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In addition to the classical functions of the musculoskeletal system and calcium homeostasis, the function of vitamin D as an immune modulator is well established. The vitamin D receptors and enzymes that metabolize vitamin D are ubiquitously expressed in most cells in the body, including T and B lymphocytes, antigen-presenting cells, monocytes, macrophages and natural killer cells that trigger immune and antimicrobial responses. Many in vitro and in vivo studies revealed that vitamin D promotes tolerogenic immunological action and immune modulation. Vitamin D adequacy positively influences the expression and release of antimicrobial peptides, such as cathelicidin, defensin, and anti-inflammatory cytokines, and reduces the expression of proinflammatory cytokines. Evidence suggestss that vitamin D's protective immunogenic actions reduce the risk, complications, and death from COVID-19. On the contrary, vitamin D deficiency worsened the clinical outcomes of viral respiratory diseases and the COVID-19-related cytokine storm, acute respiratory distress syndrome, and death. The study revealed the need for more preclinical studies and focused on well-designed clinical trials with adequate sizes to understand the role of vitamin D on the pathophysiology of immune disorders and mechanisms of subduing microbial infections, including COVID-19.Copyright © 2023 Bentham Science Publishers.
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Rising of a new virus from city of Cathay, responsible for 2019 global pandemic is caused by SARS-CoV-2marked as a great threat for populations. The member (CoV-2) from vast family of Covid virus with single-stranded RNA spread to over 216 countries and billions of individuals died all around the globe. Regardless of all strict standard operating procedures, special care and therapies, SARS-CoV-2 mutating its genomic structure and leads to shutting the world. While different therapeutic approaches face problems due to the complexity in pathogenicity mechanism of CoV-2 and its variants. Mechanism of action, genome analysis, transmission, development of broad-spectrum antiviral medications and SARS-CoV-2 vaccines have been reported which are essential for future directions to control this pandemic. Here, in this review, these domains were discussed to highlight the genome structure pathophysiology, immune response, multiple diagnostic methods, and possible treatment strategies. This review deliberates the methodologies for creating practical vaccinations and treatment cocktail to manage this eruption. © 2023, The Running Line. All rights reserved.
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Biosensors using opto electronics mechanisms are evolving as efficient (sensitive and selective) and low-cost analytical diagnostic devices for early-stage disease diagnosis, which is crucial for person-centered health and wellness management. Due to advancements in nanotechnology in the areas of sensing unit fabrication, device integration, interfacing, packaging, and sensing performance at the point-of-care (POC), personalized diagnostics are now possible, allowing doctors to tailor tests to each patient's unique disease profile and management requirements. Innovative biosensing technology is being pushed as the diagnostic tool of the future because of its potential to provide accurate results without requiring intrusive procedures. Because of this, this visionary piece of writing explores analytical methods for managing personalised health care that IP 203.8 109.10 On: Th , 16 F b 2023 14 53 21 can enhance the health of the general population. The end goal is to take control of a healthier tomorrow as Copyright: Ame can Scientific Pub shers soon as possible. Right now, the most crucial part of controllig the COVID-19 pandemic, a potentially fatal Delive ed by Ingenta respiratory viral disease, is the rapid, specific, and sensitive detection of human beta severe acute respiratory system coronavirus (SARS-CoV-2) protein.
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Background Understanding health trends and estimating the burden of disease at the national and subnational levels helps policy makers track progress and identify disparities in overall health performance. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides comprehensive estimates for Pakistan. Comparison of health indicators since 1990 provides valuable insights about Pakistan's ability to strengthen its health-care system, reduce inequalities, improve female and child health outcomes, achieve universal health coverage, and meet the UN Sustainable Development Goals. We present estimates of the burden of disease, injuries, and risk factors for Pakistan provinces and territories from 1990 to 2019 based on GBD 2019 to improve health and health outcomes in the country. Methods We used methods and data inputs from GBD 2019 to estimate socio-demographic index, total fertility rate, cause-specific deaths, years of life lost, years lived with disability, disability-adjusted life-years, healthy life expectancy, and risk factors for 286 causes of death and 369 causes of non-fatal health loss in Pakistan and its four provinces and three territories from 1990 to 2019. To generate estimates for Pakistan at the national and subnational levels, we used 68 location-years of data to estimate Pakistan-specific demographic indicators, 316 location-years of data for Pakistan-specific causes of death, 579 location-years of data for Pakistan-specific non-fatal outcomes, 296 location-years of data for Pakistan-specific risk factors, and 3089 location-years of data for Pakistan-specific covariates. Findings Life expectancy for both sexes in Pakistan increased nationally from 61 center dot 1 (95% uncertainty interval [UI] 60 center dot 0-62 center dot 1) years in 1990 to 65 center dot 9 (63 center dot 8-67 center dot 8) years in 2019;however, these gains were not uniform across the provinces and federal territories. Pakistan saw a narrowing of the difference in healthy life expectancy between the sexes from 1990 to 2019, as health gains for women occurred at faster rates than for men. For women, life expectancy increased by 8 center dot 2% (95% UI 6middot3-13middot8) between 1990 and 2019, whereas the male life expectancy increased by 7 center dot 6% (3 center dot 5-11 center dot 8). Neonatal disorders, followed by ischaemic heart disease, stroke, diarrhoeal diseases, and lower respiratory infections were the leading causes of all-age premature mortality in 2019. Child and maternal malnutrition, air pollution, high systolic blood pressure, dietary risks, and tobacco consumption were the leading all-age risk factors for death and disability-adjusted life-years at the national level in 2019. Five non-communicable diseases-ischaemic heart disease, stroke, congenital defects, cirrhosis, and chronic kidney disease-were among the ten leading causes of years of life lost in Pakistan. Burden varied by socio-demographic index. Notably, Balochistan and Khyber Pakhtunkhwa had the lowest observed gains in life expectancy. Dietary iron deficiency was the leading cause of years lived with disability for both men and women in 1990 and 2019. Low birthweight and short gestation and particulate matter pollution were the leading contributors to overall disease burden in both 1990 and 2019 despite moderate improvements, with a 23 center dot 5% (95% UI 3 center dot 8-39 center dot 2) and 27 center dot 6% (14 center dot 3-38 center dot 6) reduction in age-standardised attributable DALY rates during the study period. Interpretation Our study shows that progress has been made on reducing Pakistan's disease burden since 1990, but geographical, age, and sex disparities persist. Equitable investment in the health system, as well as the prioritisation of high-impact policy interventions and programmes, are needed to save lives and improve health outcomes. Pakistan is facing several domestic and foreign challenges-the Taliban's return to power in Afghanistan, political turmoil, catastrophic flooding, the COVID-19 pandemic-that will shape the trajectory of the country's health and development. Pakistan must address the burden of infectious disease and curb rising rates of non-communicable diseases. Prioritising these three areas will enhance Pakistan's ability to achieve universal health coverage, meet its Sustainable Development Goals, and improve the overall health outcomes.
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Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students' performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.
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Coronavirus disease (COVID-19) is an infectious disease caused by the coronavirus was first found in Wuhan, China in December 2019. It has infected more than 300 million people with more than 5 million of death cases. Until now, the virus is still evolving producing new variants of concern contributes to the increase the infection rate around the world. Thus, various diagnostic procedures are in need to help physicians in diagnosis disease certainly and rapidly. In this study, deep learning approach is used to classify normal and COVID-19 cases from CT scan images. Normalizer Free CNN network (NFNets) model is implemented on the images. Statistical measures such as accuracy, precision, sensitivity (also known as recall) are used to evaluate the performance of the model against the previous studies. Loss of 0.0842, accuracy of 0.7227, precision of 0.9751 and recall of 0.9727 are achieved. Thus, further optimization on the NFNets learning algorithm is required to improve the classification performanceClinical Relevance-Implementation of deep learning technique to automate diagnosis of diseases such as COVID-19 cases from CT scan images will simplify the clinical flow towards providing reliable intelligent aids for patient care. © 2022 IEEE.
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The covid 19 immediate impact has intensely changed global trends. At the same time, the impact covid 19 has exhilarated research interest among researchers worldwide. In Malaysia, the Movement Control Order (MCO) and travel restrictions enforced by the Malaysian Government in March 2020 have significantly hit Malaysian daily life. Therefore, understanding the topic of interest and broadening collaboration networks is critically important to advance research development. This bibliometric study examines the Covid-19 research trends in Malaysia based on publication output, prominent journals, prominent authors, affiliated countries, and author co-occurrences. Utilizing the Scopus database, 1776 articles were identified and extracted from 2020 until 2021. The result has shown that the number of articles related to Covid 19 in Malaysia significantly increased in 2021, resulting in higher cumulative total publications. Most Covid 19 research publications in Malaysia collaborated with the United Kingdom, Pakistan, and United States. The bibliometric literature in this study have declared that digital technologies have the potential to fulfill customized requirements of COVID-19 pandemic. Thinking about advance technologies and its benefits, this study is going to provide a through literature about the application of advance technologies in real-time. Despite of that, this study also mapped the literature based on topic of interest to understand the benefits and application of these technologies in different areas including pure technology, sustainability, education, tourism, psychology, food and agriculture, and the economy. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.
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Since 2019, the Coronavirus (Covid-19) pandemic has hit the whole world significantly and has been one of the worst nightmares for us. Single mother entrepreneurs have been one of the most affected groups due to COVID-19, and this group's gradual implementation of technology is called for. This article aims to determine the pandemic's implications for SMPs and whether they have taken any new strategies or changes for the survival of their business. Adopting a qualitative research approach, an open group discussion has been conducted at Padang Jawa, Klang. A total of seven SMPs have been interviewed. The COVID-19 Movement Control Order (MCO) impacts on single mother entrepreneurs and strategies have been divided into two sections: the entrepreneur's perspective on COVID-19 and their current view and approach to facing the pandemic. © School of Engineering, Taylor's University.
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Neuromarketing study is a combination between neuroscience and marketing studies. it is done to get better understanding on consumer behaviors while making purchases. Since the advent of Covid-19 or also known as Coronavirus, eCommerce platforms are widely used by the consumers to purchase goods and services. Various techniques can be used to obtained the brain signals to observe consumers' emotions. There are two types of neuromarketing approaches which are neuroimaging and non-neuroimaging techniques. The neuroimaging techniques are frequently used by the researchers to study neuromarketing as the results obtained are based on the consumers' brainwaves and is not biased to any goods or services such the results from surveys, interviews, or other traditional marketing strategies. So, this describes review on previous research which use the neuroimaging techniques to study neuromarketing, especially using electroencephalogram (EEG). © 2022 IEEE.
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The societal disruptions due to the novel coronavirus (COVID-19) pandemic are well noted, especially in the context of science, technology, engineering, and mathematics (STEM) education. Absent a concerted effort to sustain hands-on learning opportunities in STEM amid the crisis, the consequences of COVID-19 may exacerbate existing inequities and racial disparities among youth of color further stratifying the STEM fields. In the current study, we applied a mixed-method descriptive case study design, using online learning theory and culturally responsive pedagogy as our conceptual framework, to describe how participants experienced this camp, held online due to disruptions of COVID-19, in the southeastern region of the USA. We also share findings from the implementation of a justice bots project, which enabled participants to connect social justice and engineering. Participants included middle school youth, undergraduate engineering students, and in-service math and science teachers. Data sources entailed focus groups, pre-post surveys, observations, and artifacts. Our results indicated that participants experienced gains in their communication skills, positive changes in attitudes toward STEM for middle school youth, established meaningful connections, and enhanced their technical knowledge. Middle school youth reported enjoying the online summer camp environment, though they had experienced more than a year of education online. Undergraduate engineering students asserted that it was challenging to communicate coding and other technical knowledge virtually but having to do so strengthened their capacity to teach others while honing their own competencies. Lastly, in-service math and science teachers reported a better understanding of the connection between engineering and social justice based on their experiences in the camp. We conclude this article with implications for engineering education. © 2022, Purdue University Press. All rights reserved.
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E-Learning Education systems are gaining attention day-to-day because of their inclusive pertinence in the distance education system. Due to COVID-19, the online learning education system has become very popular. Most probably, all education systems have been using the IoT-based E-Learning system to continue the students' education without hindrance during the COVID lockdown. Several E-Learning IoT schemes are explored that reflect privacy and security, but still, there is no detailed scheme;hence, it needs a sustainable, secure E-Learning IoT system. The characteristics and prospects of the Internet of Things are discussed in this article. By analyzing the various functions and capabilities of the Internet of Things, this article aims to provide an overview of the various advantages and challenges of using the platform for e-learning. This paper proposed the E-Learning IoT architecture with Blockchain technology, with layers of different IoT and Blockchain concepts to secure the online education system. Also, the block diagram of the proposed architecture demonstrates how students can securely access or interact with the online learning system through Blockchain technology. By implementing the proposed e-learning IoT architecture, universities and colleges can improve their distance learning programs and increase efficiency without affecting their academic activities. Finally, the study found that e-learning positively impacts students' learning experience and overall quality of education. It also exhibited a significant positive impact on their flexibility and academic productivity. © The Author(s) 2023.
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With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student's participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students' performance. © 2023 by the authors.
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Background: As Millions of people are receiving COVID-19 vaccine around the world, a number of side effects are being reported. Menstrual cycle disturbance is also a side effect reported by hundreds of women on social media. The objective of this study was to assess the association of menstrual abnormalities with COVID-19 vaccination. Subjects and methods: This descriptive cross sectional study was conducted in Obstetrics and Gynecology OPD of Mufti Mehmood Hospital D.I. Khan from March 2021 to June 2021. The health care workers who received Sinopharm COVID-19 vaccine and consented were included. Data was collected about change in menstrual cycle pattern after COVID-19 vaccination. Results: A total of 80 subjects were included in the study with mean age of 32.6 +7.89 years. There were 36 (45.0%) doctors, 33 nurses (41.25%), 6 (7.5%) paramedics and 5 (6.25%) medical students. According to their marital status, 51 (63.75%) were married and 29 (36.25%) were unmarried. Out of 80 subjects, 16 (20%) reported disturbance in menstrual cycle pattern, 13 (81.2%) after first dose and 3 (18.75%) after both first and second dose. Among these 9 (56.25%) reported heavy menstrual bleeding, 4 (25%) had prolonged bleeding days while 3 (18.75%) had irregular bleeding pattern. Conclusion: Menstrual cycle disturbance is a reported side effect of COVID-19 vaccination. It is more commonly reported after first dose and heavy menstrual bleedings is the most common pattern followed by prolonged/irregular bleeding. © 2022 Authors.
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This paper aims to share the Zoombombing: a new phenomenon that emerged during COVID-19, its causes, and measures to prevent Zoombombing. Finally, it provides recommendations to prevent such incidents. Zoom emerged as the most popular alternative teaching tool during COVID-19. However, even though Zoom has utilized state-of-the-art encryption methods, it has underestimated privacy expectations among its exponentially growing customer base. Zoom has many options settings for users to customize and secure their videoconferencing platform to minimize Zoombombing. However, some users overlook these security features, which leaves them vulnerable to offensive material displayed by attackers in a virtual classroom setting. Furthermore, most Zoombombings were made by insiders who had legitimate access to Zoom meetings, particularly students in high school and college classes. To reduce Zoombombing, teachers can enable settings, such as waiting room, restricting access to screen-share, and physical reporting. Moreover, severe institutional punishment for the attackers and their insider supporters can discourage such activities. © 2023 EDP Sciences. All rights reserved.
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Boilers are heat exchange machines used to generating steam. Most processes in the textile industry used steam for the fixation of chemicals, washing, dyestuff, finishing agents, and drying of textile materials. Coal is used as one of the boiler fuels and continues to grow steeply in price. To optimize operating costs during the covid-19 pandemic, the company should reduce the production cost, one of which is by selecting the best coal in all aspects. Current coal selection is based solely on the price of coal per kgs and does not consider the effect of additional costs that will arise from selected coal. Coal as solid fuel contains moisture, carbon, hydrogen, sulfur, nitrogen, oxygen, and ash. The use of coal which produces a lot of waste will increase the company's expense to be paid to third parties to collect coal waste according to government regulation. Another risk for using coal is the corrosion from sulfur content inside it, and each type of coal has a different calorific value. To find out the content in the coal supplied from each supplier. Coal samples obtained from suppliers are sent by the company to the testing agency. comparing coal, a low price of coal per kg does not necessarily result in a low overall cost so that it can save production costs. © 2023 Author(s).
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Introduction: COVID-appropriate behavior refers to the development of those habits that may serve to limit the disease's spread and, thus, reduce the number of individuals affected. Vaccinated individuals may be less willing to comply with COVID-appropriate conduct due to their perception of a diminished health risk. Consequently, the present study was conducted to assess public's attitude regarding COVID preventive measures following vaccination.. Methodology: This cross sectional study was conducted among adults aged 18 and above. 200 individuals who had received either both doses of COVID-19 or at least one dosage of either COVID-19 participated in this survey, which was performed online and involved the distribution of a self-administered questionnaire via social media. Result(s): Covishield was the most commonly administered vaccination (70%), followed by Covaxin (23%). The majority of respondents adhered to mask use after vaccination (82,5%), but 15% of respondents adhered to mask use less after vaccination than before. 2.5% of respondents reported an increase in mask use. Physical distancing was shown to be less after vaccination among (65.5%) than before vaccination. 19% of study participants reported that their frequency of hand washing with soap and water decreased following vaccination. 31% of respondents said that their usage of hand sanitizer dropped following vaccination. Conclusion(s): It should be stressed to the public that getting vaccination does not make them invincible foe the various new strains in circulation of the virus. Strict policy making should be emphasized to make people follow COVID appropriate behavior at all times.Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.
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Ventricular tachycardia (VT) is a type of broad complex tachycardia originating from a focus in the ventricle. It is one of the four important rhythms which can lead to cardiac arrest. Accurate and timely diagnosis of true VT is the cornerstone for proper management in the emergency department (ED). We present an interesting case of an electrocardiographic artifact mimicking VT, which led to a diagnostic dilemma in the ED.Copyright © 2023 Rehman, Albaroudi, Akram, Ahmad, licensee HBKU Press.
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Objective: To identify the predictors of COVID-19 safety behaviors (hand washing, physical distancing, & wearing masks), in Pakistan. Methodology: This correlational study was conducted at Karakoram International University, Gilgit and Combined Military Hospital, Kharian from November 2020 to April 2021. We used newly developed COVID-19 Safety Behaviors Scale-Urdu, COVID-19 Anxiety Scale-Urdu, COVID-19 Knowledge Scale-Urdu, and a brief version of the Big Five Personality Inventory, on 911 participants (395 women). Result(s): COVID-19 related anxiety (beta = 0.02, p < 0.05) and the personality trait 'conscientiousness' (beta = 0.02, p < 0.01) significantly moderated the positive relationship between COVID-19 knowledge and COVID-19 safety behaviors. This implies that people with a dominant personality trait of 'conscientiousness' were actively seeking COVID-19 related knowledge that led to higher levels of preventive behaviors. Conclusion(s): To control the current pandemic and associated negative consequences through 'safety behaviors' it is important to educate people while keeping the demographic variables in view.Copyright © 2023, Pakistan Medical Association. All rights reserved.
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Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.
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World scenario after pandemic COVID-19 has been drastically changing and researchers more focusing on, to minimize the post-pandemic effects on economy, energy sustainability and food security. Agriculture sector is playing pivotal role in world food security and energy sustain -ability. There is high need to optimize the mechanization technologies to increase the yield in limited energy inputs and operation time to fulfill the world growing food demand. This research is mainly focused on the design development and structural analysis aiding with Finite Element Analysis (FEA) approach for Cotton Stalk Puller and Shredder machine (CSPS) to cut the crop leftovers, soil conditioning (shredding the plant waste into soil) and sowing of next crop in single run by con-serving input resources. The experimental trials revealed that there is high pressure on cutting blades, chocking of shredder section and excessive pulling load on tractor hitches, which affected the machine's performance. To mitigate deficiencies and design optimization to improve the machine safety/reliability, the structure analysis carried out. Six core components of machine including baseplate, blade, gear system, root digger, pulley and shaft has investigated as per field conditions. The results revealed that the material of blade, root digger and teeth of gear system receiving the high stress under the operational conditions which results the edge wear and damage. The carbonization up to one-millimeter thickness can provide the extra strength to bear the exces-sive load on edge layers.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).