Search

Background: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good., Objective: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided., Method: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool., Results: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively., Conclusion: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public., (© 2022 Health Libraries Group.)

Introduction. Parkinson's disease (PD) is the second most common neurological disorder. Patients with PD were affected by the COVID-19 pandemic in many different ways. This study's principal purpose is to assess PD patients' vulnerability to COVID-19 and its consequences. Method. This systematic review was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. A thorough search was conducted in the Medline (through PubMed) and Scopus databases from inception to January 30, 2022. The Joanna Briggs Institute (JBI) critical appraisal checklist was used to evaluate the studies. Results. Most of the studies (38%) had been conducted in Italy. Of the total number of studies, 17 (58%) were cross-sectional, seven (22%) were cohort, four (12%) were quasiexperimental, two (6%) were case-control, and one (3%) was a qualitative study. The PD duration in patients ranged from 3.26 to 13.40 years (IQR1: 5.7 yrs., median: 3.688 yrs., and IQR3: 8.815 yrs.). Meanwhile, the sample size ranged from 12 to 30872 participants (IQR1: 46, median: 96, and IQR3: 211). Despite worsening PD symptoms in the targeted population (persons with COVID-19 and Parkinson's disease), some studies found PD to be a risk factor for more severe COVID-19 disease. There are many adverse effects during the pandemic period in PD patients such as abnormalities of motor, nonmotor functioning, clinical outcomes, activities of daily living, and other outcomes. Conclusion. This study confirmed the negative effect of the COVID-19 pandemic on health-related quality of life and its determinants in patients with PD and their caregivers. Thus, due to the worsening symptoms of PD patients in the current pandemic, these people should be given more care and supervision to minimize their coronavirus exposure. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images. [ABSTRACT FROM AUTHOR]