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Addressing the challenge of automatically segmenting anatomical structures from brain images has been a long-standing problem, attributed to subject- and image-based variations and constraints in available data annotations. The Segment Anything Model (SAM), developed by Meta, is a foundational model trained to provide zero-shot segmentation outputs with or without interactive user inputs, demonstrating notable performance on various objects and image domains without explicit prior training. This study evaluated SAM’s performance in brain tumor segmentation using two publicly available Magnetic Resonance Imaging (MRI) datasets. The study analyzed SAM’s standalone segmentation as well as its performance when provided user interaction through point prompts and bounding box inputs. SAM exhibited versatility across configurations and datasets, with the bounding box consistently outperforming others in achieving superior localized precision, with average Dice scores of 0.68 for TCGA and 0.56 for BRATS, along with average IoU values of 0.89 and 0.65, respectively, especially for tumors with low-to-medium curvature. Inconsistencies were observed, particularly in relation to variations in tumor size, shape, and textural features. The conclusion drawn from the study is that while SAM can automate medical image segmentation, further training and careful implementation are necessary for diagnostic purposes, especially with challenging cases such as MRI scans of brain tumors. [ABSTRACT FROM AUTHOR]

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper. [ABSTRACT FROM AUTHOR]

Pancreatic cancer remains a significant health issue with limited treatment options. The tumor stroma, a complex environment made up of different cells and proteins, plays a crucial role in tumor growth and chemoresistance. Targeting tumor stroma, consisting of diverse non-tumor cells such as fibroblasts, extracellular matrix (ECM), immune cells, and also pre-vascular cells is encouraging for remodeling solid cancers, such as pancreatic cancer. Remodeling the stroma of pancreas tumors can be suggested as a strategy for reducing resistance to chemo/immunotherapy. Several studies have shown that phytochemicals from plants can affect the tumor environment and have anti-cancer properties. By targeting key pathways involved in stromal activation, phytochemicals may disrupt communication between the tumor and stroma and make tumor cells more sensitive to different treatments. Additionally, phytochemicals have immunomodulatory and anti-angiogenic properties, all of which contribute to their potential in treating pancreatic cancer. This review will provide a detailed look at how phytochemicals impact the tumor stroma and their effects on pancreatic tumor growth, spread, and response to treatment. It will also explore the potential of combining phytochemicals with other treatment options like chemotherapy, immunotherapy, and radiation. [ABSTRACT FROM AUTHOR]

A significant cause of death and long-term disability globally is brain stroke. Stroke falls into one of two categories: (1) ischemic, which accounts for roughly 85% of cases when it is caused by abrupt cessation of blood supply to a particular area of the brain, and (2) hemorrhagic, which refers to bleeding or blood leakage. To provide stroke patients with individualized therapeutic care, meta-heuristic algorithms make accurate and timely predictions. The use of meta-heuristic algorithms and machine learning in the healthcare sector is growing. A new meta-heuristic algorithm called the Mountain Gazelle Optimizer (MGO) was developed in part as a result of wild mountain gazelles' social structure but suffered from slow convergence speed. Consequently, a modified MGO (mMGO) approach uses the joint opposite selection operator, which combines the selective leading opposition and the dynamic opposite learning approaches, to improve MGO. The purpose of this study is to evaluate the performance of mMGO based on the k-nearest neighbor (kNN) classifier in predicting brain stroke in data sets taken from Kaggle. Performance was assessed on the challenging CEC 2020 benchmark test functions. Compared to seven well-known optimization algorithms, the statistical results demonstrated the superiority of mMGO. Furthermore, the experimental results of mMGO-kNN for categorizing brain stroke data sets revealed that it outperformed competitors in all data sets with an overall accuracy of 95.5%, a sensitivity of 99.34%, a specificity of 98.99%, and a precision of 99.21%. [ABSTRACT FROM AUTHOR]

Security is the primary concern in the transmission of medical images, as it involves sensitive patient information. This study introduces an optimized watermarking approach, constructed using discrete wavelet transform and singular value decomposition. The Low- level frequency bands (LL3) sub-band singular values of the host image were embedded with the singular values of a binary watermark using multiple scaling factors. These MSFs were optimized using a recently proposed firefly Photinus algorithm to balance robustness and imperceptibility. The proposed method was applied to various images, including computed tomography images, where the visual quality of the signed and attacked images was evaluated by peak signal-to-noise ratio (PSNR) and normalized cross-correlation. The performance of the proposed algorithm demonstrates significant improvements in the embedding and extraction processes, showing an enhancement in the balance between robustness and imperceptibility, with a PSNR above 79.28 dB, compared to other related works.Article Highlights: Developing a medical image watermarking approach to secure sensitive patient information. Introducing new directions to enhance the robustness and imperceptibility of the watermark against various kinds of attacks. Achieving high performance in the embedding and extraction processes. [ABSTRACT FROM AUTHOR]

Background: Bio-phosphonates can be used to lower the risk of hip and spine fractures. Additionally, they can be used to treat Paget's disease of the bones in a variety of dosages. In the procedure that replace hyaline cartilage to bone, this procedure i.e. called endochondral ossification. It starts when mesenchymal cells from the mesoderm develop into chondrocytes. Chondrocytes multiply quickly and release an extracellular matrix to create the cartilage that serves as the model for bone. Objective: To histomorphometric illustration the endochondral ossification of the mandibular angle defect repair in rats after- oral stimulation with bisphosphonate treatment. Patients and Methods: 20 rats were used in this work and the animals were divided into the following groups: 10 Rats from the control group. The bone defect was healed naturally without medicament and 10 rats were used in the experiment, and taking the biophosphonate medication helped mend the bone defect. Every single group was studied in 7 and 14 day (5 rats for each healing period) and the surgical procedure was performed for histological and Histomorphometrically examination. The data analysis with spss statistic measure & with P vale (P ≤ 0.05). Results: Active effect of the bio-phosphate medicament in the endochondral ossification and the cell that responsible for the cartilage formation and accelerated the healing of the mandibular defect with inhibition of the bone resoption and finally decrease the time that need to full healing. Conclusion: The chemical medicament that represented by biophosphonate accelerated the endochondral ossification in a short time and replacement with bone in the site of the defect. [ABSTRACT FROM AUTHOR]

This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future. [ABSTRACT FROM AUTHOR]

Background: Nanoparticles are increasingly being used as an alternative antibiotic to target microorganisms, particularly for treating bacterial infections. In current study it was recognized processes such as metal ion release, induction of oxidative stress, and non-oxidative mechanisms. Developing resistance to nanoparticles is challenging for bacterial cells due to the multiple simultaneous actions against microorganisms, and require several simultaneous gene alterations within the same bacterial cell. Objective: To determine the effect of the titanium dioxide nanoparticles (TiO2NPs) on the activity of the same bacteria that participate in dental caries (Staphylococcus aureus and Escherichia coli). Patients and Methods: The study included 50 patients who attended the dental clinic and took the samples by the wooden swap from the molar area (cervical area of inflamed gingiva and same samples taken from occlusal surface of molar). The collected samples were cultured on "Mannitol salt agar, Blood agar, and MacConkey's agar plates" for separation of the needed bacteria (Staphylococcus aureus and Escherichia coli) that showed wide spreading in the oral cavities and dental carries. TiO2NPs are then created using pulse laser ablation, followed by coating preparation the nanoparticles materials (using a hybrid sol-gel and organ silicate nanoparticles), and finally the prepared nanoparticles to the coating solution and local paint were used for antimicrobial activity of the separated bacteria using different concentrations of TiO2 in the cultures. Results: Characterization showed the phase of the TiO2NPs was spherical, with very few irregularly shaped particles, and had an average small size in millimeters. Antimicrobial activity results showed a strong bactericidal effect against Gram-positive bacteria and demonstrated greater sensitivity to TiO2 nanoparticles at lower concentrations when compared to Gram-negative bacteria. Conclusion: It was shown that nanotechnology promise in treating various infections caused by bacteria, including dental caries. Notably, nanoparticles have demonstrated broad-spectrum antibacterial effects against Gram-positive bacteria. [ABSTRACT FROM AUTHOR]

The systematic review of Business Information Modeling (BIM) plays a crucial role in understanding its significance and impact. This review allows for a comprehensive examination of the existing literature, highlighting the benefits, challenges, and success factors associated with BIM. There is a scarcity of studies dealing with this subject, and so a question about the most important advantages that will be obtained by the construction industry, especially the construction companies, as a result of the BIM application arises. Relevant previous studies were reviewed and their quality was evaluated using a systematic methodology. The current study was characterized by the use of the SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis technique. As far as is known, the current study is the first of its kind in the field of Iraqi project management. The results suggest that the BIM benefits include firm's growth, organizational performance, enhanced market value, employee motivation, and service quality. [ABSTRACT FROM AUTHOR]

This literature review emphasizes the impact of ethyl acetate organic solvent as surface treatment agent for 3-dimentional (3D) printed polymethyl methacrylate denture base and silicone soft liner on enhancing the strength of bonding of these materials. Over time, Denture soft liner materials have appeared as important components in prosthodontics, offering enhanced comfort, tissue adaptation for denture wearers and restore the inflamed and distorted mucosa. It is commonly agreed that the main drawback is debonding of the lining material from the 3D printed denture base. Therefore, there is a need for enhancing the strength of bonding of these materials [ABSTRACT FROM AUTHOR]

Attention, which is the process of noticing the surrounding environment and processing information, is one of the cognitive functions that deteriorate gradually as people grow older. Games that are used for other than entertainment, such as improving attention, are often referred to as serious games. This study examined the effectiveness of serious games on attention among elderly individuals suffering from cognitive impairment. A systematic review and meta-analyses of randomized controlled trials were carried out. A total of 10 trials ultimately met all eligibility criteria of the 559 records retrieved. The synthesis of very low-quality evidence from three trials, as analyzed in a meta-study, indicated that serious games outperform no/passive interventions in enhancing attention in cognitively impaired older adults (P < 0.001). Additionally, findings from two other studies demonstrated that serious games are more effective than traditional cognitive training in boosting attention among cognitively impaired older adults. One study also concluded that serious games are better than traditional exercises in enhancing attention. Serious games can enhance attention in cognitively impaired older adults. However, given the low quality of the evidence, the limited number of participants in most studies, the absence of some comparative studies, and the dearth of studies included in the meta-analyses, the results remain inconclusive. Thus, until the aforementioned limitations are rectified in future research, serious games should serve as a supplement, rather than a replacement, to current interventions. [ABSTRACT FROM AUTHOR]

Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases. [ABSTRACT FROM AUTHOR]

Depression is a prevalent mental condition that is challenging to diagnose using conventional techniques. Using machine learning and deep learning models with motor activity data, wearable AI technology has shown promise in reliably and effectively identifying or predicting depression. In this work, we aim to examine the performance of simple linear and non-linear models in the prediction of depression levels. We compared eight linear and non-linear models (Ridge, ElasticNet, Lasso, Random Forest, Gradient boosting, Decision trees, Support vector machines, and Multilayer perceptron) for the task of predicting depression scores over a period using physiological features, motor activity data, and MADRAS scores. For the experimental evaluation, we used the Depresjon dataset which contains the motor activity data of depressed and non-depressed participants. According to our findings, simple linear and non-linear models may effectively estimate depression scores for depressed people without the need for complex models. This opens the door for the development of more effective and impartial techniques for identifying depression and treating/preventing it using commonly used, widely accessible wearable technology. [ABSTRACT FROM AUTHOR]

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children. The objective of this study is to examine the impact of intermittent fasting during Ramadan on stress levels in school children as measured using wearable artificial intelligence (AI). Twenty-nine school children (aged 13-17 years and 12M / 17F ratio) were given Fitbit devices and their stress, activity and sleep patterns analyzed 2 weeks before, 4 weeks during Ramadan fasting and 2 weeks after. This study revealed no statistically significant difference on stress scores during fasting, despite changes in stress levels being observed for 12 of the participants. Our study may imply intermittent fasting during Ramadan poses no direct risks in terms of stress, suggesting rather it may be linked to dietary habits, furthermore as stress score calculations are based on heart rate variability, this study implies fasting does not interfere the cardiac autonomic nervous system. [ABSTRACT FROM AUTHOR]

In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ability to predict BGL using data acquired by non-invasive Wearable Devices (WDs), therefore improving diabetes monitoring and treatment. It is crucial to study the relationships between non-invasive WD features and markers of glycemic health. Therefore, this study aimed to investigate accuracy of linear and non-linear models in estimating BGL. A dataset containing digital metrics as well as diabetic status collected using traditional means was used. Data consisted of 13 participants data collected from WDs, these participants were divided in two groups young, and Adult Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. The study showed that linear and non-linear models both have high accuracy in estimating BGL using WD data (RMSE range: 0.181 to 0.271, MAE range: 0.093 to 0.142). We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics when using Machine learning approaches. [ABSTRACT FROM AUTHOR]

Introduction: Few studies have reported hepatic and cardiac iron overload in patients with end‐stage renal disease (ESRD), and the current evidence regarding the prevalence is still scarce. Aim: This review aims to estimate the prevalence of hepatic and/or cardiac iron overload quantified by magnetic resonance imaging (MRI) in patients with ESRD who receive hemodialysis (HD), peritoneal dialysis (PD), or have undergone a kidney transplant. Methods: A systematic review with meta‐analysis was conducted and reported in line with PRISMA (Preferred Reporting Items for Systematic Review and Meta‐Analysis) guidelines. MEDLINE and Embase bibliographic databases were searched using a comprehensive list of controlled vocabulary and keywords to identify relevant studies. All studies reporting the prevalence of hepatic and/or cardiac iron overload quantified by MRI in ESRD patients were considered. The Newcastle‐Ottawa scale was used to assess the methodological quality of included studies. To investigate the heterogeneity between studies, random‐effect meta‐analyses for proportions were used. Results: The review comprised seven studies that included 339 patients. Using meta‐analysis, the pooled prevalence of severe and mild to moderate hepatic iron overload quantified by MRI was 0.23 [95% CI: 0.08–0.43] and 0.52 [95% CI: 0.47–0.57], respectively. Only three studies included cardiac iron quantification, and none reported iron overload. Conclusions: This review has revealed a high prevalence of severe hepatic iron overload in patients with ESRD treated by HD. Further studies with a larger sample size are needed to determine the impact of iron overload on vital organs in patients with ESRD and guide future research in this understudied field. Proper use of iron chelation and continuous monitoring will help in the early detection of unsolicited complications; however, the low renal clearance of most iron chelators limits the options for treating iron excess in patients with ESRD. [ABSTRACT FROM AUTHOR]

Eighty one percent of adolescents aged 11–17 years are inadequately physically active worldwide. Physical activity (PA) recommendations for high school children have not been studied previously in schools in the Qatar region. The objectives of the study were: (i) to assess the level of compliance of the recommended PA and to assess if there are any gender differences; and (ii) to analyze the recommended step count compliance during school and non-school days. An observational cross-sectional study was conducted. Twenty-nine children (12 boys and 17 girls) aged 13–17 years (15.24 ± 1.46) took part in this study. Participants wore Fitbit Charge 5 wrist bands for three weeks to collect various digital biomarkers including moderate-to-vigorous physical activity (MVPA) and step counts (tracking during out-of-school time and school time). Based on this study, high school children in the two Qatar region schools did not meet the MVPA and steps/day recommendation by the established agencies: 38% of the total study group met the recommended 60 min/day of activity (50% boys, 29% girls). Gender differences were also observed in PA levels and steps per day: for non-school days, 17% met the recommended 10,000 steps/day (25% boys, 12% girls). There was a pattern of greater PA performance and steps during the weekdays as opposed to the weekend, but these values showed no robust evidence in favor of H1 or statistical significance for step counts. However, the evidence was robust in favor of H1 (difference between weekend and weekday) due to a statistically significant difference for meeting the 60 min/day activity. While further studies are required to establish if this is a general trend in Qatari schools, this pilot study does highlight the need to design more effective programs and messaging strategies to improve PA levels in the high school population. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI)--driven serious games have been used in health care to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state to plan on how to leverage them in the current and future health care needs. Objective: This study aimed to explore the features of AI-driven serious games in health care as reported by previous research. Methods: We conducted a scoping review to achieve the abovementioned objective. The most popular databases in the information technology and health fields (ie, MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM Digital Library, and Google Scholar) were searched using keywords related to serious games and AI. Two reviewers independently performed the study selection process. Three reviewers independently extracted data from the included studies. A narrative approach was used for data synthesis. Results: The search process returned 1470 records. Of these 1470 records, 46 (31.29%) met all eligibility criteria. A total of 64 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games were used for rehabilitation in most of the studies. The most common genres of serious games were role-playing games, puzzle games, and platform games. Unity was the most prominent game engine used to develop serious games. PCs were the most common platform used to play serious games. The most common algorithm used in the included studies was support vector machine. The most common purposes of AI were the detection of disease and the evaluation of user performance. The size of the data set ranged from 36 to 795,600. The most common validation techniques used in the included studies were k-fold cross-validation and training-test split validation. Accuracy was the most commonly used metric for evaluating the performance of AI models. Conclusions: The last decade witnessed an increase in the development of AI-driven serious games for health care purposes, targeting various health conditions, and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. Although the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to categorize and analyze the public response toward third/booster shots of COVID-19 on Twitter. Methods: We downloaded the COVID-19 vaccine booster shots related Tweets using the Twitter API. The collected Tweets were pre-processed to prepare them for analysis by (1) removing non-English language tweets, retweets, emojis, emoticons, non-printable characters, the punctuation marks, and the prepositions, (2) anonymizing the identity of the users, and (3) normalizing various forms of the same words. We used the state-of-the-art BertTopic modeling library to identify the most popular topics. Results: Of 165,048 Tweets collected, 36,908 Tweets were analyzed in this study. From these tweets, we identified 9 topics, which were about Biden administration, Pfizer & BioNTech, Moderna, Johnson & Johnson, eligibility for booster shots, side effects, Donald Trump, variants of the Novel Coronavirus, and conspiracy theory & propaganda. The mean of sentiment was positive in all topics. The lowest and highest mean of sentiments were for the Donald Trump topic (0.0097) and the Johnson & Johnson topic (0.1294), respectively. Conclusions: The topics identified in this study not only accurately reflect the contemporary COVID-19 discussion, but also the high degree of politicization in the USA. While the latter might be a result of our rejection of non-English tweets, it is reassuring to see our fully automated, unsupervised pipeline reliably extract such global features in the data at scale. We, therefore, believe that the methodology presented in this study is mature and useful for other infoveillance studies on a wide variety of topics. [ABSTRACT FROM AUTHOR]

Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one's quality of life and occasionally resulting in social isolation. Brain–computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer's disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety. [ABSTRACT FROM AUTHOR]

Background: Executive functions are one of the known cognitive abilities that decline with age. They are the high-order cognitive processes that enable an individual to concentrate, plan, and take action. Serious games, which are games developed for specific purposes other than entertainment, could play a positive role in improving executive functions. Several systematic reviews have pooled the evidence about the effectiveness of serious games in improving executive functions; however, they are limited by some weaknesses. Objective: This study aims to investigate the effectiveness of serious games for improving executive functions among older adults with cognitive impairment. Methods: A systematic review of randomized controlled trials (RCTs) was conducted. To retrieve relevant studies, 8 electronic databases were searched. Further, reference lists of the included studies and relevant reviews were screened, and we checked studies that cited our included studies. Two reviewers independently checked the eligibility of the studies, extracted data from the included studies, assessed the risk of bias, and appraised the quality of the evidence. We used a narrative and statistical approach, as appropriate, to synthesize results of the included studies. Results: Of 548 publications identified, 16 RCTs were eventually included in this review. Of the 16 studies, 14 studies were included in 6 meta-analyses. Our meta-analyses showed that serious games are as effective as no or passive interventions at improving executive functions (P=.29). Surprisingly, conventional exercises were more effective than serious games at improving executive functions (P=.03). Our subgroup analysis showed that both types of serious games (cognitive training games, P=.08; exergames, P=.16) are as effective as conventional exercises at improving executive functions. No difference was found between adaptive serious games and nonadaptive serious games for improving executive functions (P=.59). Conclusions: Serious games are not superior to no or passive interventions and conventional exercises at improving executive functions among older adults with cognitive impairment. However, our findings remain inconclusive due to the low quality of the evidence, the small sample size in most included studies, and the paucity of studies included in the meta-analyses. Accordingly, until more robust evidence is available, serious games should not be offered by health care providers nor used by patients for improving executive functions among older adults with cognitive impairment. Further reviews are needed to assess the long-term effect of serious games on specific executive functions or other cognitive abilities among people from different age groups with or without cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Memory, one of the main cognitive functions, is known to decline with age. Serious games have been used for improving memory in older adults. The effectiveness of serious games in improving memory has been assessed by many studies. To draw definitive conclusions about the effectiveness of serious games, the findings of these studies need to be pooled and aggregated. Objective: This study aimed to assess the effectiveness of serious games in improving memory in older adults with cognitive impairment. Methods: A systematic review of randomized controlled trials was carried out. The search sources included 8 databases, the reference lists of the included studies and relevant reviews, and the studies that cited the included studies. In total, 2 reviewers (AA and MH) independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. Extracted data were synthesized using a narrative approach and a statistical approach (ie, multilevel meta-analysis), as appropriate. Results: Of the 618 citations retrieved, 18 (2.9%) met the eligibility criteria for this review. Of these 18 studies, 15 (83%) randomized controlled trials were included in 10 multilevel meta-analyses. We found that serious games were more effective than no or passive interventions in improving nonverbal memory (P=.02; standardized mean difference [SMD]=0.46, 95% CI 0.09-0.83) and working memory (P=.04; SMD=0.31, 95% CI 0.01-0.60) but not verbal memory (P=.13; SMD=0.39, 95% CI -0.11 to 0.89). The review also showed that serious games were more effective than conventional exercises in improving verbal memory (P=.003; SMD=0.46, 95% CI 0.16-0.77) but not nonverbal memory (P=.30; SMD=-0.19, 95% CI -0.54 to 0.17) or working memory (P=.99; SMD=0.00, 95% CI -0.45 to 0.45). Serious games were as effective as conventional cognitive activities in improving verbal memory (P=.14; SMD=0.66, 95% CI -0.21 to 1.54), nonverbal memory (P=.94; SMD=-0.01, 95% CI -0.32 to 0.30), and working memory (P=.08; SMD=0.37, 95% CI -0.05 to 0.78) among older adults with cognitive impairment. Finally, the effect of adaptive serious games on working memory was comparable with that of nonadaptive serious games (P=.08; SMD=0.18, 95% CI -0.02 to 0.37). Conclusions: Serious games have the potential to improve verbal, nonverbal, and working memory in older adults with cognitive impairment. However, our findings should be interpreted cautiously given that most meta-analyses were based on a few studies (≤3) andjudged to have a low quality of evidence. Therefore, serious games should be offered as a supplement to existing proven and safe interventions rather than as a complete substitute until further, more robust evidence is available. Future studies should investigate the short- and long-term effects of serious games on memory and other cognitive abilities among people of different age groups with or without cognitive impairment. [ABSTRACT FROM AUTHOR]

Introduction: Few studies have reported cardiac and hepatic iron overload in patients with kidney failure or end‐stage renal disease and the current evidence regarding the prevalence is still scarce. To the best of the authors' knowledge and following an exhaustive search; no systematic review/meta‐analysis has estimated the aggregated prevalence of cardiac and hepatic iron overload in this patient population. Aim: This review aims to estimate the prevalence of hepatic and/or cardiac iron overload in patients with kidney failure who are receiving hemodialysis, peritoneal dialysis, or underwent kidney transplants. Methods: A systematic review with meta‐analysis will be conducted and reported in line with PRISMA guidelines. MEDLINE and Embase bibliographic databases will be searched using a comprehensive list of controlled vocabularies and keywords to identify relevant studies. All studies reporting the prevalence of hepatic and/or cardiac iron overload prevalence in patients with kidney failure will be considered. Risk of bias assessment for included studies will be conducted based on the study design. StataBE v17 and MetaXL v5.3 will be utilized to perform the meta‐analysis. Discussion: The findings of this systematic review and analysis are expected to give information on the prevalence of iron overload among patients with kidney failure, which will optimize interventions and guide future research in this understudied field. [ABSTRACT FROM AUTHOR]

The article presents the correction for a article titled "The role of blockchain to secure internet of medical things" published online on September 12, 2024.

In this study, we addressed the alternative medications that have been targeted in the clinical trials (CTs) to be evidenced as an adjuvant treatment against COVID-19. Based on the outcomes from CTs, we found that dietary supplements such as Lactoferrin, and Probiotics (as SivoMixx) can play a role enhancing the immunity thus can be used as prophylactics against COVID-19 infection. Vitamin D was proven as an effective adjuvant treatment against COVID-19, while Vitamin C role is uncertain and needs more investigation. Herbals such as Guduchi Ghan Vati can be used as prophylactic, while Resveratrol can be used to reduce the hospitalization risk of COVID-19 patients. On the contrary, there were no clinical improvements demonstrated when using Cannabidiol. This study is a part of a twophase research study. In the first phase, we gathered evidence-based information on alternative therapeutics for COVID-19 that are under CT. In the second phase, we plan to build a mobile health application that will provide evidence based alternative therapy information to health consumers. [ABSTRACT FROM AUTHOR]

We explore the effectiveness of serious games for visuospatial abilities among older adults with cognitive impairment by conducting a systematic review. Out of 548 identified publications, seven randomized controlled trials (RCTs) were included in this review. According to a meta-analysis of four RCTs, there is no statistically significant difference (p=0.28) in visuospatial abilities between serious game and control groups. Further, the included RCTs noted no statistically significant difference in the visuospatial ability when comparing serious games to conventional exercise (one study) and other serious games (two studies). One RCT demonstrated a statistically significant effect of serious games on the visuospatial ability when compared with conventional cognitive training. This review could not prove the effectiveness of serious games in enhancing visuospatial abilities for older adults with cognitive impairment. Thus, serious games should not be offered or used for enhancing visuospatial abilities amongst the elderly population with cognitive impairment. More robust RCTs are needed to make firm conclusions on the efficacy of serious games. [ABSTRACT FROM AUTHOR]

Children go through varied emotions such as happiness, sadness, and fear. At times, it may be difficult for children to express their emotions. Detecting and understanding the unexpressed emotions of children is very important to address their needs and prevent mental health issues. In this paper, we develop an artificial intelligence (AI) based Emotion Sensing Recognition App (ESRA) to help parents and teachers understand the emotions of children by analyzing their drawings. We collected 102 drawings from a local school in Doha and 521 drawings from Google and Instagram. Four different experiments were conducted using a combination of the two datasets. The deep learning model was trained using the Fastai library in Python. The model classifies the drawings into positive or negative emotions. The model accuracy ranged from 55% to 79% in the four experiments. This study showed that ESRA has the potential in identifying the emotions of children. However, the underlying algorithm needs to be trained and evaluated using more drawings to improve its current accuracy and to be able to identify more specific emotions. [ABSTRACT FROM AUTHOR]

This article intended to carry out a systematic review on the effectiveness of serious games for language processing among older adults with cognitive impairment. Out of 548 retrieved records, six randomized controlled trials (RCTs) eventually met the eligibility criteria. A meta-analysis of four studies showed that serious games are more effective than no/passive interventions in enhancing language processing among older adults with cognitive impairment (p=0.008). Further, a statistically significant effect of serious games on language processing when compared with conventional cognitive activities and conventional exercises was reported in two RCTs. Other RCTs found that exergames are as effective as computerized cognitive training games in improving language processing. Serious games should be offered or used as complementary (i.e., not a substitute) to the current interventions. For there to be definitive conclusions about the efficacy of serious games on language processing more trials are needed. [ABSTRACT FROM AUTHOR]

Over the past decade, Artificial Intelligence (AI) technologies have quickly become implemented in protecting data, including detecting fraud in healthcare organizations. This scoping review aims to explore AI solutions utilized in fraud detection occurring in treatment settings. To find relevant literature, PubMed and Google Scholar were searched. Out of 183 retrieved studies, 31 met all inclusion criteria. This review found that AI has been used to detect different types of fraud such as identify theft and kickbacks in healthcare. Additionally, this review discusses how AI techniques used in network mapping fraud can detect and visualize the hacker’s network. A proper system must be implemented in healthcare settings for successful fraud detection, which may overall improve the healthcare system. [ABSTRACT FROM AUTHOR]

This scoping review aims to identify the causes and consequences of physician burnout resulting from using Electronic Health Records (EHRs), as reported by current literature. We identified studies by searching PubMed, Wiley Online Library, and Google Scholar. Study selection and data extraction were conducted by three reviewers independently. Extracted data was then synthesized narratively. Out of 500 references retrieved, 30 studies met all eligibility criteria. We identified six main causes that lead to physician burnout related to the use of EHRs: EHRs' documentation and related tasks, EHRs' poor design, workload, overtime work, inbox alerts, and alert fatigue. We also identified the following consequences of physician burnout: low-quality care, behavioral issues, mental health complications, substance abuse, career dissatisfaction, costly turnover, and a decline in patient safety and satisfaction. [ABSTRACT FROM AUTHOR]

This review aims to provide an overview of the features of meditation apps as described in empirical literature. Nine databases were searched for this review. Search terms were related to all types of meditation. Study selection and data extraction of the included studies were conducted by two reviewers. We included 93 studies in this review. Headspace was the most common app among studies and the most common type of meditation was mindfulness. Stress was the most targeted health condition by the studies. Future research needs to focus on different mental conditions other than stress to understand the effect of meditation apps on mental health. [ABSTRACT FROM AUTHOR]

Many meditation apps have been used to improve the mental wellbeing of individuals. However, little information is available regarding the quality of the applications. This study aims to evaluate meditation apps using the Mobile Applications Rating Scale (MARS). A systematic search for meditation apps was performed on both Android Google Play and Apple iOS Store. We used two keywords to search both app stores: meditation and mindfulness. Out of 623 apps identified, 334 apps were excluded due to language, containing only reminders to meditate, or for not being accessible. A total number of 289 apps remained, of which 280 apps were excluded for being information-only focused, containing religious practices, eating habits, exercises, or for not being free. Therefore, nine apps were included in this review for evaluation. The MARS ratings used in this app review were based on scores from a prior study conducted. The mobile app Headspace had the highest average (4), which is rated as 'good' based on MARS. The remaining apps were rated as acceptable with averages that ranged from 3.2-3.7. [ABSTRACT FROM AUTHOR]

For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial Intelligence (AI) technologies being used in many different medical specialties. Recently, AI-driven technologies have been utilized in medical care for pregnancy. In this work, we present a scoping review that explores the features of AI-driven technologies used in caring for pregnant patients. This review was conducted using the Preferred Reporting Items for Systematic review and Meta Analyses extension for Scoping Reviews. Our analysis revealed that AI techniques were used in predicting pregnancy disorders such as preeclampsia and gestational diabetes, along with managing and treating ectopic pregnancies. We also found that AI technologies were used to assess risk factors and safety surveillance of pregnant women. We believe that AI-driven technologies have the potential to improve the healthcare provided to pregnant women. [ABSTRACT FROM AUTHOR]

This scoping review aims to identify the causes and consequences of physician burnout resulting from using Electronic Health Records (EHRs), as reported by current literature. We identified studies by searching PubMed, Wiley Online Library, and Google Scholar. Study selection and data extraction were conducted by three reviewers independently. Extracted data was then synthesized narratively. Out of 500 references retrieved, 30 studies met all eligibility criteria. We identified six main causes that lead to physician burnout related to the use of EHRs: EHRs' documentation and related tasks, EHRs' poor design, workload, overtime work, inbox alerts, and alert fatigue. We also identified the following consequences of physician burnout: low-quality care, behavioral issues, mental health complications, substance abuse, career dissatisfaction, costly turnover, and a decline in patient safety and satisfaction. [ABSTRACT FROM AUTHOR]

For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial Intelligence (AI) technologies being used in many different medical specialties. Recently, AI-driven technologies have been utilized in medical care for pregnancy. In this work, we present a scoping review that explores the features of AI-driven technologies used in caring for pregnant patients. This review was conducted using the Preferred Reporting Items for Systematic review and MetaAnalyses extension for Scoping Reviews. Our analysis revealed that AI techniques were used in predicting pregnancy disorders such as preeclampsia and gestational diabetes, along with managing and treating ectopic pregnancies. We also found that AI technologies were used to assess risk factors and safety surveillance of pregnant women. We believe that AI-driven technologies have the potential to improve the healthcare provided to pregnant women. [ABSTRACT FROM AUTHOR]

This review aims to provide an overview of the features of meditation apps as described in empirical literature. Nine databases were searched for this review. Search terms were related to all types of meditation. Study selection and data extraction of the included studies were conducted by two reviewers. We included 93 studies in this review. Headspace was the most common app among studies and the most common type of meditation was mindfulness. Stress was the most targeted health condition by the studies. Future research needs to focus on different mental conditions other than stress to understand the effect of meditation apps on mental health. [ABSTRACT FROM AUTHOR]

Many meditation apps have been used to improve the mental wellbeing of individuals. However, little information is available regarding the quality of the applications. This study aims to evaluate meditation apps using the Mobile Applications Rating Scale (MARS). A systematic search for meditation apps was performed on both Android Google Play and Apple iOS Store. We used two keywords to search both app stores: meditation and mindfulness. Out of 623 apps identified, 334 apps were excluded due to language, containing only reminders to meditate, or for not being accessible. A total number of 289 apps remained, of which 280 apps were excluded for being information-only focused, containing religious practices, eating habits, exercises, or for not being free. Therefore, nine apps were included in this review for evaluation. The MARS ratings used in this app review were based on scores from a prior study conducted. The mobile app Headspace had the highest average (4), which is rated as ‘good’ based on MARS. The remaining apps were rated as acceptable with averages that ranged from 3.2-3.7. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment is a mental disorder that commonly affects elderly people. Serious games, which are games that have a purpose other than entertainment, have been used as a nonpharmacological intervention for improving cognitive abilities. The effectiveness and safety of serious games for improving cognitive abilities have been investigated by several systematic reviews; however, they are limited by design and methodological weaknesses. Objective: This study aims to assess the effectiveness and safety of serious games for improving cognitive abilities among elderly people with cognitive impairment. Methods: A systematic review of randomized controlled trials (RCTs) was conducted. The following 8 electronic databases were searched: MEDLINE, Embase, CINAHL, PsycINFO, ACM Digital Library, IEEE Xplore, Scopus, and Google Scholar. We also screened reference lists of the included studies and relevant reviews, as well as checked studies citing our included studies. Two reviewers independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. We used a narrative and statistical approach, as appropriate, to synthesize the results of the included studies. Results: Fifteen studies met the eligibility criteria among 466 citations retrieved. Of those, 14 RCTs were eventually included in the meta-analysis. We found that, regardless of their type, serious games were more effective than no intervention (P=.04) and conventional exercises (P=.002) for improving global cognition among elderly people with cognitive impairment. Further, a subgroup analysis showed that cognitive training games were more effective than no intervention (P=.05) and conventional exercises (P<.001) for improving global cognition among elderly people with cognitive impairment. Another subgroup analysis demonstrated that exergames (a category of serious games that includes physical exercises) are as effective as no intervention and conventional exercises (P=.38) for improving global cognition among elderly people with cognitive impairment. Although some studies found adverse events from using serious games, the number of adverse events (ie, falls and exacerbations of pre-existing arthritis symptoms) was comparable between the serious game and control groups. Conclusions: Serious games and specifically cognitive training games have the potential to improve global cognition among elderly people with cognitive impairment. However, our findings remain inconclusive because the quality of evidence in all meta-analyses was very low, mainly due to the risk of bias raised in the majority of the included studies, high heterogeneity of the evidence, and imprecision of total effect sizes. Therefore, psychologists, psychiatrists, and patients should consider offering serious games as a complement and not a substitute to existing interventions until further more robust evidence is available. Further studies are needed to assess the effect of exergames, the safety of serious games, and their long-term effects. [ABSTRACT FROM AUTHOR]

Background: Anxiety is a mental disorder characterized by apprehension, tension, uneasiness, and other related behavioral disturbances. One of the nonpharmacological treatments used for reducing anxiety is serious games, which are games that have a purpose other than entertainment. The effectiveness of serious games in alleviating anxiety has been investigated by several systematic reviews; however, they were limited by design and methodological weaknesses. Objective: This study aims to assess the effectiveness of serious games in alleviating anxiety by summarizing the results of previous studies and providing an up-to-date review. Methods: We conducted a systematic review of randomized controlled trials (RCTs). The following seven databases were searched: MEDLINE, CINAHL, PsycINFO, ACM Digital Library, IEEE Xplore, Scopus, and Google Scholar. We also conducted backward and forward reference list checking for the included studies and relevant reviews. Two reviewers independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. We used a narrative and statistical approach, as appropriate, to synthesize the results of the included studies. Results: Of the 935 citations retrieved, 33 studies were included in this review. Of these, 22 RCTs were eventually included in the meta-analysis. Very low--quality evidence from 9 RCTs and 5 RCTs showed no statistically significant effect of exergames (games entailing physical exercises) on anxiety levels when compared with conventional exercises (P=.70) and no intervention (P=.27), respectively. Although 6 RCTs demonstrated a statistically and clinically significant effect of computerized cognitive behavioral therapy games on anxiety levels when compared with no intervention (P=.01), the quality of the evidence reported was low. Similarly, low-quality evidence from 3 RCTs showed a statistically and clinically significant effect of biofeedback games on anxiety levels when compared with conventional video games (P=.03). Conclusions: This review shows that exergames can be as effective as conventional exercises in alleviating anxiety; computerized cognitive behavioral therapy games and exergames can be more effective than no intervention, and biofeedback games can be more effective than conventional video games. However, our findings remain inconclusive, mainly because there was a high risk of bias in the individual studies included, the quality of meta-analyzed evidence was low, few studies were included in some meta-analyses, patients without anxiety were recruited in most studies, and purpose-shifted serious games were used in most studies. Therefore, serious games should be considered complementary to existing interventions. Researchers should use serious games that are designed specifically to alleviate depression, deliver other therapeutic modalities, and recruit a diverse population of patients with anxiety. [ABSTRACT FROM AUTHOR]

Background: Depression is a common mental disorder characterized by disturbances in mood, thoughts, or behaviors. Serious games, which are games that have a purpose other than entertainment, have been used as a nonpharmacological therapeutic intervention for depression. Previous systematic reviews have summarized evidence of effectiveness of serious games in reducing depression symptoms; however, they are limited by design and methodological shortcomings. Objective: This study aimed to assess the effectiveness of serious games in alleviating depression by summarizing and pooling the results of previous studies. Methods: A systematic review of randomized controlled trials (RCTs) was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The search sources included 6 bibliographic databases (eg, MEDLINE, PsycINFO, IEEE Xplore), the search engine "Google Scholar," and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. Results of the included studies were synthesized narratively and statistically, as appropriate, according to the type of serious games (ie, exergames or computerized cognitive behavioral therapy [CBT] games). Results: From an initial 966 citations retrieved, 27 studies met the eligibility criteria, and 16 studies were eventually included in meta-analyses. Very low-quality evidence from 7 RCTs showed no statistically significant effect of exergames on the severity of depressive symptoms as compared with conventional exercises (P=.12). Very low-quality evidence from 5 RCTs showed a statistically and clinically significant difference in the severity of depressive symptoms (P=.004) between exergame and control groups, favoring exergames over no intervention. Very low-quality evidence from 7 RCTs showed a statistically and clinically significant effect of computerized CBT games on the severity of depressive symptoms in comparison with no intervention (P=.003). Conclusions: Serious games have the potential to alleviate depression as other active interventions do. However, we could not draw definitive conclusions regarding the effectiveness of serious games due to the high risk of bias in the individual studies examined and the low quality of meta-analyzed evidence. Therefore, we recommend that health care providers consider offering [ABSTRACT FROM AUTHOR]

Background: In recent years, an increasing number of health chatbots has been published in app stores and described in research literature. Given the sensitive data they are processing and the care settings for which they are developed, evaluation is essential to avoid harm to users. However, evaluations of those systems are reported inconsistently and without using a standardized set of evaluation metrics. Missing standards in health chatbot evaluation prevent comparisons of systems, and this may hamper acceptability since their reliability is unclear.Objectives: The objective of this paper is to make an important step toward developing a health-specific chatbot evaluation framework by finding consensus on relevant metrics.Methods: We used an adapted Delphi study design to verify and select potential metrics that we retrieved initially from a scoping review. We invited researchers, health professionals, and health informaticians to score each metric for inclusion in the final evaluation framework, over three survey rounds. We distinguished metrics scored relevant with high, moderate, and low consensus. The initial set of metrics comprised 26 metrics (categorized as global metrics, metrics related to response generation, response understanding and aesthetics).Results: Twenty-eight experts joined the first round and 22 (75%) persisted to the third round. Twenty-four metrics achieved high consensus and three metrics achieved moderate consensus. The core set for our framework comprises mainly global metrics (e.g., ease of use, security content accuracy), metrics related to response generation (e.g., appropriateness of responses), and related to response understanding. Metrics on aesthetics (font type and size, color) are less well agreed upon-only moderate or low consensus was achieved for those metrics.Conclusion: The results indicate that experts largely agree on metrics and that the consensus set is broad. This implies that health chatbot evaluation must be multifaceted to ensure acceptability. [ABSTRACT FROM AUTHOR]

The importance of the research lies in preparing an educational field in developing motor speed and football scoring skill for cubs, depending on the stock of motor abilities and skills that were learned in a previous period and which are prerequisites for motor learning and achieving good technical performance that secures a level of performance. the researchers decided to prepare an educational field by means of assisting in the development of kinematic speed and football scoring skill for cubs in order to reach the desired and intended achievement. The research objective : Preparing an educational field with assistive means to develop motor speed and soccer scoring skill for young players. The researchers used the experimental approach in solving the research problem, and on the research community, the research community was identified with young players of ages (13-14) years for the football school to foster the sporting talent in football for the Kotha district / Babil governorate, whose number is (22) players, and they were divided equally by the method, simple randomness. As for the most important conclusions, they were: The educational field contributed by means of assisting in the development of motor velocity and the skill of scoring football for young players. [ABSTRACT FROM AUTHOR]

The importance of the research lies in preparing an educational field in developing the skills of dribbling and shooting football for cubs, depending on the stock of motor skills that were learned in a previous period, which are prerequisites for motor learning and achieving good technical performance that secures a level of performance. As for the research problem: through the experience of field researchers, they noticed that there is a weakness in the performance of the dribbling and scoring skills of the young players, and that the level of dribbling and scoring performance is not commensurate with the level required for this stage, a the direct interest in dribbling and scoring through skill exercises is not sufficient to reach high performance without concern for the effectiveness of the educational field, and this is what the cubs’ teams suffer at least in our country, and if there is interest in these variables, then it is random and includes training that does not distinguish between the important and most important dimensions and components of these variables. Therefore, the researchers decided to prepare an educational field in developing the skills of dribbling and scoring football for cubs in order to reach the desired and intended achievement. [ABSTRACT FROM AUTHOR]

Due to the corona (COVID-19) pandemic, several countries are currently conducting non-face-to-face education. Therefore, teachers of nursing colleges have been carrying out emergency remote education. This study developed a questionnaire to understand the status of Emergency Remote Learning (ERL) in nursing education internationally, translated it into 7 languages, and distributed it to 18 countries. A total of 328 nursing educators responded, and the most often used online methods were Social networking technology such as Facebook, Google+ and Video sharing platform such as YouTube. The ERL applied to nursing education was positively evaluated as 3.59 out of 5. The results of the study show that during the two semesters nursing college professors have well adapted to this unprecedent crisis of teaching. The world after COVID-19 has become a completely different place, and nursing education should be prepared for 'untact' education. [ABSTRACT FROM AUTHOR]