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Coronavirus disease (COVID-19) has significantly affected the daily life activities of people globally. To prevent the spread of COVID-19, the World Health Organization has recommended the people to wear face mask in public places. Manual inspection of people for wearing face masks in public places is a challenging task. Moreover, the use of face masks makes the traditional face recognition techniques ineffective, which are typically designed for unveiled faces. Thus, introduces an urgent need to develop a robust system capable of detecting the people not wearing the face masks and recognizing different persons while wearing the face mask. In this paper, we propose a novel DeepMasknet framework capable of both the face mask detection and masked facial recognition. Moreover, presently there is an absence of a unified and diverse dataset that can be used to evaluate both the face mask detection and masked facial recognition. For this purpose, we also developed a largescale and diverse unified mask detection and masked facial recognition (MDMFR) dataset to measure the performance of both the face mask detection and masked facial recognition methods. Experimental results on multiple datasets including the cross-dataset setting show the superiority of our DeepMasknet framework over the contemporary models. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.
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Background: The inflated use of digital screens has completely changed the lives of people physically, mentally and psychologically. The covid-19 pandemic has also compelled people of all age groups to shift to digital media. The average screen time usage is 7-9 hours a day which is alarming. Aim(s): To find out the relation of screen time with ophthalmic problems among medical students. Study Design: Cross-sectional study Place and Duration of Study: Department of Community Medicine, HITEC-IMS Taxila from 1st January 2020 to 30th June 2020. Methodology: One hundred and fifty two medical students were included using non probability convenience sampling technique. An electronic questionnaire was developed and Google forms were used for data registration. Result(s): The relation between screen time duration with difficulty in refocusing (p=0.05) and eye redness (p=0.05). No relation was found between screen duration and headache, eye strain, blurred vision and refractive errors. Conclusion(s): High screen time is found to be related to ophthalmic problems like difficulty refocussing and eye redness which if not addressed properly might result in detrimental effects. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.
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COVID-19 was declared a pandemic with no apparent treatment of this virus in late 2019. It was recommended to observe the social distance, quarantine the infected patient, and follow good hygiene practices. Further, some strategies were adopted to control the spread of COVID-19, including social distancing, use of face masks, forced lockdown, and closure of borders. Many developing countries like Pakistan are facing severe crises and economic recession. Most people affected by this pandemic are women, children, elderly, and people with immune deficiencies. Zoom has become the counter-piece of life during this pandemic because people use it for job, education and socializing. This pandemic resulted in high earnings in the IT sector. Pakistan's e-commerce market size has witnessed a new high. The growth of e-commerce and mobile phone banking market size increased rapidly. Further government can help transition the businesses towards online mode, which can help retain jobs and economic growth stability.
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Introduction: With a high fatality rate, the coronavirus disease of 2019 (COVID-19) has drawn the attention of researchers from all over the world and put a significant strain on the healthcare system. The goal of this article was to evaluate;how COVID-19 patients were affected by biochemical parameter levels, at Mayo Hospital Lahore, Pakistan. Methodology: Between June and December 2020, this retrospective analysis on 150 COVID-19 patients-82 men and 68 women-was carried out. The patients' biochemical measures and demographic data, such as gender, age, urea, LDH (lactate dehydrogenase), Cr (creatinine), and CK (creatine kinase), were gathered via electrical medical records. Based on the COVID-19 results, two groups were made the patients were divided as (death (survival = 98 and Death = 52), and the COVID-19 biochemical parameters and results were studied. Result(s):93 (62%) of the 150 patients had a mild type of COVID-19 and recovered, while 57 (38%) acquired a severely severe type and passed away. All patients were an average age of 56 years old. The patients with LDH levels above 290 had the highest mortality. The average values of CK, Cr, urea, and LDH were calculated with the help of one-sample t-test and the results depicted that, in COVID-19 patients, the values of such variables are significantly higher than the corresponding reference intervals at P value 0.0001 for all biochemical factors. Conclusion(s): In order to assess dynamic fluctuations in COVID-19 patients, some biochemical measures are useful. The findings suggest that LDH reinforcement and metabolic measures may be helpful in assessing COVID-19 consequences. Copyright © 2022, Anka Publishers. All rights reserved.
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Background: During Covid pandemic the teaching/learning shifted from face to face to online. All institutions around the world developed learning environment for the students to facilitate distant learning. Subsequently assessments also followed online. After opening of the institutions on campus learning and assessments were carried out as usual. Objective(s): To see the effectiveness of teaching methods (online versus on campus) and to suggest improvement in both methods of teaching. Study Design: Retrospective comparative study Place and Duration of Study: Hitec-IMS, Taxila Pakistan from 1st September 2021 to 29th February 2022. Methodology: One hundred and ninety six academic performances of students in both the methods of learning were enrolled. Using purposive sampling technique, the EOB results of online and on campus were analyzed. Result(s): The better performance of student in term of summative assessments during online learning. Conclusion(s): Students found online environment better for learning and performing during examinations. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.
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During the Covid-19 Pandemic, the usage of social media networks increased exponentially. People engage in education, business, shopping, and other social activities (i.e., Twitter, Facebook, WhatsApp, Instagram, YouTube). As social networking expands rapidly, its positive and negative impacts affect human health. All this leads to social crimes and illegal activities like phishing, hacking, ransomware, password attacks, spyware, blackmailing, Middle-man-attack. This research extensively discusses the social networking threats, challenges, online surveys, and future effects. We conduct an online survey using the google forms platform to collect the responses of social networking sites (SNS) users within Pakistan to show how SNS affects health positively and negatively. According to the collected response, we analyzed that 50% of the users use SNS for education purposes, 17.5% use it for shopping purposes, 58.2% use it for entertainment, 37.1% use it for communication, and 9.8% use it for other purposes. According to the response, the excessive use of SNS affects the health that 9.8% users face the physical threat, 42.8% user faces mental health issues due to excessive or inappropriate use of SN, and 50.5% users feel moral threat using Social sites. Finally, we conclude our paper by discussing the open challenges, conclusions, and future directions.
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The COVID-19 pandemic needs present resources for the mitigation of its distressing effects. Suspected cases of COVID-19 need rapid, early and accurate detection to prevent the spread on a large scale. Existing tests for COVID-19 diagnosis are slow and need a few hours to generate the results. The situation is even worsened in developing countries such as Pakistan due to the lack/limited facilities of reliable COVID detection test. Moreover, existing tests like PCR are not highly reliable and often result in an incorrect diagnosis. Early and reliable COVID-19 diagnosis is vital to reduce the complications in the treatment of COVID-19 patients. This paper presents a comparative analysis of different deep learning-based feature extraction models for COVID-19 detection. Towards this end, we have employed twelve pretrained deep learning models, i.e., Mobilnetv2, Darknet19, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Inceptionresnetv2, Resnet50, Resnet18, Resnet101 and Shufflenet for features extraction from the chest radiograph images. The support vector machine (SVM) was then trained using these features for classification of chest radiograph images into COVID or non-COVID/normal. We evaluated the performance of each deep learning model on a standard COVID-19 Radiography Database that consists of 13808 Chest X-Ray images including 3616 COVID-19 positive images and 10192 X-ray COVID-19 negative images. Our experimental results illustrate that the Mobilnetv2 model provides the most effective deep features for image representation. The accuracy of 98.5% signifies the effectiveness of the Mobilnetv2 model for reliable detection of COVID-19.
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Objective: To evaluate the challenges and coping strategies by Radiology Doctors during the COVID-19 Era. Materials and Methods: It is a mixed-method cross-sectional study done over one month in Radiology Department, Shaikh Zayed Hospital, Lahore, Pakistan with a convenient sampling technique. Results: 80% of the radiologists faced challenges during the pandemic. Age, gender, marital status, monthly income, residents, and years of residency showed significant associations with challenges faced by doctors in the radiology department during the COVID-19 pandemic. Many challenges in terms of management, psychological aspects, training education, and research work were faced by the radiologists but they coped with them heroically. Conclusion: COVID-19 posed a spectrum of unforeseen challenges to the radiologists of Shaikh Zayed Hospital, Lahore. Challenges related to management, psychological aspects for doctors, research work and training education were all tackled by the senior and junior doctors by using various coping strategies.
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COVID-19 has become the global epidemic affecting millions of people across the world. The fact that COVID-19 spreads quickly and devastating for elderly person makes this disease lethal as we witnessed a massive mortality rate in first, second, and third wave since 2020. Early diagnosis of COVID-19 is mandatory to prevent the spread and damage control, as only few nations have been able to vaccinate more than 50% of their population. The healthcare professionals commonly use the real-time polymerase chain reaction (RT-PCR) test to identify the COVID-19. Although RT-PCR test is considered more reliable among other COVID-19 detection tests;however, sensitivity of RT-PCR lies in the range of 65%-95% and took hours to diagnose the COVID-19 disease. Therefore, there exists an urgent need to develop more rapid and reliable diagnostics methods for COVID-19. In this regard, Chest X-ray and CT scan images are also being used to determine the abnormalities in the lungs of the COVID-19 patients which are found after the initial symptoms of this disease. We exploit the benefits of convolution neural network (CNN) for reliable detection of various diseases and used it for COVID-19 detection. For this purpose, we proposed a deep learning model to automatically detect the COVID-19 disease by processing the chest X-ray images. More specifically, we presented an Inception-ResNetV2 network-based deep learning model for COVID-19 detection. Performance of our model is evaluated on the publicly available COVID-19 dataset. The accuracy of 96% indicates the effectiveness of the proposed model for COVID-19 detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Objective: The characteristics of the novel corona virus ailment 2019 (COVID-19) vs influenza were not described, such as blood test data. As a result, we compared the diagnostic features of COVID-19 and flu, along with blood test data. Materials and Methods: A cross-sectional study was conducted at Nishtar Hospital, Multan. We enrolled individuals diagnosed with COVID-19 between January 1, 2020, and December 31, 2020, and had they undergo blood tests. In comparing, we enlisted an equal percentage of participants who'd been identified with flu that had blood tests. Results: During the course of the study, 228 people were identified of COVID-19 (men:women ratio, 123 [54.0 percent]:105 [46.0 percent];age, 54.68 18.98 years). We also enlisted the help of 228 flu clients (male:female, 129 [56.6 percent ]:99 [43.4 percent ];age, 69.6 21.25 years). Clients with COVID-19 had a vastly greater age range of 15 to 70 years (vs. 71 years), respiratory problems, as well as ennui than someone with flu. Nevertheless, discomfort, a body temperature greater than 38.1oC, as well as a white blood cell count greater than 9000/lL were far more prevalent in flu patient populations. Conclusions: Our findings are helpful in distinguishing COVID-19 from flu, so they'll be remarkably helpful for future practice as we understand to interoperate with COVID-19.
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Background: Vasculitis is a known, although not commonly observed, manifestation of bacterial endocarditis. It is imperative that diagnosis is made promptly and appropriately treated, as vasculitis can often be painful and uncomfortable for patients. Case: 75-year-old male is admitted to the hospital for Coronavirus Disease 2019 (COVID-19). Several weeks after recovering from his respiratory infection, patient developed a diffuse, purpuric rash that began on his forearms and gradually spread throughout his bilateral upper extremities to his hands and fingers, as well as to his shoulders and lateral chest. Skin biopsy was performed and revealed findings suggestive of leukocytoclastic vasculitis. Blood work revealed Methicillin Resistant Staph Aureus (MRSA) bacteremia, sensitive to Vancomycin. Transthoracic echocardiogram revealed native mitral valve endocarditis. Transesophageal echocardiogram was not performed due to patient's underlying comorbidities and high risk. Decision-making: Patient was diagnosed with leukocytoclastic vasculitis secondary to bacterial endocarditis. Rheumatologic workup, including antineutrophil cytoplasmic antibodies, antinuclear antibodies, serum complement levels, anti-smith antibodies and double stranded deoxyribonucleic acid, was negative. Patient was ultimately discharged on a prolonged course of Vancomycin and his diffuse rash resolved one month later. Conclusion: There are only a few case reports describing the direct association between leukocytoclastic vasculitis and infective endocarditis. It is important to consider the association of vasculitis and endocarditis in order to effectively treat because immunosuppression, particularly with steroids, is the gold standard treatment for vasculitis. Our patient experienced near complete resolution of the rash after completion of antibiotics and no other therapy was deemed necessary.
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COVID-19 has become a pandemic, with cases all over the world, with widespread disruption in some countries, such as Italy, US, India, South Korea, and Japan. Early and reliable detection of COVID-19 is mandatory to control the spread of infection. Moreover, prediction of COVID-19 spread in near future is also crucial to better plan for the disease control. For this purpose, we proposed a robust framework for the analysis, prediction, and detection of COVID-19.We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world. The estimates, analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27, 2020. We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents. The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified. The parameters of Gaussian distribution, i.e., maximumtime and width, are determined through a statistical x2-fit for the purpose of doubling times after April 21, 2020. For COVID-19 detection, we proposed a novel method based on the Histogram of Oriented Gradients (HOG) and CNN in multi-class classification scenario i.e., Normal, COVID-19, viral pneumonia etc. Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19. © 2022 Tech Science Press. All rights reserved.
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Background: SARS-CoV-2 infection (COVID-19) is clinical threat to healthy individuals around the world. Risk of disease and related complications are high among immunocompromised individuals and those with pre-existing chronic diseases. Aim: To assess the fear of Covid-19 among patients having chronic diseases and to determine its relationship with preventive practices among them. Study Design: Cross-sectional study Place and Duration of Study: Department of Community Medicine, HITEC Hospital Taxila from 1st September2020 to 31st March 2021. Methodology: Three hundred and seventeen patients having chronic diseases were included. Fear of Covid-19 scale used to assess the fear level and questions related to preventive practices. Results: Fear of Covid-19 was high among females, hypertensive, diabetics and those having cardiovascular disease. Fear was found among 133 (42%) participants. Regarding Covid-19 preventive practices, 8(2.5%) had unsatisfactory, 115 (36.3%) had satisfactory and 194(61.2%) had good preventive practices. Covid precautions were significantly practiced among those having fear. Statistically significant positive correlation was found between mean Covid fear and practices scores (r=.30, p=.001) Conclusion: Fear of Covid is a recognized risk factor for anxiety and depression among people. However, fear is found to promote risk perception and health related preventive behaviors among chronic patients that can positively ensure safety, decrease the risk of infection and serious complications among chronic patients.
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Introduction: Spontaneous bacterial peritonitis (SBP) remains a major complication of decompensated chronic liver disease and contributes to 20% in-hospital mortality.1,5 Timely diagnosis is vital in the management of SBP, and overall improved outcomes1. A systematic review of 19 studies comparing the use of Reagent strips to cyto bacteriological analysis showed the high negative predictive value of leucocyte esterase dipsticks in the diagnosis of SBP 2. The COVID 19 pandemic has seen lab capacity including microbiology reprioritised. This called for adaptation in various areas of medicine to find innovative ways to aid ease pressure on services such as turnaround time of results. Aims & Methods: The aim was to assess the applicability of the leucocyte esterase reagent test kit (LERT) as a screening bedside test on ascitic fluid samples for the rapid diagnosis of SBP, during the peak of the COVID 19 Pandemic. Methodology: This prospective study included 3 district general hospitals in Kent and Sussex catchment areas of South East England. Data was collected between 1st May 2020 to 30th June 2020 during the first wave of the pandemic. Inclusion criteria: 1. Inpatients admitted with decompensated chronic liver disease with moderate or tense ascites. 2. Patients presenting to ambulatory care units for ascites management. 3. Inpatients with malignant ascites. Ascitic fluid samples used were the same samples obtained by the primary team at the patients initial encounter. Leucocyte esterase dipstick (Multistix SG) tests performed on the ascitic fluid samples were compared with the formal microbiology manual white cell count, neutrophil count and cultures results. To ensure validity of the results, two observes (doctor, nurse, health care assistant) were required to read the results from the leucocyte esterase test kit one minute after the test was completed. Results were recorded on an excel spreadsheet stored on hospital secure drive. Results: A total of 35 patients met inclusion criteria. Aetiology of ascites and number of patients : Alcohol Liver Disease (17) NASH Cirrhosis (3), Mixed Alcohol liver disease/ Haemochromatosis (3) , Alcoholic liver disease /Hepatitis C (1), Primary Sclerosing Cholangitis (1) , Malignancy (10). Mean age : 66.7 years. 66% were male patients and 34% female. This study demonstrated the that 2 plus leucocytes on the LERT correlated with a manual absolute neutrophil count of >250cells/mm3 and requirement of one minute to analyse the LERT to improve the diagnostic sensitivity. This was not mentioned in methodology from various studies reviewed in our literature search3,4. Conclusion: This multisite study, although a small sample size, is in accordance with similar studies showing the relatively high negative predictive value of the bedside LERT kit 3,4. This was particularly useful at a time of the COVID 19 pandemic peak when lab capacity had been redirected to cope with the increased volume of lab samples.
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Background: Medical students have faced an enormous disruption to their lives due to the COVID-19 pandemic. The study aimed to assess the impact of COVID-19 on medical student’s psychological well-being in Pakistan. Following ethical approval, an online survey developed in collaboration with World Psychiatric Association (WPA) was distributed among medical students of 5 Medical colleges in the Punjab province of Pakistan between August and September 2020. Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Risk Assessment Suicidality Scale (RASS) were used to assess psychological well-being. Data was analyzed using SPSS 26.0. Results: Eleven hundred medical students responded, 756 (69%) being females. More than 2/3rd admitted that their emotional state got worse in relation to appearance of anxiety, insecurity, and sadness, compared to before the outbreak of COVID-19. Prevalence of anxiety and depressive symptoms were 48.6% and 48.1%, respectively. Female medical students, pre-clinical students, and those with a previous psychiatric history reported experiencing more anxiety and depression symptoms (P value < 0.001). One in five medical students thought that it would be better if they were dead, and 8% admitted to often think of committing suicide during the past 2 weeks. RASS and subscales (intention, life, and history) scores were higher in females and students with previous psychiatric problems. Conclusion: Our findings underscore that the impact of COVID-19 on medical students has been significant;hence, it is crucial for medical colleges to employ strategies to maintain the student’s well-being with safeguards like reassurance, support, and confidential student-centered psychiatric services. The use of virtual platforms (websites, email) to educate and screen students by staff members can create a positive impact. The limitations of this study include cross-sectional design, the possibility of selective participation being web-based survey, response bias, and the possibility of reluctance of students to report mental health problems due to stigma. © 2021, The Author(s).
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This research provides a detailed review and analysis of the data and research available online regarding the emotional and psychological impacts of COVID-19 on people of different age groups. The purpose of this review article is to identify and highlight the deep infiltrating and all-encompassing toll to the global mental health that this pandemic is causing in addition to the decline in physical functional health.
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Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan. This research also compares the performance of machine learning classifiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vector Machine, and Logistic Regression) on the COVID-19 dataset collected in Pakistan. According to the experimental results, DT and NB classifiers outperformed the other classifiers. In addition, the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network (BRANN) classifier. The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. © 2021 Tech Science Press. All rights reserved.
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COVID-19 has had a drastic impact on every field and walk of life. The long-lasting impacts of this pandemic have changed the way businesses used to be conducted and will have a strong impact on business models as well. The main objective of this qualitative study is to investigate the impact of COVID-19 on restaurants and small stalls of street food vendors in Pakistan and to suggest a way forward. A total of 30 interviews were conducted through conference calls. The findings proved that major issues faced by the restaurants are the massive decline in sales, massive layoffs, no economic activity, and no relief from the government. The major changes required in the existing business models highlighted by the interviewees are proper sanitization, changes in the sitting area, change in menus, and the need for innovative ideas to attract the customers back. The study is useful for the restaurants and street food vendors to help them out in this difficult phase and suggest a way forward to them.
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Coronavirus disease-19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic threat. This virus is supposed to be spread by human to human transmission. Cellular angiotensin-converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2 which is identical or similar in different species of animals such as pigs, ferrets, cats, orangutans, monkeys, and humans. Moreover, a recent study predicted that dogs might be secondary hosts during the evolution of SARS-CoV-2 from bat to human. Therefore, there is a possibility of spreading SARS-CoV-2 through domestic pets. There are now many reports of SARS-CoV-2 positive cases in dogs, cats, tigers, lion, and minks. Experimental data showed ferrets and cats are highly susceptible to SARS-CoV-2 as infected by virus inoculation and can transmit the virus directly or indirectly by droplets or airborne routes. Based on these natural infection reports and experimental data, whether the pets are responsible for SARS-CoV-2 spread to humans;needs to be deeply investigated. Humans showing clinical symptoms of respiratory infections have been undergoing for the COVID-19 diagnostic test but many infected people and few pets confirmed with SARS-CoV-2 remained asymptomatic. In this review, we summarize the natural cases of SARS-CoV-2 in animals with the latest researches conducted in this field. This review will be helpful to think insights of SARS-CoV-2 transmissions, spread, and demand for seroprevalence studies, especially in companion ani-mals. Copyright & ordf;2020, Taiwan Society of Microbiology. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).