The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis

5-chloro-3-(2-(2,4-dinitrophenyl) hydrazono)indolin-2-one: synthesis, characterization, biochemical and computational screening against SARS-CoV-2

Chemical prototypes with broad-spectrum antiviral activity are important toward developing new therapies that can act on both existing and emerging viruses. Binding of the SARS-CoV-2 spike protein to the host angiotensin-converting enzyme 2 (ACE2) receptor is required for cellular entry of SARS-CoV-2. Toward identifying new chemical leads that can disrupt this interaction, including in the presence of SARS-CoV-2 adaptive mutations found in variants like omicron that can circumvent vaccine, immune, and therapeutic antibody responses, we synthesized 5-chloro-3-(2-(2,4-dinitrophenyl)hydrazono)indolin-2-one (H2L) from the condensation reaction of 5-chloroisatin and 2,4-dinitrophenylhydrazine in good yield. H2L was characterised by elemental and spectral (IR, electronic, Mass) analyses. The NMR spectrum of H2L indicated a keto-enol tautomerism, with the keto form being more abundant in solution. H2L was found to selectively interfere with binding of the SARS-CoV-2 spike receptor-binding domain (RBD) to the host angiotensin-converting enzyme 2 receptor with a 50% inhibitory concentration (IC50) of 0.26 μM, compared to an unrelated PD-1/PD-L1 ligand-receptor-binding pair with an IC50 of 2.06 μM in vitro (Selectivity index = 7.9). Molecular docking studies revealed that the synthesized ligand preferentially binds within the ACE2 receptor-binding site in a region distinct from where spike mutations in SARS-CoV-2 variants occur. Consistent with these models, H2L was able to disrupt ACE2 interactions with the RBDs from beta, delta, lambda, and omicron variants with similar activities. These studies indicate that H2L-derived compounds are potential inhibitors of multiple SARS-CoV-2 variants, including those capable of circumventing vaccine and immune responses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-023-03274-5.

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

Mapping scientific knowledge discovery on COVID-19 pandemic and agriculture: a bibliometric analysis and future research directions

This paper aims at analyzing the research productivity and scientific knowledge discovery of the COVID-19 pandemic in agriculture using a bibliometric analysis approach. A total of 1514 research papers indexed in the Scopus database, covering a period of 2020 to 2022, are processed using VOSviewer and R-Studio software. The analysis of research productivity indicates that the number of research publications on COVID-19 and agriculture has exponentially increased globally, and about 80% of the research papers have been published in the top 10 countries led by the USA, India, and China. The countries are increasingly collaborating in undertaking research on COVID-19 and agriculture. Furthermore, major journals and articles with citations have been extracted to analyze the leading publication avenues and focused areas of research. The science mapping is done using co-occurrence and thematic map. With the help of co-occurrence analysis, six clusters are identified depicting major research themes, i.e., COVID-19 and agricultural supply chain disruption, COVID-19 and human health issues and coping strategies, COVID-19 and non-human and animal health, COVID-19 pandemic and environment and pollution, COVID-19 and healthcare and treatment, and COVID-19 and food nutrition from dairy and meat products. The thematic map analysis identifies potential research areas such as mental health, anxiety, and depression in the agricultural system, which may help in setting future research agenda and help devising policy supports for managing the agriculture sector better during crisis. The paper also highlights the theoretical and practical implications.

Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis

Background

The frequent occurrence of major public health emergencies globally poses a threat to people's life, health, and safety, and the convergence development of digital technology is very effective and necessary to cope with the outbreak and transmission control of public epidemics such as COVID-19, which is essential to improve the emergency management capability of global public health emergencies.

Methods

The published literatures in the Web of Science Core Collection database from 2003 to 2022 were utilized to analyze the contribution and collaboration of the authors, institutions, and countries, keyword co-occurrence analysis, and research frontier identification using the CiteSpace, VOSviewer, and COOC software.

Results

The results are shown as follows: (1) Relevant research can be divided into growth and development period and rapid development period, and the total publications show exponential growth, among which the USA, China, and the United Kingdom are the most occupied countries, but the global authorship cooperation is not close; (2) clustering analysis of high-frequency keyword, all kinds of digital technologies are utilized, ranging from artificial intelligence (AI)-driven machine learning (ML) or deep learning (DL), and focused application big data analytics and blockchain technology enabled the internet of things (IoT) to identify, and diagnose major unexpected public diseases are hot spots for future research; (3) Research frontier identification indicates that data analysis in social media is a frontier issue that must continue to be focused on to advance digital and smart governance of public health events.

Conclusion

This bibliometric study provides unique insights into the role of digital technologies in the emergency management of public health. It provides research guidance for smart emergency management of global public health emergencies.

Care Home and Home Care Staff's Learning during the COVID-19 Pandemic and Beliefs about Subsequent Changes in the Future: A Survey Study in Sweden, Italy, Germany and the United Kingdom

The aim of this study was to compare perceptions of learning from the COVID-19 pandemic and beliefs in subsequent changes for the future, among care home and home care staff, in four European countries. A 29-item on-line questionnaire was designed in English and later translated into Swedish, Italian, and German on the impact of the pandemic on stress and anxiety. Anonymous data from care staff respondents was collected in four countries between 7 October 2020 and 17 December 2010: Sweden (n = 212), Italy (n = 103), Germany (n = 120), and the United Kingdom (n = 167). While care staff in all countries reported learning in multiple areas of care practice, Italy reported the highest levels of learning and the most agreement that changes will occur in the future due to the pandemic. Conversely, care staff in Germany reported low levels of learning and reported the least agreement for change in the future. While the pandemic has strained care home and home care staff practices, our study indicates that much learning of new skills and knowledge has taken place within the workforce. Our study has demonstrated the potential of cross-border collaborations and experiences for enhancing knowledge acquisition in relation to societal challenges and needs. The results could be built upon to improve future health care and care service practices.