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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]

Introduction: Resveratrol (RSV) is a natural polyphenolic compound derived from a variety of plants that possesses a wide range of biological activities, including antioxidant, anti-inflammatory, antitumor, antibacterial, antiviral, anti-aging, antiradiation damage, anti-apoptosis, immune modulation, regulation of glucolipid metabolism, inhibition of lipid deposition, and anti-neuro. It is therefore considered a promising drug with the potential to treat a wide range of diseases. Method: In this study, using Web of Science Core Collection (WoSCC) and CiteSpace bibliometric tool, VOSviewer quantitatively visualized the number of countries, number of authors, number of institutions, number of publications, keywords, and references of 16,934 resveratrol-related papers from 2014-2023 for quantitative and qualitative analysis. Results: The results showed that an average of 1693.4 papers were published per year, with a general upward trend. China had the most publications with 5877. China Medical University was the institution with the largest number of publications and the highest number of citations in the field. The research team was mainly led by Prof. Richard Tristan, and the journal with the highest number of published papers was Molecular. Dietary polyphenols, oxidative stress, and antioxidant and anti-inflammatory effects are the most frequently cited articles. Oxidative stress, apoptosis, expression, and other keywords play an important role in connecting other branches of the field. Discussion: Our analysis indicates that the integration of nanoparticles with RSV is poised to become a significant trend. RSV markedly inhibits harmful bacteria, fosters the proliferation of beneficial bacteria, and enhances the diversity of the intestinal flora, thereby preventing intestinal flora dysbiosis. Additionally, RSV exhibits both antibacterial and antiviral properties. It also promotes osteogenesis and serves a neuroprotective function in models of Alzheimer's disease. The potential applications of RSV in medicine and healthcare are vast. A future research challenge lies in modifying its structure to develop RSV derivatives with superior biological activity and bioavailability. In the coming years, innovative pharmaceutical formulations of RSV, including oral, injectable, and topical preparations, may be developed to enhance its bioavailability and therapeutic efficacy. [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]

The document "Advances in Neuroinflammation" explores recent research on neuroinflammation and its role in neurological diseases, particularly neurodegenerative diseases. The document highlights the complex nature of neuroinflammation, its double-edged sword effect in the central nervous system, and its involvement in various diseases. Through a series of research and review papers, the authors investigate the mechanisms and potential therapeutic targets related to neuroinflammation in diseases such as Alzheimer's, ALS, glioblastoma, CIDP, insomnia, and multiple sclerosis. The document emphasizes the importance of further research to understand the inflammatory role in disease genesis using diverse tools and technologies. [Extracted from the article]

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]

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]

Following up the previously published systematic review on the same topic and realizing that new studies and evidence had emerged on the matter, we conducted an update on the same research terms. With the objective of updating the information relating environmental risk factors on neurodegenerative diseases and the geographic approaches used to address them, we searched PubMed, Web of Science and Scopus for all scientific studies considering the following three domains: neurodegenerative disease, environmental atmospheric factor and geographical analysis, using the same keywords as in the previously published systematic review. From February 2020 to February 2023, 35 papers were included versus 34 in the previous period, with dementia (including Alzheimer's disease) being the most focused disease (60.0%) in this update, opposed to multiple sclerosis on the last review (55.9%). Also, environmental pollutants such as PM2.5 and NO2 have gained prominence, being represented in 65.7% and 42.9% of the new studies, opposed to 9.8% and 12.2% in the previous review, compared to environmental factors such as sun exposure (5.7% in the update vs 15.9% in the original). The mostly used geographic approach remained the patient's residence (82.9% in the update vs 21.2% in the original and 62.3% in total), and remote sensing was used in 45.7% of the new studies versus 19.7% in the original review, with 42.0% of studies using it globally, being the second most common approach, usually to compute the environmental variable. This review has been registered in PROSPERO with the number CRD42020196188. [ABSTRACT FROM AUTHOR]

Copyright of Bezmialem Science is the property of Galenos Yayinevi Tic. LTD. STI 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.)

Oral microorganisms have been found in the cerebral milieu, suggesting the involvement of oral microbiota in the onset and course of Alzheimer's Disease (AD) through mechanisms such as amyloid-beta accumulation, tau phosphorylation and neuroinflammation. It is still uncertain whether and which oral bacteria are associated with AD. Therefore, the aim of this systematic review was to assess the current evidence for associations between oral microbiota and AD. A database search in Pubmed and Embase resulted in 623 hits. After removing duplicates, 437 papers remained. Of these, 13 papers passed the inclusion criteria and were included for quality/risk of bias assessment and data extraction. Analysis of these 13 studies revealed high heterogeneity in terms of sample size, age, study design and microbiological methods. Quality assessment using the MINORS criteria indicated reasonable to good quality across studies. As a result of the omission of two of the criteria, the quality results may have been biased. There is no conclusive evidence as to if and which oral microbiota are associated with AD since many conflicting results were reported. Although the overall quality of the studies was acceptable, the studies differed in study design and protocol. Further research is needed to clarify this association. [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]

This research paper examines the use of Electroencephalogram (EEG) signal feature extraction for diagnosing neurological disorders, specifically Alzheimer's, Parkinson's, and seizure disorders. It evaluates various methods for categorizing EEG signals, including time-domain, frequency-domain, and statistical transformations emphasizing their effectiveness in distinguishing relevant brainwave patterns (beta, alpha, theta, delta) from artifacts like eye blinks and muscle movements. The study highlights the challenges in artifact removal and provides an overview of key feature extraction techniques, particularly in the time and frequency domains. The implementation section details the application of machine learning algorithms to classify mental states using statistical features from EEG signals. The research identifies specific EEG patterns associated with Alzheimer's, Parkinson's, and seizure disorders, noting alterations in alpha, theta, and delta waves. The paper underscores the critical role of EEG feature extraction in diagnosing neurological disorders and recommends incorporating additional frequency-based methods to enhance predictive accuracy in future research. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, such as Parkinson's and Alzheimer's, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients' lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer's and Parkinson's diseases. [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]

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]

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: 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]

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.)

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]

This paper explores the multifaceted experiences of informal caregivers providing care for family members afflicted with Alzheimer's disease, examining how these experiences are influenced by social class, and the role of economic capital, cultural health capital, and social capital. Drawing upon Bourdieu's theoretical framework, this paper conducts a qualitative thematic analysis to analyze twenty-five semi-structured interviews with caregivers and three interviews with medical professionals. The paper reveals how distinct forms of capital (economic, social, and cultural health capital) shape the caregiving experiences for family members with Alzheimer's disease. Notably, upper-class caregivers leverage both economic and cultural health capital to ease the burden associated with caregiving, while lower-class caregivers harness social capital. The paper challenges the prevailing deficit model of capital by shedding light on how lower-class caregivers demonstrate resourcefulness in seeking support beyond the health system. However, the findings also underscore a persistent and widening discrepancy in the caregiving experience. The paper concludes with policy suggestions, substantive and theoretical implications, and acknowledgement of methodological limitations. [ABSTRACT FROM AUTHOR]

This article examines the relationship between cholesterol levels and Alzheimer's disease (AD), beginning with the early observation that individuals who died from heart attacks often had brain amyloid deposition. Subsequent animal model research proved that high cholesterol could hasten amyloid accumulation. In contrast, cholesterol-lowering treatments appeared to counteract this effect. Human autopsy studies reinforced the cholesterol-AD connection, revealing that higher cholesterol levels during midlife significantly correlated with higher brain amyloid pathology. This effect was especially pronounced in individuals aged 40 to 55. Epidemiological data supported animal research and human tissue observations and suggested that managing cholesterol levels in midlife could reduce the risk of developing AD. We analyze the main observational studies and clinical trials on the efficacy of statins. While observational data often suggest a potential protective effect against AD, clinical trials have not consistently shown benefit. The failure of these trials to demonstrate a clear advantage is partially attributed to multiple factors, including the timing of statin therapy, the type of statin and the appropriate selection of patients for treatment. Many studies failed to target individuals who might benefit most from early intervention, such as high-risk patients like APOE4 carriers. The review addresses how cholesterol is implicated in AD through various biological pathways, the potential preventive role of cholesterol management as suggested by observational studies, and the difficulties encountered in clinical trials, particularly related to statin use. The paper highlights the need to explore alternate therapeutic targets and mechanisms that escape statin intervention. [ABSTRACT FROM AUTHOR]

Stem cell therapies are progressively redefining the treatment landscape for a spectrum of neurological and age-related disorders. This review discusses the molecular and functional attributes of stem cells, emphasizing the roles of neural stem cells and mesenchymal stem cells in the context of neurological diseases such as stroke, multiple sclerosis, amyotrophic lateral sclerosis, traumatic brain injury, Parkinson's disease, and Alzheimer's disease. The review also explores the potential of stem cells in addressing the aging process. The paper analyzes stem cells' intrinsic properties of self-renewal, differentiation, and paracrine effects, alongside the importance of laboratory-modified stem cells like induced pluripotent stem cells and transgenic stem cells. Insights into disease-specific stem cell treatments are offered, reviewing both successes and challenges in the field. This includes the translational difficulties from rodent studies to human trials. The review concludes by acknowledging the uncharted territories that warrant further investigation, emphasizing the potential roles of stem cell-derived exosomes and indole-related molecules, and aiming at providing a basic understanding of stem cell therapies. [ABSTRACT FROM AUTHOR]

Currently, in the United States, there is no legal obligation for medical professionals or civil courts to uphold patients' Advance Directives (ADs) regarding end-of-life care. The applicability and standing of ADs prepared by Alzheimer's patients is a persistent issue in bioethics. Those who argue against giving ADs full status take two main approaches: (1) appealing to beneficence on behalf of the Alzheimer's patient and (2) claiming that there is no longer any personal equivalence between the AD's creator and the subject of the AD. In this paper, I present profound arguments against both approaches. Firstly, I argue that the principle of beneficence cannot apply in the case of Alzheimer's patients, and, secondly, that the moral and legal authority of the AD need not depend on strict equivalence of personal identity. I conclude by arguing that valid ADs protect the dignity and autonomy of Alzheimer's patients and that, therefore, there are moral obligations to uphold ADs which should be reflected in public policy and legislation. [ABSTRACT FROM AUTHOR]

The cerebral vasculature plays a significant role in the development of Alzheimer's disease (AD), however, the specific association between them remains unclear. In this paper, based on the benefits of photoacoustic imaging (PAI), including label-free, high-resolution, in vivo imaging of vessels, we investigated the structural changes of cerebral vascular in wild-type (WT) mice and AD mice at different ages, analyzed the characteristics of the vascular in different brain regions, and correlated vascular characteristics with cognitive behaviors. The results showed that vascular density and vascular branching index in the cortical and frontal regions of both WT and AD mice decreased with age. Meanwhile, vascular lacunarity increased with age, and the changes in vascular structure were more pronounced in AD mice. The trend of vascular dysfunction aligns with the worsening cognitive dysfunction as the disease progresses. Here, we utilized in vivo PAI to analyze the changes in vascular structure during the progression of AD, elucidating the spatial and temporal correlation with cognitive impairment, which will provide more intuitive data for the study of the correlation between cerebrovascular and the development of AD. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis (ALS) affect millions and present significant challenges in healthcare and treatment costs. The debate in the field pivots around two hypotheses: synaptic spread and selective vulnerability. Pioneers like Virginia Lee and John Trojanowski have been instrumental in identifying key proteins (tau, alpha-synuclein, TDP-43) central to these diseases. The synaptic spread hypothesis suggests a cell-to-cell propagation of pathogenic proteins across neuronal synapses, influencing disease progression, with studies highlighting the role of proteins like alpha-synuclein and amyloid-beta in this process. In contrast, the selective vulnerability hypothesis proposes inherent susceptibility of certain neurons to degeneration due to factors like metabolic stress, leading to protein aggregation. Recent advancements in neuroimaging, especially PET/MRI hybrid imaging, offer new insights into these mechanisms. While both hypotheses offer substantial evidence, their relative contributions to neurodegenerative processes remain to be fully elucidated. This uncertainty underscores the necessity for continued research, with a focus on these hypotheses, to develop effective treatments for these devastating diseases. [ABSTRACT FROM AUTHOR]

The complex nature of human brain tissues is important in ensuring accurate diagnosis to save human lives. Research on early detection of brain diseases has gained significant prominence within medical intelligence using highly complex model architectures with only a single label that cannot be verified. This paper introduces an innovative approach to Siamese Network Genetic Algorithm (SN‐GA) leveraging Siamese contrastive learning for classifying brain images across diverse diseases. Our core architecture is a Bi‐Convolutional Neural Network (Bi‐CNN) optimized by a genetic algorithm to enhance brain image classification. Specifically, five widely recognized transfer learning‐based architectures, namely AlexNet, Efficient‐B0, VGG‐16, ResNet‐50, and Inception‐v3, have been incorporated to evaluate the effectiveness of the proposed SN‐GA system. The performance of these models has been rigorously analyzed and compared using two distinct datasets: Brain tumors and Alzheimer's datasets. The experimental results robustly affirm the efficacy of the proposed Siamese model, yielding exceptional levels of accuracy, precision, and recall, all peaking at 97%. These findings underscore the potential and resilience of the optimized Siamese network in the context of brain disease classification, emphasizing its significance in advancing the field of medical imaging and diagnosis, with implications for early intervention and patient care. [ABSTRACT FROM AUTHOR]

Background: In recent years, the problem of cognitive impairment in the elderly has become increasingly prominent. Understanding the research trend of influencing factors of mild cognitive impairment, and provide reference for medical staff to early screening of the elderly with mild cognitive impairment. Objective: Through the visual analysis of the influence factors of the elderly with mild cognitive impairment, the current research status was discussed. Methods: The relevant literature in the field of influencing factors of mild cognitive impairment in the elderly included in the Web of Science core collection database from 2013 to 2022 was searched. Using software such as Cite Space and VOS viewer to visually analyze literature citations, country, keywords, and development trends. Results: A total of 547 relevant literatures were included, and the number of publications showed an increasing trend in the past ten years. The United States ranked first in both the number of published papers (157) and centrality (0.34), and the United States and China had a greater influence on the influencing factors of mild cognitive impairment. Alzheimer's disease, cognitive decline, the elderly, risk factors, are the research hotspot in this field. Conclusion: Cognitive decline will affect the autonomy of the elderly. Cognitive frailty, MRI is the forefront of MCI research, to understand the research hotspots and frontiers in this field, to conduct early screening and intervention guidance for people with mild cognitive impairment, so as to delay the occurrence of Alzheimer's disease, and reduce the pressure on family caregivers and society. [ABSTRACT FROM AUTHOR]

In recent years, with the aggravation of aging, the incidence of neurodegenerative diseases is increasing year by year, and the prognosis of patients is poor. Functional Near-Infrared Spectroscopy (fNIRS) is a new and non-invasive neuroimaging technology, which has been gradually deepened in the application research of neurodegenerative diseases by virtue of its unique neurooxygen signal brain functional imaging characteristics in monitoring the disease condition, making treatment plans and evaluating the treatment effect. In this paper, the mechanism of action and technical characteristics of fNIRS are briefly introduced, and the application research of fNIRS in different neurodegenerative diseases is summarized in order to provide new ideas for future related research and clinical application. [ABSTRACT FROM AUTHOR]

The design and construction of residences for persons with Alzheimer's disease (AD) have been based on the recommendations of design guides, the results of empirical tests with samples of the population, and the experience of architects and planners. The reiteration of certain patterns, criteria, and guidelines has given rise to a new type of building that has not yet been explicitly described. The aim of this paper is to determine the main characteristics of this typology. This research is based on a critical review methodology, analysing 30 care homes built over the last four decades across various global contexts. Detailed surveys of plans, projects, and buildings were carried out, allowing a comparative analysis of the architectural attributes to determine the most influential parameters for these buildings. The results indicate that environments designed with safety, accessibility, and opportunities for social interaction in mind—and, above all, those that are personalised to the needs of this collective—significantly enhance the behaviour, emotional state, and cognitive state of their residents. The main theoretical contributions include identifying and stating the key features of this type, such as small scale, basic cell housing, comprehensible organisation, and sensory stimulation of spaces, among others. The breakthrough of this study that differentiates it from other works in this field is that it provides concrete guidelines to approach the planning, design, and construction of these kinds of residences. The significance of this research lies in the definition of this unique typology, which is not characterised by its morphology, shape, or formal composition but rather focused on promoting an adequate cognitive and physiological reception of the space by the users. This building concept has important management implications, as its construction must provide for and integrate specific care services in a residential setting for people with AD. [ABSTRACT FROM AUTHOR]

Transformers have dominated the landscape of Natural Language Processing (NLP) and revolutionalized generative AI applications. Vision Transformers (VT) have recently become a new state-of-the-art for computer vision applications. Motivated by the success of VTs in capturing short and long-range dependencies and their ability to handle class imbalance, this paper proposes an ensemble framework of VTs for the efficient classification of Alzheimer's Disease (AD). The framework consists of four vanilla VTs, and ensembles formed using hard and soft-voting approaches. The proposed model was tested using two popular AD datasets: OASIS and ADNI. The ADNI dataset was employed to assess the models' efficacy under imbalanced and data-scarce conditions. The ensemble of VT saw an improvement of around 2% compared to individual models. Furthermore, the results are compared with state-of-the-art and custom-built Convolutional Neural Network (CNN) architectures and Machine Learning (ML) models under varying data conditions. The experimental results demonstrated an overall performance gain of 4.14% and 4.72% accuracy over the ML and CNN algorithms, respectively. The study has also identified specific limitations and proposes avenues for future research. The codes used in the study are made publicly available. [ABSTRACT FROM AUTHOR]

Phospholipids are crucial structural components of cells. Phosphatidylcholine and phosphatidylethanolamine (both synthesized via the Kennedy pathway) and phosphatidylserine undergo interconversion. The dysregulation of this process is implicated in various diseases. This paper discusses the role of enzymes involved in the interconversion of phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine, specifically phosphatidylethanolamine N-methyltransferase (PEMT), phosphatidylserine synthases (PTDSS1 and PTDSS2), and phosphatidylserine decarboxylase (PISD), with a focus on their biochemical properties. Additionally, we describe the effects of the deregulation of these enzymes and their roles in both oncological and non-oncological diseases, including nonalcoholic fatty liver disease (NAFLD), Alzheimer's disease, obesity, insulin resistance, and type II diabetes. Current knowledge on inhibitors of these enzymes as potential therapeutic agents is also reviewed, although in most cases, inhibitors are yet to be developed. The final section of this article presents a bioinformatic analysis using the GEPIA portal to explore the significance of these enzymes in cancer processes. [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: 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]

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]

Intracellular biothiols, including cysteine (Cys), glutathione (GSH), and homocysteine (Hcy), play a critical role in many physiological and pathological processes. Among them, GSH is the most abundant non-protein mercaptan (1–10 mM) in cells, and the change in GSH concentration level is closely related to the occurrence of many diseases, such as Parkinson's disease, Alzheimer's disease, and neurological diseases. Fluorescent probes have attracted much attention due to their advantages of high specificity, high sensitivity, high selectivity, low cost, and high quantum yield. Methods that use optical probes for selective detection of GSH in vitro and in vivo are in high demand. In this paper, we reviewed the most recent five years of research on fluorescence probes for the detection of GSH, including the specific detection of GSH, dual-channel identification of GSH and other substances, and the detection of GSH and other biothiols. According to the type of fluorophore, we classified GSH fluorescent probes into eight classes, including BODIPY, 1,8-Naphthalimide, coumarin, xanthene, rhodamine, cyanine, benzothiazoles, and others. In addition, we roundly discuss the synthesis, detection mechanism, photophysical properties, and biological applications of fluorescent probes. We hope that this review will inspire the exploration of new fluorescent probes for GSH and other related analyses. [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]

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 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]

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]

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]

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]

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]