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Background Parkinson's disease (PD) is a common movement disorder for which diagnosis mainly depends on the medical history and clinical symptoms. Exosomes are now considered an additional mechanism for intercellular communication, allowing cells to exchange proteins, lipids, and genetic material. Long noncoding (lnc) RNA in exosomes plays a critical role in many diseases, including neurodegenerative disease. Aim To study expression differences for lncRNAs in peripheral blood exosomes of PD patients compared with healthy individuals and to look for lncRNAs that might be related to the pathogenesis of PD. Materials and Methods We recruited PD patients along with age‐ and sex‐matched healthy individuals as healthy controls and evaluated levels of lncRNAs extracted from exosomes in plasma samples via next‐generation sequencing and real‐time quantitative PCR. Correlation analysis was conducted for the clinical characteristics of PD patients and the expression of selected lncRNAs. Results We found 15 upregulated and 24 downregulated exosomal lncRNAs in the PD group. According to bioinformatics analyses, we chose lnc‐MKRN2‐42:1 for further study. Interestingly, lnc‐MKRN2‐42:1 was positively correlated with the MDS‐UPDRS III score for PD patients. Conclusion Our study suggested that lnc‐MKRN2‐42:1 may be involved in the occurrence and development of PD.

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

•Helicobacter pylori may be associated with Parkinson’s disease.•Higher prevalence of H. pylori infection in PD patients than in healthy controls.•Significant association between H. pylori infection and mean UPDRS score.•H. pylori eradication seems to improve PD patients’ mean UPDRS score. The exact etiology of Parkinson’s disease (PD) remains unclear. Some evidence supports Helicobacter pylori infection as a trigger or driving event, but detection and eradication of H. pylori are not part of PD management. The aims of this case-control study and meta-analysis were to determine (i) the prevalence of H. pylori infection in PD patients, (ii) associations between H. pylori infection and clinical status, and (iii) differences in motor status in PD patients before and after H. pylori eradication. A literature search was performed using the PubMed database. The prevalence of H. pylori infection in PD, its association with the unified Parkinson’s disease rating scale (UPDRS), and the association of H. pylori eradication therapy with the UPDRS-III score were determined by calculating the odds ratios (OR) and the standardized mean differences (SMD) with 95% confidence intervals (CI). Fixed- and random-effects models were applied. Ten studies were included in the first meta-analysis (5043 PD patients, 23,449 HCs); H. pylori infection prevalence was higher in PD patients than in HCs [OR (95% CI): 1.47 (1.27, 1.70), Pz<0.00001]. In seven studies reporting UPDRS scores (150 H. pylori infected, 228 non-infected PD patients), there was a significant association between H. pylori infection and mean UPDRS scores [SMD (95% CI): 0.33 (0.12, 0.54), Pz = 0.003]. Regarding H. pylori eradication, in five studies (90 PD patients), there was a significant reduction in UPDRS-III scores after treatment [SMD (95% CI): 6.83 (2.29, 11.38), Pz = 0.003]. In conclusion, the present meta-analysis revealed a higher prevalence of H. pylori infection in PD patients suggesting that H. pylori may contribute to PD pathophysiology. In addition, the significantly lower UPDRS scores in non-infected PD patients and in patients after H. pylori eradication therapy demonstrate that the infection may deteriorate the clinical severity of the disease.

A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.

Blood neurofilament light chain (NfL) is an easily accessible, highly sensitive and reliable biomarker for neuroaxonal damage. Currently, its role in Parkinson’s disease (PD) remains unclear. Here, we demonstrate that blood NfL can distinguish idiopathic PD from atypical parkinsonian syndromes (APS) with high sensitivity and specificity. In cross-sectional studies, some found significant correlations between blood NfL with motor and cognitive function, whereas others did not. In contrast, prospective studies reported very consistent associations between baseline blood NfL with motor progression and cognitive worsening. Amongst PD subtypes, especially postural instability and gait disorder (PIGD) subtype, symptoms and scores are reliably linked with blood NfL. Different non-motor PD comorbidities have also been associated with high blood NfL levels suggesting that the neuroaxonal damage of the autonomic nervous system as well as serotonergic, cholinergic and noradrenergic neurons is quantifiable. Numerous absolute NfL cutoff levels have been suggested in different cohort studies; however, validation across cohorts remains weak. However, age-adjusted percentiles and intra-individual blood NfL changes might represent more valid and consistent parameters compared with absolute NfL concentrations. In summary, blood NfL has the potential as biomarker in PD patients to be used in clinical practice for prediction of disease severity and especially progression.

A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson’s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales and methods that often lack the ability for continuous, real-time monitoring and can be subject to interpretation bias. Recent advancements in wearable technologies, such as smartphones, smartwatches, and activity trackers (ATs), present a promising alternative for more consistent and objective monitoring. This review aims to evaluate the use of smartphones and smart wrist devices, like smartwatches and activity trackers, in the management of PD, assessing their effectiveness in symptom evaluation and monitoring and physical performance improvement. Studies were identified by searching in PubMed, Scopus, Web of Science, and Cochrane Library. Only 13 studies of 1027 were included in our review. Smartphones, smartwatches, and activity trackers showed a growing potential in the assessment, monitoring, and improvement of motor symptoms in people with PD, compared to clinical scales and research-grade sensors. Their relatively low cost, accessibility, and usability support their integration into real-world clinical practice and exhibit validity to support PD management.

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.

The Movement Disorder Society-UPDRS (MDS-UPDRS) was published in 2008, showing satisfactory clinimetric results and has been proposed as the official benchmark scale for Parkinson’s disease. The present study, based on the official MDS-UPDRS Spanish version, performed the first independent testing of the scale and adds information on its clinimetric properties. The cross-culturally adapted MDS-UPDRS Spanish version showed a comparative fit index ≥0.90 for each part (I–IV) relative to the English-language version and was accepted as the Official MDS-UPDRS Spanish version. Data from this scale, applied with other assessments to Spanish-speaking Parkinson’s disease patients in five countries, were analyzed for an independent and complementary clinimetric evaluation. In total, 435 patients were included. Missing data were negligible and moderate floor effect (30 %) was found for Part IV. Cronbach’s α index ranged between 0.79 and 0.93 and only five items did not reach the 0.30 threshold value of item-total correlation. Test–retest reliability was adequate with only two sub-scores of the item 3.17, Rest tremor amplitude, reaching κ values lower than 0.60. The intraclass correlation coefficient was higher than 0.85 for the total score of each part. Correlation of the MDS-UPDRS parts with other measures for related constructs was high (≥0.60) and the standard error of measurement lower than one-third baseline standard deviation for all subscales. Results confirm those of the original study and add information on scale reliability, construct validity, and precision. The MDS-UPDRS Spanish version shows satisfactory clinimetric characteristics.

Deep brain stimulation (DBS) significantly improves tremor, rigidity, bradykinesia, and dyskinesia for patients with Parkinson's disease (PD), but gait and speech remain inconsistent. These discrepancies underscore the need for a systematic, quantitative synthesis of existing data to clarify the impact of DBS across different motor domains. To systematically evaluate the effects of DBS on motor symptoms in PD by analyzing UPDRS-III scores and conducting subgroup analyses based on stimulation target, stimulation type, and medication status. A literature search was conducted to identify relevant studies on clinical trials and observational studies reporting pre- and post-DBS motor assessments in PubMed, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), Ovid MEDLINE, and Web of Science from inception to 15 December 2024. A total of 35 studies comprising 1,082 PD patients were included. The pooled analysis demonstrated a significant improvement in overall UPDRS-III scores post-DBS (WMD: = -1.09, 95% CI: -1.32 to -0.87,  < 0.05). Subgroup analyses showed consistent improvements across tremor, rigidity, akinesia, bradykinesia, dyskinesia, and axial symptoms, regardless of stimulation target or medication state. UPDRS Part IV scores also significantly improved, reflecting reduced motor complications. However, speech function remained unchanged, and UPDRS Part I scores initially showed no significant improvement, though significance emerged after removing sources of heterogeneity. DBS significantly improves overall motor function, particularly tremor, rigidity, and bradykinesia. However, its effects on gait and speech remain inconsistent, which shows the need for further research to refine patient selection and optimize stimulation parameters. These findings provide valuable insights into the therapeutic impact of DBS in PD management.