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Covid-19 is a viral disease that affects humans caused by a type of virus belonging to the family Coronaviridae called the SARS-CoV-2 virus. The parasitic infection associated with this disease affects the host's immune response regulation. The levels of IgG and IgM of Toxoplasma gondii in the serum of patients with COVID-19 were measured by immunoassay of the patient's sera by ELISA. Also, the level of interferon-gamma (IFN-gamma) in a covid-19 patient with or without Toxoplasmosis was evaluated. 120 samples were collected, 60 were positive for COVID-19, confirmed by clinically and radiographic examination, and 30 were in the control group. The results showed a significant difference between the infection with Covid-19 and T. gondii during the chronic phase of Toxoplasmosis compared to the negative relationship in the acute phase. The results of INF-gamma levels among Covid-19 patients were positive for all samples included in the test (30 Covid-19 patients and 30 patients COVID-19(+)/T. gondii IgG) compared to the control group. The chronic form of Toxoplasma disease, due to change in the production of this interferon, the COVID-19 infection has changed.Copyright © 2023 by Razi Vaccine & Serum Research Institute.
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Sanitization is a protective and strategic approach to contain SARS-CoV2 dissemination. As there is no feasible way to deal with the new COVID-19 pandemic, sanitization has a key role to play. A modified method to reduce the spread of the virus by constructing a fogging room with a disinfectant and a base fluid mixture has been examined. The nanometer-sized corona in micron-sized cough droplets can quickly reach inaccessible areas when infection is present. Therefore, efficient spray and jet method should be used to disinfect certain inaccessible surfaces. A stand-alone photovoltaic (PV) system represents a pollutant-free and cost-effective solution to the stated issue. The present study aims to design sizing of a small-scale solar panel -powered mobile cleaning and disinfection chamber system for coronavirus in Remote Locations. The objective is to evaluate the sizing of the PV system to power the disinfection chamber system that is used to eliminate the spread of the virus at a constant daily load profile. The system is composed of a fogging room, water tank, PV panels, and storage batteries. The disinfection chamber system requires an energy of approximately 218 Wp with about 38 hours of battery storage that can operate the system continuously after the sunset. Powering a mobile cleaning and disinfection chamber system with PV panels has plenty of advantages, including free-maintenance, easy installation, and energy saving. © 2023 AIP Publishing LLC.
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Engineering, and especially hardware and software engineers, need systems thinking and thinking mindset. Hands-on interactive assignments utilizing a combination of hardware and software have been shown to be the most effective methods of teaching systems thinking and thinking. Nevertheless, this environment was shattered by the arrival of the COVID-19 pandemic, creating a number of challenging situations. During the pandemic, remote learning and social distancing posed the biggest challenges. Educators faced a challenge when creating hands-on and laboratory -based classes, and were forced to use innovative methods like virtual laboratories online. The research described in this paper examined the effect of changes to the educational environment caused by the COVID-19 pandemic on students' cognitive abilities development related to systems thinking and thinking education. The study, which used quantitative and qualitative tools, involved 70 senior high school electronics students. According to the findings, there was a significant drop in both skills among remote group students in comparison with face-to-face group students. This study found that students are incapable of adapting to change in instruction modes if not given sufficient time, support, and communication.
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The aim of this study was to determine the immune function of human leukocyte antigens and some vital indicators in Covid 19 patients. This study was conducted at Ibn Al-Khatib hospital, Baghdad. Sixty four blood sample of Covid 19 patients (32 male and 32 female patients), while healthy volunteers group 15 male and 15 female with age between 10 to 60. Level of IL-1b, CD4, WBC, ESR, Urea, sugar test, were measured results showed a significant increase (P<0.01) in each measured of IL-1b, CD4, WBC, ESR, Urea, Sugar. The more infection of Covid 19 with some factors such as, smoking, chronic diseases. The measurement of the level of IL-1b, CD4 by means of the enzyme - linked immunosorbent assay (ELISA), and WBC, PLT, measurement method using ABX micros 60 hematology analyzer, Urea, Sugar semi-automated chemistry analyzer using Mindray BC-5000. The data was analyzed with Graph pad prism software. © 2023 Author(s).
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COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing. © 2023, Institute of Advanced Engineering and Science. All rights reserved.
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This work was performed to examine an idea about full chelation of Iron (Fe) by well-known favipiravir (Fav) as a possible mechanism of action for medication of COVID-19 patients. To this aim, formations of Fe- mediated dimers of Fav were investigated by performing density functional theory (DFT) computations of electronic and structural features for singular and dimer models. The results indicated that the models of dimers were suitable for formation, in which two cis (D1) and trans (D2) models were obtained regarding the configurations of two Fav counterparts towards each other. Energy results indicated that formation of D1 was slightly more favorable than formation of D2. Molecular orbital features affirmed hypothesized interacting sites of Fav for Fe-mediated dimers formations, in which atomic charges and other molecular orbital related representations affirmed such achievements. Moreover, detection of such dimer formation was also possible by monitoring variations of molecular orbitals features. As a consequence, formations of Fe-mediated dimers of Fav could be achievable for possible removal of excess of Fe as a proposed mechanism of action for Fav in medication of COVID-19 patients.
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Project based learning (PBL) is an effective student-centered method to improve students' understanding. However, most PBL learning techniques rely heavily on a sequence of activities which require interaction with other humans or components and equipment in the laboratory. For many years, this method has proven effective and reliable particularly in STEM education. During the year when COVID-19 hit the world, PBL based education was implemented in the same exact manner as previous years to teach a course in electronics to senior students in high school. However, remarkable deterioration was observed in students' performance within this STEM course during this unusual year of the pandemic. The only change in educational practices was that all PBL steps were carried out using remote tools and in a social distance setting. The change in results raised many questions regarding the resilience of the used methods and techniques as well as its level of reliance on circumstances as significant factors in its effectiveness. These observations triggered this study where the target was of twofold: First, the study targeted understanding the factors influencing PBL effectiveness reflected by students' performance deterioration and identifying the subgroup of factors which were altered by the COVID-19 situation. Second, based on findings from the first part, the target was to propose corrective strategies that will improve the resilience of current interventions or reduce its dependence on circumstances which might change, such as what occurred during the pandemic. Students' performance was monitored and assessed in an electronics course at a high school during the pandemic using different assessment tools. Results were compared to similar sets collected when the course was conducted before the pandemic time. Results showed that students' performance in PBL decreased as instruction moved from face-to-face to remote mode. Timely interaction was mostly affected by this sudden change within a short time reflecting a need for better preparation, communication, and innovation to improve the independence of PBL from circumstances. © American Society for Engineering Education, 2022.
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A short time ago Internet of Things (IoTs) is being applied in many fields like healthcare systems, disease forecasting, etc. Even though the IoTs has enormous promise in a variety of applications, there are several areas where it may be improved. In the present work, we have concentrated on improvement of the performance of IoT by adding two technologies such as machine learning algorithms (Naïve Bayes (NB), Random Forest (RF)) and Ant Colony Meta-Heuristic (ACMH) algorithm to select best features from data. The efficient proposed framework applied on the data of SARS-Co V2 for disease prediction to minimize the time consumption and improve the accuracy of forecasting COVID disease. Thus, the lifetime network of IoT will lead to an increase. The performance of proposed work evaluated using reliable metrics such as precision, accuracy, running time, balance accuracy, recall, and F-Measure. We conclude from the results of evaluating, that ML algorithms in IoT achieved best performance than without using ACMH algorithm;RF with ACMH in IoT framework achieved best performance that NB with ACMH algorithm. But NB is best from RF in running time with and without ACMH algorithm. © 2022 IEEE.
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In December 2019, the coronavirus pandemic started. Coronavirus desease-19 (COVID-19) is transmitted directly from contaminated surfaces via direct touch. To combat the virus, a multitude of equipment is needed. Masks are a vital element of personal protection in crowded places. As a result, determining if a person is wearing a face mask is critical to assimilating to contemporary society. To accomplish the objective, the model presented in this paper used deep learning libraries and OpenCV. This approach was chosen for safety concerns due to its high resource efficiency during deployment. The classifier was built using the MobileNetV2 structure, which was designed to be lightweight and capable of being utilized in embedded devices such as the NVIDIA Jetson Nano to do real-time mask recognition. The stages of model construction were collecting, pre-processing, splitting data, creating the model, training the model, and applying the model. This system utilized image processing techniques and deep learning to process a live video feed. When someone is not wearing a mask, the output eventually produces an alarm sound through a built-in buzzer. Experimental results and testing were used to verify the suggested system's performance. Including both training and testing, the achieved recognition rate was 99%. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
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Corona pandemic showed how artificial intelligence has become a part of our daily lives and is breaking into all fields at a high rate and in different ways. Relying on the conventional techniques to test patients such as RT -PCR has two major drawbacks;a long time to get results and a lack of test kits. Therefore, data mining with machine learning techniques has been suggested to investigate covid-19. In this work, chest x-ray image-based covid-19 detection approach is proposed. Three types of x-ray images Covid-19, Pneumonia, and Normal, are used in two frameworks: image visualization and image segmentation. First, the x-ray samples are visualized using histograms to analyze the pixel-value distributions. The visualization approach helps covid-19 specialists to discover the intensity level of infection by examining the corresponding histograms. Second, a segmentation approach is developed with a k-mean algorithm to provide extra image tuning for infected areas. Three different centroids are used to provide different tuning granularity levels. The suggested frameworks give a fast and reliable methodology to help physicians to decide whether there is a virus or not in the x-ray sample. This is done statistically by histograms and visually by monitoring the segmented infected areas. © 2021 IEEE.
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Introduction: In the present study, the mean differences of Lactate Dehydrogenase (LDH), D-dimer, C-reactive protein, interleukin-6 and ferritin levels concentrations between study groups (patients with severe COVID-19 symptoms and patients with mild symptoms) compared tothe control group. The results showed that there were significant increased concentrations of biomarkers levels in group A as compared with group B and control. Objective: This present study aims to evaluate the COVID-19 biomarkers(Lactate Dehydrogenase (LDH), D-dimer, C-reactive protein, IL-6 and ferritin concentrations) among COVID-19 patients. Methodology: A total of 75 blood samples were collected from male patients with age groups ranging between 30 and 75 years who were suffering from coronavirus. Three groups were included in this study;each group includes 25 patients. Group A patients suffering from coronavirus with severe symptoms, group B patients suffering from coronavirus with mild symptoms and group C with healthy patients as control. All parameters were measured according to standard procedures. Data were analysed in SPSS version 20 by using mean ± SD.Significant association was established by chi-square test taking p-value<0.05. Results: Increased LDH values were linked to an increase in COVID-19 toxicity. On the basis of D-dimer, the probability of mortality can be determined. C-responsive protein and ferritin serum exercises were significantly increased in COVID-19 patients compared to those with mild side effects of COVID-19. IL-6 is a key immunomodulatory cytokine in both normal and infected tissues. Conclusion: LDH, D-dimer, C-reactive protein and serum ferritin are good predictors of COVID-19 severity and may be used for the assessment of clinical outcome. Copyright (c) 2022: Author(s).
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Background: Since the start of the outbreak of the 2019 novel coronavirus disease (COVID-19), many countries have replaced traditional class education with distance education (DE) as a preventive measure. DE has been implemented in schools and universities, without neither prior regulations nor preparations. It has been implemented into society without the necessary skills and knowledge. Objectives: To assess the preferences of university students to courses delivered through distance education, to determine the difficulties and problems they had faced during their online study that had been adopted as a result of COVID-19 and the strengths and opportunities of such a teaching transition including the variables which contribute to positive online teaching outcomes. Method: In this study, the medical sciences students whom had been taught through distant education during COVID-19 pandemic, were questioned about their academic views, attitudes, and practices towards distant teaching due to COVID-19 and how such a teaching has influenced their study performance. The informed consent was taken from each participant student before going through the questionnaire form questions. The questionnaire form was sent through the Internet to all students in the targeted colleges, to give the same chance to all to be part of the study. Five hundred twenty three students completed the form;among them, 336 were medical college students (63.4%), and 187 students from pharmacy and dentistry colleges. The questionnaire was distributed in 12 December, 2020 using previously created students Facebook groups that were adopted by medical schools for communication with their students. Data collection completed in 31 January. It comprised 13 questions, including questions about their gender, residence, and stage of education. Results: About this study, (60%) of students have previous experience with electronic education, half of them take more than 10 lectures per week and (81%) of students said that they take lectures as recorded videos. The current experience characteristics of e-learning showed that Google Classroom has been the most frequent platform used (77%), about (55%) for Google Meet and (27%) mentioned Zoom. The participation of students in e-learning lectures was poor for students who answered that they have been attending less than (50%) of the lectures, while only (28%) of them attended more than (80%) of the lectures. Two thirds of the respondents mentioned that weakness of the Internet access had been the main reason behind the weakness of e-learning. Moreover, this quality of Internet might be a considerable reason behind students' loss of motive engage in e-learning, nearly (58%) of students reasons due to method of presenting the lectures, (30.6%) due to inappropriateness of time and only (14%) due to unavailability of the required electronic device. The Participants' satisfaction with their experience with the electronic education was about (39%) and preferring e-learning to class attendance education. Only (28%) of the respondents preferred to continue teaching medical science through electronic education even after the end of COVID-19 pandemic, while half of the students wish to combine electronic education with class attendance methods to teach medical sciences after the end of COVID-19 pandemic. In Iraq e-teaching approach needs significant upgrading, and the shortage in infrastructure requirements and poor training processes, most probably, had been the reasons behind any non-satisfying effectiveness and/or efficiency.
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The pandemic caused by COVID-19 had a profound impact on engineering education challenging both educators and students to innovatively continue the learning process and unveiling many of the issues hindering education systems' resilience. To explore the challenges to engineering education which were imposed by the COVID-19 pandemic, responses to the challenges, and the underlying reasons. The hypothesis is that these challenges overlaps with challenges to sudden change of instruction to become remote while belonging to four categories: Access and compatibility, Remote and hybrid assessment, Lab and experiential learning delivery, and interpersonal relations and support societies. The goal is to use the outcomes to propose themes for consideration in building a sustainable and resilient engineering education system. Students' responses to a questionnaire were analyzed utilizing quantitative and qualitative tools. 124 engineering students volunteered to participate in the questionnaire. Results were coded and categorized to allow studying their interrelations. Challenges to engineering education caused by the COVID-19 pandemic were found to belong to three categories (performance, adaptation, and accessibility-and-compatibility). These categories are interrelated in a significant moderate positive correlation. Also, socio-economic status of students, life experiences and maturity levels, as well as availability of resources by location or other means, play a significant role in improving students' adaptation to rapid changes in the education process, and consequently affects their academic performance. Education systems aiming at becoming resilient can start by improved infrastructure and training programs related to advanced technology as well as enhancing levels of equity of access for their students.
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Extraordinary circumstances created by COVID-19 resulted in accelerating transitions of engineering education while challenging traditional perceptions and format. Instructors and students were pushed out of their comfort zone making quick adjustments, while in the semester, to guarantee continuity of learning with competent quality that will achieve the desired outcomes of the process. These transitions were perceived differently by the stakeholders. The purpose of this study was to explore the impact of changes to engineering education influenced by the COVID-19 pandemic from the viewpoints of the stakeholders and to take advantage of this major system shakeup in identifying practices and traditions which have proven to be effective and potentially resilient or sustainable vs. those which presented opportunities for improvement. Perceptions and experiences during these rapid engineering education changes caused by the pandemic were collected using surveys of engineering students and instructors and contrasted with similar information reported in the literature. Results were organized, coded, and analyzed to identify achievements as well as opportunities for improvement within the educational process. Analysis revealed that both intrinsic factors such as communication and adjustments based on dynamic feedback have a significant influence on the education process. It has also shown that extrinsic factors such as the socio-economic status and access to high quality resources play a role that is as strong as intrinsic factors. Moreover, most of these factors were found to be interdependent. Results show that some existing practices in engineering education have proven effective and resilient during the pandemic with minor tweaks, such as employing technology in communication and knowledge transformation. However, a great deal of development is still needed to bring current engineering education to levels of flexibility and resilience enabling it to face any future uncertainties. Some of these improvements start at breaking away from the traditional format of engineering education. © 2021 IEEE.
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COVID-19 pandemic has been the source of most health problems in the last two years with high mortality rates and fluctuation recovery rates, recorded in different countries. The diabetes mellitus (DM) is one of the multifactorial diseases that may be affected in the COVID-19 infected people. Present research was suggested to study the DM medications in some Biomarkers of COVID-19 infected patients. D-dimer and C-reactive protein (CRP) was used in the present work;the output of the distribution study subjects, according to DM patients shows there was 73% of infected COVID-19 is suffered from diabetes mellitus, and 27% was non-diabetes patients, the D-dimer levels were elevation in DM insignificant differences (p < 0.004). The CRP level was found in non-significant elevation in DM (p < 0.203). The DM patients enrolled in the present study were treated with three types of medications, metformin+ insulin, and insulin only. We found that in a group treated with insulin only have higher levels of d-dimer and lower levels in the group treated with metformin while the group that used both drugs, shows a high level but is lower than the group that used insulin only were significant (p 0.012). The CRP shows low levels in the group that used metformin only than others in non-significant differences (p 0.037). Also, our analysis of the relation between PCR results and DM-infected patients found that the positive results were high in the DM patients than non-DM patients in significant differences (Od 0.1037 CI95% 0.0116 to 0.9272 P 0.042). It can be concluded from finding that there was a strong association between DM and d-dimer, CRP, and these markers association with types of DM medications, and the DM cases should be careful to avoid COVID-19 infection, and the infection cases must be under hospital care. © 2021, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.
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Background: Adjuvant anti-PD1 therapy reduces the risk of recurrence in resected stage III/IV melanoma and is now standard care. Limited data exist beyond registration trials. We sought to explore the use of adjuvant immunotherapy in routine clinical practice. Methods: Patients (pts) from 11 Australian centres who received adjuvant nivolumab (nivo) for resected stage III/IV melanoma were included in this study. Efficacy, toxicity, surveillance, recurrence characteristics, management and further treatment outcomes were examined. Results:471 pts received adjuvant nivo between 8/2018 to 3/2020. 318 (68%) were male, median age 64y (range 17-94), 28 (6%) were AJCC v8 IIIA, 194 (41%) IIIB, 175 (37%) IIIC, 11 (2%) IIID, and 63 (13%) IV. 65 (14%) pts had intransit only disease, 152 (37%) pts were sentinel lymph node biopsy (SLNB+) and only 9 (6%) of these had CLND. 128 (27%) had BRAF mutant (BRAFmt) melanoma. Median time from resection to start of adjuvant nivo was 1.8 months (mo) (range 0.2-4.0). Median FU was 17.5 mo. 256 (54%) pts completed 12 months of nivo, 86 (18%) ceased early for toxicity, 76 (16%) for disease recurrence, 25 (5%) other reasons (COVID-19 8, co-morbidities 7, pt choice 10);28 (6%) pts were still receiving nivo at data cut. Median duration of treatment was 10.4 mo (range 0-16.8). 117 (25%) pts recurred;76 (65%) while ON nivo and 41 (35%) OFF nivo ( > 1 month after last dose, including 20 pts who stopped early for toxicity). 24 mo RFS was 69%. Median time to recurrence was 6.0 mo (95% CI 5.1, 7.5). 56 (48%) had first recurrence with locoregional (LR) disease only and 61 (52%) had distant +/- LR recurrence. Of those who recurred with LR disease only, 46/56 (82%) underwent surgery, 15/46 (33%) then had adjuvant radiotherapy, and 15/46 (33%) had 'second adjuvant' therapy with BRAF/MEK inhibitors (15/21, 71% BRAFmt pts). 10/56 (37%) pts who recurred with LR disease subsequently recurred distantly. 58/80 (73%) pts received systemic therapy at either 1st or subsequent unresectable recurrence. For recurrences ON nivo, 18 pts received combination ipilimumab (ipi) and nivo (ORR 44%), 4 pts had ipi monotherapy (ORR 0%), 7 pts had anti-PD1 + investigational agent (ORR 57%), 11 pts had BRAF/MEK inhibitors (ORR 82%). 1 pt had PD with ongoing PD1 monotherapy. For recurrences OFF nivo, no patients responded to PD1 alone (n = 1) or with an investigational agent (n = 1), ipi+nivo (n = 3), ipi monotherapy (n = 4) or chemotherapy (n = 2);6 pts received BRAF/MEK inhibitors (ORR 50%). 2-year OS was 92%. Conclusions: Despite higher rates of discontinuation due to toxicity compared with clinical trial cohorts, the efficacy data appear similar. Most early recurrences are distant, and many with LR recurrence soon recur distantly thereafter. Second line adjuvant BRAF/MEK inhibitors are frequently used for resected LR recurrence. Both ipi+nivo and BRAF/MEK inhibitors appear to have activity after distant recurrence.