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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]

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]

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]

A commentary of the paper 'Humanin variant P3S is associated with longevity in APOE4 carriers and resists APOE4‐induced brain pathology' that appeared recently in Aging Cell. The possible association of a mitochondrial haplogroup with a disease is frequently discussed. The Humanin peptide encoded by the mtDNA has been shown to play an important regulatory role in cell metabolism. There are variants of Humanin caused by different mutations and it is known that the potent form of Humanin, termed S14G, is found naturally in the people of haplogroup U6a7a1a because they have the mutation m.A2672G; however it has not been shown that having this mutation is indeed beneficial. In their paper, the authors suggest that the mitochondrial DNA mutation, m.C2639T, may be beneficial in people who are in haplogroup N1b and also carry APOE4. The mutation changes the common form of Humanin to Humanin P3S. In the study, the researchers looked at a group of Ashkenazi women who were over the age of 95, and found that a higher proportion of them carried APOE4, suggesting that Humanin P3S protected them against the adverse effects of APOE4. A study in a mouse model supported this finding by showing treatment with Humanin P3S reduced APOE4‐induced brain pathology. In the world population, there are about 500,000 Ashkenazi in haplogroup N1b, predominantly in the subgroup N1b1b1; and there are about 9.5 million non‐Ashkenazi people with the mutation m.C2639T and are therefore also in haplogroup N1b and have Humanin P3S. However, the researchers have yet to show Humanin P3S is of benefit in non‐Ashkenazi people. This paper raises the possibility of a therapeutic use of Humanin P3S in the treatment of Alzheimer's disease. [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]

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]

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]

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]

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]

A paper titled "[Retracted] Cognitive improvements and reduction in amyloid plaque deposition by saikosaponin D treatment in a murine model of Alzheimer's disease" was published in the journal Experimental & Therapeutic Medicine. However, it was later discovered that the images presented in the paper were similar to data from other articles that had already been published by different authors at different research institutes. As a result, the paper has been retracted from the journal. The authors were given the opportunity to explain the concerns raised, but they did not respond. The editor apologizes for any inconvenience caused. [Extracted from the article]

This document is a summary of a special issue titled "Neurogenetics in Neurology" from the International Journal of Molecular Sciences. The issue includes six papers that highlight advancements in molecular genetics and genomics and their impact on human health. The papers cover various topics, such as the molecular mechanisms underlying genetics-based diseases affecting the nervous system, brain calcification as a symptom of systemic and genetic conditions, glioblastoma multiforme metabolism, the role of the survival motor neuron protein in spinal muscular atrophy, the involvement of microRNA-30c in neurological disorders, and the use of long-range interaction maps to identify candidate genes for neurodevelopmental disorders. Overall, the papers contribute to our understanding of molecular genetics and genomics and provide opportunities for further research in the field. [Extracted from the article]

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]

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]

This paper presents two experiments in which, using artificial intelligence (specifically Deep Learning with convolutional neural networks), we were able to predict Alzheimer's disease based on MRI images. In order to have better results and to minimize the computational effort in the laboratory, two pre-trained AI models were used, models trained previously on more than a million images from the ImageNet database (which provide tens of millions of clean, labelled and sorted images). The top-layers of the models were trained, for our specific task of Alzheimer’s prediction, with 500 public MRI images from Kaggle, an online community of data scientists and machine learning engineers and a subsidiary of Google. In this paper we describe the code used in the laboratory for the specific task. [ABSTRACT FROM AUTHOR]

In this commentary, we address a paper published by Johnson et al. by assessing the robustness of their method to discover diagnostic biomarkers in Alzheimer's disease (AD). In addition, we examine how these newly discovered and previously discovered biomarkers, can play a role in assisting patients with AD and those at risk for developing AD, with an emphasis on the translational hurdles that accompany such discoveries. [ABSTRACT FROM AUTHOR]

Background: Medical students' empathy toward patients with Alzheimer's is rarely found in formal medical curricula. Based on Vygotsky's theory, watching films and reflection can be considered as effective methods to improve empathy. The present study aimed to explore medical students' perceptions of empathy toward patients with Alzheimer after participating in an educational program by using interactive video based on Vygotsky's theory. Methods: This qualitative study was conducted at Tehran University of Medical Sciences in 2022. The population included all 40 medical students. Firstly, the Still Alice movie which is about the feelings of a professor who was diagnosed with Alzheimer's disease was shown to the students. Secondly, the students reflected on their experiences of watching the movie. Thirdly, a session was held for group discussion on the subject of the movie, the patient's feelings, the doctor's attitude, the social environment surrounding the patient shown in the movie, and the necessity of empathy toward patients with Alzheimer's disease. The reflection papers were analyzed using the conventional qualitative content analysis method. Results: After analyzing 216 codes from 38 reflection papers, four categories, including communication with a patient with Alzheimer's, understanding the patient with Alzheimer's as a whole, medical science development, and the student's individual ideology, were extracted. Conclusion: Reflection and group discussion after watching movie by providing opportunities for social interaction about personal interpretations will lead to active role in enhancing empathy. Based on the perceptions of the medical students, they gained a perspective to consider the patient as a whole and pay attention to establishing a proper relationship with the patient. [ABSTRACT FROM AUTHOR]

Introduction and purpose: Currently, the role of the gut microbiota in the pathogenesis of different diseases is being widely researched, the understanding of whether the dysbiosis of the gut flora demonstrates the significant role in Alzheimer disease (AD) is accentuated. The aim of this paper is to summarize the existing paper about the role of the gut microbiota - its diversity, stability and composition in this condition. The search was conducted using PubMed and Google Scholarship databases. Brief description of the state of knowledge: It is still uncertain if alteration in gut microbiome is a consequence of AD or its risk factor, but some findings suggest that the intestinal flora is able to influence the activity of the brain and lead to its dysfunctions. There is also the association between composition of the gut microbiome and AD. Results indicate that affected patients had less heterogeneity in their microbiome and the progression of AD led to alterations in the bacterial flora. Introducing modifications of the gut microbiota is considered as a therapeutic option for AD. Following personalized diet, probiotics, prebiotics and synbiotics administration or fecal microbiota transplantation (FMT) may be beneficial for affected patients. Conclusions: The subject of the role of the gut microbiota merits further research as foregoing results of conducted studies are pointing to its possibly meaningful role. Presently, still more data is needed as most of the research was collected on animal models and the analysis conducted on humans remains insufficient. We conclude that further studies are warranted in order to fully understand the pathophysiology of the disease and the possible usefulness of microbiome therapy. [ABSTRACT FROM AUTHOR]

Introduction: The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data. Methods: In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics. Results: We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement. Conclusion: Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a popular neurological disorder affecting a critical part of the world's population. Its early diagnosis is extremely imperative for enhancing the quality of patients' lives. Recently, improved technologies like image processing, artificial intelligence involving machine learning, deep learning, and transfer learning have been introduced for detecting AD. This review describes the contribution of image processing, feature extraction, optimization, and classification approach in AD recognition. It deeply investigates different methods adopted for multiclass diagnosis of AD. The paper further presents a brief comparison of existing AD studies in terms of techniques adopted, performance measures, classification accuracy, publication year, and datasets. It then summarizes the important technical barriers in reviewed works. This paper allows the readers to gain profound knowledge regarding AD diagnosis for promoting extensive research in this field. [ABSTRACT FROM AUTHOR]

The aim of this comprehensive review is to summarize recent literature on associations between periodontitis and neurodegenerative diseases, explore the bidirectionality and provide insights into the plausible pathogenesis. For this purpose, systematic reviews and meta-analyses from PubMed, Medline and EMBASE were considered. Out of 33 retrieved papers, 6 articles complying with the inclusion criteria were selected and discussed. Additional relevant papers for bidirectionality and pathogenesis were included. Results show an association between periodontitis and Alzheimer's disease, with odds ratios of 3 to 5. A bidirectional relationship is suspected. For Parkinson's disease (PD), current evidence for an association appears to be weak, although poor oral health and PD seem to be correlated. A huge knowledge gap was identified. The plausible mechanistic link for the association between periodontitis and neurodegenerative diseases is the interplay between periodontal inflammation and neuroinflammation. Three pathways are hypothesized in the literature, i.e., humoral, neuronal and cellular, with a clear role of periodontal pathogens, such as Porphyromonas gingivalis. Age, gender, race, smoking, alcohol intake, nutrition, physical activity, socioeconomic status, stress, medical comorbidities and genetics were identified as common risk factors for periodontitis and neurodegenerative diseases. Future research with main emphasis on the collaboration between neurologists and dentists is encouraged. [ABSTRACT FROM AUTHOR]

The neuro-immune axis has a crucial function both during physiological and pathological conditions. Among the immune cells, myeloid-derived suppressor cells (MDSCs) exert a pivotal role in regulating the immune response in many pathological conditions, influencing neuroinflammation and neurodegenerative disease progression. In chronic neuroinflammation, MDSCs could lead to exacerbation of the inflammatory state and eventually participate in the impairment of cognitive functions. To have a complete overview of the role of MDSCs in neurodegenerative diseases, research on PubMed for articles using a combination of terms made with Boolean operators was performed. According to the search strategy, 80 papers were retrieved. Among these, 44 papers met the eligibility criteria. The two subtypes of MDSCs, monocytic and polymorphonuclear MDSCs, behave differently in these diseases. The initial MDSC proliferation is fundamental for attenuating inflammation in Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS), but not in amyotrophic lateral sclerosis (ALS), where MDSC expansion leads to exacerbation of the disease. Moreover, the accumulation of MDSC subtypes in distinct organs changes during the disease. The proliferation of MDSC subtypes occurs at different disease stages and can influence the progression of each neurodegenerative disorder differently. [ABSTRACT FROM AUTHOR]

Background: Collaborations between patient organisations (POs) and the pharmaceutical industry can help identify and address the unmet needs of people living with a disease. In Alzheimer's disease (AD), the scale and complexity of the current unmet needs call for a broad and cross‐sectoral collaboration, including people living with Alzheimer's (PLWA), their care partners and the wider research community. Objective: This study aimed to describe learnings from the Finding Alzheimer's Solutions Together (F.A.S.T.) Council, a collaboration between POs and Roche, convened to better understand the unmet needs of PLWA and their care partners. Results: 1. Learnings from the collaboration, including clarifying objectives and members' expectations upfront, and establishing a set of guiding values and engagement principles. 2. Insights and recommendations for improving care in AD, including a wide range of unmet needs and potential solutions, systematically captured throughout the PLWA journey. These have resulted in several published reports and other outcomes, including (1) 'Portraits of care', highlighting the role of care partners, and the impact of coronavirus disease 2019 on care; (2) Clinical trial guidebook, recommending how PLWA and care partner experience can be incorporated into trial design; (3) 'Commitments Catalogue', highlighting progress by governmental organisations in achieving their commitments; and (4) a report to guide policy on improving diversity, equity and inclusion in clinical trials. Conclusions: Close collaboration between POs and the pharmaceutical industry in AD can enable effective research, in which PLWA and care partners are engaged as 'experts through experience' to help identify key unmet needs and co‐create solutions with the wider AD research community. This paper and the work undertaken by the F.A.S.T. Council may act as a blueprint for meaningful collaboration between POs and the pharmaceutical industry. Patient or Public Contribution: The paper reports the collaboration between POs, the F.A.S.T. Council and Roche to progress towards a future in which PLWA can live fulfilling lives with their disease managed well. Clinical Trial Registration: Not applicable. [ABSTRACT FROM AUTHOR]

Background: Studies suggest that individualized music listening is an effective, non-pharmacological intervention for improving the quality of life of people with dementia in the institutional care setting. Noting that most people with dementia live at home, we conduct a randomized controlled trial to assess the feasibility and effectiveness of an app-based individualized music listening intervention for people with dementia in the home care setting. The intervention is delivered by family caregivers. Methods: We will recruit N = 130 dyads consisting of one person with dementia living at home and their family caregiver. After a baseline assessment, dyads are randomly assigned by gender to either the intervention or control group. People with dementia in the intervention group listen to individualized music playlists for 20 min every other day for six weeks via the self-developed Individualized Music and Dementia app. The control group receives standard care. All dyads complete paper-and-pencil questionnaires six weeks before the start of the intervention (T0), directly before the intervention (T1), directly after the intervention (T2), and six weeks later (T3). During the intervention period, all caregivers also complete daily ecological momentary assessments via the app. During three home visits, a trained project member will observe the dyads and collect hair samples. After the intervention, semi-structured interviews will be conducted to collect information about participants' experiences with the app and intervention. The primary outcome is the attainment of individual goals established during the baseline assessment. Secondary outcomes are the well-being, physiological stress and quality of life of people with dementia and their caregivers; people with dementia's behavioural and psychological symptoms of dementia, resistance during care, and reactions to the music; caregivers' burden of care, positive aspects of care, and caregiving self-efficacy; and the quality of the caregiver-care recipient interaction. Discussion: Our study will assess the extent to which an app-based individualized music listening intervention is feasible and effective for enhancing the well-being and quality of life of people with dementia living at home and their family caregivers. Trial registration: German Clinical Trials Register DRKS00025502 and ISRCTN registry ISRCTN68084105, https://doi.org/10.1186/ISRCTN68084105 [ABSTRACT FROM AUTHOR]

This paper delves into the increasingly recognized yet complex relationship between air pollution and Neurological Diseases. Although the detrimental effects of air pollution on respiratory and cardiovascular health are well documented, its impact on neurological and cognitive disorders is an emerging area of concern. In this mini review, we explore the intricate mechanisms by which various air pollutants, such as particulate matter, nitrogen oxides, and polycyclic aromatic hydrocarbons, contribute to neurological pathologies. The focus lies on the role of oxidative stress and inflammation in exacerbating conditions like Alzheimer’s disease and Parkinson’s disease. By unraveling these connections, the paper sheds light on the broader implications of environmental factors on neurological health and underscores the urgent need for policy interventions to mitigate air pollution’s impact on the nervous system. [ABSTRACT FROM AUTHOR]

The early and effective diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer's disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments. [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]

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]

A Nature Medicine paper published in January 2024 describes eight cases of iatrogenic Alzheimer's disease in individuals who received cadaveric pituitary‐derived human growth hormone. The paper's conclusions argue for the transmissibility of Alzheimer's disease, which, if true, would create a significant public health crisis. For example, neurosurgical practices would require substantial revision, and many individuals who have undergone neurosurgical procedures would now be at considerable risk of Alzheimer's disease. A detailed review of the presented cases reveals that they do not have Alzheimer's disease, and there are alternative explanations for the cognitive decline described. In people with progressive cognitive decline, the diagnosis of Alzheimer's disease requires a demonstration of amyloid and tau pathology or amyloid and tau biomarkers. Extensive tau pathology is not demonstrated, and some also lack amyloid beta pathology. The cases described in this paper do not meet the criteria for dementia due to Alzheimer's disease by clinical and pathological standards. Highlights: Creutzfeldt‐Jakob disease has been transmitted by cadaveric growth hormone.There is no evidence for the transmission of Alzheimer's disease by cadaveric growth hormone.There is no evidence that Alzheimer's disease is transmissible. [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]

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]

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]