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Dopamine, the main catecholamine neurotransmitter plays an important role in renal, cardiovascular, central nervous systems, and pathophysiological processes. The abnormal dopamine levels can result in neurological disorders such as Parkinson's, Alzheimer's, schizophrenia, acute anxiety, neuroblastoma and also contribute to cognitive dysfunctions. Given the widespread importance of dopamine concentration levels, it is imperative to develop sensors that are able to monitor dopamine. Herein, we have developed pre-anodized disposable paper electrode modified with 1-pyrenebutyric acid, for the selective and sensitive determination of dopamine. The sensor was characterized with Fourier transform infrared spectroscopy, Energy dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, and electrochemical techniques for addressing the robust formation and electrochemical activity. The modified electrode exhibited excellent electrocatalytic activity towards dopamine without the common interference from ascorbic acid. The calibration plot for the dopamine sensor resulted linear range from 0.003 μM to 0.5 μM with a detection limit of 0.11 nM. The sensor's potential utility was tested by monitoring dopamine concentration changes in rat brain homogenates when subjected to neurotoxicity. The developed sensor was validated with gold-standard UV–Vis spectroscopy studies and computational studies were performed to understand the interaction between 1-pyrenebutyric acid and dopamine. [Display omitted] • Pre-anodized paper electrode with π-π stacked pyrene butyric acid for selective detection of dopamine. • The sensor exhibited a detection limit of 0.11 nM with excellent selectivity. • Dopamine concentration changes in rat brain homogenates when subjected to CKD induced neurotoxicity was studied. [ABSTRACT FROM AUTHOR]

A simple microfluidic device based on the use of a paper platform modified with AuNPs was developed. The ALP-like CeO 2 nanozyme first converts p-nitrophenyl phosphate (pNPP) to PNP. Fluorescence detection of Tau-441 was proposed based on the inner filter effect (IFE) of n-doped graphene quantum dots (NGQDs) and p-nitrophenol (PNP). [Display omitted] • A novel device based on the use of a paper platform modified with AuNPs was developed. • Fluorescence detection of Tau protein (Tau-441) was proposed based on the inner filter effect (IFE) of n-doped graphene quantum dots (NGQDs). • The ALP-like CeO 2 nanozyme first converts p-nitrophenyl phosphate (pNPP) to p-nitrophenol (PNP) for the rapid analysis of Tau-441 content in human serum samples. The advancement of Alzheimer's disease (AD) is directly correlated to the phosphorylation damage of Tau protein (Tau-441), which is considered the most reliable indicator for early detection of AD. Nevertheless, the concentration of Tau-441 in human serum remains considerably low. To address this issue, a paper-based fluorescence immunosensor based on internal filter effect (IFE) was developed for the detection Tau-441. The microfluidic paper-based channel was prepared and modified with a gold layer to attach primary antibodies and increase the sensitivity of the sensor. All at once, Au@CeO 2 nanorods was employed as a fluorescence probe to hydrolyze p-nitrophenyl phosphate (pNPP), which generates hydrolyzed product p-nitrophenol (PNP) can reduce or quench the fluorescence intensity of N-doped graphene quantum dots (NGQDs). The immunosensor was designed to detect an increase in the amount of Tau-441 collected as the fluorescence intensity of NGQDs decreased. The developed sensor demonstrated satisfactory results in clinical serum samples, offering a promising concept for an immunoassay tagged with nanozymes for early detection of AD. Overall, this microfluidic paper-based fluorescent immunosensor has the potential to improve early diagnosis of AD. [ABSTRACT FROM AUTHOR]

Joint analysis of varied biomarkers in blood sample will provide more accurate results for the monitoring and management of Alzheimer's disease (AD). In addition to the known β-amyloid protein (Aβ) used as a reliable clinical marker for AD diagnosis, Fetuin B also demonstrates a strong correlation with AD progression. Especially, for medical diagnostics, on-site detection of multi-analyte is quite important and therefore, it is desirable to develop a rapid and portable, multiplex electrochemical assay for simultaneous detection of Aβ and Fetuin B, which has been seldomly reported before. To achieve this goal, a multiplex paper-based electrode was prepared via vacuum filtration, by which, single-walled carbon nanotubes (SWCNTs) and AuNPs were sequentially filtered through cellulose paper with a five-electrode pattern to form a conductive underlay and electrical connections. A nanocomposite of AuNPs-loaded dendritic mesoporous silica nanoparticles (dmSiO 2 -Au) was firstly synthesized and further deposited thionine (Thi) for signal amplification. An immunosensor was correspondingly fabricated, which involved a combination of multiplex electrodes and dmSiO 2 -Au-Thi nanocomposite to facilitate simultaneous monitoring of Aβo and Fetuin B. Additionally, ferrocene (F c) was applied onto a separate working electrode to perform a ratiometric electrochemistry for enhancing accuracy. The outcomes demonstrated high specificity, sensitivity, and stability, and no obvious cross-talking between three working electrodes. Application of this sensor resulted in a successful determination of Aβo and Fetuin B levels in serum and brain tissue of transgenic AD mice. Overall, the presented electrochemical immunosensors for performing multiplex diagnostics may facilitate to the exploration of more AD-related blood markers. • A multiplex paper-based electrode was prepared. • A sandwich-based immunosensor enabled simultaneous monitoring of Aβo and Fetuin B. • Ferrocene was applied onto electrode to construct a ratiometric electrochemistry. • Analysis of Aβo and Fetuin B in APP/PS1 transgenic AD mice was realized. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection. This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1–42 (Aβ 1–42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1–42, particularly in areas with low resources and limited communication infrastructure. The schematic diagram of the overview of an offline deep learning-assisted smartphone-based paper-based microfluidic platform for screening of Alzheimer's disease. [Display omitted] • Deep learning-assisted smartphone-based platform enables offline detection. • Fully automated microfluidic paper analysis devices (μPADs). • Smartphone-based platform for detection of biomarker of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), one of the most prevalent neurodegenerative diseases, results in severe cognitive decline and irreversible memory loss. Early detection of AD is significant to patients for personalized intervention since effective cure and treatment methods for AD are still lacking. Despite the severity of the disease, existing highly sensitive AD detection methods, including neuroimaging and brain deposit-positive lesion tests, are not suitable for screening purposes due to their high cost and complicated operation. Therefore, these methods are unsuitable for early detection, especially in low-resource settings. Although regular paper-based microfluidics are cost-efficient for AD detection, they are restricted by a poor limit of detection (LOD). To address the above limitations, we report the ultrasensitive and low-cost nanocellulose paper (nanopaper)-based analytical microfluidic devices (NanoPADs) for detecting one of the promising AD blood biomarkers (glial fibrillary acidic protein, GFAP) using Surface-enhanced Raman scattering (SERS) immunoassay. Nanopaper offers advantages as a SERS substrate, such as an ultrasmooth surface, high optical transparency, and tunable chemical properties. We detected the target GFAP in artificial serum, achieving a LOD of 150 fg mL-1. The developed NanoPADs are distinguished by their cost-efficiency and ease of implementation, presenting a promising avenue for effective early detection of AD's GFAP biomarker with ultrahigh sensitivity. More importantly, our work provides the experimental routes for SERS-based immunoassay of biomarkers on NanoPADs for various diseases in the future. Ultrasensitive SERS-Based Immunoassay NanoPADs for Early Detection of AD. [Display omitted] • Ultrasensitive and low-cost detection of biomarker of Alzheimer's disease. • Nanocellulose-paper-based biosensing microfluidic devices. • Surface-enhanced Raman scattering immunoassay of GFAP. [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: Dementia occurring before age 65 is known as young-onset dementia (YOD), with Alzheimer's disease being the most common type. YOD poses unique challenges for persons and families, impacting their working-age years and family responsibilities. Person-centered interventions and services are essential to improve their quality of life and social engagement. Objective: This study aims to synthesize non-pharmacological interventions for persons with YOD and their families to inform future targeted interventions. Methods: We conducted a systematic literature search across four databases: PubMed, PsycINFO, Scopus, and CINAHL. The included articles were carefully screened, categorized, and synthesized by following Arksey and O'Malley's five stages framework. Results: We included 20 studies reported in 24 papers, with 11 studies (14 papers) on persons with YOD and nine studies (10 papers) on families. Quantitative intervention results vary, but qualitative interviews show positive feedback. Stakeholders provided positive evaluations, stating these interventions provided a sense of normalcy, facilitated communication among families, enhanced the independence of persons with YOD, and improved the families' caregiving self-efficacy, thereby reducing care burden and psychological distress. The heterogeneity among the studies posed integration challenges. Conclusions: Interventions for YOD can improve the quality of life for both persons with YOD and their families. More extensive intervention studies are urgently needed, especially in developing countries, with a focus on family-centered and life course perspectives. In future intervention research design, a more extensive incorporation of stakeholder involvement is essential for successful implementation. Moreover, the integration of new technologies shows promise as a potential avenue for intervention advancement. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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

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]

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]

Our body’s control center is the brain. Newer and newer brain disorders are being identified as time goes on. The cumulative loss of immunological and neuronal function is what leads to neurodegenerative diseases including Alzheimer’s, Parkinson’s, Hunting ton’s, and many others. According to recent trends, there will be 152 million peoples with various neurodegenerative diseases in 2050. In recent years, the use of artificial intelligence (AI), specifically deep learning (DL) has transformed the area of neurology. This paper introduces and discusses various deep learning algorithms, including Deep belief networks (DBN), Convolutional neural networks (CNN), Recurrent neural networks (RNN) and Autoencoders along with their theory and applications in neurological diseases prediction. This paper summarizes and highlights the most promising deep learning models for Alzheimer’s disease (AD) diagnosis and risk factors by training on neuroimaging datasets combining PET and MRI images gathered using diverse modalities. This study also reviews the entire MRI processing chain—from acquisition to image retrieval, from segmentation to disease prediction which has been optimized for deep learning. Deep learning models are not meant to replace healthcare professionals, but rather to aid in recognizing illnesses and better decision-making. The ultimate goal is to help healthcare providers make more accurate diagnoses and intervene more quickly. [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]

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

Graphic medicine, an interdisciplinary field situated at the crossroads of comics and healthcare, operates as a medium through which the intricate nature of experiences with illness can be articulated, challenging orthodox medical dogmatism in an engaging and accessible way. Combining the affordances of comics and the narrative power of storytelling, graphic medicine elucidates the socio-cultural stigmatization of dementia influenced by a multitude of discourses. Diverging from existing discourses that depict individuals with Alzheimer's disease (AD) as zombies, brain-dead, or empty shells, graphic memoirs reconstruct these reductive notions and represent them as imaginative, productive, and perceptive. Taking these cues, the present paper close reads some sections of Dana Walrath's (2016) Aliceheimer's: Alzheimer's Through the Looking Glass in order to demonstrate how graphic medicine reconceptualizes the preeminent hallucinatory experiences of her AD-afflicted mother, Alice, as visions. Walrath deploys collage art to epitomize Alice's ordeal with AD. In particular, Walrath deploys thought-provoking fragments from Lewis Caroll's Alice in Wonderland, strategically to proximate Alice's experiences with AD and tackle the problem of dementia and sociality. Additionally, the paper explores how the text fosters interdependence, respect, and trust to recognize and restore Alice's personhood. The paper concludes by discussing how Aliceheimer's operates as an alternative paradigm beyond the confines of biomedical and cultural models of dementia through the use of lexical puissance. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is a complex multifactorial disease that does not yet have a cure. Although many statistical and computational methods have been effective in its early prediction and prognosis, our understanding of the biological mechanisms of the disease is far from satisfactory. Due to the complex nature of the disease and the lack of diversity in existing data, a multimodal systems biology approach is best suited to elucidate the etiology of the disease while abstracting variant differences among populations of different ethnicities. Although Alzheimer's is a complex disease with many contributing factors, genetic risks have been shown to have a significant impact on its manifestation. Using a systems biology approach, this paper uses graph theory, network analysis, omics data integration, and visualization to develop a functionally coherent AD module. This genetic tool, combined with other existing biomarkers and predictive algorithms, can enhance the accuracy of AD early prediction and prognosis. [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]

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]

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]

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]

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]