Evaluating the Diagnostic Potential of Combined Salivary and Skin Biomarkers in Parkinson's Disease.

Oligomeric alpha-synuclein (α-syn) in saliva and phosphorylated α-syn deposits in the skin have emerged as promising diagnostic biomarkers for Parkinson's disease (PD). This study aimed to assess and compare the diagnostic value of these biomarkers in discriminating between 38 PD patients and 24 healthy subjects (HSs) using easily accessible biological samples. Additionally, the study sought to determine the diagnostic potential of combining these biomarkers and to explore their correlations with clinical features. Salivary oligomeric α-syn levels were quantified using competitive ELISA, while skin biopsies were analyzed through immunofluorescence to detect phosphorylated α-syn at Ser129 (p-S129). Both biomarkers individually were accurate in discriminating PD patients from HSs, with a modest agreement between them. The combined positivity of salivary α-syn oligomers and skin p-S129 aggregates differentiated PD patients from HSs with an excellent discriminative ability with an AUC of 0.9095. The modest agreement observed between salivary and skin biomarkers individually suggests that they may reflect different aspects of PD pathology, thus providing complementary information when combined. This study's results highlight the potential of utilizing a multimodal biomarker approach to enhance diagnostic accuracy in PD.

Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study.

BACKGROUND: Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life. OBJECTIVE: We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test. METHODS: This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT. RESULTS: In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. CONCLUSIONS: The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options. TRIAL REGISTRATION: ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55452.

Ligand Profiling as a Diagnostic Tool to Differentiate Patient-Derived α-Synuclein Polymorphs.

Amyloid fibrils are characteristic of many neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. While different diseases may have fibrils formed of the same protein, the supramolecular morphology of these fibrils is disease-specific. Here, a method is reported to distinguish eight morphologically distinct amyloid fibrils based on differences in ligand binding properties. Eight fibrillar polymorphs of α-synuclein (αSyn) were investigated: five generated de novo using recombinant αSyn and three generated using protein misfolding cyclic amplification (PMCA) of recombinant αSyn seeded with brain homogenates from deceased patients diagnosed with Parkinson's disease (PD), multiple system atrophy (MSA), and dementia with Lewy bodies (DLB). Fluorescence binding assays were carried out for each fibril using a toolkit of six different ligands. The fibril samples were separated into five categories based on a binary classification of whether they bound specific ligands or not. Quantitative binding measurements then allowed every fibrillar polymorph to be uniquely identified, and the PMCA fibrils derived from PD, MSA, and DLB patients could be unambiguously distinguished. This approach constitutes a novel and operationally simple method to differentiate amyloid fibril morphologies and to identify disease states using PMCA fibrils obtained by seeding with patient samples.

Tau- and α-synuclein-targeted gold nanoparticles: applications, opportunities, and future outlooks in the diagnosis and therapy of neurodegenerative diseases.

The use of nanomaterials in medicine offers multiple opportunities to address neurodegenerative disorders such as Alzheimer's and Parkinson's disease. These diseases are a significant burden for society and the health system, affecting millions of people worldwide without sensitive and selective diagnostic methodologies or effective treatments to stop their progression. In this sense, the use of gold nanoparticles is a promising tool due to their unique properties at the nanometric level. They can be functionalized with specific molecules to selectively target pathological proteins such as Tau and α-synuclein for Alzheimer's and Parkinson's disease, respectively. Additionally, these proteins are used as diagnostic biomarkers, wherein gold nanoparticles play a key role in enhancing their signal, even at the low concentrations present in biological samples such as blood or cerebrospinal fluid, thus enabling an early and accurate diagnosis. On the other hand, gold nanoparticles act as drug delivery platforms, bringing therapeutic agents directly into the brain, improving treatment efficiency and precision, and reducing side effects in healthy tissues. However, despite the exciting potential of gold nanoparticles, it is crucial to address the challenges and issues associated with their use in the medical field before they can be widely applied in clinical settings. It is critical to ensure the safety and biocompatibility of these nanomaterials in the context of the central nervous system. Therefore, rigorous preclinical and clinical studies are needed to assess the efficacy and feasibility of these strategies in patients. Since there is scarce and sometimes contradictory literature about their use in this context, the main aim of this review is to discuss and analyze the current state-of-the-art of gold nanoparticles in relation to delivery, diagnosis, and therapy for Alzheimer's and Parkinson's disease, as well as recent research about their use in preclinical, clinical, and emerging research areas.

Clinical value of video oculomotor evaluation in the differential diagnosis of multiple system atrophy and Parkinson's disease.

BACKGROUND: Multiple system atrophy (MSA) is a neurodegenerative disease that progresses rapidly and has a poor prognosis. This study aimed to assess the value of video oculomotor evaluation (VOE) in the differential diagnosis of MSA and Parkinson's disease (PD). METHODS: In total, 28 patients with MSA, 31 patients with PD, and 30 age- and sex-matched healthy controls (HC) were screened and included in this study. The evaluation consisted of a gaze-holding test, smooth pursuit eye movement (SPEM), random saccade, and optokinetic nystagmus (OKN). RESULTS: The MSA and PD groups had more abnormalities and decreased SPEM gain than the HC group (64.29%, 35.48%, 10%, p < .001). The SPEM gain in the MSA group was significantly lower than that in the PD group at specific frequencies. Patients with MSA and PD showed prolonged latencies in all saccade directions compared with those with HC. However, the two diseases had no significant differences in the saccade parameters. The OKN gain gradually decreased from the HC to the PD and the MSA groups (p < .05). Compared with the PD group, the gain in the MSA group was further decreased in the OKN test at 30°/s (Left, p = .010; Right p = .016). Receiver operating characteristic curves showed that the combination of oculomotor parameters with age and course of disease could aid in the differential diagnosis of patients with MSA and PD, with a sensitivity of 89.29% and a specificity of 70.97%. CONCLUSIONS: The combination of oculomotor parameters and clinical data may aid in the differential diagnosis of MSA and PD. Furthermore, VOE is vital in the identification of neurodegenerative diseases.

Identification and validation of N-acetylputrescine in combination with non-canonical clinical features as a Parkinson's disease biomarker panel.

Parkinson's disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3-25% of sporadic Parkinson's patients present with genetic abnormalities that could represent a risk factor, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson's disease, which highlights the critical need for biomarkers. In the present study, we prospectively collected and analyzed plasma samples from 194 Parkinson's disease patients and 197 age-matched non-diseased controls. N-acetyl putrescine (NAP) in combination with sense of smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements demonstrated combined diagnostic utility. NAP was increased by 28% in Parkinsons disease patients and exhibited an AUC of 0.72 as well as an OR of 4.79. The clinical and NAP panel demonstrated an area under the curve, AUC = 0.9 and an OR of 20.4. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson's disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.

Associations of sleep disorders with serum neurofilament light chain levels in Parkinson's disease.

BACKGROUND: Sleep disorders are a prevalent non-motor symptom of Parkinson's disease (PD), although reliable biological markers are presently lacking. OBJECTIVES: To explore the associations between sleep disorders and serum neurofilament light chain (NfL) levels in individuals with prodromal and early PD. METHODS: The study contained 1113 participants, including 585 early PD individuals, 353 prodromal PD individuals, and 175 healthy controls (HCs). The correlations between sleep disorders (including rapid eye movement sleep behavior disorder (RBD) and excessive daytime sleepiness (EDS)) and serum NfL levels were researched using multiple linear regression models and linear mixed-effects models. We further investigated the correlations between the rates of changes in daytime sleepiness and serum NfL levels using multiple linear regression models. RESULTS: In baseline analysis, early and prodromal PD individuals who manifested specific behaviors of RBD showed significantly higher levels of serum NfL. Specifically, early PD individuals who experienced nocturnal dream behaviors (ß = 0.033; P = 0.042) and movements of arms or legs during sleep (ß = 0.027; P = 0.049) showed significantly higher serum NfL levels. For prodromal PD individuals, serum NfL levels were significantly higher in individuals suffering from disturbed sleep (ß = 0.038; P = 0.026). Our longitudinal findings support these baseline associations. Serum NfL levels showed an upward trend in early PD individuals who had a higher total RBDSQ score (ß = 0.002; P = 0.011) or who were considered as probable RBD (ß = 0.012; P = 0.009) or who exhibited behaviors on several sub-items of the RBDSQ. In addition, early PD individuals who had a high total ESS score (ß = 0.001; P = 0.012) or who were regarded to have EDS (ß = 0.013; P = 0.007) or who exhibited daytime sleepiness in several conditions had a trend toward higher serum NfL levels. CONCLUSION: Sleep disorders correlate with higher serum NfL, suggesting a link to PD neuronal damage. Early identification of sleep disorders and NfL monitoring are pivotal in detecting at-risk PD patients promptly, allowing for timely intervention. Regular monitoring of NfL levels holds promise for tracking both sleep disorders and disease progression, potentially emerging as a biomarker for evaluating treatment outcomes.

Detecting the symptoms of Parkinson's disease with non-standard video.

BACKGROUND: Neurodegenerative diseases, such as Parkinson's disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures. METHODS: Twenty-eight individuals with Parkinson's disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person's body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms. RESULTS: The support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively. CONCLUSION: These results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.

Evaluation and Management of Tremor.

Tremor is a commonly encountered condition in the primary care setting and can manifest at rest, with action, or both. Common causes include Parkinson disease, essential tremor, and drug-induced tremor. In this article, the authors discuss how to examine a patient with tremor and which features of the history and examination can help clue the provider in to the appropriate diagnosis. They also review treatments for varying types of tremor and when referral to a neurologist may be necessary.

Transferrin-conjugated magnetic nanoparticles for the isolation of brain-derived blood exosomal MicroRNAs: A novel approach for Parkinson's disease diagnosis.

BACKGROUND: Brain-derived exosomes circulate in the bloodstream and other bodily fluids, serving as potential indicators of neurological disease progression. These exosomes present a promising avenue for the early and precise diagnosis of neurodegenerative conditions. Notably, miRNAs found in plasma extracellular vesicles (EVs) offer distinct diagnostic benefits due to their stability, abundance, and resistance to breakdown. RESULTS: In this study, we introduce a method using transferrin conjugated magnetic nanoparticles (TMNs) to isolate these exosomes from the plasma of patients with neurological disorders. This TMNs technique is both quick (<35 min) and cost-effective, requiring no high-priced ingredients or elaborate equipment for EV extraction. Our method successfully isolated EVs from 33 human plasma samples, including those from patients with Parkinson's disease (PD), Multiple Sclerosis (MS), and Dementia. Using quantitative polymerase chain reaction (PCR) analysis, we evaluated the potential of 8 exosomal miRNA profiles as biomarker candidates. Six exosomal miRNA biomarkers (miR-195-5p, miR-495-3p, miR-23b-3P, miR-30c-2-3p, miR-323a-3p, and miR-27a-3p) were consistently linked with all stages of PD. SIGNIFICANCE: The TMNs method provides a practical, cost-efficient way to isolate EVs from biological samples, paving the way for non-invasive neurological diagnoses. Furthermore, the identified miRNA biomarkers in these exosomes may emerge as innovative tools for precise diagnosis in neurological disorders including PD.

Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine.

AIMS: Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats. METHODS: The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD. RESULTS: Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79). CONCLUSION: This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease.

The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.

Prospective Role of PAK6 and 14-3-3γ as Biomarkers for Parkinson's Disease.

Background: Parkinson's disease is a progressive neurodegenerative disorder mainly distinguished by sporadic etiology, although a genetic component is also well established. Variants in the LRRK2 gene are associated with both familiar and sporadic disease. We have previously shown that PAK6 and 14-3-3γ protein interact with and regulate the activity of LRRK2. Objective: The aim of this study is to quantify PAK6 and 14-3-3γ in plasma as reliable biomarkers for the diagnosis of both sporadic and LRRK2-linked Parkinson's disease. Methods: After an initial quantification of PAK6 and 14-3-3γ expression by means of Western blot in post-mortem human brains, we verified the presence of the two proteins in plasma by using quantitative ELISA tests. We analyzed samples obtained from 39 healthy subjects, 40 patients with sporadic Parkinson's disease, 50 LRRK2-G2019S non-manifesting carriers and 31 patients with LRRK2-G2019S Parkinson's disease. Results: The amount of PAK6 and 14-3-3γ is significantly different in patients with Parkinson's disease compared to healthy subjects. Moreover, the amount of PAK6 also varies with the presence of the G2019S mutation in the LRRK2 gene. Although the generalized linear models show a low association between the presence of Parkinson's disease and PAK6, the kinase could be added in a broader panel of biomarkers for the diagnosis of Parkinson's disease. Conclusions: Changes of PAK6 and 14-3-3γ amount in plasma represent a shared readout for patients affected by sporadic and LRRK2-linked Parkinson's disease. Overall, they can contribute to the establishment of an extended panel of biomarkers for the diagnosis of Parkinson's disease.

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.

Psychometric properties of the Parkinson's disease Questionnaire-39 and its short form Parkinson's disease Questionnaire-8: A systematic review and meta-analysis.

Parkinson's disease (PD) affects Quality of Life (QoL), since it is responsible for cognitive impairment, non-motor, and motor symptoms. Outcome measures are fundamental for evaluating treatment's effect on QoL over time. This systematic review aimed to identify the psychometric properties of PDQ-39 and PDQ-8 in the different populations in which they were validated. The electronic databases systematically searched are MEDLINE (via PubMed), CINAHL, SCOPUS, and Web of Science; the research was conducted in July 2023. The psychometric properties considered were those of the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) checklist. Risk of bias was assessed using the COSMIN checklist. The search identified 1306 articles. 398 duplicates were eliminated; 908 articles were analyzed reading title and abstract; 799 were finally excluded because used PDQ-39 and PDQ-8 as outcome measures or were not dealing with psychometric properties; 66 articles were excluded after reading the full text. 43 articles were included in the review; meta-analysis showed all the Cronbach's alpha values were statistically significant for all the subscales of PDQ-39 and PDQ-8. PDQ-39 demonstrated to be a specific HRQoL questionnaire that is correlated with generic HRQoL questionnaires, in fact in many studies included in the review, correlations with SF-36 were found. In the last studies about psychometric properties of PDQ-8 emerged that it is a practical and informative instrument that can be easily used in clinical settings, especially in busy ones, but also in large-scale studies in which a brief instrument would be preferred.

[Associations of serum neuromarkers with clinical features of Parkinson's disease]. / Assotsiatsii syvorotochnykh neiromarkerov s klinicheskimi osobennostyami bolezni Parkinsona.

OBJECTIVE: To evaluate the clinical and laboratory correlation of biomarkers with anti- and pro-apoptotic activity with the severity of motor and non-motor symptoms depending on the progression rate of Parkinson's disease (PD). MATERIAL AND METHODS: A wide range of non-motor symptoms (emotional-affective, cognitive, psychotic and behavioral disorders, fatigue, sleep disorders and autonomic disorders) was evaluated using validated scales and a number of serum neuromarkers responsible for neuroplasticity and neuronal survival processes (BDNF, PDGF, cathepsin D) in 71 patients with PD (mean age 65 (55; 70) years, disease duration 7 (4; 9) years, age of onset 57 (49; 62) years). RESULTS: The concentration of biomarkers (BDNF, PDGF and cathepsin D) was the lowest in the group of patients with a rapid PD progression rate (p<0.001, p=0.001 and p=0.031, respectively), the severity of motor and most non-motor symptoms was higher (p=0.023 and p=0.001, respectively) compared to middle and slow progression rate. There were correlations between BDNF concentration and the severity of depression (r=-0.63, p<0.001), apathy (r=-0.48, p<0.001), impulsive behavioral disorders (r=0.500, p<0.001), level of cognitive functions (r=0.54, p<0.001), motor symptoms (r=-0.43, p<0.001); between PDGF level and the severity of motor manifestations of PD (r=-0.30, p=0.011), depression (r=-0.70, p<0.001), apathy (r=-0.460, p<0.001), the degree of severity of behavioral disorders (r=0.742, p<0.001). No significant correlations were observed between the level of cathepsin D and the severity of clinical manifestations of PD, which indicates the connection of cathepsin D with the general pathogenesis of PD. CONCLUSION: The possibility of using serum proteins of the neurotrophin subfamily and the protein associated with autophagy, cathepsin D, as biomarkers that determine the prognosis of PD, is considered.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

An α-helical peptide-based plasmonic biosensor for highly specific detection of α-synuclein toxic oligomers.

BACKGROUND: α-Synuclein (αS) aggregation is the main neurological hallmark of a group of neurodegenerative disorders, collectively referred to as synucleinopathies, of which Parkinson's disease (PD) is the most prevalent. αS oligomers are elevated in the cerebrospinal fluid (CSF) of PD patients, standing as a biomarker for disease diagnosis. However, methods for early PD detection are still lacking. We have recently identified the amphipathic 22-residue peptide PSMα3 as a high-affinity binder of αS toxic oligomers. PSMα3 displayed excellent selectivity and reproducibility, binding to αS toxic oligomers with affinities in the low nanomolar range and without detectable cross-reactivity with functional monomeric αS. RESULTS: In this work, we leveraged these PSMα3 unique properties to design a plasmonic-based biosensor for the direct detection of toxic oligomers under label-free conditions. SIGNIFICANCE AND NOVELTY: We describe the integration of the peptide in a lab-on-a-chip plasmonic platform suitable for point-of-care measurements of αS toxic oligomers in CSF samples in real-time and at an affordable cost, providing an innovative biosensor for PD early diagnosis in the clinic.

Glutamate Signaling in Patients With Parkinson Disease With REM Sleep Behavior Disorder.

BACKGROUND AND OBJECTIVES: Clinical heterogeneity of patients with Parkinson disease (PD) is well recognized. PD with REM sleep behavior disorder (RBD) is a more malignant phenotype with faster motor progression and higher nonmotor symptom burden. However, the neural mechanisms underlying this clinical divergence concerning imbalances in neurotransmitter systems remain elusive. METHODS: Combining magnetic resonance (MR) spectroscopy and [11C]ABP688 PET on a PET/MR hybrid system, we simultaneously investigated two different mechanisms of glutamate signaling in patients with PD. Patients were grouped according to their RBD status in overnight video-polysomnography and compared with age-matched and sex-matched healthy control (HC) participants. Total volumes of distribution (VT) of [11C]ABP688 were estimated with metabolite-corrected plasma concentrations during steady-state conditions between 45 and 60 minutes of the scan following a bolus-infusion protocol. Glutamate, glutamine, and glutathione levels were investigated with single-voxel stimulated echo acquisition mode MR spectroscopy of the left basal ganglia. RESULTS: We measured globally elevated VT of [11C]ABP688 in 16 patients with PD and RBD compared with 17 patients without RBD and 15 HC participants (F(2,45) = 5.579, p = 0.007). Conversely, glutamatergic metabolites did not differ between groups and did not correlate with the regional VT of [11C]ABP688. VT of [11C]ABP688 correlated with the amount of REM sleep without atonia (F(1,42) = 5.600, p = 0.023) and with dopaminergic treatment response in patients with PD (F(1,30) = 5.823, p = 0.022). DISCUSSION: Our results suggest that patients with PD and RBD exhibit altered glutamatergic signaling indicated by higher VT of [11C]ABP688 despite unaffected glutamate levels. The imbalance of glutamate receptors and MR spectroscopy glutamate metabolite levels indicates a novel mechanism contributing to the heterogeneity of PD and warrants further investigation of drugs targeting mGluR5.

Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease.

INTRODUCTION: Reliable assessment of individuals with Parkinson's disease (PD) is essential for providing adequate treatment. Clinical assessment is a complex and time-consuming task, especially for bradykinesia, since its evaluation can be influenced by the degree of experience of the examiner, patient collaboration and individual bias. Improvement of the clinical evaluation can be obtained by considering assessments from several professionals. However, this is only true when inter and intra-rater agreement are high. Recently, the Movement Disorder Society highlighted, during the COVID-19 pandemic, the need to develop and validate technologies for remote assessment of the motor status of people with PD. Thus, this study introduces an objective strategy for the remote evaluation of bradykinesia using multi-specialist analysis. METHODS: Twelve volunteers with PD participated and these were asked to execute finger tapping, hand opening/closing and pronation/supination movements. Each task was recorded and rated by fourteen PD health experts for each patient. The scores were assessed on an individual basis. Intra and inter-rater agreement and correlation were estimated. RESULTS: The results showed that agreements and correlations between experienced examiners were high with low variability. In addition, group analysis was noted as possessing the potential to solve individual inconsistency bias. CONCLUSION: Furthermore, this study demonstrated the need for a group with prior training and experience, along with indicating the importance for the development of a clinical protocol that can use telemedicine for the evaluation of individuals with PD, as well as the inclusion of a specialized mediating group. In Addition, this research helps to the development of a valid remote assessment of bradykinesia.