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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. [ABSTRACT FROM AUTHOR]

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]

Introduction. Applying computerized simulation education tool for learning in medical domains is widely used in many countries. This review is aimed at systematically investigating the computerized simulation tools developed to educate physiotherapy students and determine the effectiveness of these interventions. Methods. A comprehensive search was conducted in Medline (through PubMed) and Scopus databases from inception to Sept. 10, 2022. The studies that examined the effectiveness of computerized simulation-based interventions were included. Results. Sixteen studies were included in this systematic review. All included examinations were ranked "good" or "low risk of bias" based on the criteria utilized in the Joanna Briggs Institute (JBI) scale and the Effective Public Health Practice Project (EPHPP) tool. Most of the articles (43%) were conducted in the USA and 25% in Australia. In 43% of the total studies, the study population was only physiotherapy students, and in 12.5% of them, the scope of education was related to practical skills training. Three of the 16 reviewed articles presented positive qualitative results; thirteen quantitative investigations also declared statistically positive effects. Positive effects have been seen in areas such as improving professional and behavioral abilities, improving knowledge and self-confidence, and reducing stress. The sample size of the studies ranged from eight to 162 participants. The limited sample sizes in groups, lack of interaction, and short follow-up duration were the most consistent limitations evident within the included studies. Conclusion. Computerized simulation education approaches can help to improve physiotherapy students' skills and knowledge. They also have great potential to reduce learning costs and increase the quality of education. [ABSTRACT FROM AUTHOR]

Purpose. Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods. A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results. Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion. The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine. [ABSTRACT FROM AUTHOR]

Background. Leukemia is fatal cancer in both children and adults and is divided into acute and chronic. Acute lymphoblastic leukemia (ALL) is a subtype of this cancer. Early diagnosis of this disease can have a significant impact on the treatment of this disease. Computational intelligence-oriented techniques can be used to help physicians identify and classify ALL rapidly. Materials and Method. In this study, the utilized dataset was collected from a CodaLab competition to classify leukemic cells from normal cells in microscopic images. Two famous deep learning networks, including residual neural network (ResNet-50) and VGG-16 were employed. These two networks are already trained by our assigned parameters, meaning we did not use the stored weights; we adjusted the weights and learning parameters too. Also, a convolutional network with ten convolutional layers and 2 ∗ 2 max-pooling layers—with strides 2—was proposed, and six common machine learning techniques were developed to classify acute lymphoblastic leukemia into two classes. Results. The validation accuracies (the mean accuracy of training and test networks for 100 training cycles) of the ResNet-50, VGG-16, and the proposed convolutional network were found to be 81.63%, 84.62%, and 82.10%, respectively. Among applied machine learning methods, the lowest obtained accuracy was related to multilayer perceptron (27.33%) and highest for random forest (81.72%). Conclusion. This study showed that the proposed convolutional neural network has optimal accuracy in the diagnosis of ALL. By comparing various convolutional neural networks and machine learning methods in diagnosing this disease, the convolutional neural network achieved good performance and optimal execution time without latency. This proposed network is less complex than the two pretrained networks and can be employed by pathologists and physicians in clinical systems for leukemia diagnosis. [ABSTRACT FROM AUTHOR]

Objective. A large number of patients need critical physical rehabilitation after the stroke. This study aimed to review and report the result of published studies, in which newly emerged games were employed for physical rehabilitating in poststroke patients. Materials and Methods. This systematic review study was performed based on the PRISMA method. A comprehensive search of PubMed, Scopus, IEEE Xplore Digital Library, and ISI Web of Science was conducted from January 1, 2014, to November 9, 2020, to identify related articles. Studies have been entered in this review based on inclusion and exclusion criteria, in which new games have been used for physical rehabilitation. Results. Of the 1326 retrieved studies, 60 of them met our inclusion criteria. Virtual reality-oriented games were the most popular type of physical rehabilitation approach for poststroke patients. "The Nintendo Wii Fit" game was used more than other games. The reviewed games were mostly operated to balance training and limb mobilization. Based on the evaluation results of the utilized games, only in three studies, applied games were not effective. In other studies, games had effective outcomes for target body members. Conclusions. The results indicate that modern games are efficient in poststroke patients' physical rehabilitation and can be used alongside conventional methods. [ABSTRACT FROM AUTHOR]

Objectives. Considering the advantages of the wearable systems (such as continuous monitoring, ease of installation, and significant efficiency), they can also be used to monitor patients in emergency situations and, particularly, in the ambulance. This study is aimed at specifying, modeling, and evaluating a wearable smart blanket system for monitoring vital signs in emergency circumstances. Method. At first, all the smart blanket system requirements were specified using an author-made questionnaire, and the essential requirements were specified in the software requirements specification format. Such an anticipated smart wearable blanket is then modeled by Unified Modeling Language. Finally, the most important quality attributes (i.e., the nonfunctional requirements) of the proposed wearable smart blanket system were evaluated using a descriptive-analytical study. Results. Evaluation results of the proposed system show that using the smart blanket system could not only provide the required functionalities, but also, it improved the important quality attributes such as response time and delay in sending data packets (14% improvement), accuracy, energy consumption (18% improvement), reliability and fault-tolerance, and performance (28% improvement) in contrast to the compared related work. Conclusion. Using a smart wearable blanket in an ambulance instead of such in a huge ambulance cabin would be beneficial in terms of time and space, ease of use, and maybe cost while providing the required functionalities besides having proper quality attributes. [ABSTRACT FROM AUTHOR]