Update on the diagnosis and management of Parkinson's disease

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Cellular and Mitochondrial Toxicity of Tolcapone, Entacapone, and New Nitrocatechol Derivatives

Nitrocatechols are the standard pharmacophore to develop potent tight-binding inhibitors of catechol O-methyltransferase (COMT), which can be used as coadjuvant drugs to manage Parkinson's disease. Tolcapone is the most potent drug of this class, but it has raised safety concerns due to its potential to induce liver damage. Tolcapone-induced hepatotoxicity has been attributed to the nitrocatechol moiety; however, other nitrocatechol-based COMT inhibitors, such as entacapone, are safe and do not damage the liver. There is a knowledge gap concerning which mechanisms and chemical properties govern the toxicity of nitrocatechol-based COMT inhibitors. Using a vast array of cell-based assays, we found that tolcapone-induced toxicity is caused by direct interference with mitochondria that does not depend on bioactivation by P450. Our findings also suggest that (a) lipophilicity is a key property in the toxic potential of nitrocatechols; (b) the presence of a carbonyl group directly attached to the nitrocatechol ring seems to increase the reactivity of the molecule, and (c) the presence of cyano moiety in double bond stabilizes the reactivity decreasing the cytotoxicity. Altogether, the fine balance between lipophilicity and the chemical nature of the C1 substituents of the nitrocatechol ring may explain the difference in the toxicological behavior observed between tolcapone and entacapone.

Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

Aqueous extract of Swietenia macrophylla leaf exerts an anti-inflammatory effect in a murine model of Parkinson's disease induced by 6-OHDA

Introduction

Parkinson's disease affects 2% of the population aged over 65 years and is the second most common neurodegenerative disorder in the general population. The appearance of motor symptoms is associated with the degeneration of dopaminergic neurons in the nigrostriatal pathway. Clinically significant nonmotor symptoms are also important for severe disability with disease progression. Pharmacological treatment with levodopa, which involves dopamine restitution, results in a temporary improvement in motor symptoms. Among the mechanisms underlying the pathogenesis of the disease are exacerbated oxidative stress, mitochondrial dysfunction, and neuroinflammation. A phytochemical prospecting study showed that the aqueous extract of the leaves from Swietenia macrophylla (Melineaceae), known as mahogany, has polyphenols with antioxidant and anti-inflammatory capacity in a significantly higher percentage than leaf extracts from other Amazonian plants. Furthermore, the antioxidant and anti-inflammatory capacity of aqueous extract of mahogany leaf has already been demonstrated in an in vitro model. In this study, we hypothesized that the aqueous extract of mahogany leaf (AEML) has a neuroprotective effect in a murine model of Parkinson's disease induced by 6-hydroxidopamine (6-OHDA), due to antioxidant and anti-inflammatory properties of its phenolic compounds.

Methods

Mice were treated daily with the mahogany extract at a dose of 50 mg/kg, starting 7 days before 6-OHDA infusion until post-surgery day 7.

Results and discussion

The animals from the 6-OHDA/mahogany group, which corresponds to animals injected with the toxin and treated with aqueous extract of the mahogany leaf, presented distinct behavioral phenotypes after apomorphine challenge and were therefore subdivided into 2 groups, 6-OHDA/mahogany F1 and 6-OHDA/mahogany F2. The F1 group showed a significant increase in contralateral rotations, whereas the F2 group did not show rotations after the apomorphine stimulus. In the F1 group, there was an increase, although not significant, in motor performance in the open field and elevated plus maze tests, whereas in the F2 group, there was significant improvement, which may be related to the lesser degree of injury to the nigrostriatal dopaminergic pathway. The TH+ histopathological analysis, a dopaminergic neuron marker, confirmed that the lesion to the nigrostriatal dopaminergic pathway was more pronounced in 6-OHDA/mahogany F1 than in 6-OHDA/mahogany F2. Our main result consisted of signs of improvement in the inflammatory profile in both the F1 and F2 6-OHDA/mahogany groups, such as a lower number of IBA-1+ microglial cells in the ventral striatum and substantia nigra pars compacta and a reduction in GFAP+ expression, an astrocyte marker, in the dorsal striatum. In this study, several bioactive compounds in the aqueous extract of mahogany leaf may have contributed to the observed beneficial effects. Further studies are necessary to better characterize their applicability for treating chronic degenerative diseases with inflammatory and oxidative bases, such as Parkinson's disease.

Absence of causal relationship between Parkinson's disease and subsequent prostate cancer: evidence from meta-analysis and Mendelian randomization studies

Background

Numerous observational studies have investigated the risk of prostate cancer (PCa) in patients diagnosed with Parkinson's Disease (PD). However, the existence of a definitive association remains uncertain.

Methods

Systematic searches were performed on PubMed, Web of Science, Scopus, and Google Scholar for studies published up to October 1, 2023. For Mendelian randomized (MR) causal inference, we employed pooled data from the IPDGC and PRACTICAL Consortium. The inverse variance weighted (IVW) method served as the principal technique for estimating odds ratios (ORs) and 95% confidence intervals (CIs) for the associations under investigation.

Results

Cumulative analysis of nine studies revealed no significant association between patients diagnosed with PD and the subsequent incidence of PCa ([relative ratio] RR = 0.89, 95%CI = 0.73 to 1.08, P = 0.237). However, subgroup analyses indicated a reduced occurrence of PCa in Caucasian patients with PD (RR = 0.81, 95%CI = 0.69 to 0.95, P = 0.011). MR analyses failed to establish a significant link between increased genetic susceptibility to PD and the risk of PCa (IVW OR = 1.025, 95%CI = 0.997 to 1.054, P = 0.082). Sensitivity analyses further corroborated the robustness of these results.

Conclusion

Both observational meta-analysis and MR analysis based on genetic variation do not support an association between PD patients and the subsequent risk of PCa. Further research is warranted to unravel the potential underlying mechanisms linking these two diseases.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023473527.

Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms.

Objective

This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices.

Methods

An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale.

Results

The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001).

Conclusion

These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.

AI molecular property prediction for Parkinson's Disease reveals potential repurposing drug candidates based on the increase of the expression of PINK1

BACKGROUND AND OBJECTIVE

Parkinson's Disease (PD), a common neurodegenerative disorder and one of the major current challenges in neuroscience and pharmacology, may potentially be tackled by the modern AI techniques employed in drug discovery based on molecular property prediction. The aim of our study was to explore the application of a machine learning setup for the identification of the best potential drug candidates among FDA approved drugs, based on their predicted PINK1 expression-enhancing activity.

METHODS

Our study relies on supervised machine learning paradigm exploiting in vitro data and utilizing the scaffold splits methodology in order to assess model's capability to extract molecular patterns and generalize from them to new, unseen molecular representations. Models' predictions are combined in a meta-ensemble setup for finding new pharmacotherapies based on the predicted expression of PINK1.

RESULTS

The proposed machine learning setup can be used for discovering new drugs for PD based on the predicted increase of expression of PINK1. Our study identified nitazoxanide as well as representatives of imidazolidines, trifluoromethylbenzenes, anilides, nitriles, stilbenes and steroid esters as the best potential drug candidates for PD with PINK1 expression-enhancing activity on or inside the cell's mitochondria.

CONCLUSIONS

The applied methodology allows to reveal new potential drug candidates against PD. Next to novel indications, it allows also to confirm the utility of already known antiparkinson drugs, in the new context of PINK1 expression, and indicates the potential for simultaneous utilization of different mechanisms of action.

Parkinson's and the couple relationship: a qualitative meta-synthesis

OBJECTIVE

The aim was to synthesise the current qualitative literature on the impact of Parkinson's on the couple relationship, including individual and dyad studies.

METHODS

Noblit and Hare's meta-ethnography approach was applied; 19 studies were included in the review following a systematic search of four electronic databases. The studies included experiences of 137 People with Parkinson's and 191 partners.

FINDINGS

Analysis produced three themes: (1) Disruption of roles and responsibilities; (2) Challenges to communication and closeness; and (3) Grief, burden, and isolation. The themes are discussed with supporting extracts from the 19 included studies.

CONCLUSION

The findings highlight the challenges that couples experience and the individual and relational resources that support coping. Support should be individually tailored to each couple as the impact on the couple may change in response to individual and contextual factors. This review adds further evidence to the case for relationally focused multidisciplinary team input at all stages of Parkinson's disease.

Propagation of Parkinson's disease by extracellular vesicle production and secretion

Parkinson's disease (PD) is a common neurodegenerative condition affecting a significant number of individuals globally, resulting in the presentation of debilitating motor and non-motor symptoms, including bradykinesia, resting tremor, as well as mood and sleep disorders. The pathology of PD has been observed to spread through the central nervous system resulting in progressive brain degeneration and a poor prognosis. Aggregated forms of the protein α-synuclein, particularly intermediary aggregates, referred to as oligomers, or preformed fibrils, have been implicated as the causative agent in the degeneration of neuronal processes, including the dysfunction of axonal transport, mitochondrial activity, and ultimately cellular death. Extracellular vesicles (EVs) have been strongly implicated in the propagation of PD pathology. Current observations suggest that aggregated α-synuclein is transported between neurons via small EVs in a series of exocytosis and endocytosis cellular processes leading to the observed spread of neurotoxicity and cellular death. Despite some understanding of the role of EVs in neurodegeneration, the exact mechanism by which these lipidic particles participate in the progression of Parkinson's pathology is not entirely understood. Here we review the current understanding of the role of EVs in the propagation of PD and explore their potential as a therapeutic target.

Novel insights into the role of circular RNAs in Parkinson disease: An emerging renaissance in the management of neurodegenerative diseases

Parkinson's disease (PD), as a debilitating neurodegenerative disease, particularly affects the elderly population, and is clinically identified by resting tremor, rigidity, and bradykinesia. Pathophysiologically, PD is characterized by an early loss of dopaminergic neurons in the Substantia nigra pars compacta, accompanied by the extensive aggregation of alpha-synuclein (α-Syn) in the form of Lewy bodies. The onset of PD has been reported to be influenced by multiple biological molecules. In this context, circular RNAs (circRNAs), as tissue-specific noncoding RNAs with closed structures, have been recently demonstrated to involve in a set of PD's pathogenic processes. These RNA molecules can either up- or downregulate the expression of α-Syn, as well as moderating its accumulation through different regulatory mechanisms, in which targeting microRNAs (miRNAs) is considered the most common pathway. Since circRNAs have prominent structural and biological characteristics, they could also be considered as promising candidates for PD diagnosis and treatment. Unfortunately, PD has become a global health concern, and a large number of its pathogenic processes are still unclear; thus, it is crucial to elucidate the ambiguous aspects of PD pathophysiology to improve the efficiency of diagnostic and therapeutic strategies. In line with this fact, the current review aims to highlight the interplay between circRNAs and PD pathogenesis, and then discusses the diagnostic and therapeutic potential of circRNAs in PD progression. This study will thus be the first of its kind reviewing the relationship between circRNAs and PD.

Comparative Effectiveness of Carbidopa-Levodopa Enteral Suspension and Deep Brain Stimulation on Parkinson's Disease-Related Pill Burden Reduction in Advanced Parkinson's Disease: A Retrospective Real-World Cohort Study

Introduction

In advanced Parkinson’s disease (PD), a high pill burden is associated with poor compliance, reduced control of symptoms, and decreased quality of life. We assessed the impact of carbidopa–levodopa enteral suspension (CLES) and deep brain stimulation (DBS) on PD-related pill burden.

Methods

A retrospective cohort analysis was conducted in the IBM MarketScan and Medicare Supplemental databases. Patients with advanced PD, taking only PD medications, and initiating CLES or DBS between 9 January 2015 and 31 July 2019 were identified. CLES patients were matched to DBS patients in a 1:3 ratio based on a propensity score to balance patient characteristics. Pill burden was measured as a 30-day average number of PD-related pills per day and was captured monthly. Pill-free status was evaluated as the percentage of patients receiving CLES or DBS monotherapy. Descriptive statistics were used to compare pill counts and assess the proportion of patients on monotherapy at 6 and 12 months after initiating CLES or DBS.

Results

The cohorts included 34 CLES patients matched to 97 DBS patients. A significant reduction in PD-related pill burden was observed at 6 months after initiation of CLES or DBS (∆CLES: −5.62, p < 0.0001; ∆DBS: −1.48, p = 0.0022). PD-related pill burden reduction in CLES patients was significantly greater than in matched DBS patients at 6 months (∆: −4.14, p < 0.0001), which was sustained at 12 months after initiation. At 12 months, nearly three times more CLES patients were pill free than DBS patients (29.41% and 10.31%, respectively, p = 0.0123).

Conclusions

Device-aided therapies such as CLES and DBS are effective in significantly reducing PD-related pill burden. Patients treated with CLES were more likely to achieve pill-free status than patients receiving DBS.

Artificial Intelligent Olfactory System for the Diagnosis of Parkinson's Disease

Background: Currently, Parkinson's disease (PD) diagnosis is mainly based on medical history and physical examination, and there is no objective and consistent basis. By the time of diagnosis, the disease would have progressed to the middle and late stages. Pilot studies have shown that a unique smell was present in the skin sebum of PD patients. This increases the possibility of a noninvasive diagnosis of PD using an odor profile. Methods: Fast gas chromatography (GC) combined with a surface acoustic wave sensor with embedded machine learning (ML) algorithms was proposed to establish an artificial intelligent olfactory (AIO) system for the diagnosis of Parkinson's through smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs) from Fourth Affiliated Hospital of Zhejiang University School of Medicine, China, were smelled by the AIO system. Univariate and multivariate methods were used to identify the significant volatile organic compound (VOC) features in the chromatograms. ML algorithms, including support vector machine, random forest (RF), k nearest neighbor (KNN), AdaBoost (AB), and Naive Bayes (NB), were used to distinguish PD patients from HC based on the VOC peaks in the chromatograms of sebum samples. Results: VOC peaks with average retention times of 5.7, 6.0, and 10.6 s, respectively, corresponding to octanal, hexyl acetate, and perillic aldehyde, were significantly different in PD and HC. The accuracy of the classification based on the significant features was 70.8%. Based on the odor profile, the classification had the highest accuracy and F1 of the five models with 0.855 from NB and 0.846 from AB, respectively, in the process of model establishing. The highest specificity and sensitivity of the five classifiers were 91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating set. Conclusions: The proposed AIO system can be used to diagnose PD through the odor profile of sebum. Using the AIO system is helpful for the screening and diagnosis of PD and is conducive to further tracking and frequent monitoring of the PD treatment process.

The importance of community nurses in supporting Parkinson's patients on complex therapies

A newly updated non-oral treatment pathway for people with Parkinson's who have complex needs provides clear, instructive guidance on identifying, assessing, treating and managing individuals on these therapies. While specialists provide much of the care within these pathways, district and community nurses are essential to ensure that patients progressing to more complex phases of the condition are identified and referred for specialist assessment, as well as being instrumental in the monitoring and ongoing management required once a new therapy is in place.