Parkinsonian Symptoms Research Articles

Principal Variables Analysis for Non-Gaussian Data

Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA are based on the Pearson correlation matrix, which is not well-suited to describing the relationships between non-Gaussian variables. We propose a generalized approach to PVA enabling the use of different types of correlation, and we explore using Spearman, Gaussian copula, and polychoric correlations as alternatives to Pearson correlation. We compare performance in simulation studies varying the form of the true multivariate distribution over a range of possibilities. Our results show that on continuous non-Gaussian data, using generalized PVA with Gaussian copula or Spearman correlations provides a major improvement in performance compared to Pearson. On ordinal data, generalized PVA with polychoric correlations outperforms the rest by a wide margin. We apply generalized PVA to a dataset of 102 clinical variables measured on individuals with X-linked dystonia parkinsonism (XDP), a neurodegenerative disorder involving symptoms of both dystonia and parkinsonism. We find that using different types of correlation yields substantively different sets of principal variables; for example, parkinsonism-related metrics appear more explanatory than dystonia-related metrics on the observed data. Supplementary materials for this article are available online.

Khảo sát tình hình điều trị thuốc cho người bệnh parkinson tại Bệnh viện Đại học Y dược TP. Hồ Chí Minh

Background: Parkinson's disease is one of the most common progressive neurodegenerative disorders. There's currently no cure for Parkinson's disease. Current therapies only help patients relieve the symptoms and improve their quality of life. Objectives: To determine the rate of drugs prescribed to treat Parkinson's disease motor symptoms, the rate of the types of drug combinations, and the frequency of drug-induced motor complications in Parkinson's disease patients. Methods: This cross-sectional study was conducted at the University Medical Center HCMC, Ho Chi Minh City. The patient was diagnosed with Parkinson's disease based on the criteria of the International Parkinson's Disease and Movement Disorder Society (IPMDS). Patients' demographic data, information about Parkinson's disease, and medications used to treat Parkinson's disease were collected. Rating scales include MDS-UPRRS (Movement Disorder Society-Unified Parkinson's Disease Rating Scale), and Hoehn & Yahr. Results: Among 218 Parkinson's patients, 33 (15.1%) patients received levodopa monotherapy, 126 (57.8%) patients received combination therapy of levodopa and dopamine agonists, 07 (3.2%) ) patients received combination therapy of levodopa and trihexyphenidyl, 1 (0.5%) patient received combination therapy of dopamine agonist and trihexyphenidyl, and 51 (23.4%) patients received combination therapy of both levodopa, dopamine agonist and trihexyphenidyl. A total of 217 patients were prescribed levodopa at a median dose of 375 mg. The median daily levodopa equivalent dose was 500 mg. A total of 177 patients (81.2%) used the dopamine agonist (pramipexole) with a median dose of 0.75 mg. 27.9 % of patients used an anticholinergic drug (trihexyphenidyl) with a median dose of 2 mg. The proportion of Parkinson's disease patients with motor complications was 32.6%. Conclusion: Levodopa was the medication most commonly prescribed to treat motor symptoms in patients with Parkinson's disease. The combination of levodopa and dopamine agonist was most popularly used. Drug-induced motor complications were common in Parkinson’s disease.

Exploring Fecal Microbiota Transplantation for Modulating Inflammation in Parkinson's Disease: A Review of Inflammatory Markers and Potential Effects.

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by numerous motor and non-motor symptoms. Recent data highlight a potential interplay between the gut microbiota and the pathophysiology of PD. The degeneration of dopaminergic neurons in PD leads to motor symptoms (tremor, rigidity, and bradykinesia), with antecedent gastrointestinal manifestations, most notably constipation. Consequently, the gut emerges as a plausible modulator in the neurodegenerative progression of PD. Key molecular changes in PD are discussed in the context of the gut-brain axis. Evidence suggests that the alterations in the gut microbiota composition may contribute to gastroenteric inflammation and influence PD symptoms. Disturbances in the levels of inflammatory markers, including tumor necrosis factor-α (TNF α), interleukin -1β (IL-1β), and interleukin-6 (IL-6), have been observed in PD patients. These implicate the involvement of systemic inflammation in disease pathology. Fecal microbiota transplantation emerges as a potential therapeutic strategy for PD. It may mitigate inflammation by restoring gut homeostasis. Preclinical studies in animal models and initial clinical trials have shown promising results. Overall, understanding the interplay between inflammation, the gut microbiota, and PD pathology provides valuable insights into potential therapeutic interventions. This review presents recent data about the bidirectional communication between the gut microbiome and the brain in PD, specifically focusing on the involvement of inflammatory biomarkers.

Substantia Nigra Pathology, Contact Sports Play, and Parkinsonism in Chronic Traumatic Encephalopathy

Parkinsonism is associated with traumatic brain injury and chronic traumatic encephalopathy (CTE), a neurodegenerative disease associated with repetitive head impact (RHI) exposure, but the neuropathologic substrates that underlie parkinsonism in individuals with CTE are yet to be defined. To evaluate the frequency of parkinsonism in individuals with CTE and the association of RHI and neuropathologic substrates with parkinsonism in these individuals. This cross-sectional study included brain donors with neuropathologically diagnosed CTE without other significant neurodegenerative disease and with information on parkinsonism from the Understanding Neurologic Injury and Traumatic Encephalopathy brain bank between July 2015 and May 2022. Years of contact sports participation as a proxy for RHI. The main outcomes were frequency of parkinsonism in individuals with CTE and associations between (1) RHI with substantia nigra (SN) Lewy bodies (LBs) and neurofibrillary tangles (NFTs); (2) LBs, NFTs, and arteriolosclerosis with SN neuronal loss; and (3) SN neuronal loss, LBs, NFTs, and arteriolosclerosis with parkinsonism, tested by age-adjusted logistic regressions. Of 481 male brain donors with neuropathologically diagnosed CTE, parkinsonism occurred frequently in individuals with CTE (119 [24.7%]; 362 [75.3%] did not have parkinsonism). Participants with parkinsonism had a higher mean (SD) age at death (71.5 [13.0] years) than participants without parkinsonism (54.1 [19.3] years) (P < .001) and higher rates of dementia (104 [87.4%] vs 105 [29.0%]), visual hallucinations (45 [37.8%] vs 51 [14.1%]), and probable rapid eye movement sleep behavior disorder (52 [43.7%] vs 58 [16.0%]) (P < .001 for all). Participants with parkinsonism had a more severe CTE stage (eg, stage IV: 35 [29.4%] vs 39 [10.8%]) and nigral pathology than those without parkinsonism (NFTs: 50 of 117 [42.7%] vs 103 of 344 [29.9%]; P = .01; neuronal loss: 61 of 117 [52.1%] vs 59 of 344 [17.1%]; P < .001; and LBs: 28 of 116 [24.1%] vs 20 of 342 [5.8%]; P < .001). Years of contact sports participation were associated with SN NFTs (adjusted odds ratio [AOR], 1.04; 95% CI, 1.00-1.07; P = .03) and neuronal loss (AOR, 1.05; 95% CI, 1.01-1.08; P = .02). Nigral neuronal loss (AOR, 2.61; 95% CI, 1.52-4.47; P < .001) and LBs (AOR, 2.29; 95% CI, 1.15-4.57; P = .02) were associated with parkinsonism. However, SN neuronal loss was associated with SN LBs (AOR, 4.48; 95% CI, 2.25-8.92; P < .001), SN NFTs (AOR, 2.51; 95% CI, 1.52-4.15; P < .001), and arteriolosclerosis (AOR, 2.27; 95% CI, 1.33-3.85; P = .002). In American football players, regression analysis demonstrated that SN NFTs and neuronal loss mediated the association between years of play and parkinsonism in the context of CTE (β, 0.012; 95% CI, 0.001-0.038). In this cross-sectional study of contact sports athletes with CTE, years of contact sports participation were associated with SN tau pathology and neuronal loss, and these pathologies were associated with parkinsonism. Repetitive head impacts may incite neuropathologic processes that lead to symptoms of parkinsonism in individuals with CTE.

An overview of the role of monoamine oxidase-B in Parkinson’s disease: implications for neurodegeneration and therapy

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by both non-motor and motor symptoms, due to the loss of dopamine-producing neurons in the brain. Monoamine oxidase-B (MAO-B) inhibitors are essential in the treatment of PD, as they increase dopamine levels and could potentially slow down the progression of the disease. MAO-B inhibitors block the ability of the enzyme to degrade dopamine in the brain. MAO-B inhibitors work by inhibiting this enzyme, which raises dopamine levels and helps reduce motor symptoms, such as akinesia and stiffness in the muscles. In addition to their impact on dopamine levels, MAO-B inhibitors may possess neuroprotective properties. Research indicates that these inhibitors can shield neurons from the harmful byproducts of dopamine breakdown, such as dihydroxy acetaldehyde and hydrogen peroxide. This neuroprotective effect could potentially slow the progression of PD and protect against neuronal damage. MAO-B inhibitors are effective in treating both advanced and early stages of PD. They are recommended as initial treatments for individuals with early PD and can also be used as supplementary therapy in advanced PD to assist in managing motor complications. Additionally, MAO-B inhibitors have shown promise for the treatment of non-motor symptoms of PD, such as fatigue and sleep disturbances. MAO-B inhibitors are an important class of drugs for the treatment of PD, offering both symptomatic relief and potential disease-modifying effects. The goal of ongoing research and development of MAO-B inhibitors is to enhance their safety and selectivity profiles, which could lead to improved treatment approaches for PD and other neurodegenerative disorders.

Symptomatic vs. Disease-modifying treatments in neurological diseases: Where next?

Symptomatic vs. Disease-modifying treatments in neurological diseases: Where next? An optimal drug for chronic neurological disease would slow down disease progression in the long term, with short-term symptomatic benefits. This would shorten clinical development timelines and reduce the cost and risk level for drug developers. Henri Huttunen Chief Scientific Officer (CSO) at Herantis Pharma Plc explains. In Parkinson’s disease (PD), the development of motor symptoms is associated with the degeneration of dopamine-producing nerve cells in the midbrain. For decades, motor symptoms of PD have been treated with levodopa, a precursor of dopamine, which helps to replenish dopamine levels in brain areas coordinating movement. Levodopa is an example of a symptomatic treatment as it improves symptoms but does not interfere with the underlying causes or progression of the disease.

Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors.

The Unified Parkinson's Disease Rating Scale (UPDRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity. The rating is crucial for disease progression monitoring and treatment adjustment. This study aims to advance the capabilities of PD management by developing an innovative framework that integrates deep learning with wearable sensor technology to enhance the precision of UPDRS assessments. We introduce a series of deep learning models to estimate UPDRS Part III scores, utilizing motion data from wearable sensors. Our approach leverages a novel Multi-shared-task Self-supervised Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework that processes raw gyroscope signals and their spectrogram representations. This technique aims to refine the estimation accuracy of PD severity during naturalistic human activities. Utilizing 526 min of data from 24 PD patients engaged in everyday activities, our methodology demonstrates a strong correlation of 0.89 between estimated and clinically assessed UPDRS-III scores. This model outperforms the benchmark set by single and multichannel CNN, LSTM, and CNN-LSTM models and establishes a new standard in UPDRS-III score estimation for free-body movements compared to recent state-of-the-art methods. These results signify a substantial step forward in bioengineering applications for PD monitoring, providing a robust framework for reliable and continuous assessment of PD symptoms in daily living settings.

Zebrafish trpm7 mutants show reduced motility in free movement.

Parkinson's disease is a neurological disorder characterized by reduced motility, depression and dementia. Guamanian parkinsonism dementia with amyotrophic sclerosis is a local case of Parkinson's disease reported in the Western Pacific Islands of Guam and Rota as well as in the Kii Peninsula of Japan. A previous genetic study has suggested that Guamanian parkinsonism is attributable to a variant of the TRPM7 gene, which encodes for melastatin-related transient receptor potential (TRP) ion channels. But the link between parkinsonism and the TRPM7 gene remains elusive. Previous studies have addressed that trpm7-deficient zebrafish embryos showed defects in pigmentation and touch-evoked motor response. In this study, we identified a new viable allele of trpm7 mutant causing an I756N amino acid substitution in the first transmembrane domain. Behavioral analyses revealed that trpm7 mutants showed compromised motility with their movement distance shorter than wild-type larvae. The velocity of the movement was significantly reduced in trpm7 mutants than in wild-type larvae. Along with a previous finding of reduced dopaminergic neurons in zebrafish trpm7 mutants, reduced motility of trpm7 mutants can suggest another similarity between trpm7 phenotypes and Parkinson's disease symptoms.

Reinforcement Learning-Based Adaptive Classification for Medication State Monitoring in Parkinson's Disease.

Parkinson's Disease (PD) patients frequently transition between the 'ON' state, where medication is effective, and the 'OFF' state, affecting their quality of life. Monitoring these transitions is vital for personalized therapy. We introduced a framework based on Reinforcement Learning (RL) to detect transitions between medication states by learning from continuous movement data. Unlike traditional approaches that typically identify each state based on static data patterns, our approach focuses on understanding the dynamic patterns of change throughout the transitions, providing a more generalizable medication state monitoring method. We integrated a deep Long Short-Term Memory (LSTM) neural network and three one-class unsupervised classifiers to implement an RL-based adaptive classifier. We tested on two PD datasets: Dataset PD1 with 12 subjects (14-minute average recording) and Dataset PD2 with seven subjects (120-minute average recording). Data from wrist and ankle wearables captured transitions during 2 to 4-hour daily activities. The algorithm demonstrated its effectiveness in detecting medication states, achieving an average weighted F1-score of 82.94% when trained and tested on Dataset PD1. It performed well when trained on Dataset PD1 and tested on Dataset PD2, with a weighted F1-score of 76.67%. It surpassed other models, was resilient to severe PD symptoms, and performed well with imbalanced data. Notably, prior work has not addressed the generalizability from one dataset to another, essential for real-world applications with varied sensors. Our innovative framework revolutionizes PD monitoring, setting the stage for advanced therapeutic methods and greatly enhancing the life quality of PD patients.

Gene expression asymmetry in Parkinson's Disease; variation of CCT and BEX gene expression levels are correlated with hemisphere specific severity.

Parkinson's Disease (PD) develops unilaterally, which may be related to brain hemispheric differences in gene expression. Here we measured bulk RNA-seq levels in neuronal nuclei obtained from prefrontal cortex postmortem brain samples from males and females with PD and from healthy controls. Left and right hemispheres from each brain were related the side of symptom onset and compared. We employed two a priori approaches; first we identified genes differentially expressed between PD and controls and between left vs right PD brain hemispheres. Second, we examined the presence of, and correlates to, variable asymmetry seen in candidate PD differentially expressed genes. We found large variation among individuals with PD, and PD stratification by gene expression similarity was required for patterns of genetic asymmetry to emerge. For a subset of PD brains, hemispherical variation of CCT and BEX gene levels correlated with the side of PD symptom onset.

Efficacy and Safety of Zonisamide as Adjunctive Therapy in Motor Symptoms of Advanced Parkinson's Disease (PD) in Egyptian Population

Abstract Background Management of motor complications -especially wearing off- in advanced PD is very challenging. Zonisamide (ZNS) is an antiepileptic drug that showed beneficial effects in motor symptoms of PD in previous studies. Methods To determine the efficacy and safety of ZNS in motor symptoms of advanced PD, we conducted a one month double blinded randomized placebo-controlled crossover study in Egypt. PD patients with Hoehn &amp;amp; Yahr stage ≥ 2 (onstate) and at least 2 hours off time daily were randomized to one of three groups: ZNS 25mg, ZNS 50 mg and placebo. Primary outcome measure was change in off time based on part IV MDS-UPDRS (Movement disorders society-Unified Parkinson’s disease rating scale). Secondary outcome measures were: change in on time based on part IV MDS- UPDRS, change in total score MDS-UPDRS, score of part III MDS-UPDRS. Results Of 69 patients enrolled in the study and underwent randomization to the 3 groups, 49 patients completed one month follow-up. Only 26 patients underwent crossover and 20 patients completed one month follow-up. So, the total number of patients analyzed at 1 month was: 69 patients (25 patients ZNS 25 mg group, 18 patients ZNS 50 mg group and 26 patients placebo group). Both ZNS 25 and 50 mg groups showed significant improvement of total MDS-UPDRS score, Tremors ON score and part 4. Except for sedation, there was no significant adverse effects in both ZNS 25,50 mg groups as compared to placebo. Conclusion ZNS (25,50 mg) is safe and effective in improving motor symptoms of advanced PD as adjunctive therapy.

Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease.

To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD). This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups. A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002). Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD. The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD. Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.

547 Creation of a Machine Learning Model to Predict Cognitive Outcomes Post Deep Brain Stimulation Surgery for Parkinson’s Disease

Abstract Background Parkinson’s disease (PD) is the second most common neurodegenerative disorder and continues to affect an increasingly large portion of the world’s population. Limitations of the medical treatment of PD with levodopa have led to deep brain stimulation (DBS) therapy being used to alleviate symptoms in advanced PD patients. Despite its efficacy with motor symptoms of PD, DBS has been noted to lead to worsening of cognitive outcomes in a percentage of cases. There is currently no predictive model for individual patient cognitive outcomes post DBS. Aim The aim of this study is the creation of a machine learning (ML) model which can help facilitate decision making for clinicians regarding offering DBS to patients. ML has the potential to analyse multiple variables and produce a predictive model for neurocognitive outcomes. The volume of variables analysed by ML is beyond the remit of calculations that can be performed by individual medical professionals for each patient. Method This study used mild cognitive impairment (MCI) as the outcome, at six months post DBS surgery to measure neurocognitive impact. Clinical and radiomics variables were used to train the models. Results Two predictive models were successfully generated – random forest and logistic regression, which showed an accuracy of 0.87 and 0.74 respectively. Conclusions The study concluded that it is possible to use ML to generate a prediction model for outcomes post DBS. The model can help optimise the decision-making process behind offering DBS surgery to patients and lend insight into the possible causes of poor outcomes.

Reliability Test of Mobile Embedded Accelerometers and Gyroscopes with the Goal of Measuring Postural Stability for People with Parkinson's Disease

Abstract Parkinson's Disease (PD) is the second most common neurodegenerative disease in the United States. The cardinal symptoms of PD are tremor, rigidity, slowed movement, and impaired balance. These symptoms often interfere with the daily activities of people with Parkinson's (PwPD) and negatively affect quality of life (QoL). Therefore, monitoring PD symptoms is essential for clinical evaluations and adjusting medication to help maintain QoL for PwPD. We are developing a mobile app to conduct at-home PD symptom monitoring to provide more timely, frequent, and accurate measurements of PD symptoms. While the tremor and finger-tapping results collected in the mobile app have been discussed in previous publications, this paper focuses on the design and evaluation of postural stability tests in the app and validating the reliability of the embedded accelerometers and gyroscopes in smartphones. During the test, a shaker was employed to provide vibration in amplitude and frequency ranges similar to human postural stability signals, and both the accelerometer and gyroscope measurements were evaluated. We used signal processing algorithms to extract postural stability factors, such as the root mean square (RMS) value, the derivative of acceleration, frequency factors, etc. for the accelerations, and the ranges and RMS for the angular velocity. Our findings show that smartphone devices have good consistency over multiple trials and between devices, and motion patterns achieved from multiple data points are reliable for postural stability analysis.

Tear α-synuclein as a biomarker for Parkinson's disease: A systematic review and meta-analysis.

Parkinson's disease symptoms mostly manifest after significant and irreversible neuropathology. Hence, there is a need to identify biomarkers that can provide indications of disease before significant neuronal degeneration occurs. To estimate the difference in the concentration of α-synuclein protein in tears between individuals with Parkinson's disease and healthy controls. PubMed, Scopus, and Web of Science. The last database search was on December 20, 2023. Primary prospective studies in humans measuring the level of α-synuclein in tears and clinical outcomes reported using mean or median. Individuals with Parkinson's disease and healthy controls. The risk of bias was assessed using the Newcastle-Ottawa Scale. The I2 statistic was used to estimate heterogeneity. The outcome measure was the difference in tear total and oligomeric α-synuclein. Mean difference (MD) was used to assess the outcome. The certainty of evidence was rated following the Grading of Recommendations Assessment and Development and Evaluation (GRADE) system. Three hundred twenty-seven Parkinson's disease and 312 healthy control subjects from five studies and 177 Parkinson's disease and 166 healthy control subjects from two studies were included in total α-synuclein levels and oligomeric α-synuclein levels analysis, respectively. Total α-synuclein level was not different between Parkinson's disease and healthy controls (MD = 0.02 ng/mL [95% confidence interval {CI}: 0.00 to 0.05 ng/mL; I2 = 90%; Z = 1.79; p=0.07; number of studies = 5; GRADE rating = very low]). Stratifying the data based on disease duration, total α-synuclein was higher in subjects with Parkinson's disease duration ≥7 years compared with healthy controls (MD = 0.04 ng/mL [95% CI: 0.03 to 0.05 ng/mL; I2 = 0%; Z = 8.24, p<0.00001; number of studies = 2; GRADE rating = low]) but not different between the two groups (MD = -0.12 ng/mL (95% CI: -0.38 to 0.15 ng/mL; I2 = 93%; Z = 0.84, p=0.40; number of studies = 3; GRADE rating = very low]). Oligomeric α-synuclein level was higher in Parkinson's disease compared with controls (MD = 6.50 ng/mL [95% CI: 2.79 to 10.20 ng/mL; I2 = 94%; Z = 3.44; p=0.0006; number of studies = 2; GRADE rating = very low]). High heterogeneity between studies. Potential sources of heterogeneity could not be explored due to the limited number of studies. Tear α-synuclein has the potential to be a noninvasive biomarker for Parkinson's disease. Studies are, however, needed to increase certainty in the biomarker and establish how the protein's changes in tears correlate with Parkinson's disease progression and severity.

The protective effects of repetitive transcranial magnetic stimulation with different high frequencies on motor functions in MPTP/probenecid induced Parkinsonism mouse models.

High-frequency repeated transcranial magnetic stimulation (rTMS) stimulating the primary motor cortex (M1) is an alternative, adjunctive therapy for improving the motor symptoms of Parkinson's disease (PD). However, whether the high frequency of rTMS positively correlates to the improvement of motor symptoms of PD is still undecided. By controlling for other parameters, a disease animal model may be useful to compare the neuroprotective effects of different high frequencies of rTMS. The current exploratory study was designed to compare the protective effects of four common high frequencies of rTMS (5, 10, 15, and 20Hz) and iTBS (a special form of high-frequency rTMS) and explore the optimal high-frequency rTMS on an animal PD model. Following high frequencies of rTMS application (twice a week for 5weeks) in a MPTP/probenecid-induced chronic PD model, the effects of the five protocols on motor behavior as well as dopaminergic neuron degeneration levels were identified. The underlying molecular mechanisms were further explored. We found that all the high frequencies of rTMS had protective effects on the motor functions of PD models to varying degrees. Among them, the 10, 15, and 20Hz rTMS interventions induced comparable preservation of motor function through the protection of nigrostriatal dopamine neurons. The enhancement of brain-derived neurotrophic factor (BDNF), dopamine transporter (DAT), and vesicular monoamine transporter 2 (VMAT-2)and the suppression of TNF-α and IL-1β in the nigrostriatum were involved in the process. The efficacy of iTBS was inferior to that of the above three protocols. The effect of 5Hz rTMS protocol was weakest. Combined with the results of the present study and the possible side effects induced by rTMS, we concluded that 10Hz might be the optimal stimulation frequency for preserving the motor functions of PD models using rTMS treatment.

Time association study on a sub-acute mouse model of Parkinson’s disease

Parkinson's disease (PD) is a severe neurodegenerative disease that disturbs human health. In the laboratory researches about PD, the mice model induced by intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) was widely used. However, there has been controversy about the model effectiveness to simulate PD symptoms and pathology, and the time-varying development of behavioral and pathological characteristic after MPTP treatment remains unclear. In order to solve these problems, we designed a series of experiments to evaluate this PD model at different time points. We constructed the subacute PD mouse model by intraperitoneal injection of MPTP for 5 consecutive days. The rotarod test, open field test and the immunohistochemical staining of tyrosine hydroxylase were conducted at −5, 1, 5, 7, 14, 21 and 28 days after the last injection of MPTP. The results showed that 5 days after the last MPTP administration, typical motor disorders with significant balance function damage in rotarod test began to appear and remained stable throughout the entire experiment. Simultaneously, we also observed the loss of tyrosine hydroxylase (TH) positive cells in the substantia nigra compacta and reduction of TH content in the striatum but this pathological change in the substantia nigra compacta reversed 21 days after injection. Besides, the spontaneous movement of mice in open field test remained unchanged by MPTP. This research indicated the time-dependence of MPTP neurotoxicity that impair the motor function and histological features and confirmed the symptom occurrence time after MPTP injection, which provides a reference for the future research about MPTP-induced PD.

PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment

This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.

Pharmacological Assessment of Aqueous Ethanolic Extract of Thalictrum Foetidum Against Haloperidol-Induced Parkinson’s Like Symptoms in Animal Model: A Dose-Dependent Study With Mechanistic Approach

Introduction: Parkinson’s disease (PD) is characterized by dopamine deficiency in the corpus striatum due to the degeneration of dopaminergic neurons in the substantia nigra. Symptoms include bradykinesia, resting tremors, unstable posture, muscular rigidity, and a shuffled gait. Thalictrum foetidum is traditionally used for neurodegenerative disorders. Objectives: This study aimed to explore the therapeutic potential of aqueous ethanolic extract of Thalictrum foetidum (AETF) against Parkinson-like symptoms and to investigate its underlying mechanism. Methodology: Thirty-six albino mice were randomly divided into 6 groups (n = 6): normal control, disease control, standard treatment (levodopa/carbidopa, 100/25 mg/kg), and 3 treatment groups (AETF at 200, 400, and 600 mg/kg). One hour before treatment, haloperidol (1 mg/kg, i. p.) was administered to induce Parkinson’s disease in all groups except the normal control group. Results: Behavioral analysis showed significant improvement ( P &lt; .001) in motor function, muscular coordination, and reduced muscular rigidity and tremors. AETF also reduced oxidative stress. Histological examination of the brain showed reduced Lewy bodies, neurofibrillary tangles, and plaque formation. Conclusion: AETF alleviated PD symptoms by reducing neurodegeneration, modulating oxidative stress, and inhibiting the expression of nuclear factor-κB (NF-κB) and associated inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6).

Role of proinflammatory biomarkers in Parkinson's disease with non-motor symptoms: A systematic review and meta-analysis

Background and objectives. Neuroinflammation, characterized by microglial activation and elevated proinflammatory cytokines, plays a crucial role in Parkinson's disease (PD) pathogenesis and is linked to neurodegeneration and non-motor symptoms. This review aims to identify specific biomarkers associated with these non-motor symptoms, highlighting their roles and implications in PD management and therapy. Materials and methods. This systematic review followed PRISMA guidelines to assess proinflammatory biomarkers in PD patients with non-motor symptoms. We included 10 studies identified through comprehensive searches in PubMed/Medline, Wiley Online Library, Science Direct, Google Scholar, and Neurona. Bias was assessed using the ROBINS-I tool, and study quality was evaluated with GradePro. Meta-analysis was conducted using the Ordmeta test. Results. Proinflammatory biomarkers such as IL-6, TNF-α, CRP, and hs-CRP are notably associated with non-motor symptoms in PD. TNF-α shows a moderate correlation with depressive disorders in 194 patients (p=0.007±0.011), while IL-17A correlates similarly with anxiety, and IL-2 with mood disorders. Furthermore, TNF-α has a low yet meaningful correlation with sleep disorders. Ordmeta results reveal significant elevations of TNF-α in 52 PD patients with cognitive impairment (p=0.0003) and of IL-6 in 361 PD patients (p=0.023±0.008), underlining their roles in PD pathology. Conclusions. This study confirms that proinflammatory biomarkers such as CRP, hs-CRP, IL-6, IL-2, IL-17A, and TNF-α are significantly linked to non-motor symptoms in PD. TNF-α is notably elevated in patients with cognitive impairments and depressive disorders, while IL-6 is also significantly increased in those with cognitive issues.