Alzheimer's disease (AD). Both microglia and astrocyte activation have been reported to occur with a spatially and temporarily distinct pattern. Acting as a double-edged sword, glia-mediated neuroinflammation may be both detrimental and beneficial to the brain. In a variety of neuropathologies, microglia are activated before astrocytes, which facilitates astrocyte activation. Yet reactive astrocytes can also prevent the activation of adjacent microglia in addition to helping them become activated. Studies describe changes in the genetic profile as well as cellular and molecular responses of these two types of glial cells that contribute to dysfunctional immune crosstalk in AD. In this paper, we construct current knowledge of microglia-astrocyte communication, highlighting the multifaceted functions of microglia and astrocytes and their role in AD. A thorough comprehension of microglia-astrocyte communication could hasten the creation of novel AD treatment approaches. [ABSTRACT FROM AUTHOR]

Activation of cell-cycle machinery in Alzheimer's disease (AD) brain was reported by Mark Smith and colleagues and by other researchers. Among other biochemical processes underlying this activation, the notion that AD brain, under the onslaught of oxidative and nitrosative damage leading to neuronal loss, neurons would attempt to replenish their numbers by entering the cell cycle. However, being post-mitotic, neurons entering the cell cycle would become trapped therein, ultimately leading to death of these neurons. Yang and co-workers and the Butterfield laboratory first reported that similar activation of the cell cycle was present in the brains of individuals with amnestic mild cognitive impairment (MCI), arguably the earliest clinical stage of AD, but who demonstrate normal activities of daily living and no dementia. Activation of the cell cycle in MCI brain is consonant with the concept that this process is an early aspect in the progression of AD. This brief review article discusses these findings and recognizes the contribution of Dr. Mark Smith to the investigation of cell-cycle activation in AD brain and other aspects of AD neuropathology. [ABSTRACT FROM AUTHOR]

Background: Increased understanding of dementia risk-reduction and early detection of Alzheimer's disease and related disorders has spurred interest in the identification of risks for dementia, underlying putative biologies, or dementia itself. Implementation of such approaches require acceptability to the public. Research prior to 2012 indicated limited acceptability for population dementia screening. The changing landscape of dementia prevention research may influence recent perceptions. Additionally, perspectives from underserved populations, such as ethnic minorities and low socio-economic groups, are lacking. Objective: In this systematic review, we sought published studies since 2012 on attitudes and preferences of people with dementia, carers and the general public from ethnic minorities and low socio-economic groups regarding dementia screening. Methods: This review was preregistered on PROSPERO (CRD42023384115) and followed PRISMA guidelines. Key search terms were entered into five databases. Articles were included if they focused on population or risk screening for dementia via primary/community care-based assessments, and which included majority ethnic minority or low socio-economic groups or discretely considered these groups in data analysis. Data were synthesized narratively. Results: Seven studies reported perspectives of ethnic minorities regarding dementia screening; one study included people from low socio-economic groups. Results indicated that participants from ethnic minorities were willing to undergo dementia screening. Predictors of willingness included belief in benefits, desire to boost diversity, and to implement lifestyle changes. Unwillingness was associated with anxiety regarding results. Conclusions: Although there seems to be high acceptability for screening in the studied groups, more research is necessary to explore the practical considerations for screening such as cultural and economic barriers, trust, and post-screening actions. [ABSTRACT FROM AUTHOR]

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. CONCLUSION: The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features. [ABSTRACT FROM AUTHOR]

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. CONCLUSION: The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features. [ABSTRACT FROM AUTHOR]

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]

Background: The large number of dementia cases produces a great pressure on health and social care services, which requires efficient planning to meet the needs of patients through infrastructure, equipment, and financial, technical, and personal resources adjusted to their demands. Dementia analysis requires studies with a very precise patient characterization of both the disease and comorbidities present, and long-term follow-up of patients in clinical aspects and patterns of resource utilization and costs generated. Objective: To describe and quantify direct healthcare expenditure and its evolution from three years before and up to ten years after the diagnosis of dementia, compared to a matched group without dementia. Methods: Retrospective cohort design with follow-up from 6 to 14 years. We studied 996 people with dementia (PwD) and 2,998 controls matched for age, sex, and comorbidity. This paper adopts the provider's perspective as the perspective of analysis and refers to the costs actually incurred in providing the services. Aggregate costs and components per patient per year were calculated and modelled. Results: Total health expenditure increases in PwD from the year of diagnosis and in each of the following 7 years, but not thereafter. Health status and mortality are factors explaining the evolution of direct costs. Dementia alone is not a statistically significant factor in explaining differences between groups. Conclusion: The incremental direct cost of dementia may not be as high or as long as studies with relatively short follow-up suggest. Dementia would have an impact on increasing disease burden and mortality. [ABSTRACT FROM AUTHOR]

In the last years, many clinical studies highlighted sex-specific differences in the pathophysiology of Alzheimer's disease (AD). The recent paper published in the Journal of Alzheimer's Disease shows the influence of sex on amyloid-β plaque deposition, behavior, and dopaminergic signaling in the 5xFAD mouse model of AD, with worse alterations in female mice. This commentary focuses on the importance of recognizing sex as a key variable to consider for a more precise clinical practice, with the challenge to develop sex-specific therapeutic interventions in neurodegenerative diseases such as AD. [ABSTRACT FROM AUTHOR]

Background: Mnemonic discrimination is the behavioral ability stemming from pattern separation, which is the neural process of establishing independent and non-overlapping new memories. Over the past two decades, its assessment in various populations has contributed to a better conceptual understanding of age-related memory decline. Objective: To assess the clinical relevance of mnemonic discrimination in the memory clinics setting. Methods: This retrospective study was performed in 90 patients with a Mini-Mental State Examination (MMSE)>18 who consulted our memory clinic for the first time. All patients were tested with the Mnemonic Similarity Task, a freely available computerized test. Global cognitive function, executive function, visuoconstructional abilities, and verbal and visual episodic memory were also collected, together with the diagnosis after the initial clinical assessment (subjective cognitive complaint [SCC], mild cognitive impairment [MCI], or mild dementia). Results: Mnemonic discrimination performance was correlated with global cognitive function, executive function, and visual and verbal episodic memory scores, independent of age. It discriminated patients with SCC from those with MCI (amnestic or non-amnestic) with moderate accuracy (AUC = 0.77-0.78), similar to MMSE and the Frontal Assessment Battery (AUC = 0.74-0.84). Mnemonic discrimination performance did not distinguish between amnestic and non-amnestic MCI and the variability of the measure was important within groups. Conclusion: Mnemonic discrimination performance involves many cognitive domains and discriminates between patients with SCC and MCI with performance equivalent to "paper-and-pencil" screening tests. Further dedicated prospective studies will determine whether this task is of interest beyond research purposes, as a diagnostic or screening tool in primary care. [ABSTRACT FROM AUTHOR]

Brain diseases is a wide range of disorders and diseases that affect the brain. They can change a person's behavior, personality, and capacity for thought and function. CT images are more essential than conventional clinical tests for detecting brain hemorrhage accurately. MRI images of the brain can reveal even small abnormalities in the cranial region, helping providers diagnose a wide variety of conditions, ranging from brain stroke, cancers, aneurysms, and Alzheimer's. This paper proposes a novel Fused dual neural (FDN) network for detecting brain cancer, stroke, aneurysms, and Alzheimer using Brain Medical Images (BMI) the combination of MRI and CT. In BMI, the adaptive bilateral filter reduces noise artifacts. Google Net is used to extract features from pre-processed MRI images, and Mobile Net is used to extract features from pre-processed CT images. The integration of extracted features from Google Net and Mobile Net is fused by the Wrapper method. Finally, the Deep Belief Network is employed for classifying brain stroke, cancer, Aneurysm, and Alzheimer's diseases using BMI images. The quantitative analysis of the suggested method is determined using the parameters like specificity, recall, precision, F1 score, and accuracy. The proposed FDN achieves a high classification accuracy rate of 98.19%, 97.68%, 94.31%, and 93.82% for detecting stroke, cancer, Aneurysm, and Alzheimer respectively. The proposed FDN model improves the overall accuracy by 5.35%, 3.14%, 9.48%, 5.33%, and 0.55% better than Faster R-CNN, CNN, Inception-V3, DCNN, and Fine-tuning Network respectively. [ABSTRACT FROM AUTHOR]

Background: Health- and lifestyle factors account for a substantial part of all dementia cases, which opens the opportunity for primary prevention. However, the required behavioral change is complex and involves targeting multiple risk factors. mHealth interventions can potentially contribute to improving motivation in a low-cost and scalable way. Objective: To explore usage patterns, appreciation, and beliefs and attitudes regarding dementia risk reduction during the use of the MyBraincoach mobile app. Methods: Participants were community-dwelling middle-aged adults from the Netherlands and used either the standard (education) or extended (education+motivational triggers) app version for three months. Two panel studies were combined in this paper. Chi-square tests, t-tests and linear mixed models were used, adjusted for age, sex, and education. Results: Of all participants (n = 299, 50.2% male), 167 (55.9%) had installed the app. The most reported reason for non-use was technical problems (47%). Those who used the app were at baseline already more positive about dementia risk reduction than those who did not use the app. Of all users who completed the evaluation (n = 102), 78.4% (n = 80) stated that the app provided a positive approach towards brain health and 80.4% (n = 82) felt better informed. Younger (<60y) and lower educated participants evaluated the app most positively. Conclusion: Usage of the app was low, but users showed more positive beliefs and attitudes regarding dementia risk reduction. Most users evaluated the app positively and stated to have gained knowledge on the topic. Improving the use of the app must keep high priority in future studies. [ABSTRACT FROM AUTHOR]

Changes in diagnostic certainty can be evaluated by assessing the impact of a diagnostic test in driving decision making. Diagnostic tests can be appraised using validated measures of accuracy, i.e., sensitivity, specificity, and positive or negative predictive values against a known reference standard. However, other less well formalized factors affect diagnostic certainty. These inputs are under-researched and more difficult to quantify. Clinicians assess the significance of available data in the context of their expertise, pre-diagnostic confidence, and background knowledge of populations and disease. Inherent qualities of the diagnostic test and an individual clinician's interpretation of the meaning of test results will also affect the subsequent level of diagnostic certainty. These factors are only infrequently considered alongside the diagnostic accuracy of a test. In this paper, we present a model of the different processes which can affect diagnostic certainty in Alzheimer's disease (AD). This model builds upon existing understanding and provides further insights into the complexity of diagnostic certainty in AD and how we might improve this. [ABSTRACT FROM AUTHOR]

Background: People experiencing cognitive concerns and symptoms of depression or anxiety are at risk for Alzheimer's disease and dementia. We know physical activity can benefit cognition but understanding how to best support engagement is an ongoing challenge. Evidence-based conceptual models of factors underpinning physical activity engagement in target populations can inform intervention tailoring to address this challenge. Objective: This study (part of a pragmatic physical activity implementation trial) aimed to develop a specified model of physical activity engagement in people experiencing depressive or anxiety symptoms and cognitive concerns, to enable optimized dementia risk reduction intervention tailoring. Methods: We employed a qualitative design, triangulating data from three sources: semi-structured individual interviews with people experiencing cognitive concerns and mild to moderate depressive or anxiety symptoms; review of published evidence; and the Capability, Opportunity and Motivation system of behavior, an existing behavioral science model. Findings were integrated to develop a contextualized model of mechanisms of action for optimizing engagement. Results: Twenty-one participants were interviewed, and 24 relevant papers included. Convergent and complementary themes extended understanding of intervention needs. Findings highlighted emotional regulation, capacities to enact intentions despite barriers, and confidence in existing skills as areas of population-specific need that have not previously been emphasized. The final model provides specificity, directionality, and linked approaches for intervention tailoring. Conclusion: This study demonstrated that people experiencing cognitive concerns and symptoms of depression or anxiety require different interventions to improve physical activity engagement. This novel model can enable more precise intervention tailoring, and, ultimately, benefits for a key at-risk population. [ABSTRACT FROM AUTHOR]

Neuropsychological assessment using the Boston Process Approach (BPA) suggests that an analysis of the strategy or the process by which tasks and neuropsychological tests are completed, and the errors made during test completion convey much information regarding underlying brain and cognition and are as important as overall summary scores. Research over the last several decades employing an analysis of process and errors has been able to dissociate between dementia patients diagnosed with Alzheimer's disease, vascular dementia associated with MRI-determined white matter alterations, and Parkinson's disease; and between mild cognitive impairment subtypes. Nonetheless, BPA methods can be labor intensive to deploy. However, the recent availability of digital platforms for neuropsychological test administration and scoring now enables reliable, rapid, and objective data collection. Further, digital technology can quantify highly nuanced data previously unobtainable to define neurocognitive constructs with high accuracy. In this paper, a brief review of the BPA is provided. Studies that demonstrate how digital technology translates BPA into specific neurocognitive constructs using the Clock Drawing Test, Backward Digit Span Test, and a Digital Pointing Span Test are described. Implications for using data driven artificial intelligence-supported analytic approaches enabling the creation of more sensitive and specific detection/diagnostic algorithms for putative neurodegenerative illness are also discussed. [ABSTRACT FROM AUTHOR]

Background: There is a common agreement that Alzheimers disease (AD) is inherently complex; otherwise, a general disagreement remains on its etiological underpinning, with numerous alternative hypotheses having been proposed. Objective: To perform a scoping review of original manuscripts describing hypotheses and theories of AD published in the past decades. Results: We reviewed 131 original manuscripts that fulfilled our inclusion criteria out of more than 13,807 references extracted from open databases. Each entry was characterized as having a single or multifactorial focus and assigned to one of 15 theoretical groupings. Impact was tracked using open citation tools. Results: Three stages can be discerned in terms of hypotheses generation, with three quarter of studies proposing a hypothesis characterized as being single-focus. The most important theoretical groupings were the Amyloid group, followed by Metabolism and Mitochondrial dysfunction, then Infections and Cerebrovascular. Lately, evidence towards Genetics and especially Gut/Brain interactions came to the fore. Conclusions: When viewed together, these multi-faceted reports reinforce the notion that AD affects multiple sub-cellular, cellular, anatomical, and physiological systems at the same time but at varying degree between individuals. The challenge of providing a comprehensive view of all systems and their interactions remains, alongside ways to manage this inherent complexity. [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]

This paper presents data analysis of a large dataset of outpatient records for the purpose of establishing accurate prevalence rates of dementia and Alzheimer’s disease in Bulgaria. The research is motivated by the lack of accurate prevalence data as well as statistics about the actual number of people affected by dementia at national level. Health data from pseudonymized outpatient records (1,378,355) of 642,013 unique patients (Male 39.88%, Female 61.67%) with visits to neurologists and psychiatrists in 2018 was mapped to an OMOP CDM relational database. The size of this dataset is one of largest in the EU context. Prevalence of dementia for all age groups [30,100+] years is 1.61% (Male 0.62%, Female 0.99%) and Alzheimer’ disease prevalence is 0.39% (Male 0.15%, Female 0.24%), where 24.34% of all the patients with dementia suffer Alzheimer’s disease. The mean prevalence rates of dementia and Alzheimer's disease for ages above 64 are 9.52% (CI 95%, [6.98%, 12.04%]) and 1.04 % (CI 95%, [0.84%, 1.27%,]). A comparison with EU statistics yields 0.07% difference for dementia prevalence, while data visualization confirms the known patterns of prevalence with aging and gender specifics These results stimulate further research and support the development of a national strategy for dementia and Alzheimer’s disease. [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]

Alzheimer's disease (AD) is a progressive degenerative disease of the nervous system (CNS) with an insidious onset. Clinically, it is characterized by a full range of dementia manifestations including memory impairment, aphasia, loss of speech, loss of use, loss of recognition, impairment of visuospatial skills, and impairment of executive function, as well as changes in personality and behavior. The exact cause of AD has not yet been identified. Nevertheless, modern research indicates that genetic factors contribute to 70% of human's risk of AD. Apolipoprotein (APOE) accounts for up to 90% of the genetic predisposition. APOE is a crucial gene that cannot be overstated. In addition, innate immunity plays a significant role in the etiology and treatment of AD. Understanding the different subtypes of APOE and their interconnections is of paramount importance. APOE and innate immunity, along with their relationship to AD, are primary research motivators for in-depth research and clinical trials. The exploration of novel technologies has led to an increasing trend in the study of AD at the cellular and molecular levels and continues to make more breakthroughs and progress. As of today, there is no effective treatment available for AD around the world. This paper aims to summarize and analyze the role of APOE and innate immunity, as well as development trends in recent years. It is anticipated that APOE and innate immunity will provide a breakthrough for humans to hinder AD progression in the near future. [ABSTRACT FROM AUTHOR]

Background: Growing evidence suggests that critical periods in early life may contribute to one's risk of Alzheimer's disease and related dementias (ADRD) in later life. In this paper we explore the role that exposure to infant mortality plays in later life ADRD. Objective: To determine if exposure to early life infant mortality is associated with later mortality from ADRD. Also, we explore how these associations differ by sex and age group, along with the role of state of birth and competing risks of death. Methods: We use a sample of over 400,000 individuals aged 50 and above with the NIH-AARP Diet and Health Study with mortality follow-up, allowing us to examine how early life infant mortality rates along with other risk factors play in one's individual mortality risk. Results: We show that infant mortality rates are associated with death from ADRD among those under 65 years of age, but not those over 65 at baseline interview. Moreover, when factoring in competing risks of death, the associations are relatively unchanged. Conclusion: These results suggest that those exposed to worse adverse conditions during critical periods increase their likelihood of death from ADRD earlier than average, due to that exposure increasing their susceptibility to develop illness later on in life. [ABSTRACT FROM AUTHOR]

Background: A key challenge in studies that model outcomes, disease progression, and cost-effectiveness of existing and emerging dementia treatments is the lack of conversion criteria to translate, or 'crosswalk', scores on multiple measurement scales. Clinical status in dementia is commonly characterized in the cognitive, functional, and behavioral domains.Objective: We conducted a systematic review of peer-reviewed dementia measure crosswalks in the three domains.Methods: We systematically reviewed published literature for crosswalks between scales used to measure cognitive, functional, or behavioral outcomes in Alzheimer's and related dementias. The search was conducted in PubMed, and additional crosswalks were identified through snowballing and expert input from dementia modelers.Results: Of the reviewed articles, 2,334 were identified through a PubMed search, 842 articles were sourced from backward and forward citation snowballing, and 8 additional articles were recommended through expert input. 31 papers were eligible for inclusion, listing 74 unique crosswalks. Of those, 62 (83.8%) were between endpoints of the cognitive domain and 12 (16.2%) were either between endpoints of the functional domain or were hybrid in nature. Among crosswalks exclusively in the cognitive domain, a majority involved the Mini-Mental State Examination (MMSE) (37 crosswalks) or the Montreal Cognitive Assessment (MoCA) and its variants (25 crosswalks). MMSE was directly compared to MoCA or MoCA variants in 16 crosswalks.Conclusion: Existing crosswalks between measures of dementia focus largely on a limited selection of outcome measures, particularly MMSE and MoCA. Few crosswalks exist in the functional domain, and no crosswalks were identified for solely behavioral measures. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) is further investigated in computer intelligence and artificial intelligence. Many TL methodologies have been suggested and applied to figure out the problem of practical applications, such as in natural language processing, classification models for COVID-19 disease, Alzheimer's disease detection, etc. FTL (fuzzy transfer learning) is an extension of TL that uses a fuzzy system to pertain to the vagueness and uncertainty parameters in TL, allowing the discovery of predicates and their evaluation of unclear data. Because of the system's increasing complexity, FTL is often utilized to further infer proper results without constructing the knowledge base and environment from scratch. Further, the uncertainty and vagueness in the daily data can arise and modify the process. It has been of great interest to design an FTL model that can handle the periodicity data with fast processing time and reasonable accuracy. This paper proposes a novel model to capture data related to periodical phenomena and enhance the quality of the existing inference process. The model performs knowledge transfer in the absence of reference or predictive information. An experimental stage on the UCI and real-life dataset compares our proposed model against the related methods regarding the number of rules, computing time, and accuracy. The experimental results validated the advantages and suitability of the proposed FTL model. [ABSTRACT FROM AUTHOR]

Most types of dementia, including Alzheimer's disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems). [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder affecting 50 million people globally. It is characterized by the presence of extracellular senile plaques and intracellular neurofibrillary tangles, consisting of amyloid-β and hyperphosphorylated tau proteins, respectively. Despite global research efforts, there is currently no cure available, due in part to an incomplete understanding of the disease pathogenesis. Numerous possible mechanisms, or hypotheses, explaining the origins of sporadic or late-onset AD have been proposed, including the amyloid-β, inflammatory, vascular, and infectious hypotheses. However, despite ample evidence, the failure of multiple trial drugs at the clinical stage illuminates the possible pitfalls of these hypotheses. Systems biology is a strategy which aims to elucidate the interactions between parts of a whole. Using this approach, the current paper shows how the four previously mentioned hypotheses of AD pathogenesis can be intricately connected. This approach allows for seemingly contradictory evidence to be unified in a system-focused explanation of sporadic AD development. Within this view, it is seen that infectious agents, such as P. gingivalis, may play a central role. The data presented here shows that when present, P. gingivalis or its virulence factors, such as gingipains, may induce or exacerbate pathologies underlying sporadic AD. This evidence supports the view that infectious agents, and specifically P. gingivalis, may be suitable treatment targets in AD. [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: 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]

Alzheimer's disease (AD) is an incurable disease and a type of dementia. About 55 million people around the world have AD. However, technologies have been used to assist in the healthcare of AD, supporting physicians in the palliative care of patients. This article presents a systematic mapping study (SMS) to identify articles that use technologies to monitor patients with AD in order to show an overview of the literature, identifying gaps and research opportunities in this field. The scientific contribution of this work is to identify monitoring technologies related to AD and highlight current trends on the subject. The paper uses the term technologies as hardware infrastructure and devices or systems without considering software technologies. In addition, this article proposes a taxonomy for the domain of technologies applied to AD patients. The SMS study was conducted in six databases, including articles from 1997 to 2021. An initial search resulted in 7,781 articles. After applying filter criteria, throwing automatic selection on databases, and manual assortment, 171 articles were selected. Subsequently, a second search was performed to reduce the list of articles and filter by the specific search objective of articles focused on technologies for monitoring with tracking, resulting in 74 works. The main results obtained are: (1) a relevant number of articles (43.42%) reported solutions used in sensor-based devices; (2) several works (33.33%) have the interaction focus on Position/Distance/Proximity/Location sensor type; (3) another group of articles has a secondary focus on Emergency help (18.97%). The results indicated the need for technologies to help caregivers monitor patients, in addition to evidence of research opportunities in palliative care and support for the daily activities of AD patients. [ABSTRACT FROM AUTHOR]

For years now, Alzheimer's disease (AD) research has been stuck in a Groundhog-Day scenario: an endless time loop with no breakthrough in sight. Disagreement about the validity of the field's dominant approach, based on the Amyloid Cascade Hypothesis, has led to a seemingly unresolvable trench war between proponents and critics. Our paper evaluates the recent scientific literature on AD from a historical and philosophical perspective. We show that AD research is a classic example of the boundary work at play in a field in crisis: both parties deploy historical and philosophical references to illustrate what counts as good and bad science, as proper scientific method and appropriate scientific conduct. We also show that boundary work has proved unable to point a way out of the deadlock and argue that the science system's tools for establishing scientific quality, such as peer review and the grant system, are unlikely to resolve the crisis. Rather, they consolidate the dominant model's position even more. In conclusion, we suggest that some kind of reverse boundary-work is needed that reopens the discussion on the nature of AD, an issue that has never been settled scientifically. Drawing on historical and philosophical work, we make clear that the definition of AD as a biomedical disease for which a cure can be found has consequences, not only for funding opportunities, but also for patients and their lives. A reconsideration of the desirability of these consequences may lead to different choices with respect to research priorities and patient care. [ABSTRACT FROM AUTHOR]

Advances in biomarkers, genetics, and other data used as dementia risk evidence (DRE) are increasingly informing clinical diagnosis and management. The purpose of this Mini-Forum is to provide a solutions-based discussion of the ethical and legal gaps and practical questions about how to use and communicate these data. Investigators often use DRE in research. When participants ask for their personal results, investigators have concerns. Will data that was intended to study groups be valid for individuals? Will sharing data cause distress? Debates around sharing DRE became heated when blood-based amyloid tests and amyloid reducing drugs appeared poised to enable clinicians easily to identify people with elevated brain amyloid and reduce it with a drug. Such an approach would transform the traditional role of DRE from investigational to foundational; however, then the high costs, uncertain clinical benefits and risks of the therapy led to an urgent need for education to support clinical decision making. Further complicating DRE use are direct to consumer genetic testing and increasingly available biomarker testing. Withholding DRE becomes less feasible and public education around responsible use and understanding become vital. A critical answer to these legal and ethical issues is supporting education that clearly delineates known risks, benefits, and gaps in knowledge, and communication to promote understanding among researchers, clinicians, patients, and all stakeholders. This paper provides an overview and identifies general concepts and resource documents that support more informed discussions for individuals and interdisciplinary groups. [ABSTRACT FROM AUTHOR]

Background: Research advancements in Alzheimer's disease (AD) raise opportunities for genetic testing to improve diagnostic and risk assessment. Despite emerging developments, it is unclear how geriatricians perceive the potential clinical and personal utility of genetic testing for their patients. Geriatricians' perspectives are essential to understanding potential ethical, policy, and clinical challenges.Objective: In this paper, we report on geriatricians' perspectives on the utility of genetic testing for AD.Methods: Semi-structured interviews with California geriatricians within different practices settings to collect and characterize their perspectives on genetic testing for AD. We used an adapted grounded theory approach to analyze recorded and transcribed interviews.Results: We identified geriatricians' (n = 10) perspectives on the clinical and personal utility of testing, alongside their views on clinical care approaches for older adults. Geriatricians perceived minimal clinical utility of genetic testing for AD, though that may change with the availability of disease-modifying therapies. Yet, they recognized the potential personal utility of testing (e.g., assisting with future financial planning). Finally, geriatricians expressed concerns regarding patients' anxiety from learning about genetic status, particularly through direct-to-consumer (DTC) testing.Conclusion: Our data highlight that the decision to order genetic testing requires clinical and ethical considerations, including balancing limited clinical utility with the potential personal utility. Although DTC testing is available, geriatricians perceive that they have an important role in managing the decision to test and interpreting the results. Further research is needed to inform policy and ethical guidelines to support geriatricians' critical role to counsel patients considering clinical and DTC genetic testing. [ABSTRACT FROM AUTHOR]

In recent years, the association between the activity of platelets and risk of Alzheimer's disease (AD) risk has been noticed in numerous studies. However, there in no investigations on the role of specific intracellular pathways to explain this connection. The phosphatidylinositol 3 kinase (PI3K)/AKT pathway is one of the main regulators of cell survival which regulates cellular responses to environmental changes. This pathway also regulates the activity of platelets, and its aberrant activity has been linked to platelet dysfunction in different pathologies. On the other hand, the PI3K/AKT pathway regulates amyloid-β (Aβ) production through regulation of amyloid-β protein precursor (AβPP), BACE-1, ADAMs, and γ-secretase. In addition, alterations in the activity of all of these factors in platelets has been shown in AD-related pathologies. Therefore, this paper aims to introduce the PI3K/AKT pathway as a molecular inducer of platelet dysfunction during aging and AD progression. [ABSTRACT FROM AUTHOR]