Online ISSN: 2515-8260

Keywords : Parkinson disease

Assessment of association between glycemic status and Parkinson disease

Gary Batra; Sourav Bansal; Jugraj Singh; Arshdeep Singh Samra

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 1862-1866

Background: Parkinson disease (PD) is also a major chronic disease, and its clinical significance is increasing worldwide. The present study was conducted to assess association between glycemic status and clinical stage of PD.
Materials & Methods: 89 patients of Parkinson disease (PD) of both genders were included. Sleep quality was assessed using Parkinson’s disease sleep scale (PDSS). Presence of depression was assessed using Hamilton Depression (HAM-D) Rating Scale. Presence of motor and autonomic symptoms were recorded.
Results: out of 89, males were 49 and females were 40. Autonomic dysfunction was constipation in 42, urinary symptoms in 34, sexual dysfunction in 28 and postural giddiness in 53 patients. Non- motor symptoms was sleep disorder in 23, depression in 38, pain in 21, olfactory dysfunction in 45 and cognitive dysfunction in 61. Glycated hemoglobin <5.7% was seen in 18, 5.7- 6.4 % in 23 and >6.5% in 48. The difference was significant (P< 0.05).
Conclusion: Poor glycemic control was found in most of patients with PD.

Functionality of Pre-Prepared CNN Models using Deep Learning Technique for Detection of Parkinson Disease

Rohit Agarwal; Juginder Pal Singh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 121-125

Parkinson Disease is one of the most widely recognized neurodegenerative disorders. In
the U.S. Parkinson disease prevalence is roughly 20 cases for every 100,000 people per
year, with the mean period of beginning near 60 years. Thus, building up an automatic
system for detecting parkinson would be gainful for treating the infection without any
delay especially in remote areas. Due to the accomplishment of profound learning
calculations in breaking down clinical images, Convolution Neural Networks (CNNs)
have increased a lot of consideration for medical disease classification. What's more,
highlights realized by pre-prepared CNN models on huge scale datasets are a lot of
valuable in picture characterization errands. In this work, we evaluate the functionality of
pre-prepared CNN models used as highlight extractors followed by different classifiers
for the order of anomalous and normal MRI check pictures