Perceptions Of Students' Regarding Impact Of Covid-19 On Their Learning Ín Public Sector Universities Of Khyber Pakhtunkhwa Pakistan

It is necessary to understand the emergence and history of coronavirus due to which the whole world has been badly affected. The design of the study was survey in nature. The population of the study consisted of administration management, faculty and students of KP Universities. The target population of the study comprised of students in which the numbers of (n = 1000) students including males and females were taken as sample of the study throughout the selected 20 public section universities of KP by applying snowball and convenient sampling techniques. Quantitative approached was preferred. The key objectives of the study were: 1) To know the impact of COVID-19 on universities students. 2) To examine the coping strategies of KP Universities regarding COVID-19. 3) To determine the responses of universities students' about e-teaching and learning during COVID-19. Data were collected online through self-developed questionnaire of 5-Points Likert scale with options "Strongly Agree to Strongly Disagree" and interview by using electronic resources like email and whatsapp to know public perceptions regarding COVID-19 and its impact on students' learning. Data were delimited to the public sector universities of KP. The collected data were statistically analyzed through descriptive statistics. Results and conclusions were drawn by probing that COVID-19 badly effected students' learning. Some recommendations were given at the end regarding the control and proper solution of pandemic COVID-19 along with technology usage for e-teaching and e-learning at university level.

Perceptions Of Undergraduate Students Regarding The Role Of Information Communication Technologies (Icts) In Education After COVID-19

The main purpose of the current study was to explore the perceptions of undergraduate students about the importance of information communication technologies (ICTs) in education. All Public and Private sector universities of Khyber Pakhtunkhwa were the populations of the study in which all undergraduate students of only southern districts of Khyber Pakhtunkhwa were taken as samples of the study according to John Curry Sample Size rule of thumb. Self-developed questionnaire on the 6-Points Likert scale was used. The collected data were statistically analyzed through SPSS by using t-test, One Way ANOVA, and Linear Regression. T-test was used for gender and institution-wise comparison. One Way ANOVA was used to compare the perceptions of different respondents;and Linear Regression was used to know the effects of information communication technologies on Undergraduate students during COVID-19. Results and conclusions were drawn in which the role of ICTs was found highly successful for undergraduate students during COVID-19. The findings of the study highlighted the role of ICTs and especially the online sources in the teaching-learning process.

Entropy in Education System: Transformation of an Individual Through Meaningful Interactions in a Community of Inquiry.

Every individual is unique and may serve a unique purpose in this life. Education is widely accepted to be the means of transformation of individuals so that they may achieve their unique success or create their own lives. However, not every individual seems to be realizing their true potential. This paper explores the concept of entropy in education system as a force that is usually imagined to oppose realization of potential of an individual during life in this phenomenal world. Alternatively, the same may provide an impetus that is necessary to bring in organization in oneself to realize the hidden potential. A one group Pretest-Posttest quasi-experimental design was used to draw the conclusions on data obtained from participants of workshops in three different modes, viz. face-to-face Pre COVID-19, face-to-face in COVID-19 with SOPs, and online in COVID-19. Realization of an individual's potential was represented as a dependent variable, i.e. transformation in cognition, skills, and attitude while the independent variables taken into account were the meaningful interactions of an individual with peers and advanced learners in a designed environment. It was inferred from the results that transformation in learners' cognition (6-30 %), skills (0-20 %), and attitude (5-32 %) occurred through human discourse, in a community of inquiry.

Comparative analysis of dental pulp stem cells and stem cells from human exfoliated teeth in terms of growth kinetics, immunophenotype, self-renewal and multi lineage differentiation potential for future perspective of calcified tissue regeneration

Background and Objectives: Owing to high proliferation rate, multipotency and self-renewal capability, dental pulp stem cells (DPSC) and stem cells from human exfoliated teeth (SHED) have become stem cell source of choice for cell based regenerative therapies. We aimed to compare DPSC and SHED as stem cell sources with a future use in regeneration of calcified tissue. Methods: Explant derived human DPSC (n=9) and SHED (n=1) were cryopreserved, thawed and expanded for analysis of population doubling time, colony forming unit assay and efficiency. A growth curve was plotted to determine population doubling time, while colony forming numbers and efficiency was determined at plating cell densities of 5.6, 11.1 and 22.2 / cm2. The isolated cells were characterized for the presence of stem cell markers by immunophenotyping and immunofluorescence staining, and tri-lineage differentiation. Statistical analysis was performed by Pearson correlation, Exponential regression and two way Anova with Tukey test at p<0.05. Results: DPSC and SHED exhibited spindle shaped fibroblast like morphology. SHED was found superior than DPSC in terms of proliferation and colony forming efficiency. Immunophenotypes showed that DPSC contain 62.6±26.3 %, 90.9±14.8% and 19.8±0.1%, while SHED contain 90.5%, 97.7% and 0.1% positive cells for CD90, CD73 and CD105. DPSC were strongly positive for vimentin, CD29, CD73, while reactivity was moderate to weak against CD44 and CD90. SHED expressed vimentin, CD29, CD105, CD90 and CD44. Both were negative for CD45. Upon induction, both cell types differentiated into bone, fat and cartilage like cells. Conclusion: Cultured DPSC and SHED were proliferative and exhibited self-renewal property. Both DPSC and SHED expressed stem cell markers and were able to differentiate into bone, fat and cartilage like cells. Thus, these could be a suitable stem cell sources for cell based regenerative therapies.

Making a Joint Decision Regarding the Timing of Surgery for Elective Arthroplasty Surgery After Being Infected With COVID-19: A Systematic Review.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has caused a substantial number of patients to have their elective arthroplasty surgeries rescheduled. While it is established that patients with COVID-19 who are undergoing surgery have a significantly higher risk of experiencing postoperative complications and mortality, it is not well-known at what time after testing positive the risk of postoperative complications or mortality returns to normal. METHODS: PubMed (MEDLINE), Excerpta Medica dataBASE, and professional society websites were systematically reviewed on March 7, 2022 to identify studies and guidelines on the optimal timeframe to reschedule patients for elective surgery after preoperatively testing positive for COVID-19. Outcomes included postoperative complications such as mortality, pneumonia, acute respiratory distress syndrome, septic shock, and pulmonary embolism. RESULTS: A total of 14 studies and professional society guidelines met the inclusion criteria for this systematic review. Patients with asymptomatic COVID-19 should be rescheduled 4-8 weeks after testing positive (as long as they do not develop symptoms in the interim), patients with mild/moderate COVID-19 should be rescheduled 6-8 weeks after testing positive (with complete resolution of symptoms), and patients with severe/critical COVID-19 should be rescheduled at a minimum of 12 weeks after hospital discharge (with complete resolution of symptoms). CONCLUSIONS: Given the negative association between preoperative COVID-19 and postoperative complications, patients should have elective arthroplasty surgery rescheduled at differing timeframes based on their symptoms. In addition, a multidisciplinary and patient-centered approach to rescheduling patients is recommended. Further study is needed to examine the impact of novel COVID-19 variants and vaccination on timeframes for rescheduling surgery.

Clinico-Pathological Association of Haemoglobin At The Time of Presentation: A Predictive Tool of Survivorship in COVID-19

Objective: To determine the clinico-pathological association of haemoglobin at the time of presentation as a predictor of survivorship in patients suffering from COVID-19. Study Design: Prospective observational study. Place and Duration of Study: Combined Military Hospital Hyderabad Pakistan, from Mar to Sep 2020. Methodology: Two hundred and four patients who were symptomatic and PCR positive for COVID-19 were included in the study. Informed consent was taken from patients and approval from the institutional ethics committee was obtained. Haemoglobin values were analyzed using SPSS version 22. Results: In our study, 186 (91.2%) patients survived the disease and 18 (8.8%) patients died. The mortality rate was high in patients who presented with low haemoglobin levels at time of presentation. Conclusion: This study concluded that hemoglobin level at time of presentation could be a predictor of survivorship.

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine.

Clinical characteristics, risk factors and outcomes in patients with severe COVID-19 registered in the International Severe Acute Respiratory and Emerging Infection Consortium WHO clinical characterisation protocol: a prospective, multinational, multicentre, observational study.

Due to the large number of patients with severe coronavirus disease 2019 (COVID-19), many were treated outside the traditional walls of the intensive care unit (ICU), and in many cases, by personnel who were not trained in critical care. The clinical characteristics and the relative impact of caring for severe COVID-19 patients outside the ICU is unknown. This was a multinational, multicentre, prospective cohort study embedded in the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization COVID-19 platform. Severe COVID-19 patients were identified as those admitted to an ICU and/or those treated with one of the following treatments: invasive or noninvasive mechanical ventilation, high-flow nasal cannula, inotropes or vasopressors. A logistic generalised additive model was used to compare clinical outcomes among patients admitted or not to the ICU. A total of 40 440 patients from 43 countries and six continents were included in this analysis. Severe COVID-19 patients were frequently male (62.9%), older adults (median (interquartile range (IQR), 67 (55-78) years), and with at least one comorbidity (63.2%). The overall median (IQR) length of hospital stay was 10 (5-19) days and was longer in patients admitted to an ICU than in those who were cared for outside the ICU (12 (6-23) days versus 8 (4-15) days, p<0.0001). The 28-day fatality ratio was lower in ICU-admitted patients (30.7% (5797 out of 18 831) versus 39.0% (7532 out of 19 295), p<0.0001). Patients admitted to an ICU had a significantly lower probability of death than those who were not (adjusted OR 0.70, 95% CI 0.65-0.75; p<0.0001). Patients with severe COVID-19 admitted to an ICU had significantly lower 28-day fatality ratio than those cared for outside an ICU.

Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

Clinical characteristics, risk factors and outcomes in patients with severe COVID-19 registered in the ISARIC WHO clinical characterisation protocol: a prospective, multinational, multicentre, observational study

Due to the large number of patients with severe COVID-19, many were treated outside of the traditional walls of the ICU, and in many cases, by personnel who were not trained in critical care. The clinical characteristics and the relative impact of caring for severe COVID-19 patients outside of the ICU is unknown. This was a multinational, multicentre, prospective cohort study embedded in the ISARIC WHO COVID-19 platform. Severe COVID-19 patients were identified as those admitted to an ICU and/or those treated with one of the following treatments: invasive or non-invasive mechanical ventilation, high-flow nasal cannula, inotropes, and vasopressors. A logistic Generalised Additive Model was used to compare clinical outcomes among patients admitted and not to the ICU. A total of 40 440 patients from 43 countries and six continents were included in this analysis. Severe COVID-19 patients were frequently male (62.9%), older adults (median [IQR], 67 years [55, 78]), and with at least one comorbidity (63.2%). The overall median (IQR) length of hospital stay was 10 days (5–19) and was longer in patients admitted to an ICU than in those that were cared for outside of ICU (12 [6–23] versus 8 [4–15] days, p<0.0001). The 28-day fatality ratio was lower in ICU-admitted patients (30.7% [5797/18831] versus 39.0% [7532/19295], p<0.0001). Patients admitted to an ICU had a significantly lower probability of death than those who were not (adjusted OR:0.70, 95%CI: 0.65-0.75, p<0.0001). Patients with severe COVID-19 admitted to an ICU had significantly lower 28-day fatality ratio than those cared for outside of an ICU.

Comparative Study of Chinese and American Media Reports on the COVID-19 and Expressions of Social Responsibility: A Critical Discourse Analysis.

Critical discourse analysis aims to explore the dialectical relationship between discourse and ideology. Based on psycholinguistic research, this paper analyzes the Chinese and American media's news reports and comments on the COVID-19. It aims to expose the hidden psychological messages and ideologies behind the words. The corpus in this paper is mainly from the official media of China Daily and Time from December 2019 to January 2021 in China and the United States. This paper uses Wang Zhenhua's Appraisal Theory and Halliday's Systemic Functional Grammar as tools to make a comparative analysis of the corpus. At the textual level, languages are classified and lexical choices are analyzed followed by the analysis of the reporter's ideology after reviewing the motivation of the reporters of two countries. On the level of social responsibility expression and discourse, the paper analyzes the news reports, which are characterized by the combination of the reporter's views on the news. In the aspect of social practice, the social and cultural factors and background of news reports are analyzed. China calls for strengthening cooperation and exchanges with other countries to jointly fight the epidemic. The Chinese government has actively shared its experience and made corresponding contributions to international economic recovery. However, the US government shirks its responsibility by claiming that the effective implementation of Chinese methods and experience in China does not mean that it can achieve corresponding results in Europe and the US. At the same time, the United States provides medical supplies to other countries. This study hopes to help awaken readers' critical thinking and increase their awareness of the anti-control of mass discourse. At the same time, it is hoped that readers can view the epidemic from a more scientific perspective, understand the facts and reject the unwarranted panic. It will also help reshape Chinese and American discourse.

Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.

Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.

Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients.

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients

The novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities. Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily. A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate. This research provides a prediction method for the early identification of COVID-19 patient's outcome based on patients' characteristics monitored at home, while in quarantine. The study was performed using 287 COVID-19 samples of patients from the King Fahad University Hospital, Saudi Arabia. The data were analyzed using three classification algorithms, namely, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). Initially, the data were preprocessed using several preprocessing techniques. Furthermore, 10-k cross-validation was applied for data partitioning and SMOTE for alleviating the data imbalance. Experiments were performed using twenty clinical features, identified as significant for predicting the survival versus the deceased COVID-19 patients. The results showed that RF outperformed the other classifiers with an accuracy of 0.95 and area under curve (AUC) of 0.99. The proposed model can assist the decision-making and health care professional by early identification of at-risk COVID-19 patients effectively. [ABSTRACT FROM AUTHOR] Copyright of Scientific Programming is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

E-Triage Systems for COVID-19 Outbreak: Review and Recommendations.

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients' conditions and identifying their severity levels.

Applications of Big Data Analytics to Control COVID-19 Pandemic.

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs).

Household Items-Based Personalized Video Rehabilitation amidst the COVID-19 Pandemic: Handouts We All Wish We Had

Category: Ankle;Sports Introduction/Purpose: Rehabilitation is vital in the recovery of countless foot and ankle injuries and operations.1With our attention directed to providing care on the frontlines of the COVID-19 pandemic, rehabilitation has fallen by the wayside with poor consequences. Access to outpatient rehabilitation services and their specialized exercise equipment has significantly decreased,2 leaving patients feeling abandoned and surgeons looking for help on how to guide patients’ postoperative rehabilitation. This poster serves to introduce household items-based personalized video rehabilitation (PVR). Methods: Rehabilitation exercise videos were produced by a board-certified athletic trainer and board-certified orthopaedic foot and ankle surgeon. The videos showed how to perform rehabilitation exercises with common household items (Figure 3).The videos were produced in a structured order, explaining the purpose of the exercise, showing what household items are needed, and demonstrating the proper technique of the exercise. Following recording of the videos, they were assembled into injury/scenario-specific infographics (Figure 1). Results: 34 rehabilitation exercise videos were recorded, with an average length of two minutes, 11 seconds.11 injury/scenario- specific infographics were assembled, with each page of an infographic containing seven exercises (Figure 1), with specific information and access for each exercise (Figure 2).Patients can access the videos by clicking on the hyperlink, or by scanning the QR code. When giving infographics, healthcare professionals can guide patients on which exercises to perform. Conclusion: Household items-based PVR provides patients with an interactive infographic that gives access to concise videos of rehabilitation exercises, especially during a time of reduced rehabilitation accessibility. Healthcare professionals can assign patient- specific exercises on the infographic that are appropriate for a patients’ phase of rehabilitation. Not meant to replace the current rehabilitation model, household item-based PVR is a paradigm shift that will allow physicians to bridge gaps in care.