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Currently, the need for real-time COVID-19 detection methods with minimal tools and cost is an important challenge. The available methods are still difficult to apply, slow, costly, and their accuracy is low. In this work, a novel machine learning-based framework to predict COVID-19 is proposed, which is based on rapid inpatient clinical tests of lung and heart function. Compared with current cognition therapy techniques, the proposed framework can significantly improve the accuracy and time performance of COVID-19 diagnosis without any lab or equipment requirements. In this work, five parameters of clinical testing were adopted;Respiration rate, Heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial blood pressure. After obtaining results for these tests, a pre-trained intelligent model based on Random Forest Tree (RFT) machine learning algorithm is used for detection. This model was trained by about 13,558 records of the COVID19 testing dataset collected from King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Saudi Arabia. Experiments have shown that the proposed framework performs highly in detecting COVID infections by 96.9%. Its results can be output in minutes, which supports clinical staff in screening COVID-19 patients from their inpatient clinical data. © 2022 IEEE.
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Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.
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COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples' mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public;country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification. © 2022 IEEE.
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Background: COVID-19 pneumonia is newly recognized illness has spread rapidly around the world. It causes much morbidity and mortality. Diabetes in COVID-19 patients is individual risk factor and documented in worldwide studies to contribute to severity of the disease, increased length of stay and higher mortality. Aggressive management of blood sugars and acute diabetic complications reduce the length of stay and mortality. Method(s): The improvement project carried over for four months in King Fahd Armed Forces hospital. Randomly selected 200 patients admitted to the hospital with diabetes and COVID-19 studied. The unified treatment protocol applied for all patients and blood sugars monitored closely and optimized. Data collected on bimonthly basis and analyzed. The patient characteristics taken from data extraction tool (Oasis) of hospital. Median values for length of stay and post discharge FBS and RBS were calculated Microsoft Excel tool. Mortality rates calculated by percentages. Result(s): 200 patients studied in the 4 months study period. The median length of stay was 3 days. The mortality rate was 2.5%. The median FBS and RBS in the patient group monitored in the post discharge clinic was 130 mg/dl and 170 mg/dl respectively. The results compared with the standard international studies and improvements in primary and secondary outcomes documented. Discussion(s): The length of stay in the study population in the project was 3 days as compared to 13 days in a major international study helping rapid turnover of beds and financial savings. The mortality was 2.5% compared to 7.3% in a major published study, reflecting the implications of aggressive management of diabetes by teamwork. The median values of FBS 130 mg/dl and RBS of 170mg/dl achieved by regular follow-up and support to the patient by running postdischarged clinic definitely help reducing readmissions and acute complications of uncontrolled diabetes. Conclusion(s): COVID-19 patients with diabetes are more prone to have more severe disease, and tend to stay longer in hospital and have higher mortality compared to non-diabetics. An aggressive preemptive strategy with unified treatment protocols and readily available service of endocrinologist and effective control of diabetes and acute complications of diabetes significantly reduces the length of and mortality in COVID-19 patients with diabetes.
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The lumpy skin disease (LSD) virus of the Poxviridae family is a serious threat that mostly affects cattle and causes significant economic loss. LSD has the potential to spread widely and its rapidly across borders. Despite the availability of information, there is still no competitive vaccine available for LSD. Therefore, the current study was conducted to develop an epitope-based LSD vaccine that is efficient, secure, and biocompatible and stimulates both innate and adaptive immune responses using immunoinformatics techniques. Initially, putative virion core proteins were manipulated;B-cell and T-cell epitopes have been predicted and connected with the help of adjuvants and linkers. Numerous bioinformatics methods, including antigenicity testing, transmembrane topology screening, allergenicity assessment, conservancy analysis, and toxicity evaluation, were employed to find superior epitopes. Based on promising vaccine candidates and immunogenic potential, the vaccine design was selected. Strong interactions between TLR4 and TLR9 and the anticipated vaccine design were revealed by molecular docking. Finally, based on the high docking score, computer simulations were performed in order to assess the stability, efficacy, and compactness of the constructed vaccine. The simulation outcomes showed that the polypeptide vaccine design was remarkably stable, with high expression, stability, immunogenic qualities, and considerable solubility. Additionally, computer-based research shows that the constructed vaccine provides adequate population coverage, making it a promising candidate for use in the design of vaccines against other viruses within the Poxviridae family and potentially other virus families as well. These outcomes suggest that the epitope-based vaccine developed in this study will be a significant candidate against LSD to control and prevent LSDV-related disorders if further investigated experimentally.
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Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to discover holistic, multi-perspective (e.g., local, cultural, national, and international), and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from the academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media and Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months from March 2021 to August 2022 to showcase the public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles. Discovering system parameters are required to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The work presented in this paper has significant theoretical and practical implications in that it improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, holistic, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. © 2023 by the authors.
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Background: Face masks have become a crucial part of everyday life across the globe since COVID-19 was declared a pandemic. Objectives: To study the impact of prolonged use of facemasks on the health of the population of Jeddah, Saudi Arabia Method: This was a cross sectional study;the non-probability convenient sampling method was used to collect data on 248 subjects via online-Google form questionnaire. The questionnaire provided information on the personal, sociodemographic characteristics, and clinical aspects of the studied subjects. Statistical analysis: data were analyzed using SPSS version 23. The Chi square test of significance was used. The level of significance was 0.05. Results: Respiratory tract infection (RTI) is common in Saudi Arabia. About 25% of the subjects did not use facemasks. Almost all the subjects who used facemasks knew how to wear them and the reason for their use, and were worried about catching RTI. A minority of the studied subjects didn't know the association between chronic diseases and RTI. Males significantly felt more than females that wearing facemasks would make them embarrassed, and would affect critically their social communication. The majority of the studied subjects did not want to wear a mask when they had flu, and they thought that others should protect themselves. Facemasks interfere with smoking habits and practicing exercise. Wearing masks was associated with a feeling of difficulty of breathing when walking, causing headache, skin problems and sore throat. Also the majority could not handle wearing masks for 3 hours straight. About 60% of the subjects felt uncomfortable when they sit with people who do not wear masks. The females significantly felt that the surgical mask is better than the cloth mask, compared to males. The majority prefer to wear one mask only, and use it several times. Conclusion: Respiratory tract infection is common in Jeddah city. A great proportion of the subjects do not wear facemasks in public places, particularly when they have flu. Wearing of facemasks is associated with several clinical adverse effects. The knowledge about types and use of the facemasks is deficient in a great proportion of the population. These points will help the health care planners when they design health education programs to educate the public about use of facemasks and the ways to avoid physical side effects
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Objective: During the coronavirus disease 2019 pandemic, virtual phone clinics were adopted at King Abdulaziz Medical City, Jeddah, Saudi Arabia, as an alternative to in-person routine outpatient visits. This study aimed to assess primary caregiver perceptions of the virtual phone clinics towards routine care of pediatric patients with type 1 diabetes mellitus (T1DM). Method(s): A cross-sectional research design was employed through a web-based research tool where the primary caregivers of pediatric patients with T1DM participated. Data were analyzed descriptively and statistically using independent t-tests and one-way analysis of variance. Result(s): In total, 214 participants were included in this study. The average primary caregiver perception value towards virtual phone clinics was 66.71 +/- 23.1%, which suggested a satisfactory perception of their experience. Statistically significant differences (P >.05) were not found between the mean perception values and demographic characteristics. Higher mean primary caregiver perception values were associated with those who: 1) agreed or strongly agreed to be contented with their current state of health, 2) did not ask their physicians to be physically seen during a virtual visit, 3) preferred having four virtual visits or more, and 4) preferred having single to no in-person visits at all. Discussion/Conclusion: This study revealed a generally acceptable primary caregiver perception regarding virtual clinics. Telemedicine is recommended for routine treatment alongside regular in-clinic appointments to improve the quality of care for pediatric patients with T1DM. Copyright © 2022
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Introduction: & Background: Diabetes in COVID-19 patients is individual risk factor and documented in worldwide studies to contribute to disease severity, increased length of stay and higher mortality (fig-1). Aggressive management of blood sugars and acute diabetic complications reduce the length of stay and mortality. Methods: Randomly selected 200 patients admitted with diabetes and COVID-19 studied. The unified treatment protocol (fig-2) applied for all patients and blood sugars monitored closely and optimized. Data collected on bimonthly basis and analyzed. Patients’ characteristics taken from data extraction tool (Oasis) of hospital. Median values for length of stay and post discharge FBS and RBS were calculated Microsoft Excel tool. Mortality rates calculated by percentages. Results: 200 patients studied in the 4 months study period. The median length of stay was 3 days. The mortality rate was 2.5% (fig-3,4). The median FBS and RBS in the patient group monitored in the post discharge clinic was 130 mg/dl and 170 mg/dl respectively. The results compared with the standard international studies. Discussion: Diabetes in COVID-19 patients posed great challenge as increased severity and mortalities reported compared to non-diabetic. Taking a pre-emptive strategy to combat this problem by aggressively manage diabetes help in reducing length of stay and morbidity. The length of stay in studded population was 3 days as compared to 13 days in a major international study(Ref: 1) . Financial saving come from rapid turnover of beds. The mortality was 2.5% compared to reported 7.3% in a major study (Ref: 2), reflecting the implications of aggressive management of diabetes. Regular follow-up and support by running post-discharge clinic definitely help reducing readmissions and acute complications of uncontrolled diabetes. Conclusion: Aggressive management of diabetes in COVID-19 patients by tailored treatment protocols and dedicated teams will help to decrease the morbidity and mortality.Presentation: No date and time listed
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Selenium is an indispensable trace element for all living organisms. It is an essential structural component of several selenium-dependent enzymes, which support the human body's defense mechanism. Recently, the significance of selenium in preventing/treating COVID-19 has been documented in the literature. This review highlights the clinical studies, compositions, and patent literature on selenium to prevent/treat COVID-19. Selenium exerts its anti-COVID-19 action by reducing oxidative stress, declining the expression of the ACE-2 receptor, lowering the discharge of pro-inflammatory substances, and inhibiting the 3CLPro (main protease) and PLpro enzyme of SARS-CoV-2. The data of clinical studies, inventive compositions, and patent literature revealed that selenium monotherapy and its compositions with other nutritional supplements/drugs (vitamin, iron, zinc, copper, ferulic acid, resveratrol, spirulina, N-acetylcysteine, fish oil, many herbs, doxycycline, azithromycin, curcumin, quercetin, etc.,) might be practical to prevent/treat COVID-19. The studies have also suggested a correlation between COVID-19 and selenium deficiency. This indicates that adequate selenium supplementation may provide promising treatment outcomes in COVID-19 patients. The authors foresee the development and commercialization of Selenium-based compositions and dosage forms (spray, inhalers, control release dosage forms, etc.) to battle COVID-19. We also trust that numerous selenium-based compositions are yet to be explored. Accordingly, there is good scope for scientists to work on developing novel and inventive selenium-based compositions to fight against COVID-19. However, there is also a need to consider the narrow therapeutic window and chemical interaction of selenium before developing selenium-based compositions.
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Aim of Study: To investigate the adverse events of COVID-19 vaccinations on menstrual abnormalities in women in their reproductive period. Methods: Following a cross-sectional research design, this study included 102 women who had received the COVID-19 vaccinations. A study questionnaire adapted from the Menstruation after COVID vaccine (MECOVAC) survey was used for data collection. Results: Almost half of participants (48%) reported the incidence of menstrual abnormalities after receiving COVID-19 vaccinations, manifested as changes in frequency, length or quantity of menstruation. Participants received one, two or three doses of either Pfizer-BioNTech or Oxford-AstraZeneca vaccines. Adverse events of COVID-19 vaccines occurred mainly after the third doses of Pfizer and AstraZeneca (12.5% for both). The most frequent changes in quantity of menstrual blood among participants were variation in its quantity and having heavier menstruation (17.2% and 15.6%, respectively), which occurred after receiving the third dose of Pfizer vaccine, while menstrual blood became less among 25% of participants after receiving the third dose of AstraZeneca. Disturbance of personal life occurred in 44.9%, while 38.8% needed to visit a doctor, 26.5% underwent investigations and 32.7% took medications. Menstrual cycles of 51% returned to normal within 1-2 cycles, while those of 49% returned after more than two cycles. Conclusions: Women who receive COVID-19 vaccinations may have menstrual abnormalities. This change mainly occurs after the third dose, regardless of the brand of received vaccine. It is recommended that women be clearly informed after vaccination of the possibility of short-term menstrual abnormality and to seek proper medical advice in such conditions. Further studies are required to investigate the possible mechanisms behind these COVID-19 vaccines' adverse events.
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"In recent years, many technological innovations that have contributed to the success of the educational process, and artificial intelligence is one of these recent innovations. Artificial intelligence has become one of the technological tools used in education and demonstrated a successful emergency strategy during the pandemic of Covid-19. This study aimed to focus on the role artificial intelligence played in education in Arabs world during the last five years. This study conducted a comprehensive survey of the research published in three databases (Google Scholar, ERIC, and IEEE) to reach the goal of this study. The total number were found in three databases was 436 and 29 of them are related to ""Artificial Intelligence and Learning and Teaching and Arab world"", the s of these studies were checked according to adopted methodology. The methodology was applied to select the studies is where these studies applied, the specialization of the researcher, and the research methodology followed. Moreover, the goals of the selected papers, the results, and similarities and differences among these studies were discussed. Recommendations and future studies also discussed."
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Background/Introduction: During the coronavirus disease 2019 (COVID-19) pandemic, hospitals have shortened the number of elective surgeries performed to the advantage of patient safety. Bariatric surgery, which is the utmost operative intervention for longstanding weight loss in obese patients, is one example of such elective procedures that were postponed and delayed during the pandemic. Moreover, it has been noted that the majority of people experienced significant disruptions in their lifestyle management, including physical activity and eating practices during the COVID-19 pandemic. Objective(s): The objectives of this study is to evaluate the effect of COVID-19 pandemic on the psychological wellbeing and self-management practices among patients with obesity on bariatric surgery waiting list at King Abdulaziz Medical City, Jeddah, Saudi Arabia. Method(s): A web-based survey was sent to all patients on bariatric surgery waiting list. It included questions about demographics, COVID-19, and changes in self-management behaviors and psychological status during the COVID-19 pandemic. The Patient Health Questionnaire-9 (PHQ-9) was used to measure the severity of depression. Associations between categorical variables were established by applying the chisquare test, and the means of two groups were equated using an independent t-test and a one-way ANOVA. Result(s): Out of 437 patients identified, 208 participants successfully completed the survey. The mean PHQ-9 total score was 8.29 +/- 6.3. In terms of PHQ-9 categories, the majority had no depression (32.2%, n = 67), followed shortly by mild depression (30.8%, n = 64), and then by moderate depression (19.2%, n = 40), moderately severe depression (12.0%, n = 25), and severe depression (5.8%, n = 12). Changes in psychological status, weight, dietary habits, and physical activity that occurred during the COVID-19 pandemic in addition to the employment status were significantly correlated with the PHQ-9 Total Score (p < 0.05). Conclusion(s): Findings of this study demonstrated that obesity amidst COVID-19 plays significant influence on the psychological state of patients on the bariatric surgery waiting list, in which low psychological status and poor self-management behaviors were revealed. (Table Presented).
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This study aimed to test the factor structure of the measure of student participation in distance education. The study population consisted of all teachers in public education and faculty members in higher education in the Kingdom of Saudi Arabia by applying it to a sample of bachelor's and graduate students at the college of Education at umm al-Qura University. The (ESE) was applied to a random sample representing the study population consisting of (216) respondents. The results of the study showed that the scale consists of three main factors, with showed a high degree of construct validity through fit indices of the confirmatory factor analysis. The results have shown a gradual consistency of the measure's invariance that reaches the high level of the Measurement Invariance across the gender and study groups variables.
ABSTRACT
Selenium is an indispensable trace element for all living organisms. It is an essential structural component of several selenium-dependent enzymes, which support the human body’s defense mechanism. Recently, the significance of selenium in preventing/treating COVID-19 has been documented in the literature. This review highlights the clinical studies, compositions, and patent literature on selenium to prevent/treat COVID-19. Selenium exerts its anti-COVID-19 action by reducing oxidative stress, declining the expression of the ACE-2 receptor, lowering the discharge of pro-inflammatory substances, and inhibiting the 3CLPro (main protease) and PLpro enzyme of SARS-CoV-2. The data of clinical studies, inventive compositions, and patent literature revealed that selenium monotherapy and its compositions with other nutritional supplements/drugs (vitamin, iron, zinc, copper, ferulic acid, resveratrol, spirulina, N-acetylcysteine, fish oil, many herbs, doxycycline, azithromycin, curcumin, quercetin, etc.,) might be practical to prevent/treat COVID-19. The studies have also suggested a correlation between COVID-19 and selenium deficiency. This indicates that adequate selenium supplementation may provide promising treatment outcomes in COVID-19 patients. The authors foresee the development and commercialization of Selenium-based compositions and dosage forms (spray, inhalers, control release dosage forms, etc.) to battle COVID-19. We also trust that numerous selenium-based compositions are yet to be explored. Accordingly, there is good scope for scientists to work on developing novel and inventive selenium-based compositions to fight against COVID-19. However, there is also a need to consider the narrow therapeutic window and chemical interaction of selenium before developing selenium-based compositions.
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Background Amid the ongoing COVID-19 pandemic and its global health and socioeconomic aftereffects, the enduring state of crisis is increasingly impacting the coping capacity of the populations. In this study, we aimed to characterize the levels of psychological distress after the lifting of COVID-19 lockdown. Results The Impact of Event Scale (IES-R) and Depression, Anxiety, and Stress Scales-21 items (DASS-21) were used to screen for post-traumatic stress syndrome (PTSD), depression, anxiety, and stress. The prevalence of PTSD was 41.6% and was associated with severe or extremely severe stress (27.8%), anxiety (31.4%), and depression (39.0%). All disorders were strongly correlated with one another. The risk of developing PTSD was independently associated with residence in high COVID-19 prevalence region (OR = 2.25, p = 0.004), poor (OR = 3.98, p = 0.002), or moderate (OR = 1.63, p = 0.048) self-assessed overall physical health, psychiatric comorbidity (OR = 1.87, p = 0.036), number of COVID-19-like symptoms (OR = 1.94, p = 0.039), and severe COVID-19 morbidity in the acquaintances (OR = 1.54, p = 0.026). Four theories were proposed to explain these high figures, with a discussion of their practical implications. Conclusions The lifting of lockdown measures was associated with a substantial increase in psychological distress among the Saudi population, referring to figures reported during the lockdown. This may indicate a decline in the overall population's coping capacity with the enduring crisis.
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Background: COVID-19 is one of the recent emerging infectious diseases that were progressively declared a pandemic affecting the whole globe. Such a disease encompassed several misconceptions and conspiracy theories. This study aimed to determine the prevalence and pattern of conspiracy theories and determine associated factors and compliance with preventive measures. Methods: We conducted an analytical cross-sectional study utilizing a self-administered questionnaire on conspiracy theories and ideas. We did data analysis with the IBM SPSS version 21 was u. A p-value of <0.05 was considered for statistical significance. Descriptive statistics were performed, and we analyzed the association between variables using the Chi-square test or Kruskal-Wallis test. Results: The current study got 399 responses. We found a 35.8% prevalence of conspiracy beliefs about COVID-19 among participants. The top 4 conspiracy theory beliefs were: COVID-19 was exaggerated to make significant changes in the world's social order, COVID-19 was a secret biological weapon, big pharmaceutical companies have known about COVID-19 for a while and COVID-19 was made up to keep people's attention so that special agencies could change the world economy. Believing in conspiracy theories was significantly associated with low acceptance and compliance with preventive measures and less COVID-19 vaccine adoption. There was no difference between conspiracy believers and non-believers in regard to sociodemographic factors. Conclusion: This study finding suggest that there is still a need for continuing efforts to raise awareness and education programs to educate people, burst myths and conspiracies, and incite vaccination adoption.
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Background: COVID-19 is a disease that is caused by the coronavirus and until today, there has been no-effective treatment for this virus. In this study, our aim is to estimate the mediating effect of health behavior engagement on the associations between mental health and COVID-19-related worry, stress. Moreover, how Physical activity affect psychological health through the COVID19 pandemic in Saudi adult population. Methodology: This study was a cross-sectional survey conducted in Saudi Arabia from October 2021 to April 2022. The least possible sample size to achieve an accuracy of 5% marginal error with 95% confidence level is 384. The sample was made up of adults (male & female) who are residents of Saudi Arabia. SPSS 26 used for data entry and data analysis. Results: The study included 1698 participants, Seventy percent of them were females and 30% were males. 51.1% of participants aged between 20- 30 years old. 6.5% always and 17% usually feel nervous or anxious. 5% always and 12.9% usually feel inability to control anxiety. 65.1% of participants practiced meditation before the pandemic but only 38.3% practice it currently after the pandemic. Of the participants who used to meditate before the pandemic. Conclusion: The study concluded that increased exercise and meditation were linked with better-quality mental health and decreased the level of anxiety through the pandemic. The findings emphasize the importance of maintaining self-management health behaviors like physical activity and meditation to maintain one's mental health throughout pandemic of COVID-19.