Closing the loop for patients with Parkinson disease: where are we?

Biosensors for Parkinson's Disease: Where Are We Now, and Where Do We Need to Go?

Parkinson's Disease is the second most common neurological disease in the United States, yet there is no cure, no pinpointed cause, and no definitive diagnostic procedure. Parkinson's is typically diagnosed when patients present with motor symptoms such as slowness of movement and tremors. However, none of these are specific to Parkinson's, and a confident diagnosis of Parkinson's is typically only achieved when 60-80% of dopaminergic neurons are no longer functioning, at which point much of the damage to the brain is irreversible. This Perspective details ongoing efforts and accomplishments in biosensor research with the goal of overcoming these issues for Parkinson's diagnosis and care, with a focus on the potential impact of early diagnosis and associated opportunities to pinpoint a cause and a cure. We critically analyze the strengths and shortcomings of current technologies and discuss the ideal characteristics of a diagnostic technology toolbox to guide future research decisions in this space. Finally, we assess what role biosensors can play in facilitating precision medicine for Parkinson's patients.

Microneedles-Based Theranostic Platform: From the Past to the Future

Fully integrated theranostic devices are highly esteemed in clinical applications, offering immense potential in real-time disease monitoring and personalized care. Microneedles (MNs), as innovative and wearable devices, boast important advantages in biosensing and therapy, thus holding significant promise in the advancement of diagnostic and therapeutic platforms. Encouragingly, advancements in electrochemical sensing technology, micronano fabrication, and biocompatible materials are propelling momentum for MNs-based closed-loop systems, enhancing detection capabilities, biocompatibility, and cost-effectiveness. Moreover, the notable progress in integrating MN chips with other biochips signifies a frontier for growth. Successful clinical trials in target molecule monitoring and drug delivery domains herald excellent clinical translational prospects for the aforementioned theranostic platform. Finally, we delineate both challenges and opportunities in the development of integrated diagnostic and therapeutic MN systems, including continuous monitoring, intelligent control algorithms, safety, and regulatory considerations.

Levodopa: From Biological Significance to Continuous Monitoring

A continuous levodopa sensor can improve the quality of life for patients suffering with Parkinson's disease by enhancing levodopa titration and treatment effectiveness; however, its development is currently hindered by the absence of a specific levodopa molecular recognition element and limited insights into how real-time monitoring might affect clinical outcomes. This gap in research contributes to clinician uncertainty regarding the practical value of continuous levodopa monitoring data. This paper examines the current state of levodopa sensing and the inherent limitations in today's methods. Further, these challenges are described, including aspects such as interference from the metabolic pathway and adjunct medications, temporal resolution, and clinical questions, with a specific focus on a comprehensive selection of molecules, such as adjunct medications and structural isomers, as an interferent panel designed to assess and validate future levodopa sensors. We review insights and lessons from previously reported levodopa sensors and present a comparative analysis of potential molecular recognition elements, discussing their advantages and drawbacks.

Biosensor Strip for Rapid On-site Assessment of Levodopa Pharmacokinetics along with Motor Performance in Parkinson's Disease

While levodopa (L-Dopa) is the primary treatment for alleviating Parkinson's disease (PD), its efficacy is hindered by challenges such as a short half-life and inconsistent plasma levels. As PD progresses, the rising need for increased and more frequent L-Dopa doses coupled with symptom fluctuations and dyskinesias underscores the urgency for improved comprehension of the interplay between L-Dopa levels and PD motor symptoms. Addressing this critical need, we present a decentralized testing method using a disposable biosensor strip and a universal slope (U-slope) calibration-free approach. This enables reliable, rapid, simple, and cost-effective decentralized L-Dopa measurements from capillary blood. A pilot study with PD persons demonstrates the ability to monitor real-time L-Dopa pharmacokinetics from fingerstick blood after oral L-Dopa-Carbidopa (C-Dopa) tablet administration. Correlating capillary blood L-Dopa levels with PD motor scores revealed a well-defined inverse correlation with temporal motor fluctuations. We compared the resulting dynamic capillary blood L-Dopa levels with plasma L-Dopa levels using the traditional but clinically impractical high-performance liquid chromatography technique. By providing timely feedback on a proper L-Dopa dosing regimen in a decentralized and rapid fashion, this new biosensing platform will facilitate tailored optimal L-Dopa dosing, towards improving symptom management and enhancing health-related quality of life.

Recent Progress in Implantable Drug Delivery Systems

In recent years, tremendous effort is devoted to developing platforms, such as implantable drug delivery systems (IDDSs), with temporally and spatially controlled drug release capabilities and improved adherence. IDDSs have multiple advantages: i) the timing and location of drug delivery can be controlled by patients using specific stimuli (light, sound, electricity, magnetism, etc.). Some intelligent "closed-loop" IDDS can even realize self-management without human participation. ii) IDDSs enable continuous and stable delivery of drugs over a long period (months to years) and iii) to administer drugs directly to the lesion, thereby helping reduce dosage and side effects. iv) IDDSs enable personalized drug delivery according to patient needs. The high demand for such systems has prompted scientists to make efforts to develop intelligent IDDS. In this review, several common stimulus-responsive mechanisms including endogenous (e.g., pH, reactive oxygen species, proteins, etc.) and exogenous stimuli (e.g., light, sound, electricity, magnetism, etc.), are given in detail. Besides, several types of IDDS reported in recent years are reviewed, including various stimulus-responsive systems based on the above mechanisms, radio frequency-controlled IDDS, "closed-loop" IDDS, self-powered IDDS, etc. Finally, the advantages and disadvantages of various IDDS, bottleneck problems, and possible solutions are analyzed to provide directions for subsequent research.

Global trends and hotspots in research on acupuncture for neurodegenerative diseases over the last decade: a bibliometric and visualization analysis

Objectives

This study aimed to explore the current status and trends of acupuncture for neurodegenerative diseases (NDs) in the last decade and provide new insights for researchers in future studies.

Methods

The publications concerning acupuncture treatment for NDs published between 2014 and 2023 were extracted from the Web of Science Core Collection. We used CiteSpace and VOSviewer to analyze data on numbers of annual publications, countries, institutions, cited journals, cited authors, cited references, keywords, and citation bursts about acupuncture for NDs.

Results

A total of 635 publications were obtained from 2014 to 2023. We identified the most prolific journals, countries, institutions, authors, patterns of authorship, and the main direction of future research in the field of acupuncture for NDs in the last decade. The country, institution, and journal with the most publications are China (389 articles), Beijing University of Chinese Medicine (56 articles), and Evidence Based Complementary and Alternative Medicine (42 articles), respectively. The high-frequency keywords focused on "Alzheimer's disease," "Parkinson's disease," "acupuncture," "dementia," and "electroacupuncture." The top five keywords in terms of centrality were "cerebral ischemia," "acupuncture stimulation," "fMRI," "apoptosis," and "deep brain stimulation."

Conclusion

The results from this bibliometric study provide insight into the research trends in acupuncture therapy for NDs, and the current status and trends of the past decade, which may help researchers confirm the current status, hotspots, and frontier trends in this field.

A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease

Using deep learning has demonstrated significant potential in making informed decisions based on clinical evidence. In this study, we deal with optimizing medication and quantitatively present the role of deep learning in predicting the medication dosage for patients with Parkinson's disease (PD). The proposed method is based on recurrent neural networks (RNNs) and tries to predict the dosage of five critical medication types for PD, including levodopa, dopamine agonists, monoamine oxidase-B inhibitors, catechol-O-methyltransferase inhibitors, and amantadine. Recurrent neural networks have memory blocks that retain crucial information from previous patient visits. This feature is helpful for patients with PD, as the neurologist can refer to the patient's previous state and the prescribed medication to make informed decisions. We employed data from the Parkinson's Progression Markers Initiative. The dataset included information on the Unified Parkinson's Disease Rating Scale, Activities of Daily Living, Hoehn and Yahr scale, demographic details, and medication use logs for each patient. We evaluated several models, such as multi-layer perceptron (MLP), Simple-RNN, long short-term memory (LSTM), and gated recurrent units (GRU). Our analysis found that recurrent neural networks (LSTM and GRU) performed the best. More specifically, when using LSTM, we were able to predict levodopa and dopamine agonist dosage with a mean squared error of 0.009 and 0.003, mean absolute error of 0.062 and 0.030, root mean square error of 0.099 and 0.053, and R-squared of 0.514 and 0.711, respectively.

Blocking the Self-Destruct Program of Dopamine Neurons through Macrophage Migration Inhibitory Factor Nuclease Inhibition

Parkinson's disease (PD) is a progressive neurodegenerative condition that pathognomonically involves the death of dopaminergic neurons in the substantia nigra pars compacta, resulting in a myriad of motor and non-motor symptoms. Given the insurmountable burden of this disease on the population and healthcare system, significant efforts have been put forth toward generating disease modifying therapies. This class of treatments characteristically alters disease course, as opposed to current strategies that focus on managing symptoms. Previous literature has implicated the cell death pathway known as parthanatos in PD progression. Inhibition of this pathway by targeting poly (ADP)-ribose polymerase 1 (PARP1) prevents neurodegeneration in a model of idiopathic PD. However, PARP1 has a vast repertoire of functions within the body, increasing the probability of side effects with the long-term treatment likely necessary for clinically significant neuroprotection. Recent work culminated in the development of a novel agent targeting the macrophage migration inhibitory factor (MIF) nuclease domain, also named parthanatos-associated apoptosis-inducing factor nuclease (PAAN). This nuclease activity specifically executes the terminal step in parthanatos. Parthanatos-associated apoptosis-inducing factor nuclease inhibitor-1 was neuroprotective in multiple preclinical mouse models of PD. This piece will focus on contextualizing this discovery, emphasizing its significance, and discussing its potential implications for parthanatos-directed treatment. © 2024 International Parkinson and Movement Disorder Society.

Biopolymer-protected graphene-Fe3O4 nanocomposite based wearable microneedle sensor: toward real-time continuous monitoring of dopamine

Neurological disorders can occur in the human body as a result of nano-level variations in the neurotransmitter levels. Patients affected by neuropsychiatric disorders, that are chronic require continuous monitoring of these neurotransmitter levels for effective disease management. The current work focus on developing a highly sensitive and personalized sensor for continuous monitoring of dopamine. Here we propose a wearable microneedle-based electrochemical sensor, to continuously monitor dopamine in interstitial fluid (ISF). A chitosan-protected hybrid nanomaterial Fe3O4-GO composite has been used as a chemical recognition element protected by Nafion antifouling coating layer. The morphological and physiochemical characterizations of the nanocomposite were carried out with XRD, XPS, FESEM, EDAX and FT-IR. The principle of the developed sensor relies on orthogonal detection of dopamine with square wave voltammetry and chronoamperometric techniques. The microneedle sensor array exhibited an attractive analytical performance toward detecting dopamine in phosphate buffer and artificial ISF. The limit of detection (LOD) of the developed sensor was observed to be low, 90 nM in square wave voltammetry and 0.6 μM in chronoamperometric analysis. The practical applicability of the microneedle sensor array has been demonstrated on a skin-mimicking phantom gel model. The microneedle sensor also exhibited good long-term storage stability, reproducibility, and sensitivity. All of these promising results suggest that the proposed microneedle sensor array could be reliable for the continuous monitoring of dopamine.

Wearable Microneedle-Based Array Patches for Continuous Electrochemical Monitoring and Drug Delivery: Toward a Closed-Loop System for Methotrexate Treatment

Wearable devices based on microneedle (MN) technology have recently emerged as tools for in situ transdermal sensing or delivery in interstitial fluid (ISF). Particularly, MN-based electrochemical sensors allow the continuous monitoring of analytes in a minimally invasive manner through ISF. Exogenous small molecules found in ISF such as therapeutic drugs are ideal candidates for MN sensors due to their correlation with blood levels and their relevance for the optimal management of personalized therapies. Herein, a hollow MN array patch is modified with conductive pastes and functionalized with cross-linked chitosan to develop an MN-based voltammetric sensor for continuous monitoring of methotrexate (MTX). Interestingly, the chitosan coating avoids biofouling while enabling the adsorption of MTX at the electrode's surface for sensitive analysis. The MN sensor exhibits excellent analytical performance in vitro with protein-enriched artificial ISF and ex vivo under a Franz diffusion cell configuration. The MN sensor shows a linear range from 25 to 400 μM, which fits within the therapeutic range of high-dose MTX treatment for cancer patients and an excellent continuous operation for more than two days. Moreover, an iontophoretic hollow MN array patch is developed with the integration of both the anode and cathode in the single MN array patch. The ex vivo characterization demonstrates the transdermal on-demand drug delivery of MTX. Overall, the combination of both MN patches represents impactful progress in closed-loop systems for therapeutic drug management in disorders such as cancer, rheumatoid arthritis, or psoriasis.

Deep brain stimulation in PD: risk of complications, morbidity, and hospitalizations: a systematic review

Introduction

Parkinson's disease (PD) is a progressive and debilitating neurological disorder. While dopaminergic medication improves PD symptoms, continued management is complicated by continued symptom progression, increasing medication fluctuations, and medication-related dyskinesia. Deep brain stimulation (DBS) surgery is a well-accepted and widespread treatment often utilized to address these symptoms in advanced PD. However, DBS may also lead to complications requiring hospitalization. In addition, patients with PD and DBS may have specialized care needs during hospitalization.

Methods

This systematic review seeks to characterize the complications and risk of hospitalization following DBS surgery. Patient risk factors and modifications to DBS surgical techniques that may affect surgical risk are also discussed.

Results

It is found that, when candidates are carefully screened, DBS is a relatively low-risk procedure, but rate of hospitalization is somewhat increased for DBS patients.

Discussion

More research is needed to determine the relative influence of more advanced disease vs. DBS itself in increased rate of hospitalization, but education about DBS and PD is important to insure effective patient care within the hospital.

Expanding causal genes for Parkinson's disease via multi-omics analysis

Genome‑wide association studies (GWASs) have revealed numerous loci associated with Parkinson's disease (PD). However, some potential causal/risk genes were still not revealed and no etiological therapies are available. To find potential causal genes and explore genetically supported drug targets for PD is urgent. By integrating the expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets from multiple tissues (blood, cerebrospinal fluid (CSF) and brain) and PD GWAS summary statistics, a pipeline combing Mendelian randomization (MR), Steiger filtering analysis, Bayesian colocalization, fine mapping, Protein-protein network and enrichment analysis were applied to identify potential causal genes for PD. As a result, GPNMB displayed a robust causal role for PD at the protein level in the blood, CSF and brain, and transcriptional level in the brain, while the protective role of CD38 (in brain pQTL and eQTL) was also identified. We also found inconsistent roles of DGKQ on PD between protein and mRNA levels. Another 9 proteins (CTSB, ARSA, SEC23IP, CD84, ENTPD1, FCGR2B, BAG3, SNCA, FCGR2A) were associated with the risk for PD based on only a single pQTL after multiple corrections. We also identified some proteins' interactions with known PD causative genes and therapeutic targets. In conclusion, this study suggested GPNMB, CD38, and DGKQ may act in the pathogenesis of PD, but whether the other proteins involved in PD needs more evidence. These findings would help to uncover the genes underlying PD and prioritize targets for future therapeutic interventions.

Smartphone-based detection of levodopa in human sweat using 3D printed sensors

Parkinson's disease (PD) is one of the leading neurological disorders negatively impacting health on a global scale. Patients diagnosed with PD require frequent monitoring, prescribed medications, and therapy for extended periods as symptom severity worsens. The primary pharmaceutical treatment for PD patients is levodopa (L-Dopa) which reduces many symptoms experienced by PD patients (e.g., tremors, cognitive ability, motor dysfunction, etc.) through the regulation of dopamine levels in the body. Herein, the first detection of L-Dopa in human sweat using a low-cost 3D printed sensor with a simple and rapid fabrication protocol combined with a portable potentiostat wirelessly connected to a smartphone via Bluetooth is reported. By combining saponification and electrochemical activation into a single protocol, the optimized 3D printed carbon electrodes were able to simultaneously detect uric acid and L-Dopa throughout their biologically relevant ranges. The optimized sensors provided a sensitivity of 83 ± 3 nA/μM from 24 μM to 300 nM L-Dopa. Common physiological interferents found in sweat (e.g., ascorbic acid, glucose, caffeine) showed no influence on the response for L-Dopa. Lastly, a percent recovery of L-Dopa in human sweat using a smartphone-assisted handheld potentiostat resulted in the recovery of 100 ± 8%, confirming the ability of this sensor to accurately detect L-Dopa in sweat.

Vector enabled CRISPR gene editing - A revolutionary strategy for targeting the diversity of brain pathologies

Brain pathologies are considered one of the greatest contributors of death and disability worldwide. Neurodegenerative Alzheimer's disease is the second leading cause of death in adults, whilst brain cancers including glioblastoma multiforme in adults, and pediatric-type high-grade gliomas in children remain largely untreatable. A further compounding issue for patients with brain pathologies is that of long-term neuropsychiatric sequela - as a symptom or arising from high dose therapeutic intervention. The major challenge to effective, low dose treatment is finding therapeutics that successfully cross the blood-brain barrier and target aberrant cellular processes, while having minimum effect on essential cellular processes, and healthy bystander cells. Following over 30 years of research, CRISPR technology has emerged as a biomedical tour de force with the potential to revolutionise the treatment of both neurological and cancer related brain pathologies. The aim of this review is to take stock of the progress made in CRISPR technology in relation to treating brain pathologies. Specifically, we will describe studies which look beyond design, synthesis, and theoretical application; and focus instead on in vivo studies with translation potential. Along with discussing the latest breakthrough techniques being applied within the CRISPR field, we aim to provide a prospective on the knowledge gaps that exist and challenges that still lay ahead for CRISPR technology prior to successful application in the brain disease treatment field.

Digital health for aging populations

Growing life expectancy poses important societal challenges, placing an increasing burden on ever more strained health systems. Digital technologies offer tremendous potential for shifting from traditional medical routines to remote medicine and transforming our ability to manage health and independence in aging populations. In this Perspective, we summarize the current progress toward, and challenges and future opportunities of, harnessing digital technologies for effective geriatric care. Special attention is given to the role of wearables in assisting older adults to monitor their health and maintain independence at home. Challenges to the widespread future use of digital technologies in this population will be discussed, along with a vision for how such technologies will shape the future of healthy aging.

PARK7/DJ-1 in microglia: implications in Parkinson's disease and relevance as a therapeutic target

Microglia are the immune effector cells of the brain playing critical roles in immune surveillance and neuroprotection in healthy conditions, while they can sustain neuroinflammatory and neurotoxic processes in neurodegenerative diseases, including Parkinson's disease (PD). Although the precise triggers of PD remain obscure, causative genetic mutations, which aid in the identification of molecular pathways underlying the pathogenesis of idiopathic forms, represent 10% of the patients. Among the inherited forms, loss of function of PARK7, which encodes the protein DJ-1, results in autosomal recessive early-onset PD. Yet, although protection against oxidative stress is the most prominent task ascribed to DJ-1, the underlying mechanisms linking DJ-1 deficiency to the onset of PD are a current matter of investigation. This review provides an overview of the role of DJ-1 in neuroinflammation, with a special focus on its functions in microglia genetic programs and immunological traits. Furthermore, it discusses the relevance of targeting dysregulated pathways in microglia under DJ-1 deficiency and their importance as therapeutic targets in PD. Lastly, it addresses the prospect to consider DJ-1, detected in its oxidized form in idiopathic PD, as a biomarker and to take into account DJ-1-enhancing compounds as therapeutics dampening oxidative stress and neuroinflammation.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

A Novel Microfluidic-Based OMC-PEDOT-PSS Composite Electrochemical Sensor for Continuous Dopamine Monitoring

Fast and precise analysis techniques using small sample volumes are required for next-generation clinical monitoring at the patient's bedside, so as to provide the clinician with relevant chemical data in real-time. The integration of an electrochemical sensor into a microfluidic chip allows for the achievement of real-time chemical monitoring due to the low consumption of analytes, short analysis time, low cost, and compact size. In this work, dopamine, used as a model, is an important neurotransmitter responsible for controlling various vital life functions. The aim is to develop a novel serpentine microfluidic-based electrochemical sensor, using a screen-printed electrode for continuous dopamine detection. The developed sensor employed the composite of ordered mesoporous carbon (OMC) and poly (3,4 ethylenedioxythiophene)-poly (styrene sulfonate) (PEDOT-PSS). The performance of a microfluidic, integrated with the sensor, was amperometrically evaluated using a computer-controlled microfluidic platform. The microfluidic-based dopamine sensor exhibited a sensitivity of 20.2 ± 0.6 μA μmol L^-1, and a detection limit (LOD) of 21.6 ± 0.002 nmol L^-1, with high selectivity. This microfluidic-based electrochemical sensor was successfully employed to determine dopamine continuously, which could overcome the problem of sensor fouling with more than 90% stability for over 24 h. This novel microfluidic sensor platform provides a powerful tool for the development of a continuous dopamine detection system for human clinical application.

Artificial Neural Network-Assisted Wearable Flexible Sweat Patch for Drug Management in Parkinson's Patients Based on Vacancy-Engineered Processing of g-C3N4

Herein, we developed a flexible, low-cost non-enzymatic sweat sensing chip for in situ acquisition of bioinformation in sweat of individuals under exercise conditions to advance personal health monitoring and medication management for patients with Parkinson's disease. This low-cost, flexible, wearable sweat sensor consists of a printed screen electrode modified with g-C₃N₄ material and an external MSME element. The doping strategy and surface activation strategy of the g-C₃N₄-based exhibited efficient glucose oxidase-like activity and electrochemical activity when testing l-dopa and glucose in sweat. The optimized signal was transmitted to a smartphone for processing 12 individuals with simulated dosing, enabling continuous monitoring of l-dopa metabolism in sweat and management of dosing. The generalization ability and robustness of models constructed by methods such as multiple linear regression, artificial neural networks, and convolutional neural networks were compared cross-sectionally. Deep learning models based on artificial neural networks help develop a user-personalized medication administration reminder system, which provides a promising paradigm for reliable medication supervision for Parkinson's patients in the Internet of Things era.

Wearable chemical sensors for biomarker discovery in the omics era

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.