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The 6th International Conference on Agricultural and Biological Sciences (ABS 2020), was planned to be held in the People's Republic of China. Due to the COVID‐19 pandemic, ABS 2020, "Agricultural and Biological Sciences: Plant, Soil, Animal, and Environment”, was the first online conference in Agricultural and Biological Sciences history. Each paper addressed a slightly different topic and provided identifiable challenges and research key questions in agriculture, agronomy, food production and security, and environmental hazards. The role of Agricultural and Biological Sciences of China is the generation of research knowledge that influence everyday activities. There were 41 manuscripts submitted, of which 25 were accepted for publication. The research domains varied and included the role of specific plant on soil C‐cycling, haploid induction, and natural doubling of Zea mays L.;plant species and soil rhizosphere microflora;forest tree biomass succession and dynamics relevant to C‐sequestration;making sandy land agriculture friendly;plantation age on C, N, and P stoichiometry;codon uses pattern of Gnetum luofuoense C.Y. Cheng using transcriptome data;suitability of plant landscape unit and natural parks;regeneration protocol of Jatropha curcas L., etc. The purpose of this special section is to generate an improved communication among international scientists that we hope will lead to enhanced food security.
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BackgroundChest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.ResultsThe Spearman's correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).ConclusionsThe automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.
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In the current investigation, two novel series of (tetrahydro)thioquinazoline-N-arylacetamides and (tetrahydro)thioquinazoline-N-arylacetohydrazides were designed, synthesized and investigated for their antiviral activity against SARS-CoV-2. The thioquinazoline-N-arylacetamide 17g as well as the tetrahydrothioquinazoline-N-arylacetohydrazides 18c and 18f showed potent antiviral activity with IC50 of 21.4, 38.45 and 26.4 µM, respectively. In addition, 18c and 18f demonstrated potential selectivity toward the SARS-CoV-2 over the host cells with SI of 10.67 and 16.04, respectively. Further evaluation of the mechanism of action of the three derivatives 17g, 18c, and 18f displayed that they can inhibit the virus at the adsorption as well as at the replication stages, in addition to their virucidal properties. In addition, 17g, 18c, and 18f demonstrated satisfactory physicochemical properties as well as drug-likeness properties to be further optimized for the discovery of novel antiviral agents. The docking simulation predicted the binding pattern of the target compounds rationalizing their differential activity based on their hydrophobic interaction and fitting in the hydrophobic S2 subsite of the binding site.
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BACKGROUND: Omega-3 may alleviate the severity of coronavirus disease 2019 (COVID-19) by reducing the C-reactive protein (CRP) level, a marker for systemic inflammation. Because the scientific evidence indicating such a role is inconsistent, we aimed to evaluate the effect of Omega-3 on CRP change and CRP level in patients with COVID-19. METHODS: We conducted a comprehensive search on four databases (PubMed, Web of Science, EMBASE, and Scopus). We included all RCTs comparing Omega-3 with a control group regarding their effect on the CRP levels in patients with COVID-19. We used version two of the Cochrane risk of bias assessment tool to appraise the included studies. We extracted data to an online data extraction sheet. The primary outcomes were CRP change from baseline and CRP serum levels. RESULTS: We included four randomized controlled trials (RCTs) with 274 patients in this study. The overall effect estimate favored Omega-3 over the control group in terms of CRP change from baseline (mean difference (MD) =- 2.53, 95% confidence interval (CI): - 4.40, - 0.66) and CRP serum levels at the end of the study (MD =- 6.24, 95% CI: - 11.93, - 0.54). CONCLUSION: Omega-3 showed promising effects on systemic inflammation by reducing CRP levels in COVID-19 patients. Based on this finding, we recommend Omega-3 for COVID-19 patients for its anti-inflammatory actions.
Subject(s)
COVID-19 Drug Treatment , Fatty Acids, Omega-3 , C-Reactive Protein , Dietary Supplements , Fatty Acids, Omega-3/pharmacology , Fatty Acids, Omega-3/therapeutic use , Humans , Inflammation/drug therapy , Randomized Controlled Trials as TopicABSTRACT
With the widespread of COVID-19 in Saudi Arabia, the educational authorities issued firm directions to convert to virtual classes exploiting the available Learning Management System (LMS). However, during the academic year 2020-2021, the researchers observed that writing EFL instructors at Prince Sattam bin Abdulaziz University (PSAU), Saudi Arabia, faced diverse challenges due to having online mixed-ability classes, i.e. those classes where students have varying levels of readiness, motivation, and academic caliper. Though many previous studies explored the influence of the COVID-19 pandemic on teaching and learning practices, very few studies addressed the way technological affordances pose challenges for instructors teaching mixed-ability classes. Therefore, the present study, using mixed quantitative and qualitative research methods, sought to explore challenges that evolved due to the technological affordances of LMS to spot the persistent problems and to offer relevant solutions for upgrading, writing teaching and learning practices. The basic research design relied on an online questionnaire followed by semi-structured interviews. Findings showed that differentiated instruction proved to be the most successful strategy for teaching writing in mixed-ability online classes as it allowed the adaptation of materials, teaching and learning practices, and assessment tools to motivate low-achievers. In addition, the collaborative tools offered by the Blackboard such as the White Board, Discussion Board, Blogs, and Breakout Groups helped to meet the preferences of visual, auditory, and kinesthetic learners. Finally, further studies are recommended to explore the affordances of educational technologies regularly to identify potential benefits and limitations for offering the best teaching and learning practices.
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Higher education has been shifting to learning management systems (LMS) for decades. Some universities, like the Open University, have managed to gain international recognition by providing undergraduate degrees to students in different countries. However, in moments of emergency and international disruption higher education institutions need to adapt at unprecedented speed. This chapter focuses on the use of technology in moments of extreme internationalised interference. Using the COVID-19 pandemic as a ground for change, students enrolled in presential courses in Spain, Malta, and the United Kingdom were interviewed in order to understand how they are coping with having contact with their academic life exclusively online. The students' impressions, LMS software, and results (assignments and exams) were also discussed. Finally, the chapter analyses the solutions provided by lecturers and students. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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OBJECTIVE: The aim of this study was to assess of the effect of coronavirus disease 2019 (COVID-19) pandemic on urology practice in the Arab world during the first year of the crisis. METHODS: An Internet-based questionnaire was created and sent out via email to members of the Arab Association of Urology (AAU) using 'Google Forms'. The survey assessed participants' demographics in terms of age, gender, country of origin, type of practice and position. Impacts of COVID-19 on urological practice were assessed in terms of the changes in hospital policies regarding consultations, and elective and emergency surgical cases. Moreover, impacts of COVID-19 on urologists were assessed. RESULTS: A total of 255 AAU members across 14 Arab countries (Emirates, Egypt, Saudi Arabia, Iraq, Jordan, Algeria, Kuwait, Yemen, Qatar, Lebanon, Libya, Oman, Sudan and Syria) completed the survey; 4% were female urologists. Consultations at outpatient clinics were closed or restricted to emergency cases or replaced by telemedicine in almost 15%, 40% and 25% of hospitals, respectively. Elective surgeries were stopped or reduced to under 25% of surgical capacity in >10% and about 25% of hospitals, respectively. Almost 90% (228) reported changes in the policy for emergency theatres. Nearly 65% of hospitals offered preoperative COVID-19 testing to patients and 50% of hospitals provided personal protective equipment (PPE) to their urologists. Of 99% (253) who reported a change in urological education, 95% relied on online webinars. About 56% of respondents had their own private practice, of whom 91% continued private practice during the crisis. About 38% of participants reported exposure to intimidation (75% emotional, 20% verbal and 5% physical). CONCLUSION: The COVID-19 pandemic resulted in major changes in hospitals' policies regarding outpatient consultations, elective and emergency operative cases, and the shift to telemedicine. Arab urologists have been facing major challenges either in both the governmental or the private sectors, and some of them were exposed to emotional, verbal and even physical intimidation.
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Background Chest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated. Results The Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001). Conclusions The automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.
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Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the 'covid19census' package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes. Database URL: https://github.com/c1au6i0/covid19census.
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COVID-19/epidemiology , Databases, Factual , Datasets as Topic , Pandemics , SARS-CoV-2 , Algorithms , Comorbidity , Demography , Diabetes Mellitus/mortality , Health Surveys , Humans , Italy/epidemiology , Models, Theoretical , Software , United States/epidemiologyABSTRACT
The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.
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INTRODUCTION: This retrospective cohort analysis examines the impact of the COVID-19 pandemic in the prehospital setting in Germany. The data of two emergency physician response units of a northern German region with 1.2 million citizens was analyzed retrospectively. MATERIALS AND METHODS: We analyzed the period March 16 to April 16 for the year 2020 when the lockdown took place in Germany and compare the results for the same period for the year 2019 and 2018. 1004 patients were included. Demographic data, the type of rescue missions, the number of missions per day, the National Advisory Committee for Aeronautics Score (NACA-score), the frequency of respiratory emergencies (COVID-19 and non-COVID-19 associated), as well as the number of deaths were documented. RESULTS: Mean age was 62.3±24.8 years and 576 (56.5%) were male. Number of missions were 397, 403 and 333 in 2018, 2019 and 2020 respectively. The control room registered a 22% reduction of rescue missions for the year 2020. Even the amount of emergency calls via the emergency number 112 was reduced by 17.4% between 2018 and 2020. 150 (14.9%) missions were due to respiratory emergencies. In 2020 10 missions (28.6% of respiratory emergencies) were COVID-19 related. In 2020 the NACA score increased significantly. CONCLUSION: We found a decreasing effect of the COVID-19 pandemic in Germany on the number of emergency calls as well as missions, and an increase of the severity of cases in preclinical care for a northern German region with 1.2 million citizens. The effect of these findings caused by COVID-19 on the health care system remains to be seen.