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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]

Introduction: Alzheimer's disease and related dementias (ADRD) represent a substantial global public health challenge with multifaceted impacts on individuals, families, and healthcare systems. Brief cognitive screening tools such as the Mini-Cog© can help improve recognition of ADRD in clinical practice, but widespread adoption continues to lag. We compared the Digital Clock and Recall (DCR), a next-generation process-driven adaptation of the Mini-Cog, with the original paper-and-pencil version in a well-characterized clinical trial sample. Methods: DCR was administered to 828 participants in the Bio-Hermes-001 clinical trial (age median ± SD = 72 ± 6.7, IQR = 11; 58% female) independently classified as cognitively unimpaired (n = 364) or as having mild cognitive impairment (MCI, n = 274) or dementia likely due to AD (DLAD, n = 190). MCI and DLAD cohorts were combined into a single impaired group for analysis. Two experienced neuropsychologists rated verbal recall accuracy and digitally drawn clocks using the original Mini-Cog scoring rules. Inter-rater reliability of Mini- Cog scores was computed for a subset of the data (n = 508) and concordance between Mini-Cog rule-based and DCR scoring was calculated. Results: Inter-rater reliability of Mini-Cog scoring was good to excellent, but Rater 2's scores were significantly higher than Rater 1's due to variation in clock scores (p < 0.0001). Mini-Cog and DCR scores were significantly correlated (τB = 0.71, p < 0.0001). However, using a Mini-Cog cut score of 4, the DCR identified more cases of cognitive impairment (n = 47; χ² = 13.26, p < 0.0005) and Mini-Cog missed significantly more cases of cognitive impairment (n = 87). In addition, the DCR correctly classified significantly more cognitively impaired cases missed by the Mini-Cog (n = 44) than vice versa (n = 4; χ² = 21.69, p < 0.0001). Discussion: Our findings demonstrate higher sensitivity of the DCR, an automated, process-driven, and process-based digital adaptation of the Mini- Cog. Digital metrics capture clock drawing dynamics and increase detection of diagnosed cognitive impairment in a clinical trial cohort of older individuals. [ABSTRACT FROM AUTHOR]

Causal inference has been increasingly reliant on observational studies with rich covariate information. To build tractable causal procedures, such as the doubly robust estimators, it is imperative to first extract important features from high or even ultra‐high dimensional data. In this paper, we propose causal ball screening for confounder selection from modern ultra‐high dimensional data sets. Unlike the familiar task of variable selection for prediction modeling, our confounder selection procedure aims to control for confounding while improving efficiency in the resulting causal effect estimate. Previous empirical and theoretical studies suggest excluding causes of the treatment that are not confounders. Motivated by these results, our goal is to keep all the predictors of the outcome in both the propensity score and outcome regression models. A distinctive feature of our proposal is that we use an outcome model‐free procedure for propensity score model selection, thereby maintaining double robustness in the resulting causal effect estimator. Our theoretical analyses show that the proposed procedure enjoys a number of properties, including model selection consistency and pointwise normality. Synthetic and real data analysis show that our proposal performs favorably with existing methods in a range of realistic settings. Data used in preparation of this paper were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. [ABSTRACT FROM AUTHOR]

The aging of the population is associated with an increase in the prevalence of neurodegenerative diseases such as dementia. Our research aim was to determine how technology can help to improve the support for behavioral and psychological challenges of dementia. A systematic review of the literature was performed following PRISMA guidelines. Papers meeting the inclusion criteria and reporting technological devices aimed to the management of these challenges. After screening 1085 papers, 18 studies were eligible for inclusion in the review. Technologies identified in this search (e.g., robots, mobile, apps, computer, software, GPS, wearables, assistive technology) suggest that this non-pharmacological approach may be useful in projects aimed to the management and control of behavioral and psychological manifestations in people living with Alzheimer. The manifestations that are most successfully supported by means of technological applications and devices are depression, sleep disorders, anxiety, apathy, motor activity, and agitation. [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]

Purpose: The purpose of this study was to examine the literature surrounding dementia in the aging correctional population and assess the role of cognitive screening related to dementia detection within corrections. The literature regarding the role of dementia within the justice continuum is scant. Furthermore, correctional health researchers have not reached a consensus on the best age to administer cognitive screening in older persons or prioritizes a screening tool for the early detection of dementia. Design/methodology/approach: A key search term list including dementia screening and was developed to review the literature surrounding dementia and the aging correctional population. PubMed, Criminal Justice Abstracts (Ebsco) and the National Criminal Justice Reference Service were used within the academic search. A gray literature search using these same search terms was conducted reviewing criminal justice federal agencies and organizations for additional information on the dementia experience within correctional settings. Snowballing was used to capture relevant theoretical and empirical knowledge. Findings: Shortages in aging specialized health-care staffing presents a barrier for the clinical interpretation of Montreal Cognitive Assessment (MoCA) results. Correctional officers are also identified as useful candidates within the administration of cognitive screening with proper training. The MoCA may be the optimal cognitive screening tool for dementia, until an original cognitive screening tool is created specific to the correctional population. An age of 55 years or older may serve as the best cutoff score for classifying incarcerated individuals as older persons, and screening should be prioritized for these individuals. Finally, new specialized programs related to dementia within correctional settings are identified. Research limitations/implications: A limitation of this research is the conflicting opinions among researchers regarding the use of general cognitive screening tools within the correctional setting. Originality/value: This research can inform correctional organizational policy and practices regarding the screening of older persons suspected of dementia. Most notably, this research proposes that correctional settings should incorporate the MoCA within initial screening of all individuals 55 years of age or older, enriching the job design of correctional officer's job positions to include cognitive testing, and for correctional settings to provide dementia and age-associated training for correctional officers. Finally, this paper informs future research in the development of a cognitive assessment tool specific to the correctional population. [ABSTRACT FROM AUTHOR]

Introduction: Remote digital assessments (RDAs) such as voice recording, video and motor sensors, olfactory, hearing, and vision screenings are now starting to be employed to complement classical biomarker and clinical evidence to identify patients in the early AD stages. Choosing which RDA can be proposed to individual patients is not trivial and often time-consuming. This position paper presents a decision-making algorithm for using RDA during teleconsultations in memory clinic settings. Method: The algorithm was developed by an expert panel following the Delphi methodology. Results: The decision-making algorithm is structured as a series of yes-no questions. The resulting questionnaire is freely available online. Discussion: We suggest that the use of screening questionnaires in the context of memory clinics may help accelerating the adoption of RDA in everyday clinical practice. [ABSTRACT FROM AUTHOR]

An increasing number of people seek medical attention for mild cognitive symptoms at older age, worried that they might develop Alzheimer's disease. Some clinical practice guidelines suggest offering biomarker testing in such cases, using a brain scan or a lumbar puncture, to improve diagnostic certainty about Alzheimer's disease and enable an earlier diagnosis. Critics, on the other hand, point out that there is no effective Alzheimer treatment available and argue that biomarker tests lack clinical validity. The debate on the ethical desirability of biomarker testing is currently polarized; advocates and opponents tend to focus on their own line of arguments. In this paper, we show how the method of reflective equilibrium (RE) can be used to systematically weigh the relevant arguments on both sides of the debate to decide whether to offer Alzheimer biomarker testing. In the tradition of RE, we reflect upon these arguments in light of their coherence with other argumentative elements, including relevant facts (e.g. on the clinical validity of the test), ethical principles, and theories on societal ideals or relevant concepts, such as autonomy. Our stance in the debate therefore rests upon previously set out in-depth arguments and reflects a wide societal perspective. [ABSTRACT FROM AUTHOR]

The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer's disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder that affects millions worldwide and is expected to surge in prevalence due to aging populations. Frailty, characterized by muscle function decline, becomes more prevalent with age, imposing substantial burdens on patients and caregivers. This paper aimed to comprehensively review the current literature on AD coupled with frailty, encompassing prevalence, screening, assessment, and treatment while delving into the field's challenges and future trajectories. Frailty and AD coexist in more than 30% of cases, with hazard ratios above 120% indicating a mutually detrimental association.Various screening tools have emerged for both frailty and AD, including the Fried Frailty Phenotype (FP), FRAIL scale, Edmonton Frailty Scale (EFS), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clock Drawing Test (CDT), and General Practitioner Assessment of Cognition (GPCOG). However, none has solidified its role as the definitive gold standard. The convergence of electronic health records and brain aging biomarkers heralds a new era in AD with frailty screening and assessment. In terms of intervention, non-pharmacological strategies spanning nutrition, horticulture, exercise, and social interaction, along with pharmacological approaches involving acetylcholinesterase inhibitors (AChEIs), N-methyl-D-aspartate (NMDA) receptor antagonists, and anti-amyloid beta-protein medications, constituted cornerstones for treating AD coupled with frailty. Technological interventions like repetitive transcranial magnetic stimulation (rTMS) also entered the fold. Notably, multi-domain non-pharmacological interventions wield considerable potential in enhancing cognition and mitigating disability. However, the long-term efficacy and safety of pharmacological interventions necessitate further validation. Diagnosing and managing AD with frailty present several daunting challenges, encompassing low rates of early co-diagnosis, limited clinical trial evidence, and scarce integrated, pioneering service delivery models. These challenges demand heightened attention through robust research and pragmatic implementation. [ABSTRACT FROM AUTHOR]

Background: Literature supports the use of serious games and virtual environments to assess cognitive functions and detect cognitive decline. This promising assessment method, however, has not yet been translated into self-administered screening instruments for pre-clinical dementia.Objective: The aim of this study is to assess the performance of a novel self-administered serious game-based test, namely the Virtual Supermarket Test (VST), in detecting mild cognitive impairment (MCI) in a sample of older adults with subjective memory complaints (SMC), in comparison with two well-established screening instruments, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE).Methods: Two groups, one of healthy older adults with SMC (N = 48) and one of MCI patients (N = 47) were recruited from day centers for cognitive disorders and administered the VST, the MoCA, the MMSE, and an extended pencil and paper neuropsychological test battery.Results: The VST displayed a correct classification rate (CCR) of 81.91% when differentiating between MCI patients and older adults with SMC, while the MoCA displayed of CCR of 72.04% and the MMSE displayed a CCR of 64.89%.Conclusion: The three instruments assessed in this study displayed significantly different performances in differentiating between healthy older adults with SMC and MCI patients. The VST displayed a good CCR, while the MoCA displayed an average CCR and the MMSE displayed a poor CCR. The VST appears to be a robust tool for detecting MCI in a population of older adults with SMC. [ABSTRACT FROM AUTHOR]

Amnestic mild cognitive impairment (aMCI) is the prodromal period of more serious neurodegenerative diseases (e.g., Alzheimer's disease), characterized by declines in memory and thinking abilities. Auxiliary assessment and early diagnosis of aMCI are crucial in preventing the continued deterioration of cognitive abilities; nevertheless, this task poses a formidable challenge due to the inconspicuous nature of early symptoms. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost, and user-friendly neuroimaging technique, which is capable of detecting subtle changes in brain activity among different subjects. Moreover, multimodal fusion can assess cognition status from different perspectives and enhance auxiliary diagnosis accuracy significantly. This paper proposes an fNIRS representation and fNIRS-scales multimodal fusion method for auxiliary diagnosis of aMCI. Specifically, we convert one-dimensional time-series fNIRS signals into two-dimensional images with Gramian Angular Field and achieve end-to-end fNIRS representation with convolutional neural network. Then, we integrate the extracted features with cognitive scales at the decision-making level to improve the diagnosis accuracy of aMCI, employing the data balance strategy to prevent biased prediction. What is more, based on the fNIRS features, we also propose a data-driven cognitive scales-screening method to help the physician to assess aMCI with higher efficiency. We conducted experiments on 86 subjects (including 53 aMCI patients and 33 normal controls) recruited from Foshan First People's Hospital. The diagnosis accuracy reaches 88.02% and 93.90% with fNIRS representation and further fNIRS-scales fusion, respectively. With the cognitive scales-screening, we delete 50% scales, reducing test time but only losing 2.54% accuracy. • An efficient near-infrared characterization method based on the conversion of one-dimensional time series signals into images. • Through image conversion and data balancing, a high-precision cognitive scale and near-infrared multi-modal fusion method are proposed. • Screen cognitive scales in the preprocessing stage and fusion stage to improve detection efficiency while maintaining classification accuracy. [ABSTRACT FROM AUTHOR]

Background: In the era of disease-modifying therapies, empowering the clinical neuropsychologist's toolkit for timely identification of mild cognitive impairment (MCI) is crucial. Objective: Here we examine the clinimetric properties of the Montreal Cognitive Assessment (MoCA) for the early diagnosis of MCI due to Alzheimer's disease (MCI-AD). Methods: Data from 48 patients with MCI-AD and 47 healthy controls were retrospectively analyzed. Raw MoCA scores were corrected according to the conventional Nasreddine's 1-point correction and demographic adjustments derived from three normative studies. Optimal cutoffs were determined while previously established cutoffs were diagnostically reevaluated. Results: The original Nasreddine's cutoff of 26 and normative cutoffs (non-parametric outer tolerance limit on the 5th percentile of demographically-adjusted score distributions) were overly imbalanced in terms of Sensitivity (Se) and Specificity (Sp). The optimal cutoff for Nasreddine's adjustment showed adequate clinimetric properties (≤23.50, Se = 0.75, Sp = 0.70). However, the optimal cutoff for Santangelo's adjustment (≤22.85, Se = 0.65, Sp = 0.87) proved to be the most effective for both screening and diagnostic purposes according to Larner's metrics. The results of post-probability analyses revealed that an individual testing positive using Santangelo's adjustment combined with a cutoff of 22.85 would have 84% post-test probability of receiving a diagnosis of MCI-AD (LR+ = 5.06). Conclusions: We found a common (mal)practice of bypassing the applicability of normative cutoffs in diagnosis-oriented clinical practice. In this study, we identified optimal cutoffs for MoCA to be allocated in secondary care settings for supporting MCI-AD diagnosis. Methodological and psychometric issues are discussed. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups. Methods: The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments. Results: We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding. Conclusions: We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection. Trial registration: The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23–09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC. [ABSTRACT FROM AUTHOR]

• The review focuses on Aβ, p-tau, NfL, GFAP, NDEs, and TDP-43 as blood AD biomarkers. • Progress in the area was accelerated in part due to advanced analytic platforms. • Blood biomarkers enable broad initial screening, succeeded by in-depth assessments. • Blood biomarkers can evaluate the impact of AD-modifying treatments. • Equitable diagnosis across socioeconomics is needed to mitigate disparities. The field of blood-based biomarkers for Alzheimer's disease (AD) has advanced at an incredible pace, especially after the development of sensitive analytic platforms that can facilitate large-scale screening. Such screening will be important when more sophisticated diagnostic methods are scarce and expensive. Thus, blood-based biomarkers can potentially reduce diagnosis inequities among populations from different socioeconomic contexts. This large-scale screening can be performed so that older adults at risk of cognitive decline assessed using these methods can then undergo more complete assessments with classic biomarkers, increasing diagnosis efficiency and reducing costs to the health systems. Blood-based biomarkers can also aid in assessing the effect of new disease-modifying treatments. This paper reviews recent advances in the area, focusing on the following leading candidates for blood-based biomarkers: amyloid-beta (Aβ), phosphorylated tau isoforms (p-tau), neurofilament light (NfL), and glial fibrillary acidic (GFAP) proteins, as well as on new candidates, Neuron-Derived Exosomes contents (NDEs) and Transactive response DNA-binding protein-43 (TDP-43), based on data from longitudinal observational cohort studies. The underlying challenges of validating and incorporating these biomarkers into routine clinical practice and primary care settings are also discussed. Importantly, challenges related to the underrepresentation of ethnic minorities and socioeconomically disadvantaged persons must be considered. If these challenges are overcome, a new time of cost-effective blood-based biomarkers for AD could represent the future of clinical procedures in the field and, together with continued prevention strategies, the beginning of an era with a lower incidence of dementia worldwide. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), breast cancer (BC) and prostate cancer (PC) continue to be high in the research and innovation agenda of the European Commission (EC). This is due to their exceptionally large burden to the national health systems, the profound economic effects of opportunity costs attributable to decreased working ability, premature mortality and the ever-increasing demand for both hospital and home-based medical care. Over the last two decades, the EC has been steadily increasing both the number of proposals being funded and the amounts of financial resources being allocated to these fields of research. This trend has continued throughout four consecutive science funding cycles, namely framework programme (FP)5, FP6, FP7 and Horizon 2020 (H2020). We performed a retrospective assessment of the outputs and outcomes of EC funding in AD, BC and PC research over the 1999–2019 period by means of selected indicators. These indicators were assessed for their ability to screen the past, present and future for an array of causal relationships and long-term trends in clinical, epidemiological and public health sphere, while considering also the broader socioeconomic impact of funded research on the society at large. This analysis shows that public–private partnerships with large industry and university-based consortia have led to some of the most impactful proposals being funded over the analysed time period. New pharmaceuticals, small molecules and monoclonal antibodies alike, along with screening and prevention, have been the most prominent sources of innovation in BC and PC, extending patients' survival and enhancing their quality of life. Unlike oncology, dementia drug development has been way less successful, with only minor improvements related to the quality of supportive medical care for symptoms and more sensitive diagnostics, without any ground-breaking disease-modifying treatment(s). Significant progresses in imaging diagnostics and nanotechnology have been largely driven by the participation of medical device industry multinational companies. Clinical trials funded by the EC were conducted, leading to the development of brand-new drug molecules featuring novel mechanisms of action. Some prominent cases of breakthrough discoveries serve as evidence for the European capability to generate cutting-edge technological innovation in biomedicine. Less productive areas of research may be reconsidered as priorities when shaping the new agenda for forthcoming science funding programmes. [ABSTRACT FROM AUTHOR]

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In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer's disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon's entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures. [ABSTRACT FROM AUTHOR]

Pyrocatechol violet/copper ion-graphene oxide/alginate (PV/Cu2+-GO/Alg) hydrogel was fabricated and applied as a colorimetric sensor for monitoring urinary cysteine via an indicator-displacement assay (IDA) and Cu2+-cysteine affinity pair. The hydrogel-based sensor was formed by Ca2+ cations cross-linked PV/Cu2+-GO/Alg. The morphologies of hydrogel were characterized by field-emission scanning electron microscopy with energy-dispersive X-ray spectroscopy and Fourier-transform Raman spectroscopy. Incorporating GO into the hydrogel improved its uniformity of porosity, large surface area, and compressive strength, leading to amplified colorimetric signals of the hydrogel sensor. Under optimal conditions, this sensor offered a linear range of 0.0–0.5 g/L with a detection limit of 0.05 g/L for cysteine without interfering effects in urine. Furthermore, this hydrogel-based sensor was applied for urinary cysteine detection and validated with laser desorption ionization mass spectrometry. This platform could be used to determine cysteine at its cutoff (0.25 g/L) in human urine, which was distinguishable between normal and abnormal individuals, to evaluate an early stage of Alzheimer's disease. Eventually, this system was integrated with diapers for a wearable cysteine sensor. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate diagnosis of Alzheimer's disease (AD) in its earliest stage can prevent the disease and delay the symptoms. Therefore, more sensitive, non-invasive, and simple screening tools are required for the early diagnosis and monitoring of AD. Here, we design a self-assembled nanoparticle-mediated amplified fluorogenic immunoassay (SNAFIA) consisting of magnetic and fluorophore-loaded polymeric nanoparticles. Using a discovery cohort of 21 subjects, proteomic analysis identifies adenylyl cyclase-associated protein 1 (CAP1) as a potential tear biomarker. The SNAFIA demonstrates a low detection limit (236 aM), good reliability (R2 = 0.991), and a wide analytical range (0.320–1000 fM) for CAP1 in tear fluid. Crucially, in the verification phase with 39 subjects, SNAFIA discriminates AD patients from healthy controls with 90% sensitivity and 100% specificity in under an hour. Utilizing tear fluid as a liquid biopsy, SNAFIA could potentially aid in long-term care planning, improve clinical trial efficiency, and accelerate therapeutic development for AD. Accurate diagnosis of Alzheimer's disease in its earliest stage can prevent the disease and delay the symptoms. Here the authors identify a potential Alzheimer's biomarker from tear fluid, and develop a nanoparticle-based immunoassay for its detection, demonstrating potential in Alzheimer's diagnosis. [ABSTRACT FROM AUTHOR]

3D spheroids have emerged as powerful drug discovery tools given their high-throughput screening (HTS) compatibility. Here, we describe a method for generating functional neural spheroids by cell-aggregation of differentiated human induced pluripotent stem cell (hiPSC)-derived neurons and astrocytes at cell type compositions mimicking specific regions of the human brain. Recordings of intracellular calcium oscillations were used as functional assays, and the utility of this spheroids system was shown through disease modeling, drug testing, and formation of assembloids to model neurocircuitry. As a proof of concept, we generated spheroids incorporating neurons with Alzheimer's disease-associated alleles, as well as opioid use disorder modeling spheroids induced by chronic treatment of a mu-opioid receptor agonist. We reversed baseline functional deficits in each pilot disease model with clinically approved treatments and showed that assembloid activity can be chemogenetically manipulated. Here, we lay the groundwork for brain region-specific neural spheroids as a robust functional assay platform for HTS studies. The authors describe a method for generating functional neural spheroids and assembloids for disease modeling and drug discovery by cell aggregation of differentiated human iPSC derived neurons and astrocytes at cell type compositions mimicking specific regions of the human brain. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders, such as Alzheimer's disease (AD), negatively affect the economic and psychological system. For AD, there is still a lack of disease-altering treatments and promising cures due to its complex pathophysiology. In this study, we computationally screened the natural database of fungal metabolites against three known therapeutic target proteins of AD. Initially, a pharmacophore-based, drug-likeness category was employed for screening, and it filtered the 14 (A–N) best hits out of 17,544 fungal metabolites. The 14 best hits were docked individually against GSK-3β, the NMDA receptor, and BACE-1 to investigate the potential of finding a multitarget inhibitor. We found that compounds B, F, and L were immuno-toxic, whereas E, H, I, and J had a higher LD50 dose (5000 mg/kg). Among the examined metabolites, the Bisacremine-C (compound I) was found to be the most active molecule against GSK-3β (ΔG: −8.7 ± 0.2 Kcal/mol, Ki: 2.4 × 106 M−1), NMDA (ΔG: −9.5 ± 0.1 Kcal/mol, Ki: 9.2 × 106 M−1), and BACE-1 (ΔG: −9.1 ± 0.2 Kcal/mol, Ki: 4.7 × 106 M−1). It showed a 25-fold higher affinity with GSK-3β, 6.3-fold higher affinity with NMDA, and 9.04-fold higher affinity with BACE-1 than their native ligands, respectively. Molecular dynamic simulation parameters, such as RMSD, RMSF, Rg, and SASA, all confirmed that the overall structures of the targeted enzymes did not change significantly after binding with Bisacremine-C, and the ligand remained inside the binding cavity in a stable conformation for most of the simulation time. The most significant hydrophobic contacts for the GSK-3β-Bisacremine-C complex are with ILE62, VAL70, ALA83, and LEU188, whereas GLN185 is significant for H-bonds. In terms of hydrophobic contacts, TYR184 and PHE246 are the most important, while SER180 is vital for H-bonds in NMDA-Bisacremine-C. THR232 is the most crucial for H-bonds in BACE-1-Bisacremine-C and ILE110-produced hydrophobic contacts. This study laid a foundation for further experimental validation and clinical trials regarding the biopotency of Bisacremine-C. [ABSTRACT FROM AUTHOR]

Uncaria rhynchophylla (Gouteng in Chinese, GT) is the main medicine in many traditional recipes in China. It is commonly used to alleviate central nervous system (CNS) disorders, although its mechanism in Alzheimer's disease is still unknown. This study was designed to predict and validate the underlying mechanism in AD treatment, thus illustrating the biological mechanisms of GT in treating AD. In this study, a PPI network was constructed, KEGG analysis and GO analysis were performed, and an "active ingredient-target-pathway" network for the treatment of Alzheimer's disease was constructed. The active ingredients of GT were screened out, and the key targets were performed by molecular docking. UHPLC-Q-Exactive Orbitrap MS was used to screen the main active ingredients and was compared with the network pharmacology results, which verified that GT did contain the above ingredients. A total of targets were found to be significantly bound up with tau, Aβ, or Aβ and tau through the network pharmacology study. Three SH-SY5Y cell models induced by okadaic acid (OA), Na2S2O4, and H2O2 were established for in vitro validation. We first found that GT can reverse the increase in the hyperphosphorylation of tau induced by OA to some extent, protecting against ROS damage. Moreover, the results also indicated that GT has significant neuroprotective effects. This study provides a basis for studying the potential mechanisms of GT in the treatment of AD. [ABSTRACT FROM AUTHOR]

Background: Clock Drawing Test (CDT) is a commonly used screening tool for cognitive disorders, known for its ease of administration and scoring. Despite frequent use by clinicians, CDT is criticized for its poor predictive value in mild cases of impairment. Objective: To evaluate CDT as a screening tool for early stage of cognitive impairment in biomarker-verified Alzheimer's disease (AD) and depressive disorder (DD). Methods: We analyzed CDT of 172 patients with verified AD, 70 patients with DD, in whom neurodegenerative disorder was excluded using cerebrospinal fluid biomarkers, and 58 healthy older adults. CDT was scored using the semi-quantitative (Shulman) and itemized criteria (adapted from Mendez). Results: Logistic regression showed that for both DD and AD patients with high Mini-Mental State Examination (MMSE) scores (27 and above) the significant predicting variable is uneven number spacing. As MMSE deteriorates (24-26 points), an additional error of setting clock hands is predictive of the disease. In the low MMSE condition, CDT showed an acceptable discrimination for AD (AUC itemized 0.740, Shulman 0.741) and DD (AUC itemized 0.827, Shulman 0.739) using both scoring methods. In the high MMSE condition, discrimination rates were acceptable using itemized scoring but poor using Shulman scoring for both AD (AUC itemized 0.707, Shulman 0.677) and DD (AUC itemized 0.755, Shulman 0.667) groups. Conclusion: Ideally, modern diagnostic process should take place before the cognitive performance drops beneath the healthy range. This makes CDT of little use when screening patients with very mild cognitive deficits. [ABSTRACT FROM AUTHOR]

Background: In Kenya, many people are currently living with dementia without a formal diagnosis or support; often attributing symptoms to normal aging or as a consequence of past behaviors. Dementia screening is not commonplace within Kenya. Improving the supply (or opportunity) of dementia screening within the region may promote uptake, thus leading to more people to seek a formal diagnosis and subsequently receive support within the Kenyan healthcare system. Community Healthcare Workers (CHWs) have successfully demonstrated their value in delivering health interventions within Kenya and have strong links within local communities. Objective: To integrate and evaluate a community-level dementia screening program among older adults in rural Kenya. Methods: Through leveraging this resource, we will deliver dementia screening to older adults (≥60 years) within Makueni County, Kenya over a 6-month period. Here, we present a protocol for the process evaluation of a dementia screening program in Kenya — DEM-SKY. The process evaluation seeks to understand the adoption, implementation, continuation, and implementation determinants, using quantitative and qualitative measures. Conclusions: Gaining perspectives of different participants involved in the program (i.e., older adults, CHWs, hospital staff, and trainers), will ensure that we understand the reason for successful (or unsuccessful) delivery of DEM-SKY. [ABSTRACT FROM AUTHOR]

Background: Pre-symptomatic screening methods for detecting a higher risk of Alzheimer's disease (AD) are gaining popularity; thus, more people are seeking these tests. However, to date, not much is known about the attitudes toward pre-symptomatic AD screening. Objective: The goal of this study is to examine the psychometric properties of a tool for assessing the attitudes, barriers, and motivations to pre-symptomatic AD screening. Methods: This is a cross-sectional study performed on 208 Greek participants (189 students and 19 caregivers) provided with an online questionnaire. Psychometric properties were assessed through the examination of its construct validity (principal component analysis) and internal consistency. Results: Exploratory factor analysis revealed the presence of four factors. The first factor is labeled as "Perceived harms of testing" (10 items), the second "Acceptance of testing" (5 items), the third "Perceived benefits of testing" (6 items), and the fourth factor "Need for knowledge" (4 items). The reliability (internal consistency) of each factor separately was acceptable to good (0.70–0.87) while the internal consistency of the overall questionnaire (25 items) was good (Cronbach's α=0.82). Conclusion: PRE-ADS is a valid questionnaire that might help in the research of peoples' attitudes related to the pros and cons of pre-symptomatic screening for AD, and the development of effective counseling programs and prevention strategies. However, future research is required in the target population. [ABSTRACT FROM AUTHOR]

Background: A rapid digital instrument is needed to facilitate community-based screening of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in China. Objective: We developed a voice recognition-based cognitive assessment (Shanghai Cognitive Screening, SCS) on mobile devices and evaluated its diagnostic performance. Methods: Participants (N = 251) including healthy controls (N = 98), subjective cognitive decline (SCD, N = 42), MCI (N = 80), and mild AD (N = 31) were recruited from the memory clinic at Shanghai Sixth People's Hospital. The SCS is fully self-administered, takes about six minutes and measures the function of visual memory, language, and executive function. Participants were instructed to complete SCS tests, gold-standard neuropsychological tests and standardized structural 3T brain MRI. Results: The Cronbach's alpha was 0.910 of the overall scale, indicating high internal consistency. The SCS total score had an AUC of 0.921 to detect AD (sensitivity = 0.903, specificity = 0.945, positive predictive value = 0.700, negative predictive value = 0.986, likelihood ratio = 16.42, number needed for screening utility = 0.639), and an AUC of 0.838 to detect MCI (sensitivity = 0.793, specificity = 0.671, positive predictive value = 0.657, negative predictive value = 0.803, likelihood ratio = 2.41, number needed for screening utility = 0.944). The subtests demonstrated moderate to high correlations with the gold-standard tests from their respective cognitive domains. The SCS total score and its memory scores all correlated positively with relative volumes of the whole hippocampus and almost all subregions, after controlling for age, sex, and education. Conclusion: The SCS has good diagnostic accuracy for detecting MCI and AD dementia and has the potential to facilitate large-scale screening in the general community. [ABSTRACT FROM AUTHOR]

Background: The Rey Complex Figure Test (RCFT) has been widely used to evaluate the neurocognitive functions in various clinical groups with a broad range of ages. However, despite its usefulness, the scoring method is as complex as the figure. Such a complicated scoring system can lead to the risk of reducing the extent of agreement among raters. Although several attempts have been made to use RCFT in clinical settings in a digitalized format, little attention has been given to develop direct automatic scoring that is comparable to experienced psychologists. Therefore, we aimed to develop an artificial intelligence (AI) scoring system for RCFT using a deep learning (DL) algorithm and confirmed its validity. Methods: A total of 6680 subjects were enrolled in the Gwangju Alzheimer's and Related Dementia cohort registry, Korea, from January 2015 to June 2021. We obtained 20,040 scanned images using three images per subject (copy, immediate recall, and delayed recall) and scores rated by 32 experienced psychologists. We trained the automated scoring system using the DenseNet architecture. To increase the model performance, we improved the quality of training data by re-examining some images with poor results (mean absolute error (MAE) ≥ 5 [points]) and re-trained our model. Finally, we conducted an external validation with 150 images scored by five experienced psychologists. Results: For fivefold cross-validation, our first model obtained MAE = 1.24 [points] and R-squared ( R 2 ) = 0.977. However, after evaluating and updating the model, the performance of the final model was improved (MAE = 0.95 [points], R 2 = 0.986). Predicted scores among cognitively normal, mild cognitive impairment, and dementia were significantly different. For the 150 independent test sets, the MAE and R 2 between AI and average scores by five human experts were 0.64 [points] and 0.994, respectively. Conclusion: We concluded that there was no fundamental difference between the rating scores of experienced psychologists and those of our AI scoring system. We expect that our AI psychologist will be able to contribute to screen the early stages of Alzheimer's disease pathology in medical checkup centers or large-scale community-based research institutes in a faster and cost-effective way. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the most common cause of dementia, and due to increasing life expectancy the number of patients is expected to grow. The diagnosis of AD involves the use of biomarkers determined by an amyloid PET scan or cerebrospinal fluid analyses that are either invasive or expensive, and not available in each hospital, thus limiting their usage as a front-line screener. The TearAD study aims to use tear fluid as a potential source for AD biomarkers. In previous reports, we demonstrated that AD biomarkers amyloid-beta and tau, are measurable in tear fluid and are associated with disease severity and neurodegeration. This study aims to validate previous results in a larger cohort and evaluate the diagnostic accuracy of tear biomarkers to discriminate between individuals with and without neurodegeneration as determined by hippocampal atrophy. Methods: The TearAD study is an observational longitudinal multi-center study that will enroll 50 cognitively healthy controls, 50 patients with subjective cognitive decline, 50 patients with mild cognitive impairment and 50 patients with AD dementia from the memory clinic. Participants will be examined at baseline, after one year, and after two years follow-up. Study assessments include neuropsychological tests and ophthalmic examination. All participants will receive a MRI scan, and a subset of the study population will undergo cerebral spinal fluid collection and an amyloid PET scan. Tear fluid will be collected with Schirmer strips and levels of Aβ38, Aβ40, Aβ42, t-tau and p-tau in tear fluid will be determined using multiplex immunoassays. Blood samples will be collected from all participants. Images of the retina will be obtained with a standard, hyperspectral and ultra-wide field fundus camera. Additionally, macular pigment optical density will be measured with the macular pigment reflectometer, and cross-sectional images of the retina will be obtained through optical coherence tomography imaging. Discussion: The TearAD study will provide insight into the potential diagnostic use of tear biomarkers as a minimally invasive and low cost tool for the screening and diagnosis of AD. Trial registration: Retrospectively registered at clinicaltrials.gov (NCT05655793). [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) is a disease continuum from pathophysiologic, biomarker and clinical perspectives. With the advent of advanced technologies, diagnosing and managing patients is evolving. Methods: A systematic literature review (SLR) of practice guidelines for mild cognitive impairment (MCI) and AD dementia was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This systematic literature review (SLR) aimed to summarize current clinical practice guidelines for screening, testing, diagnosis, treatment and monitoring in the AD continuum. The results of this SLR were used to propose a way forward for practice guidelines given the possible introduction of biomarker-guided technology using blood- or plasma-based assays and disease-modifying treatments (DMTs) targeted for early disease. Results: 53 clinical practice guidelines were identified, 15 of which were published since 2018. Screening for asymptomatic populations was not recommended. Biomarker testing was not included in routine diagnostic practice. There was no consensus on which neurocognitive tests to use to diagnose and monitor MCI or AD dementia. Pharmacologic therapies were not recommended for MCI, while cholinesterase inhibitors and memantine were recommended for AD treatment. Discussion: The pre-2018 and post-2018 practice guidelines share similar recommendations for screening, diagnosis and treatment. However, once DMTs are approved, clinicians will require guidance on the appropriate use of DMTs in a clinical setting. This guidance should include strategies for identifying eligible patients and evaluating the DMT benefit-to-risk profile to facilitate shared decision-making among physicians, patients and care partners. Conclusion: Regular evidence-based updates of existing guidelines for the AD continuum are required over the coming decades to integrate rapidly evolving technologic and medical scientific advances and bring emerging approaches for management of early disease into clinical practice. This will pave the way toward biomarker-guided identification and targeted treatment and the realization of precision medicine for AD. [ABSTRACT FROM AUTHOR]

Introduction: The risk of developing Alzheimer's disease (AD) in older adults increasingly is being discussed in the literature on Post-Acute COVID-19 Syndrome (PACS). Remote digital Assessments for Preclinical AD (RAPAs) are becoming more important in screening for early AD, and should always be available for PACS patients, especially for patients at risk of AD. This systematic review examines the potential for using RAPA to identify impairments in PACS patients, scrutinizes the supporting evidence, and describes the recommendations of experts regarding their use. Methods: We conducted a thorough search using the PubMed and Embase databases. Systematic reviews (with or without meta-analysis), narrative reviews, and observational studies that assessed patients with PACS on specific RAPAs were included. The RAPAs that were identified looked for impairments in olfactory, eye-tracking, graphical, speech and language, central auditory, or spatial navigation abilities. The recommendations' final grades were determined by evaluating the strength of the evidence and by having a consensus discussion about the results of the Delphi rounds among an international Delphi consensus panel called IMPACT, sponsored by the French National Research Agency. The consensus panel included 11 international experts from France, Switzerland, and Canada. Results: Based on the available evidence, olfaction is the most long-lasting impairment found in PACS patients. However, while olfaction is the most prevalent impairment, expert consensus statements recommend that AD olfactory screening should not be used on patients with a history of PACS at this point in time. Experts recommend that olfactory screenings can only be recommended once those under study have reported full recovery. This is particularly important for the deployment of the olfactory identification subdimension. The expert assessment thatmore long-termstudies are needed after a period of full recovery, suggests that this consensus statement requires an update in a few years. Conclusion: Based on available evidence, olfaction could be long-lasting in PACS patients. However, according to expert consensus statements, AD olfactory screening is not recommended for patients with a history of PACS until complete recovery has been confirmed in the literature, particularly for the identification sub-dimension. This consensus statement may require an update in a few years. [ABSTRACT FROM AUTHOR]

Phosphorylation of tau at Ser (396, 404) (p-tau396,404) is one of the earliest phosphorylation events, and plasma p-tau396,404 level appears to be a potentially promising biomarker of Alzheimer's disease (AD). The low abundance and easy degradation of p-tau in the plasma make the lateral flow assay (LFA) a suitable choice for point-of-care detection of plasma p-tau396,404 levels. Herein, based on our screening of a pair of p-tau396,404-specific antibodies, we developed a colorimetric and surface-enhanced Raman scattering (SERS) dual-readout LFA for the rapid, highly sensitive, and robust detection of plasma p-tau396,404 levels. This LFA realized a detection limit of 60 pg/mL by the naked eye or 3.8 pg/mL by SERS without cross-reacting with other tau species. More importantly, LFA rapidly and accurately differentiated AD patients from healthy controls, suggesting that it has the potential for clinical point-of-care application in AD diagnosis. This dual-readout LFA has the advantages of simple operation, rapid, and ultra-sensitive detection, providing a new way for early AD diagnosis and intervention, especially in primary and community AD screening. [ABSTRACT FROM AUTHOR]

The 1980s saw an upsurge of research in Alzheimer's disease (AD). The necessity of standardized assessment batteries became apparent, leading to the development of standardized instruments, such as the CERAD, the CAMDEX, the CSI 'D', and later the TOOLBOX. The advent of new biological markers has led to speculation in the research community about the necessity for these instruments. As the association of biomarkers with subsequent clinical dementia remains unclear, assessment batteries are still necessary, especially with growing evidence that prodromal symptoms of AD may not be cognitive decline but emotional or behavioral symptoms. Inclusion of ethnic minority groups is also essential. [ABSTRACT FROM AUTHOR]

Cognitive screening instruments (CSI) have variable sensitivity and specificity to the cognitive changes associated with dementia syndromes, and the most recent systematic review found insufficient evidence to support the benefit of cognitive screening tools in older adults residing within the community. Consequently, there is a critical need to improve CSI methods, which have not yet incorporated advances in psychometrics, neuroscience, and technology. The primary goal of this article is to provide a framework for transitioning from legacy CSIs to advanced dementia screening measurement. In line with ongoing efforts in neuropsychology and the call for next-generation digital assessment for early detection of AD, we propose a psychometrically advanced (including application of item response theory methods), automated selective assessment model that provides a framework to help propel an assessment revolution. Further, we present a three-phase model for modernizing CSIs and discuss critical diversity and inclusion issues, current challenges in differentiating normal from pathological aging, and ethical considerations. [ABSTRACT FROM AUTHOR]

Alzheimer's pathology can be assessed and defined via Aβ and tau biomarkers. The preclinical period of Alzheimer's disease is long and lasts several decades. Although effective therapies to block pathological processes of Alzheimer's disease are still lacking, downward trends in the incidence and prevalence of dementia have occurred in developed countries. Accumulating findings support that education, cognitive training, physical exercise/activities, and a healthy lifestyle can protect cognitive function and promote healthy aging. Many studies focus on detecting mild cognitive impairment (MCI) and take a variety of interventions in this stage to protect cognitive function. However, when Alzheimer's pathology advances to the stage of MCI, interventions may not be successful in blocking the development of the pathological process. MCI individuals reverting to normal cognitive function exhibited a high probability to progress to dementia. Therefore, it is necessary to take effective measures before the MCI stage. Compared with MCI, an earlier stage, transitional cognitive decline, may be a better time window in which effective interventions are adopted for at-risk individuals. Detecting this stage in large populations relies on rapid screening of cognitive function; given that many cognitive tests focus on MCI detection, new tools need to be developed. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disease without known cure. However, early medical treatment can help control its progression and postpone intellectual decay. Since AD is preceded by a period of cognitive deterioration, the effective assessment of cognitive capabilities is crucial to develop reliable screening procedures. For this purpose, cognitive tests are extensively used to evaluate cognitive areas such as language, attention, or memory. Methods: In this work, we analyzed the potential of a visual dynamics evaluation, the rapid serial visual presentation task (RSVP), for the detection of cognitive impairment in AD. We compared this evaluation with two of the most extended brief cognitive tests applied in Spain: the Clock-drawing test (CDT) and the Phototest. For this purpose, we assessed a group of patients (mild AD and mild cognitive impairment) and controls, and we evaluated the ability of the three tests for the discrimination of the two groups. Results: The preliminary results obtained suggest the RSVP performance is statistically higher for the controls than for the patients (p-value = 0.013). Furthermore, we obtained promising classification results for this test (mean accuracy of 0.91 with 95% confidence interval 0.72, 0.97). Conclusions: Since the RSVP is a computerized, auto-scored, and potentially self-administered brief test, it could contribute to speeding-up cognitive impairment screening and to reducing the associated costs. Furthermore, this evaluation could be combined with other tests to augment the efficiency of cognitive impairment screening protocols and to potentially monitor patients under medical treatment. [ABSTRACT FROM AUTHOR]

Background: The self-administered iPad-based Cleveland Clinic Cognitive Battery (C3B) was designed specifically for the efficient screening of cognitive functioning of older adults in a primary care setting. Objective: 1) Generate regression-based norms from healthy participants to enable demographic corrections to facilitate clinical interpretation; 2) estimate test-retest reliability and practice effects; 3) examine ability to discriminate mild cognitive impairment (MCI) from healthy aging; 4) d etermine validity of screening in a distracting clinical environment; and 5) determine completion rates and patient satisfaction in a primary care setting. Methods: Study 1 (S1) recruited a stratified sample of 428 healthy adults, ages 18–89, to generate regression-based equations. S2 assessed 2-week test-retest reliability and practice effects in 30 healthy elders. S3 recruited 30 MCI patients and 30 demographically-matched healthy controls. In S4, 30 healthy elders self-administered the C3B in a distracting environment and in a quiet private room in counterbalanced order. In a demonstration project, 470 consecutive primary care patients were administered the C3B as part of routine clinical care (S5). Results: C3B performance was primarily influenced by age, education, and race (S1), had acceptably high test-retest reliability and minimal practice effects (S2), discriminated MCI from healthy controls (S3), was not negatively impacted by a distracting clinical environment (S4), had high completion rates (>92%) and positive ratings from primary care patients (S5). Conclusion: The C3B is a computerized cognitive screening tool that is reliable, validated, self-administered, and is conducive to integration into a busy primary care clinical workflow for detecting MCI, early Alzheimer's disease, and other related dementias. [ABSTRACT FROM AUTHOR]

Background: Detecting early-stage Alzheimer's disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process. Objective: Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine. Methods: The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model. Results: The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. Conclusion: The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening. [ABSTRACT FROM AUTHOR]

Alzheimer is a severe memory and cognitive impairment neurodegenerative disease that is the most common cause of dementia worldwide and characterized by the pathological accumulation of tau protein and amyloid-beta peptides. In this study, we have developed E-pharmacophore modeling to screen the eMolecules database with the help of a reported co-crystal structure bound with Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE-1). Flumemetamol, florbetaben, and florbetapir are currently approved drugs for use in the clinical diagnosis of Alzheimer's disease. Despite the benefits of commercially approved drugs, there is still a need for novel diagnostic agents with enhanced physicochemical and pharmacokinetic properties compared to those currently used in clinical practice and research. In the E-pharmacophore modeling results, it is revealed that two aromatic rings (R19, R20), one donor (D12), and one acceptor (A8) are obtained, and also that similar pharmacophoric features of compounds are identified from pharmacophore-based virtual screening. The identified screened hits were filtered for further analyses using structure-based virtual screening and MM/GBSA. From the analyses, top hits such as ZINC39592220 and en1003sfl.46293 are selected based on their top docking scores (−8.182 and −7.184 Kcal/mol, respectively) and binding free energy (−58.803 and −56.951 Kcal/mol, respectively). Furthermore, a molecular dynamics simulation and MMPBSA study were performed, which revealed admirable stability and good binding free energy throughout the simulation period. Moreover, Qikprop results revealed that the selected, screened hits have good drug-likeness and pharmacokinetic properties. The screened hits ZINC39592220 and en1003sfl.46293 could be used to develop drug molecules against Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective. [ABSTRACT FROM AUTHOR]

Introduction: Preclinical Alzheimer's disease (AD) represents the earliest phase of AD, often years before the onset of mild cognitive impairment (MCI). There is a pressing focus on identifying individuals in the preclinical AD phase to alter the trajectory or impact of the disease potentially. Increasingly, Virtual Reality (VR) technology is being used to support a diagnosis of AD. While VR technology has been applied to the assessment of MCI and AD, studies about how best to utilize VR as a screening tool for preclinical AD are limited and discordant. The objectives of this review are to synthesize the evidence pertaining to the use of VR as a screening tool for preclinical AD as well as to identify factors that need to be considered when utilizing VR to screen for preclinical AD. Methods and analysis: The methodological framework proposed by Arksey and O'Malley (2005) will be introduced to guide the conduction of the scoping review, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) (2018) will be used to organize and structure the review. PubMed, Web of Science, Scopus, ScienceDirect and Google Scholar will be used to search for literature. Obtained studies will be screened for eligibility based on predefined exclusion criteria. A narrative synthesis of eligible studies will be performed, after tabulating the extracted data from existing literature, to answer the research questions. Ethics and dissemination: Ethical approval is not required for this scoping review. Findings will be disseminated through conference presentations, publication in a peer-reviewed journal, and discussions among professional networks in the research domain combining neuroscience and information and communications technology (ICT). Registration details: This protocol has been registered on Open Science Framework (OSF). Relevant materials and potential following updates are available at https://osf.io/aqmyu. [ABSTRACT FROM AUTHOR]

Background: Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. Objective: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. Methods: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. Results: The algorithms achieved a performance of AUC 0.73 and AUC 0.77 in the respective training cohorts and AUC 0.81 in the unseen validation cohorts. Conclusion: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care. [ABSTRACT FROM AUTHOR]

The accumulation of cross-β-sheet amyloid fibrils is a hallmark of the neurodegenerative process of Alzheimer's disease (AD). Although it has been reported that green tea substances such as epicatechin (EC), epicatechin-3-gallate (ECG), epigallocatechin (EGC) and epigallocatechin-3-gallate (EGCG) could alleviate the symptoms of AD and other neurodegenerative diseases, the pharmacological mechanism remains largely unexplored. This study aimed to reveal the underlying mechanism of EC, ECG, EGC and EGCG in AD using a computer-aided screening strategy. Our results showed that the four tea polyphenols interfered with the signaling pathways of AD via calcium signaling channels, neurodegeneration-multiple disease signal pathways and others. We also identified the key residues of the interaction between VEGFA and the four active components, which included Glu64 and Phe36. Overall, we have provided valuable insights into the molecular mechanism of tea polyphenols, which could be used as a reference to improve therapeutic strategies against AD. [ABSTRACT FROM AUTHOR]

Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer's disease neuroimaging initiative study further illustrates the utility of the methodology. [ABSTRACT FROM AUTHOR]

In a recent issue of the Journal of Alzheimer's Disease, Zinman et al. investigated the role of sex in the risk of cognitive impairment in 5,969 patients with a stroke or transient ischemic attacks. Using a short validated clinical screening tool, they noted that men had a 34% higher risk of screening positive for post-stroke cognitive impairment after adjusting for age, education, and stroke severity compared to women. This study highlights that more large, prospective, and multicenter studies are needed to evaluate sex-specific changes after a stroke since sex differences exist in many aspects of stroke presentation and management. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is known to be caused by amyloid β-peptide (Aβ) misfolded into β-sheets, but this knowledge has not yet led to treatments to prevent AD. To identify novel molecular players in Aβ toxicity, we carried out a genome-wide screen in Saccharomyces cerevisiae, using a library of 5154 gene knock-out strains expressing Aβ1–42. We identified 81 mammalian orthologue genes that enhance Aβ1–42 toxicity, while 157 were protective. Next, we performed interactome and text-mining studies to increase the number of genes and to identify the main cellular functions affected by Aβ oligomers (oAβ). We found that the most affected cellular functions were calcium regulation, protein translation and mitochondrial activity. We focused on SURF4, a protein that regulates the store-operated calcium channel (SOCE). An in vitro analysis using human neuroblastoma cells showed that SURF4 silencing induced higher intracellular calcium levels, while its overexpression decreased calcium entry. Furthermore, SURF4 silencing produced a significant reduction in cell death when cells were challenged with oAβ1–42, whereas SURF4 overexpression induced Aβ1–42 cytotoxicity. In summary, we identified new enhancer and protective activities for Aβ toxicity and showed that SURF4 contributes to oAβ1–42 neurotoxicity by decreasing SOCE activity. [ABSTRACT FROM AUTHOR]

Cognitive screening is often a first step to document cognitive status of patients suspected having Alzheimer's disease (AD). Unfortunately, screening neuropsychological tests are often insensitivity in the detection. The goal of this study was to develop a simple and sensitive screening neuropsychological test to facilitate early detection of AD. This study recruited 761 elderly individuals suspected of having AD and presenting various cognitive statuses (mean age: 77.69 ± 8.45 years; proportion of females: 65%; cognitively unimpaired, CU, n = 133; mild cognitive impairment, MCI, n = 231; dementia of Alzheimer's type, DAT, n = 397). This study developed a novel screening neuropsychological test incorporating assessments of the core memory deficits typical of early AD and an interview on memory function with an informant. The proposed History-based Artificial Intelligence-Show Chwan Assessment of Cognition (HAI-SAC) was assessed in terms of psychometric properties, test time, and discriminative ability. The results were compared with those obtained using other common screening tests, including Cognitive Abilities Screening Instrument (CASI), Montreal Cognitive Assessment (MoCA), and an extracted Mini-Mental State Examination score from CASI. HAI-SAC demonstrated acceptable internal consistency. Factor analysis revealed two factors: memory (semantic and contextual) and cognition-related information from informants. The assessment performance of HAI-SAC was strongly correlated with that of the common screening neuropsychological tests addressed in this study. HAI-SAC outperformed the other tests in differentiating CU individuals from patients with MCI (sensitivity: 0.87; specificity: 0.58; area under the curve [AUC]: 0.78) or DAT (sensitivity: 0.99; specificity: 0.89; AUC: 0.98). Performance of HAI-SAC on differentiating MCI from DAT was on par with performances of other tests (sensitivity: 0.78; specificity: 0.84; AUC: 0.87), while the test time was less than one quarter that of CASI and half that of MoCA. HAI-SAC is psychometrically sound, cost-effective, and sensitive in discriminating the cognitive status of AD. [ABSTRACT FROM AUTHOR]

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