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1.
Pakistan Journal of Medical and Health Sciences ; 17(2):756-758, 2023.
Article in English | EMBASE | ID: covidwho-20237837

ABSTRACT

Objective: This study aimed to evaluate the psychological distress experienced by healthcare and non-healthcare professionals working in a hospital setting during the Coronavirus Disease 2019 (COVID-19) pandemic. Methodology: This survey-based cross-sectional study included 361 professionals (288 healthcare and 73 non-healthcare professionals) working at Ziauddin University Hospital, Karachi, Pakistan. Psychological distress was assessed using Depression Anxiety Stress Scale - 21 (DASS-21). Result(s): There was a higher prevalence of anxiety, depression, and stress among healthcare professionals as compared to the non-healthcare professionals, as indicated by the mean depression, anxiety, and stress scores on DASS-21 (p<0.05). The univariate logistic regression analysis showed that the odds of psychological distress were similar in both genders and individuals of all age groups. Healthcare professionals were twice more likely to be severely depressed and stressed as compared to non-healthcare professionals (p<0.05). Conclusion(s): This study concludes that psychological distress is more prevalent among healthcare workers than non-healthcare workers.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

2.
Journal of Population Therapeutics and Clinical Pharmacology ; 30(9):e178-e186, 2023.
Article in English | EMBASE | ID: covidwho-20233238

ABSTRACT

Background: At our hospital, people with COVID-19 (coronavirus disease 2019) had a high rate of pulmonary barotrauma. Therefore, the current study looked at barotrauma in COVID-19 patients getting invasive and non-invasive positive pressure ventilation to assess its prevalence, clinical results, and features. Methodology: Our retrospective cohort study comprised of adult COVID-19 pneumonia patients who visited our tertiary care hospital between April 2020 and September 2021 and developed barotrauma. Result(s): Sixty-eight patients were included in this study. Subcutaneous emphysema was the most frequent type of barotrauma, reported at 67.6%;pneumomediastinum, reported at 61.8%;pneumothorax, reported at 47.1%. The most frequent device associated with barotrauma was CPAP (51.5%). Among the 68 patients, 27.9% were discharged without supplemental oxygen, while 4.4% were discharged on oxygen. 76.5% of the patients expired because of COVID pneumonia and its complications. In addition, 38.2% of the patients required invasive mechanical breathing, and 77.9% of the patients were admitted to the ICU. Conclusion(s): Barotrauma in COVID-19 can pose a serious risk factor leading to mortality. Also, using CPAP was linked to a higher risk of barotrauma.Copyright © 2021 Muslim OT et al.

3.
Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies ; 4:303-314, 2023.
Article in English | Web of Science | ID: covidwho-2309256

ABSTRACT

Online social media has been evolved as a universal platform for sharing information. Termination being shared on these platforms can be dubious or filthy. Propaganda is one of the systematic methods by which behavior of user can be manipulated. In this work, various machine learning methods are used for detecting such types of information on online social media. Data is collected d from Twitter using its API with the help of various ambiguous hashtags. The results showed that proposed Long Short Term Memory (LSTM) based propaganda identification showed better results than other machine learning techniques. An accuracy of 77.15% is achieved using the proposed approach. In the future BERT model can be used for achieving better Accuracy.

4.
Pakistan Journal of Medical and Health Sciences ; 16(12):483-486, 2022.
Article in English | EMBASE | ID: covidwho-2266120

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is currently spreading fast around the world. The rate of acute kidney damage (AKI) in patients hospitalized with Covid-19, as well as the outcomes related with it, are unknown. The goal of this study was to see if having acute kidney damage (AKI) increased the risk of severe infection and death in COVID-19 patients. It also described the symptoms, risk factors, and outcomes of AKI in Covid-19 patients. Material(s) and Method(s): We undertook a retrospective cohort from June 2020 and March 2021 to examine the connection between AKI and patient outcomes COVID-19. Result(s): The most common comorbid condition was hypertension and diabetes followed by chronic kidney disease and ischemic heart disease. Most of the patients who required low dose oxygen with nasal prongs, face masks, or rebreathing masks were in control groups (76.2% vs. 50.6%;p <.001). More patients in AKI group needed non-invasive ventilation and invasive mechanical ventilation compared to control group (33.8% vs. 19.9%;p .001, 15.6% vs. 3.9%;p <.001 respectively. Patients in the AKI group had higher levels of C-reactive protein, lactate dehydrogenase, D-dimer, and serum. Of 145 patients who developed AKI, 29 (20%) needed hemodialysis. Of 29 patients who needed hemodialysis, 18 (62%) expired. A higher number of patients in the control group were discharged than patients in the AKI group (82.1% vs. 56.9%;p <.001). One hundred five patients were expired, with higher mortality in the AKI group (41.7% vs. 12.4%;p <.001). Conclusion(s): COVID-19 patients admitted to the hospital, AKI is associated with a shockingly high fatality rate.Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

5.
Sustainability ; 15(2), 2023.
Article in English | Web of Science | ID: covidwho-2231840

ABSTRACT

People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.

6.
Iraqi Journal of Science ; 63(10):4488-4498, 2022.
Article in English | Scopus | ID: covidwho-2164575

ABSTRACT

COVID-19 affected the entire world due to the unavailability of the vaccine. The social distancing was a contributing factor that gave rise to the usage of Online Social Networks. It has been seen that people share the information that comes to them without verifying its source . One of the common forms of information that is disseminated that have a radical purpose is propaganda. Propaganda is organized and conscious method of molding conclusions and impacting an individual's contemplations to accomplish the ideal aim of proselytizer. For this paper, different propagandistic tweets were shared in the COVID-19 Era. Data regarding COVID-19 propaganda was extracted from Twitter. Labelling of data was performed manually using different propaganda identification techniques and Hybrid feature engineering was used to select the essential features. Ensemble machine learning classifiers were used for performing the binary classification. Adaboost shows an accuracy of 98.7%, which learns from a weak learning algorithm by updating the weights. © 2022 University of Baghdad-College of Science. All rights reserved.

7.
Mobile Information Systems ; 2022, 2022.
Article in English | Web of Science | ID: covidwho-2005523

ABSTRACT

The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining "Term Frequency (TF)/Inverse Document Frequency (IDF)," "Bag of Words," and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

8.
Pakistan Journal of Medical and Health Sciences ; 16(7):103-105, 2022.
Article in English | EMBASE | ID: covidwho-1980038

ABSTRACT

Background The Covid-19 pandemic have forced the education sector of every country to adopt a relatively unconventional method of teaching i.e., Online Education. However, it faces many challenges of its own. Aim: To discuss the challenges to online medical education during the Covid-19 pandemic in medical colleges of Lahore, Pakistan. Methods: A cross-sectional type of quantitative study using a self-administered online questionnaire using Google Forms® was administered to 508 students from all the medical colleges of Pakistan. Results: Majority of subjects preferred face-to-face learning (70.9%) before Covid-19 pandemic and had beginner level online exposure (60. 6%). Overall, 85.8% of participants found the impact of shifting to online learning as negative. A variety of challenges were faced by the students with decreased attention span being most common followed by strain on mental health, problem in communication and taking exams. Conclusion: Covid-19 related lockdown led to new culture of education. Medical students had difficulty adapting to this but a faction of student wanted to use and discover online learning more.

9.
Pakistan Journal of Medical and Health Sciences ; 16(6):201-204, 2022.
Article in English | EMBASE | ID: covidwho-1939788

ABSTRACT

Background: WHO recognized COVID-19 a pandemic on March 12, 2020 and National Health Commission officially declared it as a Class-B infectious disease. The technological advancements enabled the teaching staffs to keep their students involved during this period of COVID-19 pandemic. Online classes become the efficient medium to learn by staying at home. Aim: To find out the challenges faced by mothers during online learning in order to devise a systematic plan for smooth and effective learning in case of another crises like COVID-19. Method: It was a cross sectional study carried out at CMH LMC&IOD, in which a user-defined questionnaire was introduced to the participants which were mothers of school going children from all over the city. The questionnaire got 161 responses in total, but two were incomplete so 159 were considered while doing the analysis. The results were analyzed using SPSS25. Results: In this study,46.9% mothers were of age 40 and above and 47.5% were between 31 -40 years of age. Rest were 30 and below 57.5 % children used laptops to study online, while 34.4 % used a mobile. 7% had their own tablets and only 1.1 used desktops. Only 14.4% mothers supervised their children during all this time. 58.7% however managed supervising studies with other tasks and 26.9% said their children could study online unsupervised. Only 20% mothers thought their children are taking interest in online schooling. 54.5% found their children struggling37.5% mothers thought that their children were learning much less through this online mode of education and 43.8% thought that online learning is somewhat less. Conclusion: Among the various the challenges faced by working mothers and housewives during online education of their children the most important challenge was to keep their children focused on study. We discovered that the online study had little effect on grades because most mothers had to take on the role of teacher as well as supervising.

10.
Impact of Infodemic on Organizational Performance ; : 1-9, 2021.
Article in English | Scopus | ID: covidwho-1810481

ABSTRACT

The objective of this descriptive study is to highlight the issues related to the mental health of employees during COVID-19. This chapter reviews the impact of the COVID-19 outbreak on mental health of employees, and different diseases (such as anxiety, insomnia, and depression) impact employees' behavior and performance. Post-review of the latest articles, blogs, term papers, reports of the World Health Organization, and newspapers confirms the impact of COVID-19 on the physical and mental health of the employees and performance of the organizations negatively. Specifically, this study has discussed the different psychological problems like anxiety, depression, insomnia, social isolation with employees working in Pakistan. This study also highlights the measures took by the Pakistani government against COVID-19 and their results. © 2021, IGI Global.

11.
Pakistan Journal of Medical and Health Sciences ; 16(2):903-906, 2022.
Article in English | EMBASE | ID: covidwho-1791219

ABSTRACT

Background: The biggest dilemma of today's world is COVID-19. This pandemic situation has completely engulfed the globe with a rapidly increasing number of cases and has affected a great number of lives along with their lifestyle including the educational sector. Objective: This study explores the impact of COVID-19, how frequent lockdown, and online learning have affected the mental health of the students of medical college. Methods: A cross-sectional study was conducted among the medical students of Karachi, in September 2021, in Jinnah Medical and Dental College. A total of 312 medical students were enrolled in the study. Out of which 208 were females and 104 were males. Depression Anxiety Stress Scale-21 (DASS-21) was used to assess the mental health of students of medical college. Results: The results of the study revealed that a total of 312 medical students were enrolled in the study;belonging to the age group of (18-25) years. Approximately 104 (66 %) of female students experienced depression, 44 (21%) anxiety, and 60 (28%) stress. Hence gender and year of study of the participants were found to be significant (p-value<0.05). The odds of first-year students showed high levels of anxiety as compared to final-year students (OR = 1.679, 95% CI [1.202-2.634], P = 0.002). Conclusion: This study will help in making relevant policies, mental health strategies and providing a better framework for the medical colleges and universities which is essential for the mental health of students.

12.
Cancer Research ; 82(4 SUPPL), 2022.
Article in English | EMBASE | ID: covidwho-1779478

ABSTRACT

Background: Infection with SARS-CoV-2 has led to a global pandemic and has significantly impacted the care of cancer patients. Breast cancer patients receiving active systemic therapy need protection against COVID19 but the efficacy of vaccines in this population is unknown. Although specific biomarkers associated with protection from SARS-CoV-2 infection have yet to be identified, measurement of serum antibody activity is generally accepted as a surrogate of in vivo humoral response to vaccine. This study evaluates the efficiency and durability of binding antibodies to SARS-CoV-2 spike (S) protein in response to COVID19 vaccine in breast cancer patients receiving systemic treatment. Methods: Breast cancer patients, who were unvaccinated, partially or fully vaccinated with Pfizer-BioNTech BNT162b2 (PF), Moderna mRNA-1273 (Mod) or Johnson & Johnson AD26.COV2.S (J&J) were enrolled in this prospective longitudinal study. Eligible patients were on systemic treatment with cytotoxic chemotherapy, chemotherapy plus a checkpoint inhibitor (CPI), CPI alone or a CDK 4/6 inhibitor. Longitudinal blood samples are being collected at baseline, prior to vaccination in unvaccinated patients (T0), 2 weeks after the first vaccine dose and before Sthe second dose for the mRNA vaccines (T1), 1 month (T2), 3 months (T3), 6 months (T4) and 12 month post vaccination. For J&J, there was no T1 timepoint. Roche Elecsys® Anti-SARS-CoV-2 S receptor binding domain (RBD) antibody immunoassay was used to measure antibody titers (range 0.4 to 250 U/mL). Cut points of <0.8 U/mL = negative, ≥0.8 U/mL = seropositive, were based on validated product specifications. Results: Of the 84 breast cancer patients enrolled, 9 had documented COVID infection at baseline and were excluded from analysis. Mean age was 58 years;99% were female, 85% were Caucasian, 49% had early stage disease and 51% had metastatic breast cancer. 67% were receiving cytotoxic chemotherapy, 20% a CKD 4/6 inhibitor, 13% a CPI with or without chemotherapy. 61.2% were vaccinated with PF, 34.3% with Mod and 4.5% with J&J vaccines. Seropositivity rate for the entire group was 10% at T0, 78% at T1, 98% at T2 and 100% at T3. Seropositivity rates of all cohorts at different timepoints are shown in the table. Mean titers for all patients were 12.6 U/mL at T0, 102.3 U/mL at T1, 204.4 U/mL at T2 and 214.6 U/mL at T3 timepoints. Similar incremental increase in antibody levels was observed in all cohorts (Table). Conclusions: 78% of the patients with breast cancer on active systemic treatment were seropositive after the first dose of COVID19 vaccine and 98% after the second dose. The antibody response was maintained at 3 months, with 100% seropositivity rate. 6-month antibody response will be available at the time of presentation. Durability of antibody response at 6 and 12 months will help determine the timing of additional vaccine booster doses in this population. Importantly, this study has found that active treatment with chemotherapy, immunotherapy or CDK4/6 inhibitor therapy does not impact antibody response to SARS-CoV-2 vaccination in patients with breast cancer. Table: Seropositivity rate and mean Anti-S protein antibody levels by cohort at each time point. T0= baseline, T1=after first vaccine dose (mRNA vaccines), T2= 4 weeks after 2 doses of mRNA vaccine or after single dose of J&J vaccine, T3=3 months after the first dose of vaccine.

13.
3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021 ; : 2089-2095, 2021.
Article in English | Scopus | ID: covidwho-1774610

ABSTRACT

The exponential growth of smart devices and gadgets connected to the Internet of Things (IoT) produces massive amounts of data. The number of internet-connected devices is predicted to outnumber people by 25 to 50 billion by 2025. The new concept IoT refers to the combination of wired and wireless embedded communication technologies, sensors, actuators (transducers), and internet-connected items. The data generated by these IoT devices is in high velocity, variety, and varsity in accordance with location and time. IoT is generating data so it one of the primary sources of big data. Intelligent analysis and processing need an hour to develop smart IoT applications to tackle this huge volume of data. This article accesses various computing frameworks such as Cloud Computing, Fog Computing and Edge Computing environments for smarter IoT applications. This article also presents how machine learning algorithms can be incorporated in IoT data to get better insights. Various machine learning algorithms are explained and how these algorithms can be applied to data to get a higher level of information. This studies key contribution is how computing frameworks can be integrated with machine learning techniques in the healthcare sector to get better insights from the data. Mostly in this article, IoT and machine learning algorithms are discussed with respect to COVID-19. Moreover, the potential application areas and open issues of IoT data analytics and Machine Learning are discussed. © 2021 IEEE.

14.
Iraqi Journal of Science ; 62(11):4092-4100, 2021.
Article in English | Scopus | ID: covidwho-1636719

ABSTRACT

Suicidal ideation is one of the severe mental health issues and a serious social problem faced by our society. This problem has been usually dealt with through the psychological point of view, using clinical face to face settings. There are various risk factors associated with suicides, including social isolation, anxiety, depression, etc., that decrease the threshold for suicide. The COVID-19 pandemic further increases social isolation, posing a great threat to the human population. Posting suicidal thoughts on social media is gaining much attention due to the social stigma associated with the mental health. Online Social Networks (OSN) are increasingly used to express the suicidal thoughts. Recently, a top Indian actor industry took the harsh step of suicide. The last Instagram posts revealed signs of depression, which if anticipated could have saved the precious life. Recent research indicated that the public information on social media provides valuable insights on detecting the users with the suicidal ideation. The motive of this study is to provide a systematic review of the work done already in the use of social media for suicide prevention and propose a novel classification approach that classifies the suicide related tweets/posts into three levels of distress. Moreover, our proposed classification task which was implemented through various machine learning techniques revealed high accuracy in classifying the suicidal posts. Among all algorithms, the best performing algorithm was that of the decision tree, with an F1 score ranging 0.95-0.97. After thoroughly studying the work achieved by different researchers in the area of suicide prevention, our study critically analyses those works and finds various research gaps and solves some of them. We believe that our work will motivate research community to look into other gaps that will in turn help psychiatrists, psychologists, and counsellors to protect individuals suffering from suicidal ideation. © 2021 University of Baghdad-College of Science. All rights reserved.

15.
3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1526320

ABSTRACT

It is essential to understand what topics related to the COVID19 pandemic forms informative and uninformative content on social networks instead of general information (which contains both informative and uninformative). Uninformative content is mainly based on personal opinions and is more suitable for sentimental analysis. Whereas informative content is based on facts, figures, and reports;therefore, it is beneficial to gain a more in-depth understanding for a better strategic response to COVID-19. Despite knowing this fact, there is still a lack of study performed to investigate the aspects of informative content to gain an in-depth understanding of COVID-19 discussed topics. We aim to fill this gap through the study presented in this paper. We used the dataset containing 4719 'informative' and 5281 'uninformative' labeled tweets to realize informative aspects. Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) are popular topic modeling techniques. However, since both are based on an unsupervised approach, it is still unknown whether LDA or LSA effectively categorizes documents and how an appropriate number of topics can be determined. Therefore, we used both techniques to analyze tweets' content. Results show that LDA outperforms LSA by achieving a topic coherence score of 0.619 on uninformative and 0.599 on informative. In addition, based on LDA's results, it is also observed that most of the words that form informative content are death, case, coronavirus, people, confirmed, total, positive, tested, number, reported indicating tested, and death cases are the most concerned topics. On the other hand, words like immunity, fatality, protocol, thread, tourist, queue, blockade, eradication, prediction, detention, concerned are most likely to form uninformative content. © 2021 IEEE.

16.
Proc. - IEEE Int. Conf. Adv. Comput., Commun. Control Netw., ICACCCN ; : 128-134, 2020.
Article in English | Scopus | ID: covidwho-1142773

ABSTRACT

The enormous growth of Social Networking Sites (SNS) resulted in more virtual engagement of people in the last decade. Amount of data generated through these SNS is enormous, allowing researchers to analyse this Big data. People share their opinions and thoughts related to any topic of interest. As suicide is one the leading cause of death worldwide, it has become a hot topic on which different researchers are working. The Covid19 further amplified the crisis due to social isolation which is the main risk factor for suicide. The problem has usually been analysed and dealt through a physiological point of view using Questionnaires and face to face settings but social stigma prevents its efficacy. In our research, we use well-known machine learning algorithms for multi-classification of Suicidal risk on social media so that individuals having high risk could be identified and counselled properly to save precious human lives. The data has been experimented through four popular machine learning algorithms: Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine and Decision tree. The results generated are impressive with F1 Score ranging from 0.74 to 0.97. The Best performing algorithm was Decision tree that achieved an F-measure of 0.97, 0.94 and 0.96 for classifying suicidal text into three levels of concern. © 2020 IEEE.

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