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Background: There is no molecular test for Alzheimer's disease (AD) using self-collected samples, nor is there a definitive molecular test for AD. We demonstrate an accurate and potentially definitive TempO-Seq® gene expression test for AD using fingerstick blood spotted and dried on filter paper, a sample that can be collected in any doctor's office or can be self-collected. Objective: Demonstrate the feasibility of developing an accurate test for the classification of persons with AD from a minimally invasive sample of fingerstick blood spotted on filter paper which can be obtained in any doctor's office or self-collected to address health disparities. Methods: Fingerstick blood samples from patients clinically diagnosed with AD, Parkinson's disease (PD), or asymptomatic controls were spotted onto filter paper in the doctor's office, dried, and shipped to BioSpyder for testing. Three independent patient cohorts were used for training/retraining and testing/retesting AD and PD classification algorithms. Results: After initially identifying a 770 gene classification signature, a minimum set of 68 genes was identified providing classification test areas under the ROC curve of 0.9 for classifying patients as having AD, and 0.94 for classifying patients as having PD. Conclusions: These data demonstrate the potential to develop a screening and/or definitive, minimally invasive, molecular diagnostic test for AD and PD using dried fingerstick blood spot samples that are collected in a doctor's office or clinic, or self-collected, and thus, can address health disparities. Whether the test can classify patients with AD earlier then possible with cognitive testing remains to be determined. [ABSTRACT FROM AUTHOR]

Simple, portable, and low-cost paper-based sensors are alternative devices that have the potential to replace high-cost sensing technologies. The compatibility of the paper base biosensors for both chemical and biochemical accentuates its feasibility for application in clinical diagnosis, environmental monitoring, and food quality monitoring. High concentration of copper in blood serum and urine is associated with diseases like liver diseases, carcinomas, acute and chronic infections, rheumatoid arthritis, etc. Detection of copper concentration can give an early sign of Alzheimer disease. Apart from that genetic Wilson's disease can be detected by evaluating the concentration of copper in the urine. In view of the above advantages, a novel and the highly sensitive paper-based sensor has been designed for the selective detection of Cu2+ ions. The fast and highly sensitive chemiresistive multi-dye system sensor can detect Cu2+ ions selectively in as low as 2.23 ppm concentration. Least interference has been observed for counter ion in the detection of Cu2+. Copper chloride, nitrate, and acetate were used to validate the detection process. This assay provides a very high selectivity of Cu2+ ion over other metal cations such as Na+, Mg2+, Ca2+, etc. The easy preparation and high stability of dye solutions, easy functionalization of the paper-based sensors, high selectivity over other cations, low interference of counter anion, and significantly low detection limit of 2.23 ppm make it an effective Cu2+ ion sensor for real-time application in near future. [ABSTRACT FROM AUTHOR]

Given the key roles of the cerebellum in motor, cognitive, and affective operations and given the decline of brain functions with aging, cerebellar circuitry is attracting the attention of the scientific community. The cerebellum plays a key role in timing aspects of both motor and cognitive operations, including for complex tasks such as spatial navigation. Anatomically, the cerebellum is connected with the basal ganglia via disynaptic loops, and it receives inputs from nearly every region in the cerebral cortex. The current leading hypothesis is that the cerebellum builds internal models and facilitates automatic behaviors through multiple interactions with the cerebral cortex, basal ganglia and spinal cord. The cerebellum undergoes structural and functional changes with aging, being involved in mobility frailty and related cognitive impairment as observed in the physio-cognitive decline syndrome (PCDS) affecting older, functionally-preserved adults who show slowness and/or weakness. Reductions in cerebellar volume accompany aging and are at least correlated with cognitive decline. There is a strongly negative correlation between cerebellar volume and age in cross-sectional studies, often mirrored by a reduced performance in motor tasks. Still, predictive motor timing scores remain stable over various age groups despite marked cerebellar atrophy. The cerebello-frontal network could play a significant role in processing speed and impaired cerebellar function due to aging might be compensated by increasing frontal activity to optimize processing speed in the elderly. For cognitive operations, decreased functional connectivity of the default mode network (DMN) is correlated with lower performances. Neuroimaging studies highlight that the cerebellum might be involved in the cognitive decline occurring in Alzheimer's disease (AD), independently of contributions of the cerebral cortex. Grey matter volume loss in AD is distinct from that seen in normal aging, occurring initially in cerebellar posterior lobe regions, and is associated with neuronal, synaptic and beta-amyloid neuropathology. Regarding depression, structural imaging studies have identified a relationship between depressive symptoms and cerebellar gray matter volume. In particular, major depressive disorder (MDD) and higher depressive symptom burden are associated with smaller gray matter volumes in the total cerebellum as well as the posterior cerebellum, vermis, and posterior Crus I. From the genetic/epigenetic standpoint, prominent DNA methylation changes in the cerebellum with aging are both in the form of hypo- and hyper-methylation, and the presumably increased/decreased expression of certain genes might impact on motor coordination. Training influences motor skills and lifelong practice might contribute to structural maintenance of the cerebellum in old age, reducing loss of grey matter volume and therefore contributing to the maintenance of cerebellar reserve. Non-invasive cerebellar stimulation techniques are increasingly being applied to enhance cerebellar functions related to motor, cognitive, and affective operations. They might enhance cerebellar reserve in the elderly. In conclusion, macroscopic and microscopic changes occur in the cerebellum during the lifespan, with changes in structural and functional connectivity with both the cerebral cortex and basal ganglia. With the aging of the population and the impact of aging on quality of life, the panel of experts considers that there is a huge need to clarify how the effects of aging on the cerebellar circuitry modify specific motor, cognitive, and affective operations both in normal subjects and in brain disorders such as AD or MDD, with the goal of preventing symptoms or improving the motor, cognitive, and affective symptoms. [ABSTRACT FROM AUTHOR]

Enzyme-inorganic hybrid nanoflowers (HNFs) have shown excellent sensing capabilities due to their large specific surface area as well as the simplicity and mildness of the preparation process. However, coupling HNFs to electrodes to fabricate a uniform and controllable enzymatic electrochemical sensing interface remains a challenge. Here, we proposed an aptamer-induced in-situ fabrication strategy for preparing an HNF-based electrochemical sensor with ideal performance. Central to this strategy is the introduction of acetylcholinesterase (AChE)-specific binding aptamer (Apt), which induces the in-situ growth of AChE-copper phosphate (Cu3(PO4)2) HNFs on the surface of carbon paper (CP). In addition, a dense gold nanoparticle (AuNP) layer was electrodeposited on the CP for anchoring Apt and further extending the electroactive surface area. The prepared AChE-Cu3(PO4)2 HNF/Apt/AuNP/CP biosensor exhibited a wide detection range from 1 to 107 pM for the four organophosphorus inhibitors, including isocarbophos, dichlorvos, methamidophos, and parathion, with detection limits down to 0.016, 0.028, 0.071, and 0.113 pM, respectively. With the reactivation of pralidoxime chloride, the electrode can still recover 98.1% of the response after five times of repeated use. In real sample detection, the biosensor achieved high recoveries from 96.45% to 100.13%. The detection target may be extendable to other AChE inhibitors (e.g., drugs for Alzheimer's disease). This study demonstrates for the first time the feasibility of using aptamers as an inducer to fabricate an electrochemical enzyme sensing interface in-situ. This strategy can be used to fabricate other enzyme-based biosensors and therefore has broader applications. [ABSTRACT FROM AUTHOR]

Purpose: This article adopts an embodiment lens to explore the individual leisure experiences of people living with dementia when engaging in nature-based pursuits. It focuses on how people living with dementia frame their everyday experiences of nature and how these are shaped by any cognitive challenges and/or other comorbidities affecting physical health. Design/methodology/approach: Taking a phenomenological research approach, we interviewed 15 people living with dementia and 15 family carers of people with dementia to explore how people with dementia engage with nature as a subjective leisure experience. We analysed their accounts using reflexive thematic analysis. Findings: The findings reveal how people living with dementia frame their experiences of nature-based pursuits through three interlinked themes of 'bodily feelings and emotions', 'sense of self and identity' and 'connectivity to others'. Originality/value: The paper contributes to knowledge by examining through the lens of embodiment a neglected and overlooked dimension of everyday leisure: how nature is encountered, negotiated and enjoyed. The paper illustrates how nature and the outdoors may help people living with dementia to continue to enjoy prior leisure pursuits and thus achieve a degree of continuity in their everyday lives. [ABSTRACT FROM AUTHOR]

Background and Aims: Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three‐dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis. Methods: We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings. Results and Conclusion: The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL‐based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation. [ABSTRACT FROM AUTHOR]

The diagnosis of neurodegenerative diseases (NDDs) remains challenging, and existing therapeutic approaches demonstrate little efficacy. NDD drug delivery can be achieved through the utilization of nanostructures, hence enabling multimodal NDD theranostics. Nevertheless, both biomembrane and non-biomembrane nanostructures possess intrinsic shortcomings that must be addressed by hybridization to create novel nanostructures with versatile applications in NDD theranostics. Hybrid nanostructures display improved biocompatibility, inherent targeting capabilities, intelligent responsiveness, and controlled drug release. This paper provides a concise overview of the latest developments in hybrid nanostructures for NDD theranostics and emphasizes various engineering methodologies for the integration of diverse nanostructures, including liposomes, exosomes, cell membranes, and non-biomembrane nanostructures such as polymers, metals, and hydrogels. The use of a combination technique can significantly augment the precision, intelligence, and efficacy of hybrid nanostructures, therefore functioning as a more robust theranostic approach for NDDs. This paper also addresses the issues that arise in the therapeutic translation of hybrid nanostructures and explores potential future prospects in this field. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Pathophysiology is the property of Jinan University, School of Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Alzheimer's disease is a complex, multi-factorial, and multi-parametric neurodegenerative etiology. Mathematical models can help understand such a complex problem by providing a way to explore and conceptualize principles, merging biological knowledge with experimental data into a model amenable to simulation and external validation, all without the need for extensive clinical trials. We performed a scoping review of mathematical models describing the onset and evolution of Alzheimer's disease as a result of biophysical factors following the PRISMA standard. Our search strategy applied to the PubMed database yielded 846 entries. After using our exclusion criteria, only 17 studies remained from which we extracted data, which focused on three aspects of mathematical modeling: how authors addressed continuous time (since even when the measurements are punctual, the biological processes underlying Alzheimer's disease evolve continuously), howmodels were solved, and how the high dimensionality and non-linearity of models were managed. Most articles modeled Alzheimer's disease at the cellular level, operating on a short time scale (e.g., minutes or hours), i.e., the micro view (12/17); the rest considered regional or brain-level processes with longer timescales (e.g., years or decades) (the macro view). Most papers were concerned primarily with amyloid beta (n = 8), few described both amyloid beta and tau proteins (n = 3), while some considered more than these two factors (n = 6). Models used partial differential equations (n = 3), ordinary differential equations (n = 7), and both partial differential equations and ordinary differential equations (n = 3). Some did not specify their mathematical formalism (n = 4). Sensitivity analyses were performed in only a small number of papers (4/17). Overall, we found that only two studies could be considered valid in terms of parameters and conclusions, and two more were partially valid. This puts the majority (n = 13) as being either invalid or with insufficient information to ascertain their status. This was themain finding of our paper, in that serious shortcomings make their results invalid or non-reproducible. These shortcomings come from insufficient methodological description, poor calibration, or the impossibility of experimentally validating or calibrating the model. Those shortcomings should be addressed by future authors to unlock the usefulness of mathematicalmodels in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

An attempt is made in this paper to diagnose brain-related diseases like sarcoma, fatal stroke disease, cerebral disease, and Alzheimer's disease by using saliency information from magnetic resonance and computed tomography source images. The saliency information for each source image is computed using guided and median filters. The obtained saliency maps are used for computing the weight maps of each source image by using image statistics. The obtained weights are used to fuse the approximate and detailed layers of the source images by using the weighted average fusion technique. The proposed algorithm is simulated in MATLAB for various benchmark data sets of brains taken from the brain atlas provided by Harvard Medical School, available at https://www.med.harvard.edu/aanlib. In order to test the efficacy of the novel method, comparative analysis is performed in terms of image quality assessment metrics like mean, mutual information, average gradient, standard deviation, spatial frequency, etc. From the analysis, this paper concludes that the proposed algorithm improved gradient information in the fused image by 35.7%, entropy by 5.7%, spatial frequency by 32.7%, edge strength by 14.5%, and minimized the information loss by 43.6%. Therefore, the novel method of weight map computation produces a detailed and noise-free image, which is helpful for better diagnosis in clinical applications. [ABSTRACT FROM AUTHOR]

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

Background: Frontotemporal dementia (FTD) is a neurodegenerative disease with clinical, pathological, and genetic heterogeneity. FTD is receiving increasing attention because it is the second leading cause of early-onset dementia after Alzheimer's disease. This study aimed to analyse the research trends and hotspots of FTD from 2000 to 2022 using bibliometrics. Methods: Papers related to FTD from 2000 to 2020 were systematically searched through the Web of Science Core Collection (WOSCC). Citespace and Vosviewer software were used to visually analyse the retrieved data of countries/regions, institutions, journals, authors, references, and keywords. Microsoft Excel was used to generate the annual publications and growth trends. Results: There were 10,227 papers included in the bibliometric analysis. The annual publication output on FTD has increased significantly from 2000 to 2022, with papers published in 934 academic journals and 87 countries/regions. The Journal of Alzheimer's Disease was the most popular, with 488 papers about FTD. The most productive countries/regions, institutions, and authors are the United States (n = 4,037), the University of California San Francisco (n = 687), and Miller, Bruce L. (n = 427), respectively. The article by Katya Rascovsky and her colleagues published on Brain in 2011 was the most cocited paper, with 625 citations. The research hotspots in this field were the clinical diagnostic criteria, subdivision, and pathological mechanism of FTD, such as tau protein, chromosome 17, progranulin, TDP-43, and C9orf72. Conclusion: The future research direction is based on biomarkers and pathological mechanisms to diagnose and differential diagnose FTD from the aspects of behavior, neuropathology, neuroimaging, and serum markers. [ABSTRACT FROM AUTHOR]

Introduction: A rapid and reliable neuropsychological protocol is essential for the efficient assessment of neurocognitive constructs related to emergent neurodegenerative diseases. We developed an AI-assisted, digitally administered/scored neuropsychological protocol that can be remotely administered in -10min. This protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illnesses. Methods: The protocol was administered to 77 ambulatory care/memory clinic patients (56.40% women; 88.50% Caucasian). The protocol includes a 6-word version of the Philadelphia (repeatable) Verbal Learning Test [P(r)VLT], three trials of 5 digits backward fromthe Backwards Digit Span Test (BDST), and the "animal" fluency test. The protocol provides a comprehensive set of traditional "core" measures that are typically obtained through paper-and-pencil tests (i.e., serial list learning, immediate and delayed free recall, recognition hits, percent correct serial order backward digit span, and "animal" fluency output). Additionally, the protocol includes variables that quantify errors and detail the processes used in administering the tests. It also features two separate, norm-referenced summary scores specifically designed to measure executive control and memory. Results: Using four core measures, we used cluster analysis to classify participants into four groups: cognitively unimpaired (CU; n = 23), amnesticmild cognitive impairment (MCI; n = 17), dysexecutive MCI (n = 23), and dementia (n = 14). Subsequent analyses of error and process variables operationally defined key features of amnesia (i.e., rapid forgetting, extra-list intrusions, profligate responding to recognition foils); key features underlying reduced executive abilities (i.e., BDST items and dysexecutive errors); and the strength of the semantic association between successive responses on the "animal" fluency test. Executive and memory index scores effectively distinguished between all four groups. There was over 90% agreement between how cluster analysis of digitally obtained measures classified patients compared to classification using a traditional comprehensive neuropsychological protocol. The correlations between digitally obtained outcome variables and analogous paper/pencil measures were robust. Discussion: The digitally administered protocol demonstrated a capacity to identify patterns of impaired performance and classification similar to those observed with standard paper/pencil neuropsychological tests. The inclusion of both core measures and detailed error/process variables suggests that this protocol can detect subtle, nuanced signs of early emergent neurodegenerative illness efficiently and comprehensively. [ABSTRACT FROM AUTHOR]

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service—Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes. [ABSTRACT FROM AUTHOR]

Background: Aging has been implicated in many chronic inflammatory diseases, including periodontitis. Periodontitis is an inflammatory disease caused by longterm irritation of the periodontal tissues by the plaque biofilm on the surface of the teeth. However, only a few bibliometric analyses have systematically studied this field to date. This work sought to visualize research hot spots and trends in aging associated with periodontitis from 2002 to 2023 through bibliometric approaches. Methods: Graphpad prism v8.0.2 was used to analyse and plot annual papers, national publication trends and national publication heat maps. In addition, CtieSpace (6.1.6R (64-bit) Advanced Edition) and VOSviewer (version 1.6.18) were used to analyse these data and visualize the scientific knowledge graph. Results: The number of documents related to aging associated with periodontitis has steadily increased over 21 years. With six of the top ten institutions in terms of publications coming from the US, the US is a major driver of research in this area. journal of periodontology is the most published journal in the field. Tonetti MS is the most prolific authors and co-cited authors in the field. Journal of Periodontology and Journal of Clinical Periodontology are the most popular journals in the field with the largest literature. Periodontitis, Alzheimer's disease, and peri-implantitis are current hot topics and trends in the field. Inflammation, biomarkers, oxidative stress cytokines are current research hotspots in this field. Conclusion: Our research found that global publications regarding research on aging associated with periodontitis increased dramatically and were expected to continue increasing. Inflammation and aging, and the relationship between periodontitis and systemic diseases, are topics worthy of attention. [ABSTRACT FROM AUTHOR]

Copyright of Gaceta Médica de Caracas is the property of Academia Nacional de Medicina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of the present study was to explore whether Computer-Based Cognitive Training (C-BCT) versus Paper-Pencil Cognitive Training (P-PCT) is more beneficial in improving cognitive and language deficits in Greek patients living with Alzheimer's disease (pwAD). Twenty pwAD were assigned to two groups: (a) the C-BCT group, receiving a computer-based cognitive training program using the RehaCom software, and (b) the P-PCT group, which received cognitive training using paper and pencil. The cognitive training programs lasted 15 weeks and were administered twice a week for approximately one hour per session. The analyses of each group's baseline versus endpoint performance demonstrated that the P-PCT group improved on delayed memory, verbal fluency, attention, processing speed, executive function, general cognitive ability, and activities of daily living. In contrast, the C-BCT group improved on memory (delayed and working), naming, and processing speed. Comparisons between the two groups (C-BCT vs. P-PCT) revealed that both methods had significant effects on patients' cognition, with the P-PCT method transferring the primary cognitive benefits to real-life activities. Our findings indicate that both methods are beneficial in attenuating cognitive and language deficits in pwAD. The need for large-scale neurobehavioral interventions to further clarify this issue, however, remains a priority. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is the most common form of dementia and is a challenge for the modern world due to the due to aging populations. Researchers are trying their best to understand the mechanisms of onset and find effective ways to treat and prevent this disease. The purpose of the following paper is to present the impact of physical activity in the development of Alzheimer's disease. Numerous studies confirm that regular workouts have a positive reflection in the form of slowing the aging of the brain and alleviation of neuropsychiatric symptoms in affected individuals. During muscle work irisin is produced, which plays an important role in the formation of neurons and exhibits an anti-inflammatory effects. This review also looks at the aspect of maintaining proper composition of the gut microbiota, whose imbalance can promote the process of neuronal inflammation and cognitive dysfunction. Attention is also given to issues of training intensity and the effect of of exercise on blood flow in the brain. All of these elements play an important role in preventing and slowing down neurodegeneration. Purpose : The purpose of this scientific paper is to review the current knowledge of the effects of physical activity on the development of Alzheimer's disease. Review methods : We conducted our study as a literature review based on information gathered from PubMed, Embase, Google Scholar using combinations of the following keywords : Alzheimer's disease ; dementia ; physical activity ; cerebral blood flow ; gut-brain axis ; irisin. The state of knowledge : Alzheimer's disease is the most common type of dementia and one of the main neurodegenerative disorders affecting elderly people. Numerous studies confirm that regular exercise has a positive effect on slowing down brain aging and alleviating neuropsychiatric symptoms in patients. According to current knowledge, nerve cell degeneration can be exacerbated by peripheral inflammation that occurs in obesity. An imbalance of bacterial flora in the gastrointestinal tract causes systemic inflammation in the body. This in turn leads to excessive production of pro-inflammatory cytokines and activation of microglia so that the blood-brain barrier becomes leaky. Maintaining the appropriate composition of the gut microbiota plays an important role, as its imbalance can promote neuronal inflammation and cognitive impairment. During physical activity, irisin protein is produced in muscle tissue, which plays an important role in neuronal formation and exhibits anti-inflammatory effects. Currently, there is evidence that irisin can provide a number of beneficial effects, mainly through mechanisms that positively affect the gut microbiome. Attention is also given to issues of training intensity and the effect of exercise on cerebral blood flow. All these factors play an important role in preventing and slowing down neurodegeneration. Conclusion : Based on a review of the available literature, a significant effect of physical activity on the development of Alzheimer's disease can be demonstrated. The accumulated data suggest that physical activity is one of the main protective factors against the development and progression of Alzheimer's disease. Physical activity is associated with a reduction in cardiovascular risk factors and an increase in neuroprotective changes. The neuroprotective effect of physical exercise is dependent on its intensity. In addition, the role of the gut microbiota has been shown to be important for maintaining normal cognitive function. The protein irisin may also have significant neuroprotective potential in relation to the development and progression of cognitive disorders. Physical activity may be one of the strategies to prevent an increase in the incidence of Alzheimer's disease with positive health, economic and social implications in the future. [ABSTRACT FROM AUTHOR]

Background: An application of artificial intelligence is machine learning, which allows computer programs to learn and create data. Methods: In this work, we aimed to evaluate the performance of the MySLR machine learning platform, which implements the Latent Dirichlet Allocation (LDA) algorithm in the identification and screening of papers present in the literature that focus on mutations of the apolipoprotein E (ApoE) gene in Italian Alzheimer's Disease patients. Results: MySLR excludes duplicates and creates topics. MySLR was applied to analyze a set of 164 scientific publications. After duplicate removal, the results allowed us to identify 92 papers divided into two relevant topics characterizing the investigated research area. Topic 1 contains 70 papers, and topic 2 contains the remaining 22. Despite the current limitations, the available evidence suggests that articles containing studies on Italian Alzheimer's Disease (AD) patients were 65.22% (n = 60). Furthermore, the presence of papers about mutations, including single nucleotide polymorphisms (SNPs) ApoE gene, the primary genetic risk factor of AD, for the Italian population was 5.4% (n = 5). Conclusion: The results show that the machine learning platform helped to identify case-control studies on ApoE gene mutations, including SNPs, but not only conducted in Italy. [ABSTRACT FROM AUTHOR]

Rare variants, comprising the vast majority of human genetic variations, are likely to have more deleterious impact in the context of human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases. Author summary: Existing rare variants association methods often lack statistical power when sample sizes are small. Here we propose a novel integrative statistical framework, the carrier statistic, which can leverage paired genotype and gene expression data to quantify the functional impact of rare variants and enhance detection power of rare variants responsible for disease. Extensive simulations demonstrate that carrier statistic provides well-calibrated false discovery rates, shows substantially higher sensitivity compared to existing methods, and remains robust under unbalanced case-control ratios. Through analyzing real multi-omics dataset for Alzheimer's disease, we identified 16 rare variants within 15 genes with extreme carrier statistics. We hope that the results presented in this paper can highlight the promise of the carrier statistic approach and will encourage future disease studies to collect both genotype and gene expression data for the same individuals. As multi-omics and genome sequencing data continue to expand, we anticipate that carrier statistics will be a valuable tool for elucidating the molecular mechanism underlying human complex diseases. [ABSTRACT FROM AUTHOR]

Objective. Recent studies have indicated that repetitive transcranial magnetic stimulation (rTMS) could enhance cognition in Alzheimer's Disease (AD) patients, but to now the molecular-level interaction mechanisms driving this effect remain poorly understood. While cognitive scores have been the primary measure of rTMS effectiveness, employing molecular-based approaches could offer more precise treatment predictions and prognoses. To reach this goal, it is fundamental to assess the electric field (E-field) and the induced current densities (J) within the stimulated brain areas and to translate these values to in vitro systems specifically devoted in investigating molecular-based interactions of this stimulation. Approach. This paper offers a methodological procedure to guide dosimetric assessment to translate the E-field induced in humans (in a specific pilot study) into in vitro settings. Electromagnetic simulations on patients' head models and cellular holders were conducted to characterize exposure conditions and determine necessary adjustments for in vitro replication of the same dose delivered in humans using the same stimulating coil. Main results. Our study highlighted the levels of E-field and J induced in the target brain region and showed that the computed E-field and J were different among patients that underwent the treatment, so to replicate the exposure to the in vitro system, we have to consider a range of electric quantities as reference. To match the E-field to the levels calculated in patients' brains, an increase of at least the 25% in the coil feeding current is necessary when in vitro stimulations are performed. Conversely, to equalize current densities, modifications in the cells culture medium conductivity have to be implemented reducing it to one fifth of its value. Significance. This dosimetric assessment and subsequent experimental adjustments are essential to achieve controlled in vitro experiments to better understand rTMS effects on AD cognition. Dosimetry is a fundamental step for comparing the cognitive effects with those obtained by stimulating a cellular model at an equal dose rigorously evaluated. [ABSTRACT FROM AUTHOR]

Background: Currently, there are more than 55 million people living with dementia worldwide. Supporting people with dementia to live as independently as possible in their communities is a global public health objective. There is limited research exploring the implementation of such interventions in the community context. The aim of the review was to create and refine programme theory – in the form of context mechanism-outcome configurations – on how the characteristics of dementia-friendly communities (DFCs) as geographical locations interact with their social and organisational contexts to understand what works for whom and why. Methods: This realist review sourced literature from 5 electronic databases: Cochrane Library, CINAHL, Medline, Scopus, PsychINFO and Google Scholar, as well as relevant websites such as Alzheimer's Society to identify grey literature. Methodological rigour was assessed using the Joanna Briggs Institute critical appraisal tool. Results: Seven papers were included in this realist review that focused on DFCs in a geographical context The implementation of DFC interventions emerged as a process characterised by two pivotal implementation phases, intricately linked with sub-interventions. The first intervention, termed Hierarchy Commitment (I1a/b), involves the formalisation of agreements by businesses and organizations, along with the implementation of dementia-friendly action plans. Additionally, Educational Resources (I1c) play a significant role in this phase, engaging individuals with dementia and their caregivers in educational initiatives. The second phase, Geographical/Environmental Requirements (I2), encompasses the establishment of effective dementia-friendly signage, accessible meeting places, and community support. Conclusions: This realist review highlighted a theoretical framework that might guide the development of dementia-friendly communities to enhance the experiences of individuals with dementia and their caregivers within DFCs. Emphasising the need for a theoretical framework in developing geographical DFCs, the review outlines contextual elements, mechanisms, and outcomes, providing a foundation for future studies. The ultimate goal is to establish a robust body of evidence for the sustainable implementation of dementia-friendly communities, thereby improving the quality of life for those with dementia. Study registration: This study is registered as PROSPERO 2022 CRD42022317784. [ABSTRACT FROM AUTHOR]

Background: Kefir is a complex microbial community that plays a critical role in the fermentation and production of bioactive peptides, and has health-improving properties. The composition of kefir can vary by geographic localization and weather, and this paper focuses on a Brazilian sample and continues previous work that has successful anti-Alzheimer properties. In this study, we employed shotgun metagenomics and peptidomics approaches to characterize Brazilian kefir further. Results: We successfully assembled the novel genome of Lactobacillus kefiranofaciens (LkefirU) and conducted a comprehensive pangenome analysis to compare it with other strains. Furthermore, we performed a peptidome analysis, revealing the presence of bioactive peptides encrypted by L. kefiranofaciens in the Brazilian kefir sample, and utilized in silico prospecting and molecular docking techniques to identify potential anti-Alzheimer peptides, targeting β-amyloid (fibril and plaque), BACE, and acetylcholinesterase. Through this analysis, we identified two peptides that show promise as compounds with anti-Alzheimer properties. Conclusions: These findings not only provide insights into the genome of L. kefiranofaciens but also serve as a promising prototype for the development of novel anti-Alzheimer compounds derived from Brazilian kefir. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is an imaging tool employed to analyze brain structures, aiding in diagnosis and treatment planning. Alzheimer's disease (AD), a progressive neurodegenerative disorder leading to memory and cognitive function impairments, is the primary cause of dementia. Early detection of Mild Cognitive Impairment (MCI), a precursor to AD, is crucial for timely treatment. Diagnosis of atrophy, especially in the hippocampus, as a reliable biomarker for early diagnosis of Alzheimer's disease can be done using an MRI scan. This paper presents a new method for AD detection utilizing discrete surface Ricci flow theory, which creates Riemannian metrics on hippocampus surfaces with user-defined Gaussian curvatures. First, the surface of the hippocampus is extracted from the brain's subcortical surface. Then, Euclidean Ricci flow is applied to map this surface onto a sphere. Edge lengths of the triangular mesh are calculated, resulting in a feature vector. This vector is used as input for a classifier that distinguishes between brains affected by AD and healthy ones. The model is trained using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and its accuracy, precision, sensitivity, and specificity performance are evaluated. Experimental results show an accuracy rate of over 90% in classifying AD and healthy hippocampus. The multiclass classification model achieves impressive performance metrics, with accuracy, precision, sensitivity, and specificity at 83, 80, 83, and 82%, respectively. These results are exceptionally satisfactory and outperform alternative methods. [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]

Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disorder, characterized pathologically by extracellular deposition of β-amyloid (Aβ) into senile plaques and intracellular accumulation of hyperphosphorylated tau (pTau) as neurofibrillary tangles. Clinically, AD patients show memory deterioration with varying cognitive dysfunctions. The exact molecular mechanisms underlying AD are still not fully understood, and there are no efficient drugs to stop or reverse the disease progression. In this review, we first provide an update on how the risk factors, including APOE variants, infections and inflammation, contribute to AD; how Aβ and tau become abnormally accumulated and how this accumulation plays a role in AD neurodegeneration. Then we summarize the commonly used experimental models, diagnostic and prediction strategies, and advances in periphery biomarkers from high-risk populations for AD. Finally, we introduce current status of development of disease-modifying drugs, including the newly officially approved Aβ vaccines, as well as novel and promising strategies to target the abnormal pTau. Together, this paper was aimed to update AD research progress from fundamental mechanisms to the clinical diagnosis and therapies. [ABSTRACT FROM AUTHOR]

Many researchers have studied the role of air pollutants on cognitive function, changes in brain structure, and occurrence of dementia. Due to the wide range of studies and often contradictory results, the present systematic review was conducted to try and clarify the relationship between air pollutants and dementia. To identify studies for this review, a systematic search was conducted in Scopus, PubMed, and Web of Science databases (without historical restrictions) until May 22, 2023. The PECO statement was created to clarify the research question, and articles that did not meet the criteria of this statement were excluded. In this review, animal studies, laboratory studies, books, review articles, conference papers and letters to the editors were avoided. Also, studies focused on the effect of air pollutants on cellular and biochemical changes (without investigating dementia) were also excluded. A quality assessment was done according to the type of design of each article, using the checklist developed by the Joanna Briggs Institute (JBI). Finally, selected studies were reviewed and discussed in terms of Alzheimer's dementia and non-Alzheimer's dementia. We identified 14,924 articles through a systematic search in databases, and after comprehensive reviews, 53 articles were found to be eligible for inclusion in the current systematic review. The results showed that chronic exposure to higher levels of air pollutants was associated with adverse effects on cognitive abilities and the presence of dementia. Studies strongly supported the negative effects of PM2.5 and then NO2 on the brain and the development of neurodegenerative disorders in old age. Because the onset of brain structural changes due to dementia begins decades before the onset of disease symptoms, and that exposure to air pollution is considered a modifiable risk factor, taking preventive measures to reduce air pollution and introducing behavioral interventions to reduce people's exposure to pollutants is advisable. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive degenerative disorder that causes behavioral changes, cognitive decline, and memory loss. Currently, AD is incurable, and the few available medicines may, at best, improve symptoms or slow down AD progression. One main challenge in drug delivery to the brain is the presence of the blood–brain barrier (BBB), a semi-permeable layer around cerebral capillaries controlling the influx of blood-borne particles into the brain. In this paper, a mathematical model of drug transport to the brain is proposed that incorporates two mechanisms of BBB crossing: transcytosis and diffusion. To account for the structural damage and accumulation of harmful waste in the brain caused by AD, the diffusion is assumed to be anomalous and is modeled using spatial Riemann–Liouville fractional-order derivatives. The model's parameters are taken from published experimental observations of the delivery to mice brains of the orally administered AD drug donepezil hydrochloride. Numerical simulations suggest that drug delivery modalities should depend on the BBB fitness and anomalous diffusion and be tailored to AD severity. These results may inspire novel brain-targeted drug carriers for improved AD therapies. [ABSTRACT FROM AUTHOR]

Spatial orientation and navigation are complex cognitive functions that integrate sensory information, attention, and memory, enabling individuals to locate themselves in their environment. These abilities decline with age, signaling cognitive impairment in neurological patients, and significantly limit the autonomy of the elderly. Current neuropsychological assessments fall short in accurately measuring everyday wayfinding abilities, particularly in borderline cases of cognitive decline. This paper reviews various neuropsychological assessments, including Benton's Judgment of Line Orientation Test, the Almeria Spatial Memory Recognition Test, the Spatial Span subtest from the Wechsler Memory Scale, and the Spatial Orientation in Immersive Virtual Environment Maze Test, evaluating their effectiveness in delineating spatial orientation and navigation skills. The review identifies significant gaps in the validity and reliability of these tests, particularly in their shortened versions, and highlights the potential of virtual reality environments as promising tools for improving diagnostic precision. The findings underscore the need for further research to refine these tools, ensuring they accurately capture cognitive decline and improve the differential diagnosis of neurodegenerative conditions like Alzheimer's disease. Such advancements hold promise for enhancing the quality of care and autonomy for the elderly. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the most prevalent kind of dementia and is a long-term degenerative disease. Pathologically, it is defined by the development of extracellular amyloid-β plaques and intracellular neurofibrillary tangles made up of hyperphosphorylated tau protein. This causes neuronal death, particularly in the hippocampus and cortex. Mesenchymal stem cell (MSC)-derived exosomes have been identified as possibly therapeutic and have promise for Alzheimer's disease due to their regenerative characteristics. Methods: A systematic retrieval of information was performed on PubMed. A total of 60 articles were found in a search on mesenchymal stem cells, exosomes, and Alzheimer's disease. A total of 16 ongoing clinical trials were searched and added from clinicaltrials.gov. We added 23 supporting articles to help provide information for certain sections. In total, we included 99 articles in this manuscript: 50 are review articles, 13 are preclinical studies, 16 are clinical studies, 16 are ongoing clinical trials, and 4 are observational studies. Appropriate studies were isolated, and important information from each of them was understood and entered into a database from which the information was used in this article. The clinical trials on mesenchymal stem cell exosomes for Alzheimer's disease were searched on clinicaltrials.gov. Results: Several experimental investigations have shown that MSC-Exo improves cognitive impairment in rats. In this review paper, we summarized existing understanding regarding the molecular and cellular pathways behind MSC-Exo-based cognitive function restoration, with a focus on MSC-Exo's therapeutic potential in the treatment of Alzheimer's disease. Conclusion: AD is a significant health issue in our culture and is linked to several important neuropathological characteristics. Exosomes generated from stem cells, such as mesenchymal stem cells (MSCs) or neural stem cells (NSCs), have been examined more and more in a variety of AD models, indicating that they may be viable therapeutic agents for the treatment of diverse disorders. Exosome yields may be increased, and their therapeutic efficacy can be improved using a range of tailored techniques and culture conditions. It is necessary to provide standardized guidelines for exosome manufacture to carry out excellent preclinical and clinical research. [ABSTRACT FROM AUTHOR]

Aging and age-related diseases are serious public health issues that are receiving growing attention from researchers. Lutein has a critical function in the prevention and management of these issues. Possible mechanisms mainly include suppressing inflammation and oxidative stress, regulating cell activity, and modulating the levels of toxic substances. In this narrative review paper, we sum up the most current developments in the study of the effects of lutein on aging and five age-related diseases (age-related macular degeneration, cataracts, Alzheimer's disease, Parkinson's disease, and osteoporosis), and fundamental mechanisms are reviewed. The bioavailability of lutein and the strategies to improve its bioavailability are discussed. This piece of work can bring a clearer comprehension of the protective effects of lutein against aging and age-related diseases and can be also helpful for developing lutein as functional food and dietary supplements for these age-related diseases. [ABSTRACT FROM AUTHOR]

The blood–brain barrier (BBB) plays a vital role in safeguarding the central nervous system by selectively controlling the movement of substances between the bloodstream and the brain, presenting a substantial obstacle for the administration of therapeutic agents to the brain. Recent breakthroughs in nanoparticle-based delivery systems, particularly metal–organic frameworks (MOFs), provide promising solutions for addressing the BBB. MOFs have become valuable tools in delivering medications to the brain with their ability to efficiently load drugs, release them over time, and modify their surface properties. This review focuses on the recent advancements in molecular-based approaches for treating brain disorders, such as glioblastoma multiforme, stroke, Parkinson's disease, and Alzheimer's disease. This paper highlights the significant impact of MOFs in overcoming the shortcomings of conventional brain drug delivery techniques and provides valuable insights for future research in the field of neurotherapeutics. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), diabetes mellitus (DM), inflammatory bowel diseases (IBD), and rheumatoid arthritis (RA) are chronic conditions affecting millions globally. Despite differing clinical symptoms, these diseases share pathophysiological mechanisms involving metabolic and immune system dysregulation. This paper examines the intricate connections between these disorders, focusing on shared pathways such as insulin resistance, lipid metabolism dysregulation, oxidative stress, and chronic inflammation. An important aspect is the role of amyloid-beta plaques and tau protein tangles, which are hallmark features of AD. These protein aggregates are influenced by metabolic dysfunction and inflammatory processes similar to those seen in DM, RA, and IBD. This manuscript explores how amyloid and tau pathologies may be exacerbated by shared metabolic and immune dysfunction. Additionally, this work discusses the gut–brain axis and the influence of gut microbiota in mediating disease interactions. Understanding these commonalities opens new avenues for multi-targeted therapeutic approaches that address the root causes rather than merely the symptoms of these conditions. This integrative perspective could lead to more effective interventions and improved patient outcomes, emphasizing the importance of a unified approach in managing these interconnected diseases. [ABSTRACT FROM AUTHOR]

Advances in biomarker-based diagnostic modalities, recent approval of anti-amyloid monoclonal antibodies for early Alzheimer's disease (AD; mild cognitive impairment or mild dementia due to AD) and late-stage clinical development of other disease-modifying therapies for AD necessitate a significant paradigm shift in the early detection, diagnosis and management of AD. Anti-amyloid monoclonal antibodies target the underlying pathophysiological mechanisms of AD and have demonstrated a significant reduction in the rate of clinical decline in cognitive and functional outcome measures in patients with early AD. With growing recognition of the benefit of early interventions in AD, an increasing number of people may seek diagnosis for their subjective cognitive problems in an already busy medical system. Various factors such as limited examination time, lack of expertise for cognitive assessment and limited access to specialized tests can impact diagnostic accuracy and timely detection of AD. To overcome these challenges, a new model of care will be required. In this paper, we provide practical guidance for institutional readiness for anti-amyloid therapies for early AD in Asia, in terms of best practices for identifying eligible patients and diagnosing them appropriately, safe administration of anti-amyloid monoclonal antibodies and monitoring of treatment, managing potential adverse events such as infusion reactions and amyloid-related imaging abnormalities, and cross-disciplinary collaboration. Education and training will be the cornerstone for the establishment of new pathways of care for the identification of patients with early AD and delivery of anti-amyloid therapies in a safe and efficient manner to eligible patients. [ABSTRACT FROM AUTHOR]

Background: Dauricine (DAU), a benzyl tetrahydroisoquinoline alkaloid isolated from the root of Menispermum dauricum DC, exhibits promising anti-Alzheimer's disease (AD) effects, but its underlying mechanisms remain inadequately investigated. This paper aims to identify potential targets and molecular mechanisms of DAU in AD treatment. Methods: Network pharmacology and molecular docking simulation method were used to screen and focus core targets. Various transgenic Caenorhabditis elegans models were chosen to validate the anti-AD efficacy and mechanism of DAU. Results: There are 66 potential DAU-AD target intersections identified from 100 DAU and 3036 AD-related targets. Subsequent protein-protein interaction (PPI) network analysis identified 16 core targets of DAU for anti-AD. PIK3CA, AKT1 and mTOR were predicted to be the central targets with the best connectivity through the analysis of "compound-target-biological process-pathway network". Molecular docking revealed strong binding affinities between DAU and PIK3CA, AKT1, and mTOR. In vivo experiments demonstrated that DAU effectively reduced paralysis in AD nematodes caused by Aβ aggregation toxicity, downregulated expression of PIK3CA, AKT1, and mTOR homologues (age-1, akt-1, let-363), and upregulated expression of autophagy genes and the marker protein LGG-1. Simultaneously, DAU increased lysosomal content and enhanced degradation of the autophagy-related substrate protein P62. Thioflavin T（Th-T）staining experiment revealed that DAU decreased Aβ accumulation in AD nematodes. Further experiments also confirmed DAU's protein scavenging activity in polyglutamine (polyQ) aggregation nematodes. Conclusion: Collectively, the mechanism of DAU against AD may be related to the activation of the autophagy-lysosomal protein clearance pathway, which contributes to the decrease of Aβ aggregation and the restoration of protein homeostasis. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions. Methods: In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependenciesmodeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). Results: On this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these twomodalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models. Conclusion: We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis. [ABSTRACT FROM AUTHOR]

Neurological diseases have consistently represented a significant challenge in both clinical treatment and scientific research. As research has progressed, the significance of mitochondria in the pathogenesis and progression of neurological diseases has become increasingly prominent. Mitochondria serve not only as a source of energy, but also as regulators of cellular growth and death. Both oxidative stress and mitophagy are intimately associated with mitochondria, and there is mounting evidence that mitophagy and oxidative stress exert a pivotal regulatory influence on the pathogenesis of neurological diseases. In recent years, there has been a notable rise in the prevalence of cerebral ischemia/reperfusion injury (CI/RI), vascular dementia (VaD), and Alzheimer's disease (AD), which collectively represent a significant public health concern. Reduced levels of mitophagy have been observed in CI/RI, VaD and AD. The improvement of associated pathology has been demonstrated through the increase of mitophagy levels. CI/RI results in cerebral tissue ischemia and hypoxia, which causes oxidative stress, disruption of the blood-brain barrier (BBB) and damage to the cerebral vasculature. The BBB disruption and cerebral vascular injury may induce or exacerbate VaD to some extent. In addition, inadequate cerebral perfusion due to vascular injury or altered function may exacerbate the accumulation of amyloid β (Aβ) thereby contributing to or exacerbating AD pathology. Intravenous tissue plasminogen activator (tPA; alteplase) and endovascular thrombectomy are effective treatments for stroke. However, there is a narrow window of opportunity for the administration of tPA and thrombectomy, which results in a markedly elevated incidence of disability among patients with CI/RI. It is regrettable that there are currently no there are still no specific drugs for VaD and AD. Despite the availability of the U.S. Food and Drug Administration (FDA)-approved clinical first-line drugs for AD, including memantine, donepezil hydrochloride, and galantamine, these agents do not fundamentally block the pathological process of AD. In this paper, we undertake a review of the mechanisms of mitophagy and oxidative stress in neurological disorders, a summary of the clinical trials conducted in recent years, and a proposal for a new strategy for targeted treatment of neurological disorders based on both mitophagy and oxidative stress. [ABSTRACT FROM AUTHOR]

Human apolipoprotein E (ApoE) was first identified as a polymorphic gene in the 1970s; however, the genetic association of ApoE genotypes with late-onset sporadic Alzheimer's disease (sAD) was only discovered 20 years later. Since then, intensive research has been undertaken to understand the molecular effects of ApoE in the development of sAD. Despite three decades' worth of effort and over 10,000 papers published, the greatest mystery in the ApoE field remains: human ApoE isoforms differ by only one or two amino acid residues; what is responsible for their significantly distinct roles in the etiology of sAD, with ApoE4 conferring the greatest genetic risk for sAD whereas ApoE2 providing exceptional neuroprotection against sAD. Emerging research starts to point to a novel and compelling hypothesis that the sialoglycans posttranslationally appended to human ApoE may serve as a critical structural modifier that alters the biology of ApoE, leading to the opposing impacts of ApoE isoforms on sAD and likely in the peripheral systems as well. ApoE has been shown to be posttranslationally glycosylated in a species-, tissue-, and cell-specific manner. Human ApoE, particularly in brain tissue and cerebrospinal fluid (CSF), is highly glycosylated, and the glycan chains are exclusively attached via an O-linkage to serine or threonine residues. Moreover, studies have indicated that human ApoE glycans undergo sialic acid modification or sialylation, a structural alteration found to be more prominent in ApoE derived from the brain and CSF than plasma. However, whether the sialylation modification of human ApoE has a biological role is largely unexplored. Our group recently first reported that the three major isoforms of human ApoE in the brain undergo varying degrees of sialylation, with ApoE2 exhibiting the most abundant sialic acid modification, whereas ApoE4 is the least sialylated. Our findings further indicate that the sialic acid moiety on human ApoE glycans may serve as a critical modulator of the interaction of ApoE with amyloid β (Aβ) and downstream Aβ pathogenesis, a prominent pathologic feature in AD. In this review, we seek to provide a comprehensive summary of this exciting and rapidly evolving area of ApoE research, including the current state of knowledge and opportunities for future exploration. [ABSTRACT FROM AUTHOR]

The hip fracture causes significant disabilities in many elderly people. Many studies around the world have identified various risk factors for the hip fracture. The aim of this study was to systematically investigate the risk factors of hip fractures. This study is a systematic review of risk factors for hip fractures. All published papers in English and Persian languages on patients in Iran and other countries between 2002 - 2022 were examined. The search strategy used keywords matching the mesh, including : predictors, hip fracture, and disability. Articles were selected from international databases (PubMed, Proquest,Web of Sience, Scopus, Google scholar and Persian(Sid,Magiran), and the Newcastle Ottawa Scale was used to assess the risk of bias. The study has identified several factors that were significantly correlated with the risk of hip fracture, including age, cigarette and alcohol consumption, visual and hearing problems, low BMI levels, history of falling, weakness, and diseases such as stroke, cardiovascular disease, high blood pressure, arthritis, diabetes, dementia, Alzheimer's, Parkinson's, liver and kidney diseases, bone density, osteoporosis, vertebral fracture, and hyperthyroidism. However, the study did not find any significant correlations between the consumption of calcium and vitamin D, history of fractures, cognitive disorders, schizophrenia, and household income, and the risk of hip fracture. The results of this study reveal the determining role of some risk factors in hip fracture in older persons. Therefore, it is recommended that health policy makers provide the possibility of early intervention for some changeable factors. [ABSTRACT FROM AUTHOR]

Over the past decades, literature in dementia ethics has extensively looked at moral questions revolving around the care of older people living with dementia. Particularly prevalent are autonomy-related concerns regarding topics such as advance directives. In this paper, we argue that these discussions are crucially premised on how dementia is understood and represented. Despite the multiplicity of dementia presentations in people, the dominant discourse predominantly frames dementia as 'monstrous,' an 'enemy,' a destructive experience in need of eradication. We contend that such a monolithic approach, from a moral standpoint, is problematic in several respects. Indeed, framing heavily influences the way dementia is understood and experienced, leading to stigmatization, bias, and distress. Not only does it influence decisions and discussions on advance directives, but we argue that this flawed understanding of dementia is rooted in and contributes to epistemic harm. In the first section, we introduce the ethics of advance directives. More specifically, we introduce the view developed by Dworkin who has largely influenced the debate by making the case for advance directives by grounding them in the principles of autonomy and beneficence. In the second section, we show how dementia is still mostly framed monolithically as a 'destructive experience.' We then show that this framing is problematic because it oversteps the different pathologies dementia implies, which leads to an inaccurate representation of the condition. In the third section, we present possible alternative framings: dementia as normal aging, a person-centered care framework, and an embodied view. In the fourth section, drawing on recent developments in the epistemic injustice literature, we explore how maintaining and utilizing flawed understandings of dementia may lead to distinct moral-epistemic harms for those living with dementia and inform ongoing discussions on advance directives. Finally, in the concluding section, we return to the case of advance directives and what the implications of rethinking dementia are. [ABSTRACT FROM AUTHOR]

Type 2 diabetes mellitus (T2DM) is common and increasing in prevalence worldwide, with devastating public health consequences. While peripheral insulin resistance is a key feature of most forms of T2DM and has been investigated for over a century, research on brain insulin resistance (BIR) has more recently been developed, including in the context of T2DM and non-diabetes states. Recent data support the presence of BIR in the aging brain, even in non-diabetes states, and found that BIR may be a feature in Alzheimer’s disease (AD) and contributes to cognitive impairment. Further, therapies used to treat T2DM are now being investigated in the context of AD treatment and prevention, including insulin. In this review, we offer a definition of BIR, and present evidence for BIR in AD; we discuss the expression, function, and activation of the insulin receptor (INSR) in the brain; how BIR could develop; tools to study BIR; how BIR correlates with current AD hallmarks; and regional/cellular involvement of BIR. We close with a discussion on resilience to both BIR and AD, how current tools can be improved to better understand BIR, and future avenues for research. Overall, this review and position paper highlights BIR as a plausible therapeutic target for the prevention of cognitive decline and dementia due to AD. [ABSTRACT FROM AUTHOR]

Recently, Alzheimer's disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer's Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback's quasi-refraction position for further improving the optimal position's quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively. [ABSTRACT FROM AUTHOR]

Featured Application: Utilization of Rosmarinus officinalis extracts and/or its bioactives as ingredients in functional products, foods, cosmetics, and pharmaceuticals, with anti-inflammatory health-promoting neuroprotective effects against inflammation-related neurodegenerative disorders, such as Alzheimer's and Parkinson's Diseases. Neurodegenerative disorders (NDs) are conditions marked by progressively escalating inflammation that leads to the degeneration of neuronal structure and function. There is an increasing interest in natural compounds, especially those from pharmaceutical plants, with neuroprotective properties as part of potential therapeutic interventions. Thus, the rich bioactive content of the perennial herb rosemary (Rosmarinus officinalis) is thoroughly reviewed in this article, with an emphasis on its pleiotropic pharmacological properties, including its antioxidant, anti-inflammatory, and neuroprotective health-promoting effects. In addition, a comprehensive analysis of the existing scientific literature on the potential use of rosemary and its bioactive constituents in treating neurodegenerative disorders was also conducted. Rosemary and its bioactives' chemical properties and neuroprotective mechanisms are discussed, focusing on their ability to mitigate oxidative stress, reduce inflammation, and modulate neurotransmitter activity. The role of rosemary in enhancing cognitive function, attenuating neuronal apoptosis, and promoting neurogenesis is outlined. Key bioactive components, such as rosmarinic acid and carnosic acid, are also highlighted for their neuroprotective act. The promising outcomes of the conducted pre-clinical studies or clinical trials confirm the efficacy of rosemary in preventing or alleviating Alzheimer's and Parkinson's diseases both in vitro (in cells) and in vivo (in animal models of NDs). From this perspective, the applications of rosemary's bio-functional compounds and extracts in the food, cosmetics, and pharmaceutical sectors are also presented; in the latter, we discuss their use against neurodegenerative disorders, either alone or as adjuvant therapies. This paper critically evaluates these studies' methodological approaches and outcomes, providing insights into the current state of the clinical research and identifying potential avenues for future investigation. All findings presented herein contribute to the growing body of literature and support the exploration of natural compounds as promising candidates for novel applications and neuroprotective interventions, paving the way for more applied scientific research. [ABSTRACT FROM AUTHOR]

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer's disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e. shear range, rotation, zoom range, channel shift). The first approach is a deep Siamese network (DSN), and the second approach involves using a cross-domain strategy with customized VGG-16. We performed experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to evaluate the performance of our proposed models. Our experimental results demonstrate superior performance in classifying the three binary classification tasks: NC vs. AD, NC vs. MCI, and MCI vs. AD. Specifically, we achieved a classification accuracy of 97.68%, 94.25%, and 92.18% for the three cases, respectively. Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI, AD, and normal control individuals using GM scans. Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD. [ABSTRACT FROM AUTHOR]

Background: Maintaining the structural and functional integrity of the blood–brain barrier (BBB) is vital for neuronal equilibrium and optimal brain function. Disruptions to BBB performance are implicated in the pathology of neurodegenerative diseases. Main body: Early indicators of multiple neurodegenerative disorders in humans and animal models include impaired BBB stability, regional cerebral blood flow shortfalls, and vascular inflammation associated with BBB dysfunction. Understanding the cellular and molecular mechanisms of BBB dysfunction in brain disorders is crucial for elucidating the sustenance of neural computations under pathological conditions and for developing treatments for these diseases. This paper initially explores the cellular and molecular definition of the BBB, along with the signaling pathways regulating BBB stability, cerebral blood flow, and vascular inflammation. Subsequently, we review current insights into BBB dynamics in Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis. The paper concludes by proposing a unified mechanism whereby BBB dysfunction contributes to neurodegenerative disorders, highlights potential BBB-focused therapeutic strategies and targets, and outlines lessons learned and future research directions. Conclusions: BBB breakdown significantly impacts the development and progression of neurodegenerative diseases, and unraveling the cellular and molecular mechanisms underlying BBB dysfunction is vital to elucidate how neural computations are sustained under pathological conditions and to devise therapeutic approaches. [ABSTRACT FROM AUTHOR]

The growing popularity of deep learning (DL) in recent years has encouraged researchers to diversify their applications further. The limitations and shortcomings of an individual model are subdued through ensemble learning (EL), which combines the predictions of multiple models that are trained separately, thereby improving the overall accuracy and robustness. Deep ensemble learning (DEL) models leverage the combined diversity of different deep learning models. This paper provides an overview of traditional, novel, and state-of-the-art deep ensemble methods for application in Alzheimer's disease (AD) and other intelligent healthcare applications, including bagging, boosting, stacking, homogeneous/heterogeneous ensembles, explicit/implicit ensembles, negative correlation-based deep ensemble models and decision fusion. For this research study, an extensive exploration was conducted across prominent academic databases, including Google Scholar, ProQuest, DBLP, Science Direct, MDPI, IEEE Xplore, and Springer. The investigation encompassed a meticulous search for literature between 2018 and 2023 to ascertain the study's most current and relevant data. The results are presented through various methodologies, including flow charts, graphs, figures, and comparative tables, ensuring a comprehensive and visually accessible representation of the findings. This survey paper presents performance results from diverse ensemble methods applied to deep learning models. This reveals significant performance enhancements on specific datasets and model combinations, showcasing the impactful role of ensembling in surpassing individual model outcomes. Our findings also highlight nuanced correlations between ensemble techniques and data characteristics, offering actionable insights for implementing optimized ensemble-based deep learning models in clinical settings. This novel contribution underscores our paper's advancement in Alzheimer's detection methodologies, uniting comprehensive data analysis, ensemble effectiveness, and valuable considerations. [ABSTRACT FROM AUTHOR]

Aim: This study aims to (1) introduce the emergent concept of the retrograde plunge experienced by people living with Alzheimer's disease and (2) illustrate how nurses can accompany those experiencing this phenomenon, as well as their family and formal carers, using the seven‐phase inquiry process proposed by Smith and Liehr. Design: Discursive paper. Methods: After describing the phenomenon and the Story Theory, the 7‐phase Inquiry Process presents the fictional story of Mrs. Lurie, a nursing home resident. Results: Story theory allows nurses to accompany Mrs. Lurie. The emergent concept of retrograde plunge is explained. Different tools such as genograms, story paths and eco‐maps are presented. This discussion demonstrates how some perspectives can contribute to a better description of the retrograde plunge phenomenon. The narrative care approach can help give voices to people experiencing retrograde phenomena. The adoption of a perspective that considers embodied language could help to better understand the needs of a person. Conclusion: There is a lack of consistency and uniformity regarding the understanding of the retrograde phenomenon. This impacts the quality of care for people and the scientific knowledge, research and education of healthcare professionals. This issue should be addressed in future studies. Implications for the Profession and/or Patient Care: This article shows how story theory helps nurses accompany those facing a retrograde plunge phenomenon, helps them tell their own stories and finds a way to resolve the situation. Impact: This article paves the way for further developments that must now be realized by the international community of experts involved in the care of people with Alzheimer's disease, from practical, academic and research perspectives. No Patient or Public Contribution: No patient or public was involved in the design or drafting of the discursive paper. What Problem did the Study Address?: The retrograde plunge is a phenomenon in which people living with Alzheimer's disease return to their past lives and re‐experience with certain events.The retrograde plunge is a well‐known concept that is poorly described in scientific literature and is sometimes misunderstood by families and professional caregivers.This discursive article explains the retrograde plunge phenomenon experienced by people living with Alzheimer's disease. What were the Main Findings?: This article demonstrates how story theory helps nurses and families support people with retrograde plunges.This highlights the lack of consistency and uniformity in this phenomenon, which has implications for the quality of care, education and research.Story theory and the seven‐phase inquiry process proposed by Smith and Liehr (in Middle‐range theory for nursing, Springer Publishing Company, 2023) help to better understand and explain the retrograde plunge phenomenon. Where and on Whom will the Research have an Impact?: This article advances nurses' knowledge in the different fields of practice, teaching and research.This article proposes links among the retrograde plunge phenomenon, embodied language and narrative care approach; this could offer further ways to develop nursing knowledge.This study has the potential to advance the knowledge of practice and education and offers new opportunities for research. Trial and Protocol Registration: There was no trial or protocol registration as this article is a discussion. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques. [ABSTRACT FROM AUTHOR]