Diagnosis Of Parkinson's Disease Research Articles

Associations of street-view greenspace with Parkinson’s disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

IntroductionProtective associations of greenspace with Parkinson’s disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). MethodsWe created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007–2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. ResultsThere were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. ConclusionIncreasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Generation of induced pluripotent stem cells (NIMHi015-A) from a Parkinson’s Disease patient harbouring a homozygous Exon 3 deletion in the PRKN gene

The Parkin (PRKN) gene mutation is prevalent in young-onset Parkinson's disease (PD), typically emerging before age 30, accompanied by early motor symptoms. Induced pluripotent stem cells (iPSCs) were derived from peripheral blood mononuclear cells of a PD patient with an exon 3 deletion in PRKN using Sendai-virus reprogramming. PD diagnosis was confirmed via the Unified Parkinson's Disease Rating Scale (UPDRS). Characterization of the iPSC line ensured self-renewal and pluripotency. This resource serves as a valuable platform for drug screening and elucidating the pathophysiology of this mutation, facilitating advancements in PD research.

Early dopaminergic replacement treatment initiation benefits motor symptoms in patients with Parkinson's disease.

Parkinson's disease (PD) generally progresses slowly, but it is controversial whether delaying treatment accelerates the progression. Determine the correlation between the time of dopaminergic replacement treatment initiation and the severity of clinical symptoms in PD, including motor and non-motor symptoms. PD patients were divided between 155 people who were diagnosed de novo and 165 PD patients receiving dopamine replacement therapy. Basic patient characteristics included gender, age, age at onset, disease duration, and the time of dopaminergic replacement treatment initiation. We used MDS-UPDRS scores to evaluate the severity of motor symptoms and we also used the scale to assess the severity of non-motor symptoms such as cognition, mood, sleep, and quality of life. The mean time between symptom onset and the initiation of drug treatment was 31.0 (22.5) months. After adjusting for age, sex, age at onset, and disease duration, we found that the MDS-Unified Parkinson's Disease Rating Scale (UPDRS)-III score increased faster in the de novo group with a similar disease duration (F = 8.7, p = 0.0034) than the treatment group. The cumulative incidence of progression to H-Y score 3 in de novo PD group over disease duration was 39.7% in 50months and 92.2% in 100 months, while in treated group such cumulative incidence was 15.5% in 50 months, 51.4% in 100 months and 81.5% in 150 months. The cumulative incidence of patients in the de novo PD group was higher than that in the treated group (p = 0.001), suggesting that untreated patients were more likely to progress to the advanced stages. Symptoms onset, the time between symptom onset and treatment initiation, age, sex, and disease duration explained 28.95% of the total variation in the MDS-UPDRS-III score for motor symptoms. In drug-naïve patients, the time between symptom onset and treatment initiation explained 20.1% of the total variation in the MDS-UPDRS-III score for motor symptoms (t = 6.15, p < 0.001). These data in our study showed that early dopaminergic replacement treatment have played a positive role in PD patients, while dopaminergic replacement delayed treatment might be detrimental to motor symptoms and non-motor state of PD patient. Recognizing early stage symptoms of PD and early diagnosis are of great significance to treatment.

Integrative analysis of plasma and substantia nigra in Parkinson's disease: unraveling biomarkers and insights from the lncRNA-miRNA-mRNA ceRNA network.

Parkinson's disease (PD) is a rapidly growing neurological disorder characterized by diverse movement symptoms. However, the underlying causes have not been clearly identified, and accurate diagnosis is challenging. This study aimed to identify potential biomarkers suitable for PD diagnosis and present an integrative perspective on the disease. We screened the GSE7621, GSE8397-GPL96, GSE8397-GPL97, GSE20163, and GSE20164 datasets in the NCBI GEO database to identify differentially expressed (DE) mRNAs in the substantia nigra (SN). We also screened the GSE160299 dataset from the NCBI GEO database to identify DE lncRNAs and miRNAs in plasma. We then constructed 2 lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) regulatory networks based on the ceRNA hypothesis. To understand the biological function, we performed Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses for each ceRNA network. The receiver operating characteristic analyses (ROC) was used to assess ceRNA results. We identified 7 upregulated and 29 downregulated mRNAs as common DE mRNAs in the 5 SN datasets. In the blood dataset, we identified 31 DE miRNAs (9 upregulated and 22 downregulated) and 332 DE lncRNAs (69 upregulated and 263 downregulated). Based on the determined interactions, 5 genes (P2RX7, HSPA1, SLCO4A1, RAD52, and SIRT4) appeared to be upregulated as a result of 10 lncRNAs sponging 4 miRNAs (miR-411, miR-1193, miR-301b, and miR-514a-2/3). Competing with 9 genes (ANK1, CBLN1, RGS4, SLC6A3, SYNGR3, VSNL1, DDC, KCNJ6, and SV2C) for miR-671, a total of 26 lncRNAs seemed to function as ceRNAs, influencing genes to be downregulated. In this study, we successfully constructed 2 novel ceRNA regulatory networks in patients with PD, including 36 lncRNAs, 5 miRNAs, and 14 mRNAs. Our results suggest that these plasma lncRNAs are involved in the pathogenesis of PD by sponging miRNAs and regulating gene expression in the SN of the brain. We propose that the upregulated and downregulated lncRNA-mediated ceRNA networks represent mechanisms of neuroinflammation and dopamine neurotransmission, respectively. Our ceRNA network, which was associated with PD, suggests the potential use of DE miRNAs and lncRNAs as body fluid diagnostic biomarkers. These findings provide an integrated view of the mechanisms underlying gene regulation and interactions in PD.

Identifying Barriers and Facilitators to Accessing Care for Historically Marginalized Communities Affected by Parkinson Disease: A Qualitative Study.

Parkinson disease (PD) is the second most common neurodegenerative disease. Members of the Black Diaspora (MBD) and Hispanic/Latinx people are less likely to receive a timely diagnosis following the onset of symptoms and more likely to experience greater disease severity due to late diagnosis. Historically marginalized populations (i.e., MBD, Hispanic, and Latinx communities) are not accurately represented in research; this, along with many other barriers, compounds underreporting and lack of recognition of PD. It is important to understand barriers to early diagnosis and healthcare access for these historically marginalized populations from the community's perspective. Our team conducted two focus groups to identify barriers and facilitators to PD healthcare-seeking behavior. We sought to identify which barriers are modifiable to ultimately improve engagement in neurological care for MBD and Hispanic individuals affected by PD. We enrolled 15 participants (13 female; African/African American/Black n = 10, Hispanic/Puerto Rican n = 3, other n = 2) for two focus groups. Discussions revealed sources of barriers to healthcare-seeking behavior in three main domains: legacy of racism in the United States, ancestral cultural environment, and healthcare system access. These sources influenced individuals' PD knowledge and familiarity. Additionally, participants expressed a desire to know more about PD and called for increased community-based programming for education and awareness. This paper uses a community-based participatory research approach to describe the experiences of MBD, Hispanic, and Latinx people in Manhattan and the surrounding areas in relation to possible sources of healthcare disparities and delayed PD diagnosis. These sources have broad implications and should be addressed through collaborative community programming.

Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset.

Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5. We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets. Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75. The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

Real-world Chinese herbal medicine for Parkinson's disease: a hospital-based retrospective analysis of electronic medical records.

Parkinson's disease (PD) is a progressive neurodegenerative condition. Chinese medicine therapies have demonstrated effectiveness for PD in controlled settings. However, the utilization of Chinese medicine therapies for PD in real-world clinical practice and the characteristics of patients seeking these therapies have not been thoroughly summarized. The study retrospectively analyzed initial patient encounters (PEs) with a first-listed diagnosis of PD, based on electronic medical records from Guangdong Provincial Hospital of Chinese Medicine between July 2018 and July 2023. A total of 3,206 PEs, each corresponding to an individual patient, were eligible for analyses. Approximately 60% of patients made initial visits to the Chinese medicine hospital after receiving a PD diagnosis, around 4.59 years after the onset of motor symptoms. Over 75% of the patients visited the Internal Medicine Outpatient Clinic at their initial visits, while a mere 13.85% visited PD Chronic Care Clinic. Rest tremor (61.98%) and bradykinesia (52.34%) are the most commonly reported motor symptoms, followed by rigidity (40.70%). The most commonly recorded non-motor symptoms included constipation (31.88%) and sleep disturbance (25.27%). Integration of Chinese medicine and conventional medicine therapies was the most common treatment method (39.15%), followed by single use of Chinese herbal medicine (27.14%). The most frequently prescribed herbs for PD included Glycyrrhiza uralensis Fisch. (gan cao), Astragalus mongholicus Bunge (huang qi), Atractylodes macrocephala Koidz. (bai zhu), Angelica sinensis (Oliv.) Diels (dang gui), Rehmannia glutinosa (Gaertn.) DC. (di huang), Paeonia lactiflora Pall. (bai shao), Bupleurum chinense DC. (chai hu), Citrus aurantium L. (zhi qiao/zhi shi/chen pi), Panax ginseng C. A. Mey. (ren shen), and Poria cocos (Schw.) Wolf (fu ling). These herbs contribute to formulation of Bu zhong yi qi tang (BZYQT). Patients typically initiated Chinese medical care after the establishment of PD diagnosis, ~4.59 years post-onset of motor symptoms. The prevalent utilization of CHM decoctions and patented Chinese herbal medicine products, underscores its potential in addressing both motor and non-motor symptoms. Despite available evidence, rigorous clinical trials are needed to validate and optimize the integration of CHM, particularly BZYQT, into therapeutic strategies for PD.

Auto Diagnosis of Parkinson's Disease Via a Deep Learning Model Based on Mixed Emotional Facial Expressions.

Parkinson's disease (PD) is a common degenerative disease of the nervous system in the elderly. The early diagnosis of PD is very important for potential patients to receive prompt treatment and avoid the aggravation of the disease. Recent studies have found that PD patients always suffer from emotional expression disorder, thus forming the characteristics of "masked faces". Based on this, we thus propose an auto PD diagnosis method based on mixed emotional facial expressions in the paper. Specifically, the proposed method is cast into four steps: Firstly, we synthesize virtual face images containing six basic expressions (i.e., anger, disgust, fear, happiness, sadness, and surprise) via generative adversarial learning, in order to approximate the premorbid expressions of PD patients; Secondly, we design an effective screening scheme to assess the quality of the above synthesized facial expression images and then shortlist the high-quality ones; Thirdly, we train a deep feature extractor accompanied with a facial expression classifier based on the mixture of the original facial expression images of the PD patients, the high-quality synthesized facial expression images of PD patients, and the normal facial expression images from other public face datasets; Finally, with the well-trained deep feature extractor, we thus adopt it to extract the latent expression features for six facial expression images of a potential PD patient to conduct PD/non-PD prediction. To show real-world impacts, we also collected a new facial expression dataset of PD patients in collaboration with a hospital. Extensive experiments are conducted to validate the effectiveness of the proposed method for PD diagnosis and facial expression recognition.

Disease burden of Parkinson's disease in China and its provinces from 1990 to 2021: findings from the global burden of disease study 2021

Parkinson's disease (PD) has become a public health concern with global ageing. However, comprehensive assessments of the temporal and geographical trend of PD disease burden in China remain insufficient. This study aimed to examine the burden of PD by age, gender, and geographical region in China during 1990-2021. Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we analysed the incidence, prevalence, mortality, and DALY burden of PD in 33 Chinese provinces/regions. We compared the national figure with the global average and the corresponding estimates from the G20 countries. The estimated annual percentage change (EAPC) was used to quantify the temporal trends of PD burden during 1990-2021. We further assessed the PD burden by age and gender during 1990-2021. We used a decomposition analysis to investigate the changes in the number of new cases, patients, and deaths of PD during 1990-2021. In 2021, China recorded the highest age-standardised incidence and prevalence of PD among the G20 countries, at 24.3 per 100,000 and 245.7 per 100,000, respectively, figures that were much higher than the global average. During 1990-2021, the age-standardised incidence of PD in China increased by 89.7%, and the age-standardised prevalence by 167.8%, both marking the largest increases among the G20 countries. In contrast, the age-standardised mortality for PD has significantly decreased since 1990, whereas the age-standardised DALY rate for PD has remained relatively unchanged since 1990. The PD burden gradually increased with age, especially in the elderly population aged ≥65 years. During 1990-2021, the burden in males consistently surpassed that in females, with the gender difference widening over time. The increase in new cases and patients of PD was primarily driven by changes in age-specific rates, while the rise in PD-related deaths was largely attributable to population ageing. The disease burden of PD varied considerably across the Chinese provinces. In 2021, age-standardised incidence and prevalence of PD were generally higher in China's southeastern coastal regions than in the western regions, and age-standardised DALY rates were higher in the northern regions than in other regions. The disease burden of PD in China has consistently risen over the past three decades, particularly among elderly men. The increasing causative factors and population aging highlight the need for enhancing public health intervention and resource allocation, especially in etiological research, early diagnosis, preventive strategies, and region-specific management for PD. Ministry of Science and Technology of the People's Republic of China (2022YFC2304900, 2022YFC2505100); National Key R&D Program of China (2022YFC2505100, 2022YFC2505103, 2018YFC1315300); Outstanding Young Scholars Support Program (grant number: 3111500001); Epidemiology modeling and risk assessment (grant number: 20200344), and Xi'an Jiaotong University Young Scholar Support Grant (grant number: YX6J004).

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

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). 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). 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%. 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.

Risk of developing Parkinson's disease and early stage diagnosis through the primary healthcare system

Risk of developing Parkinson's disease and early stage diagnosis through the primary healthcare system

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Towards Early Intervention: Detecting Parkinson's Disease through Voice Analysis with Machine Learning

Introduction/ Background This study aims to utilize machine learning algorithms for early detection of Parkinson's Disease (PD) through voice recording analysis. Employing advanced machine learning techniques and a comprehensive dataset of voice samples, the objective is to develop a non-invasive, accurate, and reliable method for PD diagnosis, contributing to early intervention and management of the disease. Parkinson's Disease (PD) is a prevalent neurodegenerative disorder impacting millions globally. Early and accurate diagnosis is crucial for effective management and treatment. This study leverages Machine Learning (ML) algorithms to analyze voice recordings, aiming to improve PD detection. Materials and Methods We utilized a dataset of 195 voice samples with 23 attributes, applying machine learning algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). The dataset was preprocessed, balanced, and evaluated using various performance metrics. Results The K-Nearest Neighbors (KNN) model demonstrated superior performance, achieving high precision (0.96-1.00), recall (0.97-1.00), and F1-scores (0.98-0.99) for both PD and non-PD classes, demonstrating an overall accuracy of 0.98 across 59 samples. This showcases its effectiveness in PD detection via voice analysis. Discussion This research underscores the potential of ML in revolutionizing PD detection through non-invasive methods. By comparing various algorithms, the study not only identifies the most effective model but also contributes to the broader understanding of applying ML techniques in healthcare. Conclusion The study's findings advocate for the KNN model as a promising tool for early and accurate PD diagnosis through voice analysis. The success of this model opens avenues for future research, including the exploration of more advanced algorithms and the integration of these models into practical diagnostic applications.

Novel Variants Linked to the Prodromal Stage of Parkinson's Disease (PD) Patients.

The symptoms of most neurodegenerative diseases, including Parkinson's disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Data were obtained from the Parkinson's Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Genetic biomarkers are crucial for the early detection of Parkinson's disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.

Diagnosing Parkinson’s disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography–mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

Leveraging ANFIS with Adam and PSO optimizers for Parkinson's disease

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Improving Medication Management for Inpatients with a Secondary Diagnosis of Parkinson Disease.

Patients who have Parkinson disease require individualized medication regimens to optimize care. A review of the medication management of patients admitted to a tertiary care hospital with a secondary diagnosis of Parkinson disease found significant departures from the patients' home regimen. Medication regimens are often altered by health care teams unfamiliar with Parkinson disease-specific care in order to conform to standard hospital medication orders and administration times, potentially resulting in increased patient falls, delirium, and mortality.A nurse-led multidisciplinary team consisting of pharmacy, nursing, informatics, neurology, and quality personnel implemented a quality improvement (QI) project between July 2020 and July 2022 to identify patients with Parkinson disease, including those with a secondary diagnosis and those undergoing deep brain stimulation, and customize medication management in order to reduce length of stay, mortality, falls, falls with harm, and 30-day readmissions. The QI project team also evaluated patient satisfaction with medication management.Among patients with a secondary diagnosis of Parkinson disease, the proportion who had medication histories conducted by a pharmacy staff member increased from a baseline of 53% to more than 75% per month. For all patients with Parkinson disease, those whose medication history was taken by a pharmacy staff member had orders matching their home regimen 89% of the time, whereas those who did not had orders matching the home regimen only 40% of the time. Among patients with a secondary diagnosis of Parkinson disease, the length-of-stay index decreased from a baseline of 1 to 0.94 and observed-to-expected mortality decreased from 1.03 to 0.78. The proportion of patients experiencing a fall decreased from an average of 5% to 4.08% per quarter, while the proportion of patients experiencing a fall with harm decreased from an average of 1% to 0.75% per quarter. The rate of 30-day readmissions decreased from 10.81% to 4.53% per quarter. Patient satisfaction scores were 1.95 points higher for patients who had medication histories taken by pharmacy than for those who did not (5 versus 3.05).

Artificial Intelligence Diagnosis of Parkinson's Disease From MRI Scans.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia, affecting approximately 6.1 million people worldwide, according to estimates from the Parkinson's Foundation. Early and accurate diagnosis of PD is crucial for effective management and treatment. In this study, we aimed to develop an artificial intelligence (AI) model capable of distinguishing between magnetic resonance imaging (MRI) scans of individuals with PD and those without PD. A total of 442 MRI scans were utilized for training the AI model, comprising 221 scans of individuals diagnosed with PD and 221 scans of healthy controls. The dataset, obtained from a publicly available image dataset on Kaggle.com, was randomly split into three sets: training, validation, and testing, with 80%, 10%, and 10% of the data allocated to each set, respectively. Leveraging Google's Collaboration platform for model training, the AI model achieved exceptional performance, with accuracy, precision, recall (sensitivity), specificity, and F1-score all measuring at high levels. Additionally, the area under the receiver operating characteristic curve (AUC) for the model was found to be 1, indicating strong discrimination between PD and non-PD cases. This study presents a novel AI model capable of accurately identifying PD from MRI scans with high precision and reliability, offering promise for enhancing early diagnosis and personalized treatment strategies for individuals affected by PD.

Detecting Misfolded α-Synuclein in Blood Years before the Diagnosis of Parkinson's Disease.

Identifying individuals with Parkinson's disease (PD) already in the prodromal phase of the disease has become a priority objective for opening a window for early disease-modifying therapies. The aim was to evaluate a blood-based α-synuclein seed amplification assay (α-syn SAA) as a novel biomarker for diagnosing PD in the prodromal phase. In the TREND study (University of Tuebingen) biennial blood samples of n = 1201 individuals with/without increased risk for PD were taken prospectively over 4 to 10 years. We retrospectively analyzed blood samples of 12 participants later diagnosed with PD during the study to detect and amplify pathological α-syn conformers derived from neuronal extracellular vesicles using (1) immunoblot analyses with an antibody against these conformers and (2) an α-syn-SAA. Additionally, blood samples of n = 13 healthy individuals from the TREND cohort and n = 20 individuals with isolated rapid eye movement sleep behavior disorder (iRBD) from the University Hospital Cologne were analyzed. All individuals with PD showed positive immunoblots and a positive α-syn SAA at the time of diagnosis. Moreover, all PD patients showed a positive α-syn SAA 1 to 10 years before clinical diagnosis. In the iRBD cohort, 30% showed a positive α-syn SAA. All healthy controls had a negative SAA. We here demonstrate the possibility to detect and amplify pathological α-syn conformers in peripheral blood up to 10 years before the clinical diagnosis of PD in individuals with and without iRBD. The findings of this study indicate that this blood-based α-syn SAA assay has the potential to serve as a diagnostic biomarker for prodromal PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Reduced Prevalence of Parkinson's Disease in Patients Prescribed Calcineurin Inhibitors.

Preclinical evidence suggests calcineurin inhibitors (CNIs) combat α-synuclein-induced neuronal dysfunction and motor impairments. However, whether CNIs prevent or treat Parkinson's disease (PD) in humans has never been investigated. We seek to ascertain if prescription of CNIs is linked to a decreased prevalence of PD in a varied patient population and to glimpse into the mechanism(s) and target site through which CNIs might decrease PD prevalence. We analyzed electronic health records (EHRs) from patients prescribed the brain penetrant CNI tacrolimus (TAC), the peripherally restricted CNI cyclosporine (CySp), or the non-CNI sirolimus (SIR). For comparison, EHRs from a diverse population from the same network served as a general population-like control. After propensity-score matching, prevalence, odds, and hazards of PD diagnoses among these cohorts were compared. Patients prescribed CNIs have decreased odds of PD diagnosis compared to the general population-like control, while patients prescribed SIR do not. Notably, patients prescribed TAC have a decreased prevalence of PD compared to patients prescribed SIR or CySp. Our results suggest CNIs, especially those acting within the brain, may prevent PD. The reduced prevalence of PD in patients prescribed TAC, compared to patients prescribed SIR, suggests that mechanisms of calcineurin inhibition- other than immunosuppression, which is common to both drugs- are driving the reduction. Therefore, CNIs may provide a promising therapeutic approach for PD.