An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein–lipid signatures in plasma

The Analyst ◽  
2019 ◽  
Vol 144 (4) ◽  
pp. 1334-1344 ◽  
Author(s):  
Consuelo Pizarro ◽  
Isabel Esteban-Díez ◽  
María Espinosa ◽  
Fernando Rodríguez-Royo ◽  
José-María González-Sáiz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differential Diagnosis ◽  
Disease Stage ◽  
Lipoprotein Lipid ◽  
Alzheimer’S Diseases ◽  
Alzheimer's Diseases

An NMR-based lipidomic approach has been applied to provide an optimal discrimination strategy for differential diagnosis of Parkinson's and Alzheimer's diseases and for staging purposes of Parkinson's patients.

scholarly journals Retrograde Amnesia in Parkinson’s Disease

1984 ◽  
Vol 11 (2) ◽  
pp. 297-301 ◽  
Author(s):  
Morris Freedman ◽  
Peter Rivoira ◽  
Nelson Butters ◽  
Daniel S. Sax ◽  
Robert G. Feldman
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Retrograde Amnesia ◽  
Memory Loss ◽  
Remote Memory ◽  
Time Periods ◽  
Alzheimer’S Diseases ◽  
Normal Controls ◽  
Alzheimer's Diseases

ABSTRACT:Retrograde amnesia was assessed in demented and non-demented Parkinson’s patients using a test of remote memory spanning the years from 1920-1979. Results indicated that the demented patients 1) scored significantly below normal controls and 2) had equal impairment for all time periods. This pattern was like that seen in other dementing illnesses (i.e., Huntington’s and Alzheimer’s diseases), but different from that in amnesic disorders, such as Korsakoff s syndrome. The data, therefore, suggest qualitative differences in pattern of remote memory loss between the dementias and amnesic syndromes.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals High-resolution ultrasound changes of the vagus nerve in idiopathic Parkinson’s disease (IPD): a possible additional index of disease

2021 ◽  
Author(s):  
F. Sartucci ◽  
T. Bocci ◽  
M. Santin ◽  
P. Bongioanni ◽  
G. Orlandi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Resolution ◽  
Vagus Nerve ◽  
Imaging Modality ◽  
Disease Stage ◽  
Motor Nucleus ◽  
Idiopathic Parkinson’S Disease ◽  
Idiopathic Parkinson's Disease ◽  
Additional Index

Abstract Background and rationale Histopathological studies revealed degeneration of the dorsal motor nucleus of the vagus nerve (VN) early in the course of idiopathic Parkinson’s disease (IPD). Degeneration of VN axons should be detectable by high-resolution ultrasound (HRUS) as a thinning of the nerve trunk. In order to establish if the VN exhibits sonographic signs of atrophy in IPD, we examined patients with IPD compared with age-matched controls. Material and methods We measured the caliber (cross-sectional area, CSA) and perimeter of the VN in 20 outpatients with IPD (8 females and 12 males; mean age 73.0 + 8.6 years) and in age-matched controls using HRUS. Evaluation was performed by blinded raters using an Esaote MyLab Gamma device in conventional B-Mode with an 8–19 MHz probe. Results In both sides, the VN CSA was significantly smaller in IPD outpatients than in controls (right 2.37 + 0.91, left 1.87 + 1.35 mm2 versus 6.0 + 1.33, 5.6 + 1.26 mm2; p <0.001), as well as the perimeter (right 5.06 + 0.85, left 4.78 + 1.74 mm versus 8.87 + 0.86, 8.58 + 0.97 mm; p <0.001). There were no significant correlations between VN CSA and age, the Hoehn and Yahr scale, L-dopa therapy, and disease duration. Conclusion Our findings provide evidence of atrophy of the VNs in IPD patients by HRUS. Moreover, HRUS of the VN represent a non-invasive easy imaging modality of screening in IPD patients independent of disease stage and duration and an interesting possible additional index of disease.

scholarly journals Telerehabilitation in Parkinson's disease: Influence of cognitive status

2016 ◽  
Vol 10 (4) ◽  
pp. 327-332 ◽  
Author(s):  
Alice Estevo Dias ◽  
João Carlos Papaterra Limongi ◽  
Wu Tu Hsing ◽  
Egberto Reis Barbosa
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Status ◽  
Disease Stage ◽  
Voice Rehabilitation ◽  
Voice Treatment ◽  
Technological Competence ◽  
Yahr Scale ◽  
Invaluable Tool ◽  
Remote Therapy

ABSTRACT Background: The need for efficacy in voice rehabilitation in patients with Parkinson's disease is well established. Given difficulties traveling from home to treatment centers, the use of telerehabilitation may represent an invaluable tool for many patients. Objective: To analyze the influence of cognitive performance on acceptance of telerehabilitation. Methods: Fifty patients at stages 2-4 on the Hoehn-Yahr scale, aged 45-87 years old, with cognitive scores of19-30 on the Mini-Mental State Examination, and 4-17 years of education were enrolled. All patients were submitted to evaluation of voice intensity pre and post in-person treatment with the Lee Silverman Voice Treatment (LSVT) and were asked to fill out a questionnaire regarding their preferences between two options of treatment and evaluating basic technological competence. Results: Comparisons between pre and post-treatment values showed a mean increase of 14dBSPL in vocal intensity. When asked about potential acceptance to participate in future telerehabilitation, 38 subjects agreed to take part and 12 did not. For these two groups, 26% and 17% self-reported technological competence, respectively. Agreement to engage in remote therapy was positively associated with years of education and cognitive status. Conclusion: Responses to the questionnaire submitted after completion of traditional in-person LSVT showed that the majority of patients (76%) were willing to participate in future telerehabilitation. Age, gender, disease stage and self-reported basic technological skills appeared to have no influence on the decision, whereas other factors such as cognitive status and higher school education were positively associated with acceptance of the new therapy approach.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI SUBTIPE GEJALA MOTORIK PENYAKIT PARKINSON

2020 ◽  
Vol 5 (1) ◽  
pp. 343
Author(s):  
Attiya Istarini ◽  
Yuliarni Syafrita ◽  
Restu Susanti
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Age At Onset ◽  
Motor Symptom ◽  
Disease Stage ◽  
Motor Symptoms ◽  
Research Subjects ◽  
Cross Sectional ◽  
Duration Of Disease ◽  
Cross Sectional Design

<p><strong><em>Background</em></strong><em>: Parkinson's disease (PD) is a chronic neurodegenerative disease that manifests as movement disorders. Based on motor symptoms, PD is classified into subtypes of tremor and postural instability gait disorders (PIGD). The motor symptoms subtype is a predictor of disease progression, therapeutic response, and quality of life for Parkinson's patients. The purpose of this study is to identify some  factors that influence motor symptoms in Parkinson's disease.</em><strong><em>Methods:</em></strong><em> This research use cross sectional design. Samples were selected by consecutive sampling method that met the inclusion and exclusion criteria. Research subjects were 58 people. Statistical analysis using SPSS. p values &lt;0.05 were considered statistically significant.</em><strong><em>Results:</em></strong><em> This research include 58 patients, 55.2% were men with range of age 63.5 ± 8.5 years old. The mean age at onset was 57.9 ± 9.5 years and duration of disease 6.1 ± 4.6 years. Motor symptoms 53.4% dominant tremor. There was a significant relationship between disease stage and motor symptom subtypes (p &lt;0.001). There is no relationship between the patient's age, age at onset and duration of the disease with motor symptom subtypes.</em><strong><em>Conclusions:</em></strong><em> There is a relationship between disease stage and motor symptom. The patient's age, age at onset and duration of the disease are not related to the motor symptoms of Parkinson's patients.</em></p>

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [123I]FP-CIT SPECT

2014 ◽  
Vol 42 (1) ◽  
pp. 112-119 ◽  
Author(s):  
I. Huertas-Fernández ◽  
F. J. García-Gómez ◽  
D. García-Solís ◽  
S. Benítez-Rivero ◽  
V. A. Marín-Oyaga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differential Diagnosis ◽  
Learning Models ◽  
Vascular Parkinsonism ◽  
Machine Learning Models
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