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Medicina
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Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd...
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Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd Edition

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Neurology".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 427

Special Issue Editor

Dr. Anastasia Bougea

E-Mail Website
Guest Editor
1st Department of Neurology, Memory & Movement Disorder Clinic, Eginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
Interests: Parkinsonian syndromes; dementias and biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Parkinson’s disease (PD) is the second most common multi-systemic neurodegenerative disorder that is characterized by a broad spectrum of motor and non-motor symptoms (NMS). Atypical Parkinsonism is a less common group of sporadic, neurodegenerative diseases of the central nervous system but more severe than PD. The most common forms are multiple system atrophy (MSA), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and dementia with Lewy bodies (DLB). Neuroanatomically, NMS may be subdivided into cortical manifestations (psychosis and cognitive impairment), basal ganglia symptoms (impulse control disorders, apathy, and restlessness or akathisia), brainstem symptoms (depression, anxiety, and sleep disorders), and peripheral nervous system disturbances (orthostatic hypotension (OH), constipation, pain), and sensory disturbances. NMS is often overlooked by physicians and dismissed by patients, making its management difficult and a major burden for patients and caregivers. Unfortunately, there is very little existing data about NMS, their neurobiology, their potential biomarkers, their monitoring, and their treatment. Moreover, there is growing evidence of accurate monitoring of NMS by wearable sensors for PD. It is, therefore, essential that researchers and practitioners comprehensively address the factors related to NMS in order to improve the quality of life for PD patients.

The aim of the 2nd edition of this Special Issue is to welcome back research articles, opinion/perspective articles, and review articles (narrative reviews, systematic reviews, and meta-analyses), as well as preclinical studies with animal models.

Dr. Anastasia Bougea
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Parkinson’s disease (PD)
  • non-motor symptoms
  • atypical Parkinsonism
  • genetics
  • biomarkers
  • wearable sensors

Published Papers (1 paper)

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Research

12 pages, 324 KiB  
Article
An Artificial Neural Network Predicts Gender Differences of Motor and Non-Motor Symptoms of Patients with Advanced Parkinson’s Disease under Levodopa–Carbidopa Intestinal Gel
by Anastasia Bougea, Tajedin Derikvand and Efthymia Efthimiopoulou
Medicina 2024, 60(6), 873; https://doi.org/10.3390/medicina60060873 - 26 May 2024
Viewed by 296
Abstract
Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson’s disease (PD) under levodopa–carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical [...] Read more.
Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson’s disease (PD) under levodopa–carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical outcomes of patients with advanced PD after two years of LCIG therapy. Materials and Methods: This was a longitudinal, 24-month observational study of 59 patients with advanced PD in a multicenter registry under LCIG treatment from September 2019 to September 2021, including 43 movement disorder centers. The data set includes 649 measurements of patients, which make an irregular time series, and they are turned into regular time series during the preprocessing phase. Motor status was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS) Parts III (off) and IV. The NMS was assessed by the NMS Questionnaire (NMSQ) and the Geriatric Depression Scale (GDS), the quality of life by PDQ-39, and severity by Hoehn and Yahr (HY). Multivariate linear regression, ARIMA, SARIMA, and Long Short-Term Memory–Recurrent NeuralNetwork (LSTM-RNN) models were used. Results: LCIG significantly improved dyskinesia duration and quality of life, with men experiencing a 19% and women a 10% greater improvement, respectively. Multivariate linear regression models showed that UPDRS-III decreased by 1.5 and 4.39 units per one-unit increase in the PDQ-39 and UPDRS-IV indexes, respectively. Although the ARIMA-(2,0,2) model is the best one with AIC criterion 101.8 and validation criteria MAE = 0.25, RMSE = 0.59, and RS = 0.49, it failed to predict PD patients’ features over a long period of time. Among all the time series models, the LSTM-RNN model predicts these clinical characteristics with the highest accuracy (MAE = 0.057, RMSE = 0.079, RS = 0.0053, mean square error = 0.0069). Conclusions: The LSTM-RNN model predicts, with the highest accuracy, gender-dependent clinical outcomes in patients with advanced PD after two years of LCIG therapy. Full article
(This article belongs to the Special Issue Non-motor Disorders in Parkinson’s Disease and Other Parkinsonian Syndromes, 2nd Edition)
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