Review of Technological Challenges in Personalised Medicine and Early Diagnosis of Neurodegenerative Disorders

Prediction and detection of terminal diseases using Internet of Medical Things: A review

The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full potential in healthcare. This review examines these challenges and evaluates the effectiveness of AI-IoMT techniques in predicting chronic and terminal diseases, including cardiovascular conditions, Alzheimer's disease, and cancers. We analyze a range of Machine Learning (ML) and Deep Learning (DL) approaches (e.g., XGBoost, Random Forest, CNN, LSTM), alongside advanced strategies like federated learning, transfer learning, and blockchain, to improve model robustness, data security, and interoperability. Findings highlight that transfer learning and ensemble methods enhance model adaptability across clinical settings, while blockchain and federated learning effectively address privacy and data standardization. Ultimately, the review emphasizes the importance of data harmonization, secure frameworks, and multi-disease models as critical research directions for scalable, comprehensive AI-IoMT solutions in healthcare.

Extracellular vesicles: new horizons in neurodegeneration

Extracellular vesicles (EVs) are lipid-enclosed nanovesicles secreted by diverse cell types that orchestrate intercellular communication through cargo delivery. Their pivotal roles span from supporting the development of normal central nervous system (CNS) to contributing to the pathogenesis of neurological diseases. Particularly noteworthy is their involvement in the propagation of pathogenic proteins, such as those involved in neurodegenerative disorders, and nucleic acids, closely linking them to disease onset and progression. Moreover, EVs have emerged as promising diagnostic biomarkers for neurological disorders and as tools for disease staging, owing to their ability to traverse the blood-brain barrier and their specific, stable, and accessible properties. This review comprehensively explores the realm of CNS-derived EVs found in peripheral blood, encompassing their detection methods, transport mechanisms, and diverse roles in various neurodegenerative diseases. Furthermore, we evaluate the potentials and limitations of EVs in clinical applications and highlight prospective research directions in this rapidly evolving field.

The role of nuclear medicine in neurodegenerative diseases: a narrative review

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Lewy body dementia, are associated with the accumulation of brain proteins, leading to neuroinflammation, disruption of cellular clearance mechanisms, and neuronal death. Nuclear medicine, utilizing technologies like PET and SPECT, plays a crucial role in diagnosing and managing these disorders. Recent advancements in nuclear medicine have enhanced the understanding of disease pathophysiology and facilitated the development of tailored therapeutics. This study aims to address gaps in understanding nuclear medicine's potential to improve early diagnosis, monitor disease progression, and evaluate therapeutic effectiveness. In this review, we analyzed 28 papers and summarized their findings. PET radioligands have revolutionized the in vivo measurement of pathological targets in neurological diseases, offering new insights into the pathophysiology of neurodegenerative conditions. Amyloid PET has emerged as a reliable diagnostic imaging tool, accurately identifying cerebral amyloid-beta accumulation and enabling early differential diagnosis in clinical settings. Furthermore, radiopharmaceuticals such as [18F]Flortaucipir, [18F]FDOPA, and TSPO ligands provide significant advancements in the diagnosis and treatment of neurodegenerative disorders.

Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach

PURPOSE

Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain-specific assessment of neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous clinical manifestation corresponds to heterogeneous cortical disease burden, suggesting customized, high-resolution assessment of cortical pathology as a candidate biomarker for domain-specific assessment.

METHOD

Herein, we investigate the potential of the recently proposed Mosaic Approach (MAP), a normative framework for quantifying individual cortical disease burden with respect to a population-representative cohort, in predicting domain-specific clinical progression. Using MRI and clinical data from 135 recently diagnosed PD patients from the Parkinson's Progression Markers Initiative, we first defined an extremity-specific motor score. We then identified cortical regions corresponding to "extremity functions" and restricted MAP, respectively, and contrasted the explanatory power of the extremity-specific MAP to unrestricted MAP. As control conditions, domain-related but less specific general motor function and nondomain-specific cognitive scores were considered. We also tested the predictive power of the restricted MAP in predicting disease progression over 1 and 3 years using support vector machines. The restricted, extremity-specific MAP yielded higher explanatory power for extremity-specific motor function at baseline as opposed to the unrestricted, whole-brain MAP. On the contrary, for general motor function, the unrestricted, whole-brain MAP yielded higher power.

FINDING

No associations were found for cognitive function. The extremity-specific MAP predicted extremity-specific motor progression over 1 and 3 years above chance level. The MAP framework allows for domain-specific prediction of customized PD disease progression, which can inform machine learning, thereby contributing to personalized PD patient care.

New Insights into the Development of Donepezil-Based Hybrid and Natural Molecules as Multi-Target Drug Agents for Alzheimer's Disease Treatment

Alzheimer's disease (AD) involves a complex pathophysiology with multiple interconnected subpathologies, including protein aggregation, impaired neurotransmission, oxidative stress, and microglia-mediated neuroinflammation. Current treatments, which generally target a single subpathology, have failed to modify the disease's progression, providing only temporary symptom relief. Multi-target drugs (MTDs) address several subpathologies, including impaired aggregation of pathological proteins. In this review, we cover hybrid molecules published between 2014 and 2024. We offer an overview of the strategies employed in drug design and approaches that have led to notable improvements and reduced hepatotoxicity. Our aim is to offer insights into the potential development of new Alzheimer's disease drugs. This overview highlights the potential of multi-target drugs featuring heterocycles with N-benzylpiperidine fragments and natural compounds in improving Alzheimer's disease treatment.

Development of Nanocomposite Microspheres for Nasal Administration of Deferiprone in Neurodegenerative Disorders

Elevated brain iron levels are characteristic of many neurodegenerative diseases. As an iron chelator with short biological half-life, deferiprone leads to agranulocytosis and neutropenia with a prolonged therapeutic course. Its inclusion in sustained-release dosage forms may reduce the frequency of administration. On the other hand, when administered by an alternative route of administration, such as the nasal route, systemic exposure to deferiprone will be reduced, thereby reducing the occurrence of adverse effects. Direct nose-to-brain delivery has been raised as a non-invasive strategy to deliver drugs to the brain, bypassing the blood-brain barrier. The aim of the study was to develop and characterize nanocomposite microspheres suitable for intranasal administration by combining nano- and microparticle-based approaches. Nanoparticles with an average particle size of 213 ± 56 nm based on the biodegradable polymer poly-ε-caprolactone were developed using the solvent evaporation method. To ensure the deposition of the particles in the nasal cavity and avoid exhalation or deposition into the small airways, the nanoparticles were incorporated into composite structures of sodium alginate obtained by spray drying. Deferiprone demonstrated sustained release from the nanocomposite microspheres and high iron-chelating activity.

How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.

Metal Toxicity and Dementia Including Frontotemporal Dementia: Current State of Knowledge

Frontotemporal dementia (FTD) includes a number of neurodegenerative diseases, often with early onset (before 65 years old), characterized by progressive, irreversible deficits in behavioral, linguistic, and executive functions, which are often difficult to diagnose due to their similar phenotypic characteristics to other dementias and psychiatric disorders. The genetic contribution is of utmost importance, although environmental risk factors also play a role in its pathophysiology. In fact, some metals are known to produce free radicals, which, accumulating in the brain over time, can induce oxidative stress, inflammation, and protein misfolding, all of these being key features of FTD and similar conditions. Therefore, the present review aims to summarize the current evidence about the environmental contribution to FTD-mainly dealing with toxic metal exposure-since the identification of such potential environmental risk factors can lead to its early diagnosis and the promotion of policies and interventions. This would allow us, by reducing exposure to these pollutants, to potentially affect society at large in a positive manner, decreasing the burden of FTD and similar conditions on affected individuals and society overall. Future perspectives, including the application of Artificial Intelligence principles to the field, with related evidence found so far, are also introduced.

Inflammatory Bowel Disease: A Comprehensive Analysis of Molecular Bases, Predictive Biomarkers, Diagnostic Methods, and Therapeutic Options

In inflammatory bowel diseases (IBDs), such as Crohn's disease (CD) and ulcerative colitis (UC), the immune system relentlessly attacks intestinal cells, causing recurrent tissue damage over the lifetime of patients. The etiology of IBD is complex and multifactorial, involving environmental, microbiota, genetic, and immunological factors that alter the molecular basis of the organism. Among these, the microbiota and immune cells play pivotal roles; the microbiota generates antigens recognized by immune cells and antibodies, while autoantibodies target and attack the intestinal membrane, exacerbating inflammation and tissue damage. Given the altered molecular framework, the analysis of multiple molecular biomarkers in patients proves exceedingly valuable for diagnosing and prognosing IBD, including markers like C reactive protein and fecal calprotectin. Upon detection and classification of patients, specific treatments are administered, ranging from conventional drugs to new biological therapies, such as antibodies to neutralize inflammatory molecules like tumor necrosis factor (TNF) and integrin. This review delves into the molecular basis and targets, biomarkers, treatment options, monitoring techniques, and, ultimately, current challenges in IBD management.

Exercise to Counteract Alzheimer's Disease: What Do Fluid Biomarkers Say?

Neurodegenerative diseases (NDs) represent an unsolved problem to date with an ever-increasing population incidence. Particularly, Alzheimer's disease (AD) is the most widespread ND characterized by an accumulation of amyloid aggregates of beta-amyloid (Aβ) and Tau proteins that lead to neuronal death and subsequent cognitive decline. Although neuroimaging techniques are needed to diagnose AD, the investigation of biomarkers within body fluids could provide important information on neurodegeneration. Indeed, as there is no definitive solution for AD, the monitoring of these biomarkers is of strategic importance as they are useful for both diagnosing AD and assessing the progression of the neurodegenerative state. In this context, exercise is known to be an effective non-pharmacological management strategy for AD that can counteract cognitive decline and neurodegeneration. However, investigation of the concentration of fluid biomarkers in AD patients undergoing exercise protocols has led to unclear and often conflicting results, suggesting the need to clarify the role of exercise in modulating fluid biomarkers in AD. Therefore, this critical literature review aims to gather evidence on the main fluid biomarkers of AD and the modulatory effects of exercise to clarify the efficacy and usefulness of this non-pharmacological strategy in counteracting neurodegeneration in AD.

A Diagnostic Model for Parkinson's Disease Based on Anoikis-Related Genes

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, and its pathological mechanisms are thought to be closely linked to apoptosis. Anoikis, a specific type of apoptosis, has recently been suggested to play a role in the progression of Parkinson's disease; however, the underlying mechanisms are not well understood. To explore the potential mechanisms involved in PD, we selected genes from the GSE28894 dataset and compared their expression in PD patients and healthy controls to identify differentially expressed genes (DEGs), and selected anoikis-related genes (ANRGs) from the DEGs. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression approach and multivariate logistic regression highlighted five key genes-GSK3B, PCNA, CDC42, DAPK2, and SRC-as biomarker candidates. Subsequently, we developed a nomogram model incorporating these 5 genes along with age and sex to predict and diagnose PD. To evaluate the model's coherence, clinical applicability, and distinguishability, we utilized receiver operating characteristic (ROC) curves, the C-index, and calibration curves and validated it in both the GSE20295 dataset and our center's external clinical data. In addition, we confirmed the differential expression of the 5 model genes in human blood samples through qRT-PCR and Western blotting. Our constructed anoikis-related PD diagnostic model exhibits satisfactory predictive accuracy and offers novel insights into both diagnosis and treatment strategies for Parkinson's disease while facilitating its implementation in clinical practice.

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

The neurotransmitter puzzle of Alzheimer's: Dissecting mechanisms and exploring therapeutic horizons

Alzheimer's Disease (AD) represents a complex interplay of neurological pathways and molecular mechanisms, with significant impacts on patients' lives. This review synthesizes the latest developments in AD research, focusing on both the scientific advancements and their clinical implications. We examine the role of microglia in AD, highlighting their contribution to the disease's inflammatory aspects. The cholinergic hypothesis, a cornerstone of AD research, is re-evaluated, including the role of Alpha-7 Nicotinic Acetylcholine Receptors in disease progression. This review places particular emphasis on the neurotransmission systems, exploring the therapeutic potential of GABAergic neurotransmitters and the role of NMDA inhibitors in the context of glutamatergic neurotransmission. By analyzing the interactions and implications of neurotransmitter pathways in AD, we aim to shed light on emerging therapeutic strategies. In addition to molecular insights, the review addresses the clinical and personal aspects of AD, underscoring the need for patient-centered approaches in treatment and care. The final section looks at the future directions of AD research and treatment, discussing the integration of scientific innovation with patient care. This review aims to provide a comprehensive update on AD, merging scientific insights with practical considerations, suitable for both specialists and those new to the field.

The selfish network: how the brain preserves behavioral function through shifts in neuronal network state

Neuronal networks possess the ability to regulate their activity states in response to disruptions. How and when neuronal networks turn from physiological into pathological states, leading to the manifestation of neuropsychiatric disorders, remains largely unknown. Here, we propose that neuronal networks intrinsically maintain network stability even at the cost of neuronal loss. Despite the new stable state being potentially maladaptive, neural networks may not reverse back to states associated with better long-term outcomes. These maladaptive states are often associated with hyperactive neurons, marking the starting point for activity-dependent neurodegeneration. Transitions between network states may occur rapidly, and in discrete steps rather than continuously, particularly in neurodegenerative disorders. The self-stabilizing, metastable, and noncontinuous characteristics of these network states can be mathematically described as attractors. Maladaptive attractors may represent a distinct pathophysiological entity that could serve as a target for new therapies and for fostering resilience.

The mechanism of cuproptosis in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with an increased morbidity. The pathogenesis PD has not been fully elucidated, and whatever mechanism is involved, it ultimately leads to dopamine (DA) neuronal apoptosis. Cuproptosis is a novel form of cell death. Its morphology, biochemical properties, and mechanism of action differ from known forms of cell death, such as apoptosis, autophagy, necrosis and pyroptosis. Copper binds to the lipoylated components of the tricarboxylic acid cycle, causing proteotoxic stress that ultimately leads to cellular cuproptosis. PD has biochemical features such as mitochondrial dysfunction and decreased levels of copper and glutathione in brain regions. This is closely related to the cuproptosis mechanism. However, the specific link between the pathogenesis of PD and cuproptosis is unclear. Herein, we summarizes cuproptosis as the cause of DA neuronal death in PD, and the relationship between cuproptosis and the PD pathogenesis. This article provides a research basis for targeted cuproptosis for PD.

Serological Antibodies against Kidney, Liver, and Spleen Membrane Antigens as Potential Biomarkers in Patients with Immune Disorders

Immune disorders arise from complex genetic and environmental factors, which lead to dysregulation at the cellular and inflammatory levels and cause tissue damage. Recent research highlights the crucial role of reactive antibodies in autoimmune diseases and graft rejection, but their complex determination poses challenges for clinical use. Therefore, our study aimed to ascertain whether the presence of reactive antibodies against membrane antigens in tissues from both animal models and humans could serve as biomarkers in patients with autoimmune disorders. To address this issue, we examined the binding profile of serological antibodies against a diverse panel of cell membranes from the spleen, liver, and kidney tissues of monkeys, rats, and humans. After developing the cell membrane microarrays, human sera were immunologically assayed. The study was first conducted on sera from two groups, healthy subjects and patients with inflammatory and autoimmune disorders, and then optimized for kidney transplant patient sera. A significant increase in antibody reactivity against specific monkey kidney and spleen membranes was observed in the serum of patients with lupus nephritis, while kidney transplant patients showed a significant enhancement against human tissues and human embryonic kidney 293 cells. These results show the potential importance for clinical and basic research purposes of studying the presence of specific IgG against membrane antigens in patients' serum as potential biomarkers of immune disorders. However, it is important to note that these results need to be verified in further studies with a larger sample size to confirm their relevance.

Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.