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

The diagnosis of Alzheimer’s disease (AD) is frequently missed or delayed in clinical practice. To remedy this situation, we developed a screening, paper-based (P-ELISA) platform to detect β-amyloid peptide 1–42 (Aβ42) and provide rapid results using a small volume, easily accessible plasma sample instead of cerebrospinal fluid. The protocol outlined herein only requires 3 μL of sample per well and a short operating time (i.e., only 90 min). The detection limit of Aβ42 is 63.04 pg/mL in a buffer system. This P-ELISA-based approach can be used for early, preclinical stage AD screening, including screening for amnestic mild cognitive impairment (MCI) due to AD. It may also be used for treatment and stage monitoring purposes. The implementation of this approach may provide tremendous impact for an afflicted population and may well prompt additional and expanded efforts in both academic and commercial communities.

The early diagnosis of Alzheimer’s disease (AD) remains a challenge for medical scientists worldwide, leading to a number of research efforts that focus on biosensor development for AD biomarkers. However, the application of these complicated biosensors is limited in medical diagnosis, due to the difficulties in robust sensing platform development, high costs, and the necessity for technical professionals. We successfully developed a robust straightforward manufacturing process for the fabrication of multi-chamber paper devices using the wax printing method and exploited it to detect amyloid beta 42 oligomers (AβO42, a significant biomarker of AD) using copper-enhanced gold nanoprobe colorimetric immunoblotting. Small hydrophilic reaction chambers could concentrate the target sample to the desired size to improve the sensing performance. The copper-enhanced gold nanoprobe immunoblot using the designed multi-chamber platform exhibited a highly sensitive performance with a limit of detection of 320 pg/mL by the naked eye and 23.7 pg/mL by a smartphone camera. This process from sensing manufacture to sensing conduction is simple to perform whenever medical technicians require time- and cost-savings, without complicated instruments or the need for technical professionals, making it feasible to serve as a diagnostic tool worldwide for the early monitoring of AD and scalable devices for the sensing application of various biomarkers in clinical settings.

Avocado (Persea americana) is a widely consumed fruit and a rich source of nutrients and phytochemicals. Its industrial processing generates peels and seeds which represent 30% of the fruit. Environmental issues related to these wastes are rapidly increasing and likely to double, according to expected avocado production. Therefore, this work aimed to evaluate the potential of hexane and ethanolic peel (PEL-H, PEL-ET) and seed (SED-H, SED-ET) extracts from avocado as sources of neuroprotective compounds. Minerals, total phenol (TPC), total flavonoid (TF), and lipid contents were determined by absorption spectroscopy and gas chromatography. In addition, phytochemicals were putatively identified by paper spray mass spectrometry (PSMS). The extracts were good sources of Ca, Mg, Fe, Zn, ω-6 linoleic acid, and flavonoids. Moreover, fifty-five metabolites were detected in the extracts, consisting mainly of phenolic acids, flavonoids, and alkaloids. The in vitro antioxidant capacity (FRAP and DPPH), acetylcholinesterase inhibition, and in vivo neuroprotective capacity were evaluated. PEL-ET was the best acetylcholinesterase inhibitor, with no significant difference (p > 0.05) compared to the control eserine, and it showed neither preventive nor regenerative effect in the neuroprotection assay. SED-ET demonstrated a significant protective effect compared to the control, suggesting neuroprotection against rotenone-induced neurological damage.

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.

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.

Alzheimer’s disease is a progressive neurodegenerative disorder with a complex etiology, and effective interventions to prevent or delay its onset remain a global health challenge. In recent years, there has been growing interest in the potential role of probiotic and vitamin supplementation as complementary strategies for Alzheimer’s disease prevention. This review paper explores the current scientific literature on the use of probiotics and vitamins, particularly vitamin A, D, E, K, and B-complex vitamins, in the context of Alzheimer’s disease prevention and management. We delve into the mechanisms through which probiotics may modulate gut–brain interactions and neuroinflammation while vitamins play crucial roles in neuronal health and cognitive function. The paper also examines the collective impact of this combinational therapy on reducing the risk factors associated with Alzheimer’s disease, such as oxidative stress, inflammation, and gut dysbiosis. By providing a comprehensive overview of the existing evidence and potential mechanisms, this review aims to shed light on the promise of probiotic and vitamin co-supplementation as a multifaceted approach to combat Alzheimer’s disease, offering insights into possible avenues for future research and clinical application.

Panax ginseng C.A. Meyer (P. ginseng) is one of the more common traditional Chinese medicines (TCMs). It contains numerous chemical components and exhibits a range of pharmacological effects. An enormous burden is placed on people’s health and life by Alzheimer’s disease (AD), a neurodegenerative condition. Recent research has shown that P. ginseng’s chemical constituents, particularly ginsenosides, have a significant beneficial impact on the prevention and management of neurological disorders. To understand the current status of research on P. ginseng to improve AD, this paper discusses the composition of P. ginseng, its mechanism of action, and its clinical application. The pathogenesis of AD includes amyloid beta protein (Aβ) generation and aggregation, tau protein hyperphosphorylation, oxidant stress, neuroinflammation, mitochondrial damage, and neurotransmitter and gut microbiota disorders. This review presents the key molecular mechanisms and signaling pathways of the active ingredients in P. ginseng involved in improving AD from the perspective of AD pathogenesis. A P. ginseng-related signaling pathway network was constructed to provide effective targets for the treatment of AD. In addition, the application of spatial metabolomics techniques in studying P. ginseng and AD is discussed. In summary, this paper discusses research perspectives for the study of P. ginseng in the treatment of AD, including a systematic and in-depth review of the mechanisms of action of the active substances in P. ginseng, and evaluates the feasibility of applying spatial metabolomics in the study of AD pathogenesis and pharmacological treatment.

Two of the most prevalent neurodegenerative disorders (NDDs), Alzheimer’s disease (AD) and Parkinson’s disease (PD), present significant challenges to healthcare systems worldwide. While the etiologies of AD and PD differ, both diseases share commonalities in synaptic dysfunction, thereby focusing attention on the role of neurotransmitters. The possible functions that platelets may play in neurodegenerative illnesses including PD and AD are becoming more acknowledged. In AD, platelets have been investigated for their ability to generate amyloid-ß (Aß) peptides, contributing to the formation of neurotoxic plaques. Moreover, platelets are considered biomarkers for early AD diagnosis. In PD, platelets have been studied for their involvement in oxidative stress and mitochondrial dysfunction, which are key factors in the disease’s pathogenesis. Emerging research shows that platelets, which release glutamate upon activation, also play a role in these disorders. Decreased glutamate uptake in platelets has been observed in Alzheimer’s and Parkinson’s patients, pointing to a systemic dysfunction in glutamate handling. This paper aims to elucidate the critical role that glutamate receptors play in the pathophysiology of both AD and PD. Utilizing data from clinical trials, animal models, and cellular studies, we reviewed how glutamate receptors dysfunction contributes to neurodegenerative (ND) processes such as excitotoxicity, synaptic loss, and cognitive impairment. The paper also reviews all current medications including glutamate receptor antagonists for AD and PD, highlighting their mode of action and limitations. A deeper understanding of glutamate receptor involvement including its systemic regulation by platelets could open new avenues for more effective treatments, potentially slowing disease progression and improving patient outcomes.

This paper delves into the intricate structure and functionality of the brain’s glymphatic system, bringing forth new dimensions in its neuroscientific understanding. This paper commences by exploring the cerebrospinal fluid (CSF)—its localization, production, and pivotal role within the central nervous system, acting as a cushion and vehicle for nutrient distribution and waste elimination. We then transition into an in-depth study of the morphophysiological aspects of the glymphatic system, a recent discovery revolutionizing the perception of waste clearance from the brain, highlighting its lymphatic-like characteristics and remarkable operations. This paper subsequently emphasizes the glymphatic system’s potential implications in Alzheimer’s disease (AD), discussing the connection between inefficient glymphatic clearance and AD pathogenesis. This review also elucidates the intriguing interplay between the glymphatic system and the circadian rhythm, illustrating the optimal functioning of glymphatic clearance during sleep. Lastly, we underscore the hitherto underappreciated involvement of the glymphatic system in the tumoral microenvironment, potentially impacting tumor growth and progression. This comprehensive paper accentuates the glymphatic system’s pivotal role in multiple domains, fostering an understanding of the brain’s waste clearance mechanisms and offering avenues for further research into neuropathological conditions.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized primarily by a decline in cognitive function. However, the etiopathogenesis of AD is unclear. N6-methyladenosine (m6A) is abundant in the brain, and it is interesting to explore the relationship between m6A and AD causes. In this paper, the gene expression of METTL3 and NDUFA10 were found to correlate with the Mini-mental State Examination (MMSE), which is a clinical indicator of the degree of dementia. METTL3 is involved in post-transcriptional methylation and the formation of m6A. NDUFA10 encodes the protein with NADH dehydrogenase activity and oxidoreductase activity in the mitochondrial electron transport chain. The following three characteristics were observed in this paper: 1. The lower the expression level of NDUFA10, the smaller the MMSE, and the higher the degree of dementia. 2. If the expression level of METTL3 dropped below its threshold, the patient would have a risk of AD with a probability close to 100%, suggesting a basic necessity for m6A to protect mRNA. 3. The lower the expression levels of both METTL3 and NDUFA10, the more likely the patient would suffer from AD, implying the coherence between METTL3 and NDUFA10. Regarding the above discovery, the following hypothesis is presented: METTL3 expression level is downregulated, then the m6A modification level of NDUFA10 mRNA is also decreased, thereby reducing the expression level of NDUFA10-encoded protein. Furthermore, the abnormal expression of NDUFA10 contributes to the assembly disorder of mitochondrial complex I and affects the process of the electron respiratory chain, with the consequent development of AD. In addition, to confirm the above conclusions, the AI Ant Colony Algorithm was improved to be more suitable for discovering the characteristics of AD data, and the SVM diagnostic model was applied to mine the coherent effects on AD between METTL3 and NDUFA10. In conclusion, our findings suggest that dysregulated m6A leads to altered expression of its target genes, thereby affecting AD’s development.

The concreteness effect (CE), namely a better performance with concrete compared to abstract concepts, is a constant feature in healthy people, and it usually increases in persons with aphasia (PWA). However, a reversal of the CE has been reported in patients affected by the semantic variant of Primary Progressive Aphasia (svPPA), a neurodegenerative disease characterized by anterior temporal lobe (ATL) atrophy. The present scoping review aims at identifying the extent of evidence regarding the abstract/concrete contrast in Alzheimer’s disease (AD) and svPPA and associated brain atrophy. Five online databases were searched up to January 2023 to identify papers where both concrete and abstract concepts were investigated. Thirty-one papers were selected and showed that while in patients with AD, concrete words were better processes than abstract ones, in most svPPA patients, there was a reversal of the CE, with five studies correlating the size of this effect with ATL atrophy. Furthermore, the reversal of CE was associated with category-specific impairments (living things) and with a selective deficit of social words. Future work is needed to disentangle the role of specific portions of the ATL in concept representation.

Alzheimer’s disease (AD) is the world’s most common neurodegenerative disease, expected to affect up to one-third of the elderly population in the near future. Among the major challenges in combating AD are the inability to reverse the damage caused by the disease, expensive diagnostic tools, and the lack of specific markers for the early detection of AD. This paper highlights promising research directions for molecular markers in AD diagnosis, including the diagnostic potential of microRNAs. The latest molecular methods for diagnosing AD are discussed, with particular emphasis on diagnostic techniques prior to the appearance of full AD symptoms and markers detectable in human body fluids. A collection of recent studies demonstrates the promising potential of molecular methods in AD diagnosis, using miRNAs as biomarkers. Up- or downregulation in neurodegenerative diseases may not only provide a new diagnostic tool but also serve as a marker for differentiating neurodegenerative diseases. However, further research in this direction is needed.

Alzheimer’s disease (AD) is a neurological condition that worsens with ageing and affects memory and cognitive function. Presently more than 55 million individuals are affected by AD all over the world, and it is a leading cause of death in old age. The main purpose of this paper is to review the phytochemical constituents of different plants that are used for the treatment of AD. A thorough and organized review of the existing literature was conducted, and the data under the different sections were found using a computerized bibliographic search through the use of databases such as PubMed, Web of Science, Google Scholar, Scopus, CAB Abstracts, MEDLINE, EMBASE, INMEDPLAN, NATTS, and numerous other websites. Around 360 papers were screened, and, out of that, 258 papers were selected on the basis of keywords and relevant information that needed to be included in this review. A total of 55 plants belonging to different families have been reported to possess different bioactive compounds (galantamine, curcumin, silymarin, and many more) that play a significant role in the treatment of AD. These plants possess anti-inflammatory, antioxidant, anticholinesterase, and anti-amyloid properties and are safe for consumption. This paper focuses on the taxonomic details of the plants, the mode of action of their phytochemicals, their safety, future prospects, limitations, and sustainability criteria for the effective treatment of AD.

There is a paucity of empirical research on the use of non-pharmacological interventions to both treat and curb the spread of Alzheimer’s disease (AD) across the globe. This paper examines the biochemical and clinical outlook and the social implications of the condition in relation to psychological aspects that may indicate a direction for further interventions. There is a scarcity of research on the effectiveness of using various psychological aspects of AD, a disease characterized by a process of transition from health and independence to a dependent state with a progressive loss of memory and functional skills. The paper investigates the biochemical and psychological aspects of AD and their significance for improving quality of life for patients with this disease. Psychological interventions based on, among other factors, biochemical studies, are conducted to improve the emotional wellbeing of AD patients and may assist in slowing down the progression of the disease. To date, however, no effective methods of AD treatment have been established.

Neurodegenerative diseases (NDs) are a major cause of disability and are related to brain development. The neurological signs of brain lesions can vary from mild clinical shortfalls to more delicate and severe neurological/behavioral symptoms and learning disabilities, which are progressive. In this paper, we have tried to summarize a collective view of various NDs and their possible therapeutic outcomes. These diseases often occur as a consequence of the misfolding of proteins post-translation, as well as the dysfunctional trafficking of proteins. In the treatment of neurological disorders, a challenging hurdle to cross regarding drug delivery is the blood–brain barrier (BBB). The BBB plays a unique role in maintaining the homeostasis of the central nervous system (CNS) by exchanging components between the circulations and shielding the brain from neurotoxic pathogens and detrimental compounds. Here, we outline the current knowledge about BBB deterioration in the evolving brain, its origin, and therapeutic interventions. Additionally, we summarize the physiological scenarios of the BBB and its role in various cerebrovascular diseases. Overall, this information provides a detailed account of BBB functioning and the development of relevant treatments for neurological disorders. This paper will definitely help readers working in the field of neurological scientific communities.

Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain magnetic resonance imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 43.83%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to construct functional connectivity (FC) in the brain for the diagnosis and analysis of brain disease. Current studies typically use the Pearson correlation coefficient to construct dynamic FC (dFC) networks, and then use this as a network metric to obtain the necessary features for brain disease diagnosis and analysis. This simple observational approach makes it difficult to extract potential high-level FC features from the representations, and also ignores the rich information on spatial and temporal variability in FC. In this paper, we construct the Latent Space Representation Network (LSRNet) and use two stages to train the network. In the first stage, an autoencoder is used to extract potential high-level features and inner connections in the dFC representations. In the second stage, high-level features are extracted using two perspective feature parses. Long Short-Term Memory (LSTM) networks are used to extract spatial and temporal features from the local perspective. Convolutional neural networks extract global high-level features from the global perspective. Finally, the fusion of spatial and temporal features with global high-level features is used to diagnose brain disease. In this paper, the proposed method is applied to the ANDI rs-fMRI dataset, and the classification accuracy reaches 84.6% for NC/eMCI, 95.1% for NC/AD, 80.6% for eMCI/lMCI, 84.2% for lMCI/AD and 57.3% for NC/eMCI/lMCI/AD. The experimental results show that the method has a good classification performance and provides a new approach to the diagnosis of other brain diseases.

Growing evidence supports the presence of social cognition deficits and social behavior alterations in major and minor neurocognitive disorders (NCDs). Even though the ability to identify socio-emotional changes has significantly improved in recent years, there is still no specific treatment available. Thus, we explored evidence of drug therapies targeting social cognition alterations in NCDs. Papers were selected according to PRISMA guidelines by searching on the PubMed and Scopus databases. Only papers reporting information on pharmacological interventions for the treatment of social cognition and/or social behavioral changes in major and/or minor NCDs were included. Among the 171 articles entered in the paper selection, only 9 papers were eligible for the scope of the review. Trials testing pharmacological treatments for socio-emotional alterations in NCDs are poor and of low-medium quality. A few attempts with neuroprotective, psychoactive, or immunomodulating drugs have been made. Oxytocin is the only drug specifically targeting the social brain that has been tested with promising results in frontotemporal dementia. Its beneficial effects in long-term use have yet to be evaluated. No recommendation can currently be provided. There is a long way to go to identify and test effective targets to treat social cognition changes in NCDs for the ultimate benefit of patients and caregivers.

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

Alzheimer’s disease (AD) is a complex chronic disease of the brain characterized by several neurodegenerative mechanisms and is responsible for most dementia cases in the elderly. Declining immunity during ageing is often associated with peripheral chronic inflammation, and chronic neuroinflammation is a constant component of AD brain pathology. In the Special Issue published in 2021 eight papers were collected regarding different aspects of neurodegeneration associated with AD. Five papers presented and discussed infectious agents involved in brain AD pathology and three discussed data regarding receptors regulation and possible treatment of the disease. Below I will discuss and further elaborate on topics related to infections, inflammation, and neurodegenerative pathways in AD and brain senescence. The topic presented here may contribute to early intervention protocols for preventing or slowing the progression of cognitive deterioration in the elderly.

Dementia is the most common neurodegenerative disorder globally. Disease progression is marked by declining cognitive function accompanied by changes in mobility. Increased sedentary behaviour and, conversely, wandering and becoming lost are common. Global positioning system (GPS) solutions are increasingly used by caregivers to locate missing people with dementia (PwD) but also offer a non-invasive means of monitoring mobility patterns in PwD. We performed a systematic search across five databases to identify papers published since 2000, where wearable or portable GPS was used to monitor mobility in patients with common dementias or mild cognitive impairment (MCI). Disease and GPS-specific vocabulary were searched singly, and then in combination, identifying 3004 papers. Following deduplication, we screened 1972 papers and retained 17 studies after a full-text review. Only 1/17 studies used a wrist-worn GPS solution, while all others were variously located on the patient. We characterised the studies using a conceptual framework, finding marked heterogeneity in the number and complexity of reported GPS-derived mobility outcomes. Duration was the most frequently reported category of mobility reported (15/17), followed by out of home (14/17), and stop and trajectory (both 10/17). Future research would benefit from greater standardisation and harmonisation of reporting which would enable GPS-derived measures of mobility to be incorporated more robustly into clinical trials.

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

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

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.

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

Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual’s mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson’s disease, Alzheimer’s disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson’s cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials.

Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.

Biological tissues in nature are fractal due to their self-similarity and porosity properties. These properties change with the progress of some diseases, including brain tissue in leading neurological disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Thus, there is an unmet clinical need to develop a tool for accurate and early diagnosis of AD and PD conditions. Although the whole brain tissues in AD and PD have been extensively studied, their local structural alterations at the nano-to-submicron levels have not been explored. In this paper, we measure the local structural alterations in different brain regions of AD and PD patients by measuring their change in fractal dimensions via optical microscopy. Our results show an increase in the fractal dimension value of ~5–10% in the affected regions of the brain tissues relative to their respective controls. For AD cases, the structural alteration is attributed to the aberrant deposition of amyloid beta protein and neurofibrillary tangles in the brain, and for PD, the gradual loss of dopaminergic neurons and abnormal accumulation of α-synuclein in the brain. The work will enhance the further understanding of alterations in the brain structures in AD and PD and its detection.

Changes in epitranscriptome with N6-methyladenine (m6A) modification could be involved in the development of multiple diseases, which might be a prevalent modification of messenger RNAs (mRNAs) in eukaryotes. The m6A modification might be performed through the action of methyltransferases, demethylases, and methylation-binding proteins. Importantly, the m6A methylation may be associated with various neurological disorders including Alzheimer’s disease (AD), Parkinson’s disease (PD), depression, aging-related diseases, and/or aging itself. In addition, the m6A methylation might functionally regulate the eukaryotic transcriptome by influencing the splicing, export, subcellular localization, translation, stability, and decay of mRNAs. Neurodegenerative diseases may possess a wide variety of phenotypes, depending on the neurons that degenerate on occasion. Interestingly, an increasing amount of evidence has indicated that m6A modification could modulate the expression of autophagy-related genes and promote autophagy in neuronal cells. Oxidative stresses such as reactive oxygen species (ROS) could stimulate the m6A RNA methylation, which may also be related to the regulation of autophagy and/or the development of neurodegenerative diseases. Both m6A modification and autophagy could also play critical roles in regulating the health condition of neurons. Therefore, a comprehensive understanding of the m6A and autophagy relationship in human diseases may benefit in developing therapeutic strategies in the future. This paper reviews advances in the understanding of the regulatory mechanisms of m6A modification in the occurrence and development of neurodegenerative diseases and/or aging, discussing the possible therapeutic procedures related to mechanisms of m6A RNA methylation and autophagy.

The processing of the amyloid precursor protein (APP) is a critical event in the formation of amyloid plaques. Platelets contain most of the enzymatic machinery required for APP processing and correlates of intracerebral abnormalities have been demonstrated in platelets of patients with AD. The goal of the present paper was to analyze studies exploring platelet APP metabolism in Alzheimer’s disease patients trying to assess potential reliable peripheral biomarkers, to offer new therapeutic solutions and to understand the pathophysiology of the AD. According to the PRISMA guidelines, we performed a systematic review through the PubMed database up to June 2020 with the search terms: “((((((APP) OR Amyloid Precursor Protein) OR AbetaPP) OR Beta Amyloid) OR Amyloid Beta) OR APP-processing) AND platelet”. Thirty-two studies were included in this systematic review. The papers included are analytic observational studies, namely twenty-nine cross sectional studies and three longitudinal studies, specifically prospective cohort study. The studies converge in an almost unitary way in affirming that subjects with AD show changes in APP processing compared to healthy age-matched controls. However, the problem of the specificity and sensitivity of these biomarkers is still at issue and would deserve to be deepened in future studies.

We have entered an era of a steep increase in the absolute and relative number of older people. This well-come phenomenon represents a major challenge for health care. However, maturational changes in sleep associated with aging do not easily appear as main factors, even though sleep alterations in the aging process lead to many detrimental consequences. In this editorial paper, we summarize the present knowledge about the main aging-related sleep modifications and their relevance for health problems and cognitive decline. Then, we present the papers published in the Special Issue “Disturbances of Sleep Among Older People”.

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

Alzheimer’s disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer’s disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research.

Methionine oxidation to the sulfoxide form (MSox) is a poorly understood post-translational modification of proteins associated with non-specific chemical oxidation from reactive oxygen species (ROS), whose chemistries are linked to various disease pathologies, including neurodegeneration. Emerging evidence shows MSox site occupancy is, in some cases, under enzymatic regulatory control, mediating cellular signaling, including phosphorylation and/or calcium signaling, and raising questions as to the speciation and functional nature of MSox across the proteome. The 5XFAD lineage of the C57BL/6 mouse has well-defined Alzheimer’s and aging states. Using this model, we analyzed age-, sex-, and disease-dependent MSox speciation in the mouse hippocampus. In addition, we explored the chemical stability and statistical variance of oxidized peptide signals to understand the needed power for MSox-based proteome studies. Our results identify mitochondrial and glycolytic pathway targets with increases in MSox with age as well as neuroinflammatory targets accumulating MSox with AD in proteome studies of the mouse hippocampus. Further, this paper establishes a foundation for reproducible and rigorous experimental MSox-omics appropriate for novel target identification in biological discovery and for biomarker analysis in ROS and other oxidation-linked diseases.

Antibodies that can selectively remove rogue proteins in the brain are an obvious choice to treat neurodegenerative disorders (NDs), but after decades of efforts, only two antibodies to treat Alzheimer’s disease are approved, dozens are in the testing phase, and one was withdrawn, and the other halted, likely due to efficacy issues. However, these outcomes should have been evident since these antibodies cannot enter the brain sufficiently due to the blood–brain barrier (BBB) protectant. However, all products can be rejuvenated by binding them with transferrin, preferably as smaller fragments. This model can be tested quickly and at a low cost and should be applied to bapineuzumab, solanezumab, crenezumab, gantenerumab, aducanumab, lecanemab, donanemab, cinpanemab, and gantenerumab, and their fragments. This paper demonstrates that conjugating with transferrin does not alter the binding to brain proteins such as amyloid-β (Aβ) and α-synuclein. We also present a selection of conjugate designs that will allow cleavage upon entering the brain to prevent their exocytosis while keeping the fragments connected to enable optimal binding to proteins. The identified products can be readily tested and returned to patients with the lowest regulatory cost and delays. These engineered antibodies can be manufactured by recombinant engineering, preferably by mRNA technology, as a more affordable solution to meet the dire need to treat neurodegenerative disorders effectively.

Alzheimer’s Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.

The glymphatic system is a “pseudo-lymphatic” perivascular network distributed throughout the brain, responsible for replenishing as well as cleansing the brain. Glymphatic clearance is the macroscopic process of convective fluid transport in which harmful interstitial metabolic waste products are removed from the brain intima. This paper addresses the glymphatic system, its dysfunction and the major consequences of impaired clearance in order to link neurodegeneration and glymphatic activity with lifestyle choices. Glymphatic clearance can be manipulated by sleep deprivation, cisterna magna puncture, acetazolamide or genetic deletion of AQP4 channels, but how lifestyle choices affect this brain-wide clearance system remains to be resolved. This paper will synthesize existing literature on glymphatic clearance, sleep, Alzheimer’s disease and lifestyle choices, in order to harness the power of this mass transport system, promote healthy brain ageing and possibly prevent neurodegenerative processes. This paper concludes that 1. glymphatic clearance plays a major role in Alzheimer’s pathology; 2. the vast majority of waste clearance occurs during sleep; 3. dementias are associated with sleep disruption, alongside an age-related decline in AQP4 polarization; and 4. lifestyle choices such as sleep position, alcohol intake, exercise, omega-3 consumption, intermittent fasting and chronic stress all modulate glymphatic clearance. Lifestyle choices could therefore alter Alzheimer’s disease risk through improved glymphatic clearance, and could be used as a preventative lifestyle intervention for both healthy brain ageing and Alzheimer’s disease.

Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

This paper introduces the concept of an intelligent vase that combines the cultivation of plants with psychotherapy for the Alzheimer’s adjuvant therapy (from now on, referred to as AD) of patients. RECALL (The name of intelligent vase which defined by this paper), which includes hardware and software applications, as well as a unique hand gesture interaction method, is able to collect patients’ hand gestures and play photos, to remind patients of the plant’s growth status. To evaluate our conceptual system, we created a preliminary prototype. We conducted an experiment with leap motion. This project tries to explore a kind of interaction method by cultivating plants to simulate cognitive training activities, to stimulate memories through family pictures, to slow down the cognitive decline from AD, to incorporate non-drug intervention into the family life of AD patients, to reduce the expenditure, time, and energy costs, and to improve the quality of life of patients and their families.

Withania somnifera (L.) Dunal is a medicinal plant belonging to the traditional Indian medical system, showing various therapeutic effects such as anti-cancer, anti-inflammatory, anti-microbial, anti-diabetic, and hepatoprotective activity. Of great interest is W. somnifera’s potential beneficial effect against neurodegenerative diseases, since the authorized medicinal treatments can only delay disease progression and provide symptomatic relief and are not without side effects. A systematic search of PubMed and Scopus databases was performed to identify preclinical and clinical studies focusing on the applications of W. somnifera in preventing neurodegenerative diseases. Only English articles and those containing the keywords (Withania somnifera AND “neurodegenerative diseases”, “neuroprotective effects”, “Huntington”, “Parkinson”, “Alzheimer”, “Amyotrophic Lateral Sclerosis”, “neurological disorders”) in the title or abstract were considered. Reviews, editorials, letters, meta-analyses, conference papers, short surveys, and book chapters were not considered. Selected articles were grouped by pathologies and summarized, considering the mechanism of action. The quality assessment and the risk of bias were performed using the Cochrane Handbook for Systematic Reviews of Interventions checklist. This review uses a systematic approach to summarize the results from 60 investigations to highlight the potential role of W. somnifera and its specialized metabolites in treating or preventing neurodegenerative diseases.

Neurodegenerative diseases involve the progressive dysfunction and loss of neurons in the central nervous system and thus present a significant challenge due to the absence of effective therapies for halting or reversing their progression. Based on the characteristics of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), which have prolonged incubation periods and protracted courses, exploring non-invasive physical therapy methods is essential for alleviating such diseases and ensuring that patients have an improved quality of life. Photobiomodulation (PBM) uses red and infrared light for therapeutic benefits and functions by stimulating, healing, regenerating, and protecting organizations at risk of injury, degradation, or death. Over the last two decades, PBM has gained widespread recognition as a non-invasive physical therapy method, showing efficacy in pain relief, anti-inflammatory responses, and tissue regeneration. Its application has expanded into the fields of neurology and psychiatry, where extensive research has been conducted. This paper presents a review and evaluation of studies investigating PBM in neurodegenerative diseases, with a specific emphasis on recent applications in AD and PD treatment for both animal and human subjects. Molecular mechanisms related to neuron damage and cognitive impairment are scrutinized, offering valuable insights into PBM’s potential as a non-invasive therapeutic strategy.

Alzheimer’s disease is the sixth most common cause of death in the United States (U.S.), with one in three adults 65 years of age and older dying of the disease each year. Deaths from Alzheimer’s have more than doubled between 2000 and 2019, killing more adults than both breast cancer and prostate cancer. In 2021, Alzheimer’s disease resulted in 36 deaths per 100,000 in the U.S. In Mississippi, deaths from Alzheimer’s have almost doubled between 2011 and 2021, resulting in 52.9 deaths per 100,000. Women have a higher mortality rate from Alzheimer’s than men. Alzheimer’s is a progressive disease that develops through seven stages. There are effective strategies to prevent the onset of Alzheimer’s. Methods: This paper reviews the risk factors, mortality trends, etiology, and prognosis of Alzheimer’s in Mississippi with a focus on prevention. Results: The southern diet with foods high in sugar and sodium, along with sedentary and poor lifestyle choices, increases mortality risk from Alzheimer’s disease for women in Mississippi, specifically due to women over 65 having higher rates of obesity and hypertension. Conclusion: Understanding the epidemiology and risk factors of Alzheimer’s in Mississippi will help inform communities, policies, and programs to prevent disease occurrence.

Alzheimer’s disease is a neurodegenerative condition marked by the progressive deterioration of cognitive abilities, memory impairment, and the accumulation of abnormal proteins, specifically beta-amyloid plaques and tau tangles, within the brain. Despite extensive research efforts, Alzheimer’s disease remains without a cure, presenting a significant global healthcare challenge. Recently, there has been an increased focus on antibody-based treatments as a potentially effective method for dealing with Alzheimer’s disease. This paper offers a comprehensive overview of the current status of research on antibody-based molecules as therapies for Alzheimer’s disease. We will briefly mention their mechanisms of action, therapeutic efficacy, and safety profiles while addressing the challenges and limitations encountered during their development. We also highlight some crucial considerations in antibody-based treatment development, including patient selection criteria, dosing regimens, or safety concerns. In conclusion, antibody-based therapies present a hopeful outlook for addressing Alzheimer’s disease. While challenges remain, the accumulating evidence suggests that these therapies may offer substantial promise in ameliorating or preventing the progression of this debilitating condition, thus potentially enhancing the quality of life for the millions of individuals and families affected by Alzheimer’s disease worldwide.

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

Multiple sclerosis (MS) and Alzheimer’s disease (AD) cause retinal thinning that is detectable in vivo using optical coherence tomography (OCT). To date, no papers have compared the two diseases in terms of the structural differences they produce in the retina. The purpose of this study is to analyse and compare the neuroretinal structure in MS patients, AD patients and healthy subjects using OCT. Spectral domain OCT was performed on 21 AD patients, 33 MS patients and 19 control subjects using the Posterior Pole protocol. The area under the receiver operating characteristic (AUROC) curve was used to analyse the differences between the cohorts in nine regions of the retinal nerve fibre layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL) and outer nuclear layer (ONL). The main differences between MS and AD are found in the ONL, in practically all the regions analysed (AUROCFOVEAL = 0.80, AUROCPARAFOVEAL = 0.85, AUROCPERIFOVEAL = 0.80, AUROC_PMB = 0.77, AUROCPARAMACULAR = 0.85, AUROCINFERO_NASAL = 0.75, AUROCINFERO_TEMPORAL = 0.83), and in the paramacular zone (AUROCPARAMACULAR = 0.75) and infero-temporal quadrant (AUROCINFERO_TEMPORAL = 0.80) of the GCL. In conclusion, our findings suggest that OCT data analysis could facilitate the differential diagnosis of MS and AD.

Non-invasive drug delivery across the blood–brain barrier (BBB) represents a significant advancement in treating neurological diseases. The BBB is a tightly packed layer of endothelial cells that shields the brain from harmful substances in the blood, allowing necessary nutrients to pass through. It is a highly selective barrier, which poses a challenge to delivering therapeutic agents into the brain. Several non-invasive procedures and devices have been developed or are currently being investigated to enhance drug delivery across the BBB. This paper presents a review and a prospective analysis of the art and science that address pharmacology, technology, delivery systems, regulatory approval, ethical concerns, and future possibilities.