scholarly journals Parkinson’s Disease Diagnosis using Spiral Test on Digital Tablets

Author(s):  
Najd Al-Yousef ◽  
Raghad Al- ◽  
Reema Al- ◽  
Reem Al-Abdullatif ◽  
Felwa Al-Mutairi ◽  
...  
Author(s):  
Yareth Gopar-Cuevas ◽  
Ana P. Duarte-Jurado ◽  
Rosa N. Diaz-Perez ◽  
Odila Saucedo-Cardenas ◽  
Maria J. Loera-Arias ◽  
...  

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Petr G. Lokhov ◽  
Dmitry L. Maslov ◽  
Steven Lichtenberg ◽  
Oxana P. Trifonova ◽  
Elena E. Balashova

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is developed and used within a single laboratory. The holistic metabolomic LDT integrating the currently available data on human metabolic pathways, changes in the concentrations of low-molecular-weight compounds in the human blood during diseases and other conditions, and their prevalent location in the body was developed. That is, the LDT uses all of the accumulated metabolic data relevant for disease diagnosis and high-resolution mass spectrometry with data processing by in-house software. In this study, the LDT was applied to diagnose early-stage Parkinson’s disease (PD), which currently lacks available laboratory tests. The use of the LDT for blood plasma samples confirmed its ability for such diagnostics with 73% accuracy. The diagnosis was based on relevant data, such as the detection of overrepresented metabolite sets associated with PD and other neurodegenerative diseases. Additionally, the ability of the LDT to detect normal composition of low-molecular-weight compounds in blood was demonstrated, thus providing a definition of healthy at the molecular level. This LDT approach as a screening tool can be used for the further widespread testing for other diseases, since ‘omics’ tests, to which the metabolomic LDT belongs, cover a variety of them.


2021 ◽  
Author(s):  
Shengfang Song ◽  
Zhehui Luo ◽  
Chenxi Li ◽  
Xuemei Huang ◽  
Eric J. Shiroma ◽  
...  

2021 ◽  
pp. 132368
Author(s):  
Jiapei Yang ◽  
Lei Wang ◽  
Yue Su ◽  
Lingyue Shen ◽  
Xihui Gao ◽  
...  

2021 ◽  
pp. 1-10
Author(s):  
Keran Wang ◽  
Zhehui Luo ◽  
Chenxi Li ◽  
Xuemei Huang ◽  
Eric J. Shiroma ◽  
...  

Background: Literature shows an inverse association of circulating cholesterol level with the risk of Parkinson’s disease (PD); this finding has important ramifications, but its interpretation has been debated. Objective: To longitudinally examine how blood total cholesterol changes during the development of PD. Methods: In the Health, Aging and Body Composition study (n = 3,075, 73.6±2.9 years), blood total cholesterol was measured at clinic visit years 1, 2, 4, 6, 8, 10, and 11. We first examined baseline cholesterol in relation to PD risk, adjusting for potential confounders and competing risk of death. Then, by contrasting the observed with expected cholesterol levels, we examined the trajectory of changes in total cholesterol before and after disease diagnosis. Results: Compared to the lowest tertile of baseline total cholesterol, the cumulative incident ratio of PD and 95%confidence interval was 0.41 (0.20, 0.86) for the second tertile, and 0.69 (0.35, 1.35) for the third tertile. In the analysis that examined change of total cholesterol level before and after PD diagnosis, we found that its level began to decrease in the prodromal stage of PD and became statistically lower than the expected values∼4 years before disease diagnosis (observed-expected difference, –6.68 mg/dL (95%confidence interval: –13.14, –0.22)). The decreasing trend persisted thereafter; by year-6 post-diagnosis, the difference increased to –13.59 mg/dL (95%confidence interval: –22.12, –5.06), although the linear trend did not reach statistical significance (p = 0.10). Conclusion: Circulating total cholesterol began to decrease in the prodromal stage of PD, which may in part explain its reported inverse association with PD.


Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2630 ◽  
Author(s):  
Erika Rovini ◽  
Carlo Maremmani ◽  
Filippo Cavallo

Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.


2019 ◽  
Vol 23 (4) ◽  
pp. 1437-1449 ◽  
Author(s):  
Haijun Lei ◽  
Zhongwei Huang ◽  
Feng Zhou ◽  
Ahmed Elazab ◽  
Ee-Leng Tan ◽  
...  

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