Wearable Electrochemical Sensors in Parkinson's Disease

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.

Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review

This review screened 296 articles on wearable sensors for home monitoring of people with Parkinson's Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson's Disease.

Electrochemical Biosensor for Point-of-Care Testing of Low-Abundance Biomarkers of Neurological Diseases

The neurofilament protein light chain (NEFL) is a potential biomarker of neurodegenerative diseases, and interleukin-6 (IL-6) is also closely related to neuroinflammation. Especially, NEFL and IL-6 are the two most low-abundance known protein markers of neurological diseases, making their detection very important for the early diagnosis and prognosis prediction of such kinds of diseases. Nevertheless, quantitative detection of low concentrations of NEFL and IL-6 in serum remains quite difficult, especially in the point-of-care test (POCT). Herein, we developed a portable, sensitive electrochemical biosensor combined with smartphones that can be applied to multiple scenarios for the quantitative detection of NEFL and IL-6, meeting the need of the POCT. We used a double-antibody sandwich configuration combined with polyenzyme-catalyzed signal amplification to improve the sensitivity of the biosensor for the detection of NEFL and IL-6 in sera. We could detect NEFL as low as 5.22 pg/mL and IL-6 as low as 3.69 pg/mL of 6 μL of serum within 2 h, demonstrating that this electrochemical biosensor worked well with serum systems. Results also showed its superior detection capabilities over those of high-sensitivity ELISA for serum samples. Importantly, by detecting NEFL and IL-6 in sera, the biosensor showed its potential for the POCT model detection of all known biomarkers of neurological diseases, making it possible for the mass screening of patients with neurodegenerative diseases.

Fakhri M, T. Salim E, Subramaniam S. Exploring faradaic and non-faradaic electrochemical impedance spectroscopy approaches in Parkinson's disease diagnosis

Parkinson's disease is a neurodegenerative condition defined by the progressive death of dopaminergic neurons in the brain. The diagnosis of Parkinson's disease often uses time-consuming clinical evaluations and subjective assessments. Electrochemical Impedance Spectroscopy (EIS) is a useful technique for electroanalytical devices due to its label-free performance, in-situ measurements, and low cost. The development of reliable diagnostic tools for Parkinson's disease can be significantly enhanced by exploring novel techniques like faradaic and non-faradaic EIS detection methods. These techniques have the ability to identify specific biomarkers or changes in electrochemical properties linked to Parkinson's disease, allowing for an early and accurate diagnosis. Faradaic EIS detection methods utilize redox processes on the electrode surface, while non-faradaic EIS methods rely on charge transfer or capacitive properties. EIS can identify biomarkers or changes in electrical properties as indicators of Parkinson's disease by measuring impedance at different frequencies. By combining both faradaic and non-faradaic EIS approaches, it may be possible to obtain a comprehensive understanding of the electrochemical changes occurring in Parkinson's disease patients. This may lead to the development of more effective diagnostic techniques and potentially opening up new avenues for personalized treatment strategies. This review explores the current research on faradaic and non-faradaic EIS approaches for diagnosing Parkinson's disease using electrochemical impedance spectroscopy.

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.

Unlocking the future of brain research: MOFs, TMOs, and MOFs/TMOs for electrochemical NTMs detection and analysis

The central nervous system relies heavily on neurotransmitters (NTMs), and NTM imbalances have been linked to a wide range of neurological conditions. Thus, the development of reliable detection techniques is essential for advancing brain studies. This review offers a comprehensive analysis of metal-organic frameworks (MOFs), transition metal oxides (TMOs), and MOFs-derived TMOs (MOFs/TMOs) as materials for electrochemical (EC) sensors targeting the detection of key NTMs, specifically dopamine (DA), epinephrine (EP), and serotonin (SR). The unique properties and diverse families of MOFs and TMOs, along with their nanostructured hybrids, are discussed in the context of EC sensing. The review also addresses the challenges in detecting NTMs and proposes a systematic approach to tackle these obstacles. Despite the vast amount of research on MOFs and TMOs-based EC sensors for DA detection, the review highlights the gaps in the literature for MOFs/TMOs-based EC sensors specifically for EP and SR detection, as well as the limited research on microneedles (MNs)-based EC sensors modified with MOFs, TMOs, and MOFs/TMOs for NTMs detection. This review serves as a foundation to encourage researchers to further explore the potential applications of MOFs, TMOs, and MOFs/TMOs-based EC sensors in the context of neurological disorders and other health conditions related to NTMs imbalances.

An Amperometric Biosensor Based on a Bilayer of Electrodeposited Graphene Oxide and Co-Crosslinked Tyrosinase for L-Dopa Detection in Untreated Human Plasma

L-Dopa, a bioactive compound naturally occurring in some Leguminosae plants, is the most effective symptomatic drug treatment for Parkinson's disease. During disease progression, fluctuations in L-DOPA plasma levels occur, causing motor complications. Sensing devices capable of rapidly monitoring drug levels would allow adjusting L-Dopa dosing, improving therapeutic outcomes. A novel amperometric biosensor for L-Dopa detection is described, based on tyrosinase co-crosslinked onto a graphene oxide layer produced through electrodeposition. Careful optimization of the enzyme immobilization procedure permitted to improve the long-term stability while substantially shortening and simplifying the biosensor fabrication. The effectiveness of the immobilization protocol combined with the enhanced performances of electrodeposited graphene oxide allowed to achieve high sensitivity, wide linear range, and a detection limit of 0.84 μM, suitable for L-Dopa detection within its therapeutic window. Interference from endogenous compounds, tested at concentrations levels typically found in drug-treated patients, was not significant. Ascorbic acid exhibited a tyrosinase inhibitory behavior and was therefore rejected from the enzymatic layer by casting an outer Nafion membrane. The proposed device was applied for L-Dopa detection in human plasma, showing good recoveries.

Accelerating the development of implantable neurochemical biosensors by using existing clinically applied depth electrodes

In this study, an implantable stereo-electroencephalography (sEEG) depth electrode was functionalised with an enzyme coating for enzyme-based biosensing of glucose and L-glutamate. This was done because personalised medicine could benefit from active real-time neurochemical monitoring on small spatial and temporal scales to further understand and treat neurological disorders. To achieve this, the sEEG depth electrode was characterised using cyclic voltammetry (CV), differential pulse voltammetry (DPV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS) using several electrochemical redox mediators (potassium ferri/ferrocyanide, ruthenium hexamine chloride, and dopamine). To improve performance, the Pt sensors on the sEEG depth electrode were coated with platinum black and a crosslinked gelatin-enzyme film to enable enzymatic biosensing. This characterisation work showed that producing a useable electrode with a good electrochemical response showing the expected behaviour for a platinum electrode was possible. Coating with Pt black improved the sensitivity to H₂O₂ over unmodified electrodes and approached that of well-defined Pt macro disc electrodes. Measured current showed good dependence on concentration, and the calibration curves report good sensitivity of 29.65 nA/cm²/μM for glucose and 8.05 nA/cm²/μM for L-glutamate with a stable, repeatable, and linear response. These findings demonstrate that existing clinical electrode devices can be adapted for combined electrochemical and electrophysiological measurement in patients and obviate the need to develop new electrodes when existing clinically approved devices and the associated knowledge can be reused. This accelerates the time to use and application of in vivo and wearable biosensing for diagnosis, treatment, and personalised medicine.

Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Validation of Pressure-Sensing Insoles in Patients with Parkinson's Disease during Overground Walking in Single and Cognitive Dual-Task Conditions

There is a need for unobtrusive and valid tools to collect gait parameters in patients with Parkinson's disease (PD). The novel promising tools are pressure-sensing insoles connected to a smartphone app; however, few studies investigated their measurement properties during simple or challenging conditions in PD patients. This study aimed to examine the validity and reliability of gait parameters computed by pressure-sensing insoles (FeetMe^® insoles, Paris, France). Twenty-five PD patients (21 males, mean age: 69 (7) years) completed two walking assessment sessions. In each session, participants walked on an electronic pressure-sensitive walkway (GaitRite^®, CIR System Inc., Franklin, NJ, USA) without other additional instructions (i.e., single-task condition) and while performing a concurrent cognitive task (i.e., dual-task condition). Spatiotemporal gait parameters were measured simultaneously using the pressure-sensing insoles and the electronic walkway. Concurrent validity was assessed by correlation coefficients and Bland-Altman methodology. Test-retest reliability was examined by intraclass correlation coefficients (ICC) and minimal detectable changes (MDC). The validity results showed moderate to excellent correlations and good agreement between the two systems. Concerning test-retest reliability, moderate-to-excellent ICC values and acceptable MDC demonstrated the repeatability of the measured gait parameters. Our findings support the use of these insoles as complementary instruments to conventional tools during single and dual-task conditions.