The new world: lived experience of agricultural workers about the COVID-19 pandemic crisis (case study: Chardavol Township)

The purpose of this study is to examine the lived experiences of agricultural workers in the Chardavol Township about the new world. The present study is a qualitative research that has been done using an interpretive paradigm and interpretive phenomenological method. The statistical population of the study includes all villagers active in the agricultural sector in Zanjire Sofla village in Chardavol Township in Ilam province. 14 participants were selected by purposive sampling method until theoretical saturation. The semi-structured interview method was used to collect information from participants and Van Mannen's (1990) method was used to analyze the data obtained from the semi-structured interviews. The results showed that a main theme entitled "New World" and 7 sub-themes including new lifestyle, a distinct consumption pattern, the integration and synergy of tensions, understanding the cross-sectional remedial shock, the symmetry of old and new vulnerabilities, socio-protective isolation and low government presence and the tendency to counter-value measures and the experience of the new sin is experienced by the participants.

Whose Security Matters?

Claims to security are everywhere. They are used by states to justify invading other nations1 and to derogate from international law obligations.2 They are invoked by governments as reasons to exclude foreign nationals from their territory;3 surveil their citizens;4 and kill citizens and foreigners alike by remote control.5 Some experts use security claims to underscore the seriousness of global threats, like COVID-19.6 Security claims are also used by communities to defend their rights and well-being from those threatening them, including the state itself.7 Still others criticize the use of security discourse - in at least some circumstances - describing it as undermining the rule of law.8 Embedded within these claims is a view about whose security matters most - something that is also implicitly reflected in J. Benton Heath's four-part typology of security claims described in his recent article, Making Sense of Security.9 This essay explores the importance of whose security matters to Heath's framework. It does so by examining one political movement currently challenging the U.S. national security state. This movement is led by members of groups targeted and disadvantaged by U.S. national security policies - namely, Muslim, African, Middle Eastern, and South Asian communities. In that movement's recently released policy agenda, Abolishing the War on Terror & Building Communities of Care: A Grassroots Policy Agenda for the Biden-Harris Administration and 117th Congress (Abolishing the War on Terror), 10 its leaders call for abolishing the national security state and the War on Terror that it birthed.11 © Maryam Jamshidi 2022. Published by Cambridge University Press on behalf of The American Society of International Law.

Epidemiology of Diseases and Mortality in a Pediatric Intensive Care Unit in Qazvin, Iran

Background: Depending on the level of care and the availability of pediatric intensive care unit (PICU) facilities, the mortality rate of acutely ill children varies in PICUs. Referral of patients from other medical centers, admission during working or off-work hours, and nosocomial infections are the most important risk factors for the high mortality rates in PICUs. Objectives: The present study aimed to investigate the characteristics and factors related to the risk of mortality in pediatric patients admitted to the PICU of a pediatric hospital in Qazvin, Iran. Methods: This cross-sectional study was performed on children admitted to the PICU of a pediatric hospital in Qazvin, Iran, between June 2017 and June 2020. During this period, a total of 1504 children, aged one month to 13 years, were admitted to the PICU, and 106 cases expired. The patients’ clinical data (ie, demographic characteristics, underlying disease, cause of death, and length of hospital stay) was extracted from their medical records. A prolonged length of stay was defined as more than 28 days of PICU admission. Results: A total of 106 children, with a mean age of 3.89 ± 3.23 years, expired during the study, with 41 (38.7%) cases being male. Among the investigated cases, 61 (57%) were < 2 years, 18 (17%) were 2 - 5 years old, and 27 (26%) were ≥ 6 years. In these patients, sepsis (13/82, 15.85%) and pneumonia (10/82, 12.19%) were the main causes of death. Other mortalities (14/106) were due to infectious diseases (gastroenteritis, influenza, and coronavirus disease) and non-infectious diseases (aspiration, anaphylaxis, and electrocution). The majority of children with a prolonged length of stay were < 2 years (17/23, 74%). The length of PICU stay was shorter in children with a lower weight percentile (P = 0.016). Conclusions: Following infectious diseases, congenital abnormalities and genetic disorders were the most common causes of pediatric mortality. Chronically ill children were more likely to be underweight and develop nutritional disorders, leading to the deterioration of their condition.

Automated detection of coronavirus disease (COVID-19) by using data-mining techniques: a brief report

Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. Several data mining methods exist but finding a suitable one is very important. Today, coronavirus disease (COVID-19) has become one of the causing deadly diseases in the world. The early diagnosis of pandemic coronavirus disease has a significant impact in preventing death. This study aims to extract the key indications of the disease and find the best data mining methods that enhance the accuracy of coronavirus disease diagnosis. Methods: In this study, to obtain high accuracy in diagnosing COVID-19 disease, a complete and effective workflow over data mining methods was proposed, which includes these steps: data pre-analyzing, indication selection, model creation, the measure of performance, and display of results. Data and related indications of patients with COVID-19 were collected from Kerman Afzalipour Hospital and Rafsanjan, Ali Ebn Abi Taleb Hospital. Prediction structures were made and tested via different combinations of the disease indications and seven data mining methods. To discover the best key indications, three criteria including accuracy, validation and F-value were applied and to discover the best data mining methods, accuracy and validation criteria were considered. For each data mining method, the criteria were measured independently and all results were reported for analysis. Finally, the best key indications and data mining methods that can diagnose COVID-19 disease with high accuracy were extracted. Results: 9 key indications and 3 data mining methods were obtained. Experimental results show that the discovered key indications and the best-operating data mining method (i.e. SVM) attain an accuracy of 83.19% for the diagnosis of coronavirus disease. Conclusion: Due to key indications and data mining methods obtained from this study, it is possible to use this method to diagnose coronavirus disease in different people of different clinical indications with high accuracy.

A Conceptual Deep Learning Framework for COVID-19 Drug Discovery

The analytical and experimental methods used for the development of drugs have some disadvantages in the aspect of the needed time for preparation of the desired parenthetical products and the efficiency of them, which not only can the risk for failure increase, particularly when pathogens are impossible to be cultivated under laboratory conditions, but these approaches can also lead to achieving arrays of antigens that are not able to provide sufficient immunity to combat the targeted disease. On the other hand, artificial intelligence (AI) and its new branches, including deep learning (DL) and machine learning (ML) techniques can be deployed for drug development purposes in order to alleviate the difficulties associated with conventional methods. Moreover, intelligent methods will provide researchers with the opportunity to use some user-friendly and efficient services to conquer such problems. In this respect, a conceptual DL framework has been studied in order to demonstrate the capability and applicability of these methods. Accordingly, a framework has been proposed to show how COVID-19 drug development can benefit from the potentials of AI and DL. © 2021 IEEE.

Molecular modelling of the antiviral action of Resveratrol derivatives against the activity of two novel SARS CoV-2 and 2019-nCoV receptors.

The pandemic threat of COVID-19 causes serious concern for people and world organizations. The effect of Coronavirus disease on the lifestyle and economic status of humans is undeniable, and all of the researchers (biologists, pharmacists, physicians, and chemists) can help decrease its destructive effects. The molecular docking approach can provide a fast prediction of the positive influence the targets on the COVID-19 outbreak. In this work, we choose resveratrol (RV) derivatives (22 cases) and two newly released coordinate structures for COVID-19 as receptors [Papain-like Protease of SARS CoV-2 (PBD ID: 6W9C) and 2019-nCoV RNA-dependent RNA Polymerase (PBD ID: 6M71)]. The results show that conformational isomerism is significant and useful parameter for docking results. A wide spectrum of interactions such as Van der Waals, conventional hydrogen bond, Pi-donor hydrogen bond, Pi-Cation, Pi-sigma, Pi-Pi stacked, Amide-Pi stacked and Pi-Alkyl is detected via docking of RV derivatives and COVID-19 receptors. The potential inhibition effect of RV-13 (-184.99 kj/mol), and RV-12 (-173.76 kj/mol) is achieved at maximum value for 6W9C and 6M71, respectively.