The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface

Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progression, necessitating personalized diagnostic approaches. Current procedures are often costly and invasive, lacking consideration of sex-based differences. This study introduces an explainable machine learning (ML) system to predict and differentiate the progression of AD based on sex, using non-invasive, easily collectible predictors such as neuropsychological test scores and sociodemographic data, enabling its application in every day clinical settings. The ML model uses SHapley Additive explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to provide clear insights into its decision-making, making complex outcomes easier to interpret. The system includes a user-friendly graphical interface designed in collaboration with clinicians, supporting its integration into medical practice. The study extends the cohort to include healthy and Mild Cognitive Impairment subjects, aiming to support early diagnosis in AD pre-clinical stages. The ML system was trained on a large dataset of 2407 subjects from the ADNI open dataset, enhancing its robustness and applicability. By focusing on sex-specific features and utilizing longitudinal data, the system aims to improve prediction accuracy and early detection of AD, ultimately advancing personalized diagnostic and therapeutic approaches. Key findings highlight the significance of the Mini-Mental State Examination, Rey Auditory Verbal Learning Test, Logical Memory - Delayed Recall, and educational attainment in AD diagnosis and progression, with sex-based disparities. Despite performance metrics based on precision, recall, and weighted F1-score demonstrating model efficacy, future research should address the limitations of relying on a single dataset.

Revolutionizing Neuroimmunology: Unraveling Immune Dynamics and Therapeutic Innovations in CNS Disorders

Neuroimmunology is reshaping the understanding of the central nervous system (CNS), revealing it as an active immune organ rather than an isolated structure. This review delves into the unprecedented discoveries transforming the field, including the emerging roles of microglia, astrocytes, and the blood-brain barrier (BBB) in orchestrating neuroimmune dynamics. Highlighting their dual roles in both repair and disease progression, we uncover how these elements contribute to the intricate pathophysiology of neurodegenerative diseases, cerebrovascular conditions, and CNS tumors. Novel insights into microglial priming, astrocytic cytokine networks, and meningeal lymphatics challenge the conventional paradigms of immune privilege, offering fresh perspectives on disease mechanisms. This work introduces groundbreaking therapeutic innovations, from precision immunotherapies to the controlled modulation of the BBB using nanotechnology and focused ultrasound. Moreover, we explore the fusion of immune modulation with neuromodulatory technologies, underscoring new frontiers for personalized medicine in previously intractable diseases. By synthesizing these advancements, we propose a transformative framework that integrates cutting-edge research with clinical translation, charting a bold path toward redefining CNS disease management in the era of precision neuroimmunology.

Insights into AlphaFold's breakthrough in neurodegenerative diseases

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

Alzheimer's Disease Pathology and Assistive Nanotheranostic Approaches for Its Therapeutic Interventions

Alzheimer's disease (AD) still prevails and continues to increase indiscriminately throughout the 21st century, and is thus responsible for the depreciating quality of health and associated sectors. AD is a progressive neurodegenerative disorder marked by a significant amassment of beta-amyloid plaques and neurofibrillary tangles near the hippocampus, leading to the consequent loss of cognitive abilities. Conventionally, amyloid and tau hypotheses have been established as the most prominent in providing detailed insight into the disease pathogenesis and revealing the associative biomarkers intricately involved in AD progression. Nanotheranostic deliberates rational thought toward designing efficacious nanosystems and strategic endeavors for AD diagnosis and therapeutic implications. The exceeding advancements in this field enable the scientific community to envisage and conceptualize pharmacokinetic monitoring of the drug, sustained and targeted drug delivery responses, fabrication of anti-amyloid therapeutics, and enhanced accumulation of the targeted drug across the blood-brain barrier (BBB), thus giving an optimistic approach towards personalized and precision medicine. Current methods idealized on the design and bioengineering of an array of nanoparticulate systems offer higher affinity towards neurocapillary endothelial cells and the BBB. They have recently attracted intriguing attention to the early diagnostic and therapeutic measures taken to manage the progression of the disease. In this article, we tend to furnish a comprehensive outlook, the detailed mechanism of conventional AD pathogenesis, and new findings. We also summarize the shortcomings in diagnostic, prognostic, and therapeutic approaches undertaken to alleviate AD, thus providing a unique window towards nanotheranostic advancements without disregarding potential drawbacks, side effects, and safety concerns.

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

BACKGROUND

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research.

OBJECTIVE

The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

METHODS

This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels.

RESULTS

To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers.

CONCLUSIONS

The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials

The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.

Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is the most common form of dementia. Currently, there is no single test that can diagnose AD, especially in understudied populations and developing countries. Instead, diagnosis is based on a combination of medical history, physical examination, cognitive testing, and brain imaging. Exosomes are extracellular nanovesicles, primarily composed of RNA, that participate in physiological processes related to AD pathogenesis such as cell proliferation, immune response, and neuronal and cardiovascular function. However, the identification and understanding of the potential role of long non-coding RNAs (lncRNAs) in AD diagnosis remain largely unexplored. Here, we clinically, cognitively, and genetically characterized a sample of 15 individuals diagnosed with AD (cases) and 15 controls from Barranquilla, Colombia. Advanced bioinformatics, analytics and Machine Learning (ML) techniques were used to identify lncRNAs differentially expressed between cases and controls. The expression of 28,909 lncRNAs was quantified. Of these, 18 were found to be differentially expressed and harbored in pivotal genes related to AD. Two lncRNAs, ENST00000608936 and ENST00000433747, show promise as diagnostic markers for AD, with ML models achieving > 95% sensitivity, specificity, and accuracy in both the training and testing datasets. These findings suggest that the expression profiles of lncRNAs could significantly contribute to advancing personalized AD diagnosis in this community, offering promising avenues for early detection and follow-up.

Innovative Therapeutic Strategies in Alzheimer's Disease: A Synergistic Approach to Neurodegenerative Disorders

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disorders, even nearly a century after its discovery, due to the elusive nature of its causes. The development of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, opening new possibilities for treatment. Despite these advancements, conventional therapies for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, underscoring the need for innovative solutions. This necessity has led to the incorporation advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET

BACKGROUND

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET.

METHODS

We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients.

RESULTS

The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%).

CONCLUSION

Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Navigating the Alzheimer's Treatment Landscape: Unraveling Amyloid-beta Complexities and Pioneering Precision Medicine Approaches

A variety of cutting-edge methods and good knowledge of the illness's complex causes are causing a sea change in the field of Alzheimer's Disease (A.D.) research and treatment. Precision medicine is at the vanguard of this change, where individualized treatment plans based on genetic and biomarker profiles give a ray of hope for customized therapeutics. Combination therapies are becoming increasingly popular as a way to address the multifaceted pathology of Alzheimer's by simultaneously attacking Aβ plaques, tau tangles, neuroinflammation, and other factors. The article covers several therapeutic design efforts, including BACE inhibitors, gamma- secretase modulators, monoclonal antibodies (e.g., Aducanumab and Lecanemab), and anti- Aβ vaccinations. While these techniques appear promising, clinical development faces safety concerns and uneven efficacy. To address the complicated Aβ pathology in Alzheimer's disease, a multimodal approach is necessary. The statement emphasizes the continued importance of clinical trials in addressing safety and efficacy concerns. Looking ahead, it suggests that future treatments may take into account genetic and biomarker traits in order to provide more personalized care. Therapies targeting Aβ, tau tangles, neuroinflammation, and novel drug delivery modalities are planned. Nanoparticles and gene therapies are only two examples of novel drug delivery methods that have the potential to deliver treatments more effectively, with fewer side effects, and with better therapeutic results. In addition, medicines that target tau proteins in addition to Aβ are in the works. Early intervention, based on precise biomarkers, is a linchpin of Alzheimer's care, emphasizing the critical need for detecting the disease at its earliest stages. Lifestyle interventions, encompassing diet, exercise, cognitive training, and social engagement, are emerging as key components in the fight against cognitive decline. Data analytics and art are gaining prominence as strategies to mitigate the brain's inflammatory responses. To pool knowledge and resources in the fight against Alzheimer's, international cooperation between scientists, doctors, and pharmaceutical companies is still essential. In essence, a complex, individualized, and collaborative strategy will characterize Alzheimer's research and therapy in the future. Despite obstacles, these encouraging possibilities show the ongoing commitment of the scientific and medical communities to combat A.D. head-on, providing a glimmer of hope to the countless people and families touched by this savage sickness.

Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Decoding the genetic relationship between Alzheimer's disease and type 2 diabetes: potential risk variants and future direction for North Africa

Introduction

Alzheimer's disease (AD) and Type 2 diabetes (T2D) are both age-associated diseases. Identification of shared genes could help develop early diagnosis and preventive strategies. Although genetic background plays a crucial role in these diseases, we noticed an underrepresentation tendency of North African populations in omics studies.

Materials and methods

First, we conducted a comprehensive review of genes and pathways shared between T2D and AD through PubMed. Then, the function of the identified genes and variants was investigated using annotation tools including PolyPhen2, RegulomeDB, and miRdSNP. Pathways enrichment analyses were performed with g:Profiler and EnrichmentMap. Next, we analyzed variant distributions in 16 worldwide populations using PLINK2, R, and STRUCTURE software. Finally, we performed an inter-ethnic comparison based on the minor allele frequency of T2D-AD common variants.

Results

A total of 59 eligible papers were included in our study. We found 231 variants and 363 genes shared between T2D and AD. Variant annotation revealed six single nucleotide polymorphisms (SNP) with a high pathogenic score, three SNPs with regulatory effects on the brain, and six SNPs with potential effects on miRNA-binding sites. The miRNAs affected were implicated in T2D, insulin signaling pathways, and AD. Moreover, replicated genes were significantly enriched in pathways related to plasma protein binding, positive regulation of amyloid fibril deposition, microglia activation, and cholesterol metabolism. Multidimensional screening performed based on the 363 shared genes showed that main North African populations are clustered together and are divergent from other worldwide populations. Interestingly, our results showed that 49 SNP associated with T2D and AD were present in North African populations. Among them, 11 variants located in DNM3, CFH, PPARG, ROHA, AGER, CLU, BDNF1, CST9, and PLCG1 genes display significant differences in risk allele frequencies between North African and other populations.

Conclusion

Our study highlighted the complexity and the unique molecular architecture of North African populations regarding T2D-AD shared genes. In conclusion, we emphasize the importance of T2D-AD shared genes and ethnicity-specific investigation studies for a better understanding of the link behind these diseases and to develop accurate diagnoses using personalized genetic biomarkers.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

Advances in fucoxanthin chemistry and management of neurodegenerative diseases

BACKGROUND

Neurodegenerative diseases are chronic, currently incurable, diseases of the elderly, which are characterized by protein misfolding and neuronal damage. Fucoxanthin, derived from marine brown algae, presents a promising candidate for the development of effective therapeutic strategies.

HYPOTHESIS AND PURPOSE

The relationship between neurodegenerative disease management and fucoxanthin has not yet been clarified. This study focuses on the fundamental mechanisms and targets of fucoxanthin in Alzheimer's and Parkinson's disease management, showing that communication between the brain and the gut contributes to neurodegenerative diseases and early diagnosis of ophthalmic diseases. This paper also presents, new insights for future therapeutic directions based on the integrated application of artificial intelligence.

CONCLUSION

Fucoxanthin primarily binds to amyloid fibrils with spreading properties such as Aβ, tau, and α-synuclein to reduce their accumulation levels, alleviate inflammatory factors, and restore mitochondrial membranes to prevent oxidative stress via Nrf2 and Akt signaling pathways, involving reduction of specific secretases. In addition, fucoxanthin may serve as a preventive diagnosis for neurodegenerative diseases through ophthalmic disorders. It can modulate gut microbes and has potential for the alleviation and treatment of neurodegenerative diseases.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

A multi-expert ensemble system for predicting Alzheimer transition using clinical features

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.

Artificial intelligence in dementia

PURPOSE OF REVIEW

Artificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. A comprehensive review of emerging trends will allow physicians and other healthcare providers to better anticipate and understand these powerful tools.

RECENT FINDINGS

Machine learning models that utilize cerebral biomarkers are demonstrably effective for dementia identification and prediction; however, cerebral biomarkers are relatively expensive and not widely available. As eye images harbor several ophthalmic biomarkers that mirror the state of the brain and can be clinically observed with routine imaging, eye-based machine learning models are an emerging area, with efficacy comparable with cerebral-based machine learning models. Emerging machine learning architectures like recurrent, convolutional, and partially pretrained neural networks have proven to be promising frontiers for feature extraction and classification with ocular biomarkers.

SUMMARY

Machine learning models that can accurately distinguish those with symptomatic Alzheimer's dementia from those with mild cognitive impairment and normal cognition as well as predict progressive disease using relatively inexpensive and accessible ocular imaging inputs are impactful tools for the diagnosis and risk stratification of Alzheimer's dementia continuum. If these machine learning models can be incorporated into clinical care, they may simplify diagnostic efforts. Recent advancements in ocular-based machine learning efforts are promising steps forward.

Antioxidant and Neuroprotective Effects of Carnosine: Therapeutic Implications in Neurodegenerative Diseases

Neurodegenerative diseases (NDs) constitute a global challenge to human health and an important social and economic burden worldwide, mainly due to their growing prevalence in an aging population and to their associated disabilities. Despite their differences at the clinical level, NDs share fundamental pathological mechanisms such as abnormal protein deposition, intracellular Ca²⁺ overload, mitochondrial dysfunction, redox homeostasis imbalance and neuroinflammation. Although important progress is being made in deciphering the mechanisms underlying NDs, the availability of effective therapies is still scarce. Carnosine is a natural endogenous molecule that has been extensively studied during the last years due to its promising beneficial effects for human health. It presents multimodal mechanisms of action, being able to exert antioxidant, anti-inflammatory and anti-aggregate activities, among others. Interestingly, most NDs exhibit oxidative and nitrosative stress, protein aggregation and inflammation as molecular hallmarks. In this review, we discuss the neuroprotective functions of carnosine and its implications as a therapeutic strategy in different NDs. We summarize the existing works that study alterations in carnosine metabolism in Alzheimer’s disease and Parkinson’s disease, the two most common NDs. In addition, we review the beneficial effect that carnosine supplementation presents in models of such diseases as well as in aging-related neurodegeneration.