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Purpose: The nutritional and anthropometric status can be essential in determining their immune response to vaccines. The purpose of this paper was to investigate the association between diet quality and anthropometric indices with the side effects of the Pfizer-BioNTech COVID-19 vaccine and the SARS-CoV-2 immunoglobulin G titer among Kurdish adults. Design/methodology/approach: This cross-sectional survey-based study was conducted between December 2021 and February 2022. This paper included data on 115 adults, 20-89 years old, from the Kurdistan region. Dietary information was collected using a short food frequency questionnaire, and diet quality was assessed using a plant-based healthy diet score. A blood test was performed to measure the SARS-CoV-2 immunoglobin G (IgG) titer after the vaccination's first and second doses. Findings: Overweight and obese subjects reported more local pain, myalgia, headache, local bruising and local reactions after receiving the first dose of the vaccine (p = 0.04). People on a less healthy diet reported more local pain, myalgia and headache (p = 0.04) and more local bruising and reactions (p = 0.01) after receiving the second dose of the vaccine. On the other hand, the authors observed that those with healthy dietary habits had more IgG titer after the first and second doses of vaccination than those with less healthy dietary habits (p = 0.001). Originality/valueThe results showed that participants with a healthy diet and normal weight status had fewer side effects of the Pfizer-BioNTech COVID-19 vaccine than obese people and those with a less healthy diet.
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Background: Smoking is considered to be one of the main risk factors that may affect the severity of coronavirus disease 2019 (COVID-19). Previously, several meta-analyses with a limited or small sample size and insufficient methodology have been conducted investigating the impact of smoking on disease severity. Here, we use a more accurate method to identify the effect of smoking on COVID-19 disease severity. Methods: BMC, PubMed, Science Direct, Wiley, Springer, and Google Scholar websites were used to search for and select reliable articles to be included in the current analysis. Research articles that mentioned the relationship between smoking and COVID-19 severity were included. Results: Twenty-six research articles detailing 15, 713 confirmed COVID-19 cases comprising patients who smoke were selected to be included in this analysis. The analysis showed a relationship between smoking, severe COVID-19, and non-severe COVID-19 (OR=0:11;95%CI: 0.10-0.11;p<0.00001). Only 15% (2407) of the smokers suffered severe COVID-19, with the other 85% (13306) of smokers experiencing non-severe COVID-19. Conclusion: The current analysis found that only 15% of severe COVID-19 cases were smokers. Therefore, smoking is not significantly correlated with severe covid19. Copyright © 2023, Journal of Iranian Medical Council. All rights reserved. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Purpose: The aim of this study is to analyze whether time-driven benchmarking might be a helpful tool in assessing healthcare operations during the COVID-19 pandemic. Theoretical framework: The research examines the progress of eleven hospital procedures to analyze and evaluate them. This section also focuses on how time and cost data from the two hospitals we are exploring might be utilized to improve operations and performance, particularly in light of our time-driven benchmark. Design/methodology/approach: The research focuses on how to analyze time-driven benchmarking for Measuring Health Services Performance under COVID-19 Pandemic. This assessment entails the use of a strategic approach to determine the results of the review process from all financial and non-financial components of studies, research, and scientific papers found online and elsewhere. Findings: The results showed that the TD-ABC consisting of perspectives provides an innovative approach to evaluating the requirements for implementing the time-driven benchmarking in Two Iraqi hospitals, which helps Measuring Health Services Performance under COVID-19 Pandemic. Research, Practical & Social implications: The study examined the challenges and constraints of whether time-driven benchmarking might be a helpful tool in assessing healthcare operations during the COVID-19 pandemic. Originality/value: The study's originality value by assessing how to analyze time-driven benchmarking for Measuring Health Services Performance during the COVID-19 Pandemic in Two Iraqi hospitals. © AOS-Estratagia and Inovacao. All rights reserved.
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Despite much focus being placed on safety study across various industries, the application or the implementation of safety management systems (SMS) within the aviation sector and tourism sector for the safety of travellers is mostly ignored. It is surprising, given the wide field of technology study, that there are not many studies examining safety issues and connecting them with technology applications for the safety of travellers. The aim of this research is to fill the gap by explaining the correlational relationship between safety and technology in the context of aviation and tourism. This chapter extends the discussion on safety in the context of aviation and tourism by highlighting the importance of ensuring safety for travellers throughout their tourism activity or journey from one destination to another destination. This study opts to highlight the role of technology to ensure the safety of travellers in air travel and tourism activities. © 2023 selection and editorial matter, Azizul Hassan and Nor Aida Abdul Rahman;individual chapters, the contributors.
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PurposeThe nutritional and anthropometric status can be essential in determining their immune response to vaccines. The purpose of this paper was to investigate the association between diet quality and anthropometric indices with the side effects of the Pfizer-BioNTech COVID-19 vaccine and the SARS-CoV-2 immunoglobulin G titer among Kurdish adults. Design/methodology/approachThis cross-sectional survey-based study was conducted between December 2021 and February 2022. This paper included data on 115 adults, 20-89 years old, from the Kurdistan region. Dietary information was collected using a short food frequency questionnaire, and diet quality was assessed using a plant-based healthy diet score. A blood test was performed to measure the SARS-CoV-2 immunoglobin G (IgG) titer after the vaccination's first and second doses. FindingsOverweight and obese subjects reported more local pain, myalgia, headache, local bruising and local reactions after receiving the first dose of the vaccine (p = 0.04). People on a less healthy diet reported more local pain, myalgia and headache (p = 0.04) and more local bruising and reactions (p = 0.01) after receiving the second dose of the vaccine. On the other hand, the authors observed that those with healthy dietary habits had more IgG titer after the first and second doses of vaccination than those with less healthy dietary habits (p = 0.001). Originality/valueThe results showed that participants with a healthy diet and normal weight status had fewer side effects of the Pfizer-BioNTech COVID-19 vaccine than obese people and those with a less healthy diet.
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Introduction: Operating theatres and surgical resource consumption comprise a significant proportion of all healthcare costs. Inefficiencies in theatre lists remain an important focus for cost management. During COVID-19 pandemic, The number of patients On theatre waiting lists has surged. Hence there is a pressing need to utilise already limited theatre time and fraught resources with innovative methods. In this systematic review we consider The Golden Patient Initiative (GPI), in which The first patient On The operating list is pre-assessed The day prior to surgery, and we aimed to assess its overall efficacy. Method(s): A literature search using four databases: MEDLINE, CINAHL, EMBASE, and The Cochrane library identified all clinical research concerning The GPI. Two independent authors screened articles against eligibility criteria, using a process adapted from PRISMA guidelines. Data extracted included outcomes measured, follow-uP period and study design. The result displayed significant heterogeneity;therefore a narrative review was conducted. Result(s): 13 of 73 eligible articles were included for analysis. Outcomes included delay in theatre start time, number of surgical case cancellations and changes to total case numbers. Across The studies, a 19-30 minute improvement of theatre start time was reported (P<0.05), as was a statistically significant decrease in case cancellations. Conclusion(s): Our analysis gives encouraging conclusions for theatre efficiency with The GPI, a low cost solution which can easily be implemented to helP improve patient safety lead to cost savings. However, at present it is largely implemented amongst local trusts, hence larger multicentre studies are required to provide conclusive evidence. Take-home message: Our analysis gives encouraging conclusions for greater theatre efficiency with The GPI, a low cost solution which can easily be implementedtohelP improve patient safety leadto cost savings.
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The Internet of Things (IoT) for healthcare can improve patient monitoring more effectively, especially since the occurrence of the novel coronavirus (COVID-19) disease in 2019. Integrating sensors with long range (LoRa) technology, which provides long-range, low-power, and secure data transmission, can ensure better patient treatment and disease surveillance. This study is aimed at evaluating and understanding the LoRa performance as the wireless platform in IoT health monitoring. The MH-ET Live MAX30102 sensor is used to measure blood oxygen saturation and pulse rate, while TTGO LoRa32 SX1276 is used as the wireless platform. Results show that to obtain accurate readings from the sensor, users must be in rested condition, place their fingertip onto the sensor properly for a few moments without any movement, and use the body part of the fingertip only. In outdoor environment tests in the suburban area, the LoRa SX1276 transceiver's performance for the line-of-sight (LoS) transmission shows that the signal-to-noise ratio (SNR) and RSSI recorded at 1300-meter distance are -6.5 dB and -118 dBm, respectively. Non-line-of-sight (NLoS) test shows that LoRa still communicates with each other after eight blocks of houses with an approximate displacement of 240 meters apart between the modules, with RSSI and SNR values of -113 dBm and -5.42 dB, respectively. The analysis using LoRa Modem Calculator Tool proved the theoretical performances and effectiveness of LoRa communications.
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In this paper, we performed a comparative analysis using machine learning algorithms named support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), and convolution neural network (CNN) to classify pneumonia level (mild, progressive, and severe stage) of the COVID-19 confirmed patients. More precisely, the proposed model consists of two phases: first, the model computes the volume and density of lesions and opacities of the CT images using morphological approaches. In the second phase, we use machine learning algorithms to classify the pneumonia level of the confirmed COVID-19 patient. Extensive experiments have been carried out and the results show the accuracy of 91.304%, 91.4%, 87.5%, 95.622% for kNN, SVM, DT, and CNN, respectively. © Springer International Publishing AG 2018.
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The world is getting advanced towards technology rapidly, therefore an updated and advanced traditional class room learning was required in this modern era of education which could provide easy and quality learning for students. A complete solution having all variety of applications was essentially needed through which students can attend lectures from their home as well in this pandemic era. A cost-effective solution was developed named as automated class lecture recording system, utilizing a low-cost minimal effort card, single sized board PC. The proposed research provides a platform through which students can attend and revise high quality lectures in their preferred time. The proposed research comprises of raspberry pi, operating system (noobs), pi-cam and framework. Videos were recorded and streamed using Pi cam. Raspberry pi was programmed in such a way that it is capable to process all data i.e., video frames and audio packets in an asynchronous way and at the end videos and audio are merged. After completion of lecture, recording the sound and video documents can be combined and sent to the referred/desired storage device from where instructor can upload it to location or on dedicated site from where students can access it. Thus, the proposed system, provided benefit to students if someone is absent or not attending lecture due to illness or some other reasons. The proposed research also provides the solution of increasing number of expenses for students as it can save travel expenses and accommodation costs. Although there are so many competitive solutions are available for the class lecture recording system but these systems cannot be acknowledged as cost-effective solution. Moreover, some free solutions are there but they don't provide high quality and merging mechanism with the video of the instructor and lecture as well. Therefore, the proposed research can be considered as the most cost effective and efficient solution for traditional class room teachings. Cost survey for lecture recording system was also completed to recognize the importance and significance of the proposed design which showed that the proposed solution providers cost in Pakistan. © 2021 IEEE.
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In developing countries, medical diagnosis is expensive and time consuming. Hence, automatic diagnosis can be a good cheap alternative. This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks (CNNs). These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists. The deep CNNs allow direct learning from the medical images. However, the accessibility of classified data is still the largest challenge, particularly in the field of medical imaging. Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification. However, because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19, transfer learning is not usually a robust solution. Single-Image Super-Resolution (SISR) can facilitate learning to enhance computer vision functions, apart from enhancing perceptual image consistency. Consequently, it helps in showing the main features of images. Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis, this paper introduces a hybrid CNN model, namely SIGTra, to generate super-resolution versions of X-ray and CT images. It depends on a Generative Adversarial Network (GAN) for the super-resolution reconstruction problem. Besides, Transfer learning with CNN (TCNN) is adopted for the classification of images. Three different categories of chest X-ray and CT images can be classified with the proposed model. A comparison study is presented between the proposed SIGTra model and the other related CNN models for COVID-19 detection in terms of precision, sensitivity, and accuracy. © 2021 Tech Science Press. All rights reserved.
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BACKGROUND: Healthcare workers (HCWs) are at the frontline defense against coronavirus disease 2019 (COVID-19) pandemic. AIM: The study aimed to describe the characteristics and appraise potential risk factors of COVID-19 transmission among HCWs who tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in one of Cairo University Hospitals. METHOD: Cross-sectional descriptive analysis of confirmed polymerase chain reaction (PCR) positive versus negative cases for COVID-19. RESULTS: Through March–June 2020, (145/846;17%) suspected HCWs were tested for COVID-19 by PCR;out of them (70/145;48.3%) were confirmed as positive, these positive cases represented (70/846;8.3%) of all HCWs of the hospital. About 33% of confirmed COVID-19 positive HCWs acquired the infection from the healthcare while only (13/70;19%) from community settings, and no clear exposure data were identified in (34/70;48%) of cases. Most of symptomatic cases showed a positive PCR test for SARS-CoV-2 versus asymptomatic cases, p < 0.001. There was no statistical significance regarding gender, age, presence of comorbidity, workload or the type of acquisition. CONCLUSION: HCWs are at an increased risk of COVID-19 infection at the workplace. Strict implementation of infection control measures is of crucial role in preventing transmission of COVID-19 infection in health-care settings.
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Contact tracing is a very effective way to control the COVID-19-like pandemics. It aims to identify individuals who closely contacted an infected person during the incubation period of the virus and notify them to quarantine. However, the existing systems suffer from privacy, security, and efficiency issues. To address these limitations, in this paper, we propose an efficient and privacy-preserving Blockchain-based infection control system. Instead of depending on a single authority to run the system, a group of health authorities, that form a consortium Blockchain, run our system. Using Blockchain technology not only secures our system against single point of failure and denial of service attacks, but also brings transparency because all transactions can be validated by different parties. Although contact tracing is important, it is not enough to effectively control an infection. Thus, unlike most of the existing systems that focus only on contact tracing, our system consists of three integrated subsystems, including contact tracing, public places access control, and safe-places recommendation. The access control subsystem prevents infected people from visiting public places to prevent spreading the virus, and the recommendation subsystem categorizes zones based on the infection level so that people can avoid visiting contaminated zones. Our analysis demonstrates that our system is secure and preserves the privacy of the users against identification, social graph disclosure, and tracking attacks, while thwarting false reporting (or panic) attacks. Moreover, our extensive performance evaluations demonstrate the scalability of our system (which is desirable in pandemics) due to its low communication, computation, and storage overheads. IEEE
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While many patients infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) eventually produce neutralising antibodies, the degree of susceptibility of previously infected individuals to reinfection by SARS-CoV-2 is currently unknown. To better understand the impact of the immunoglobulin (IgG) level on reinfection in recovered coronavirus disease 2019 (COVID-19) patients, anti-nucleocapsid IgG levels against SARS-CoV-2 were measured in 829 patients with a previously confirmed infection just after their recovery. Notably, 87 of these patients had no detectable IgG concentration. While there was just one case of asymptomatic reinfection 4.5 months after the initial recovery amongst patients with detectable anti-nucleocapsid IgG levels, 25 of the 87 patients negative for anti-nucleocapsid IgG were reinfected within one to three months after their first infection. Therefore, patients who recover from COVID-19 with no detectable anti-nucleocapsid IgG concentration appear to remain more susceptible to reinfection by SARS-CoV-2, with no apparent immunity. Also, although our results suggest the chance is lower, the possibility for recovered patients with positive anti-nucleocapsid IgG findings to be reinfected similarly exists.
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Both laboratory investigations and body composition quantification measures (e.g., computed tomography, CT) portray muscle loss in symptomatic Coronavirus disease 2019 (COVID-19) patients. Muscle loss is associated with a poor prognosis of the disease. The exact mechanism of muscle damage in COVID-19 patients, as well as the long-term consequences of muscle injury in disease survivors, are unclear. The current review briefly summarizes the literature for mechanisms, assessment measures, and interventions relevant to skeletal muscle insult in COVID-19 patients. Muscle injury is likely to be attributed to the cytokine storm, disease severity, malnutrition, prolonged physical inactivity during intensive care unit (ICU) stays, mechanical ventilation, and myotoxic drugs (e.g., dexamethasone). It has been assessed by imaging and non-imaging techniques (e.g., CT and electromyography), physical performance tests (e.g., six-minute walk test), anthropometric measures (e.g., calf circumference), and biomarkers of muscle dystrophy (e.g., creatine kinase). Interventions directed toward minimizing muscle loss among COVID-19 patients are lacking. However, limited evidence shows that respiratory rehabilitation improves respiratory function, muscle strength, quality of life, and anxiety symptoms in recovering older COVID-19 patients. Neuromuscular electrical stimulation may restore muscle condition in ICU-admitted patients, albeit empirical evidence is needed. Given the contribution of malnutrition to disease severity and muscle damage, providing proper nutritional management for emaciated patients may be one of the key issues to achieve a better prognosis and prevent the after-effects of the disease. Considerable attention to longer-term consequences of muscle injury in recovering COVID-19 patients is necessary.
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Telehealth has the potential to improve patient access to professional healthcare. In this paper we examine the publicly held perceptions of healthcare professionals on Twitter regarding telehealth platforms pre and during the COVID-19 pandemic. Sentiment analysis and Epistemic Network Analysis (ENA) were used to investigate whether there were changes in the perceptions and opinions of telehealth expressed on Twitter by healthcare professionals between the time period of January to April 2019 and January to May 2020, during the initial medical system response to COVID-19. Findings suggest that professionals’ perceptions shifted from telehealth as innovation during COVID-19 to focus on the pervasive need for safe access and delivery to care. Overall, sentiment on telehealth was found to be positive, with advances made in payment for telehealth care delivery and the easing of some of the restrictions on telehealth practice in 2020, though concerns on access to care through telehealth platforms remain prevalent. © 2021, Springer Nature Switzerland AG.