Background Parkinson’s disease (PD) is a common neurodegenerative disease that is linked to several motor and nonmotor symptoms, including sleep disturbances. Patient quality of life has been shown to be disproportionally impacted by disease. Objectives To investigate sleep quality among individuals with PD, and to assess the severity of sleep disturbances and their impact on daytime activities. Subjects and methods A case‒control with 44 patients with Parkinson’s disease and 80 apparently healthy control participants was recruited from several hospitals and clinics. Each participant provided a thorough medical history and underwent a physical examination, and a questionnaire comprising the standard PSQI was used to assess sleep quality. Independent samples t test and Spearman’s correlation analysis were used with a p value equal to or less than 0.05 which was considered significant. Results The mean global PSQI score was 11.55 ± 4.412 for PD patients and 5.73 ± 3.22 for the control group with significant p value, Sleep latency onset was 75.57 min for PD patients and 22.81 min for the control group with significant p value. There was no significant correlation between age and other sleep-related variables. A total of 86.4% of patients with Parkinson’s disease suffered from varying degrees of daytime dysfunction compared to 61.25% of the controls. Conclusion Parkinson’s disease patients had poorer sleep quality than the controls. Age and sex were not found to be expected as a factor for sleep quality in patients with Parkinson’s disease. Daytime dysfunction rates are high in patients with Parkinson’s disease.
Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B
... Show MoreThe sunflower plants are attacked by serious seed and soil-borne pathogens including charcoal rot disease that caused by
Morphological and molecular identification was done, using universal primers for molecular identification. Finally, a greenhouse experiment was conducted, and
The etiology of Crohn's disease (CD) is still unknown. However, many factors, including a dysregulated immune system, altered microbiota, inheritance, and environmental factors, have been implicated. This work was conducted to estimate the effect of fungal microbiota on two bone mineral density markers, RANKL and sclerostin, in addition to the correlation between these markers and vitamin B12, D3, and zinc in CD patients, along with their potential effect on fungal microbiota and vice versa. Peripheral blood and carry-Blair Stool samples were collected from 88 participants (60 newly diagnosed with CD without treatment and 28 healthy controls) to detect serum levels of RANKL and sclerostin, and culture media were used to grow, isolat
... Show MoreThe present study evaluated the anti- Helicobacter pylori IgG, IgA and the role of virulence factor of H. pylori Vacuolating associated cytotoxin gene (Vac A) as a risk factors for CAD. The levels of serum IgG and IgA was done by indirect immunofluorescent (IIF) whereas Vac A measured by enzyme linked immunosorbent assay (ELISA). Ibn Al-Bitar specialist center for cardiac surgery laboratory and Ministry of Health/ Baghdad/ Iraq, between May and October 2018. Seventy Iraqi patients with CAD were enrolled in this study, their ages ranged between 40-84 years ; and 20 individuals as a control group which was divided into 2 subgroups: 10 apparently healthy volunteers (negative control) and the other subgroup contained 10 with normal coronary art
... Show MoreBackground :Atherosclerosis is the most
frequent underlying cause of ischemic heart
disease and a major cause of death all over the
world. This study was carried out to analyze and
compare the angiographic findings in patients
with diabetes mellitus versus non diabetics with
coronary heart disease , and to correlate these
findings with some risk factors for coronary
heart disease.
Methods: A total of 100 patients were studied,
50 with diabetes mellitus, and 50 non diabetics.
This study was carried out at Al-Sadr teaching
hospital in Basrah, Southern Iraq during the
period April 2009- September 2009. All patients
were known to have coronary heart disease. Risk
factors for coronary heart disease
Background:This is a prospective study of three children presented to us in the Orbital clinic in AL ShahidGazi Al Hariri Hospital with painless proptosiswith suspension of Hydatid disease.Objectives: : Orbital hydatid disease is a rare lesion accounting for less than 1% of the total lesions of the body (1, 2). Orbital cysts presented as a primary lesion in our study which is rare to have such lesion without involvement of other organs (3). Humans represent the intermediate host where the commonly affected organ are liver and the lung (10-15%) (4). Methods:This is a prospective study of three Children presented to us in the Orbital clinic in Al Shahid Ghazi Alhariri Hospital with painless proptosis with suspension of Hydatid disease, dep
... Show MoreAutomated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breat
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