Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely; however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on the incidence of COVID-19 infection cases. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the pandemic vulnerability index at city level (PVI-CI) for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections, along with an objective ranking for the vulnerability of cities. Thus, it provides critical wisdom needed for urban healthcare policy and resource management. The calculation method for the pandemic vulnerability index and the associated analytical process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas, and more resilient planning for future pandemics in cities across the world.

Quercetin attenuates viral infections by interacting with target proteins and linked genes in chemicobiological models.

Medicinally active compounds in the flavonoid class of phytochemicals are being studied for antiviral action against various DNA and RNA viruses. Quercetin is a flavonoid present in a wide range of foods, including fruits and vegetables. It is said to be efficient against a wide range of viruses. This research investigated the usefulness of Quercetin against Hepatitis C virus, Dengue type 2 virus, Ebola virus, and Influenza A using computational models. A molecular docking study using the online tool PockDrug was accomplished to identify the best binding sites between Quercetin and PubChem-based receptors. Network-pharmacological assay to opt to verify function-specific gene-compound interactions using STITCH, STRING, GSEA, Cytoscape plugin cytoHubba. Quercetin explored tremendous binding affinity against NS5A protein for HCV with a docking score of - 6.268 kcal/mol, NS5 for DENV-2 with a docking score of - 5.393 kcal/mol, VP35 protein for EBOV with a docking score of - 4.524 kcal/mol, and NP protein for IAV with a docking score of - 6.954 kcal/mol. In the network-pharmacology study, out of 39 hub genes, 38 genes have been found to interact with Quercetin and the top interconnected nodes in the protein-protein network were (based on the degree of interaction with other nodes) AKT1, EGFR, SRC, MMP9, MMP2, KDR, IGF1R, PTK2, ABCG2, and MET. Negative binding energies were noticed in Quercetin-receptor interaction. Results demonstrate that Quercetin could be a potential antiviral agent against these viral diseases with further study in in-vivo models. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-022-00132-2.

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.

The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics.

There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

CRISPR is a useful biological tool for detecting nucleic acid of SARS-CoV-2 in human clinical samples.

The recent pandemic of novel coronavirus disease (COVID-19) has spread globally and infected millions of people. The quick and specific detection of the nucleic acid of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) remains a challenge within healthcare providers. Currently, quantitative reverse transcription-polymerase chain reaction (RT-qPCR) is the widely used method to detect the SARS-CoV-2 from the human clinical samples. RT-qPCR is expensive equipment and needs skilled personnel as well as lengthy detection time. RT-qPCR limitation needed an alternative healthcare technique to overcome with a fast and cheaper detection method. By applying the principles of CRISPR technology, several promising detection methods giving hope to the healthcare community. CRISPR-based detection methods include SHERLOCK-Covid, STOP-Covid, AIOD-CRISPR, and DETECTR platform. These methods have comparative advantages and drawbacks. Among these methods, AIOD-CRISPR and DETECTR are reasonably better diagnostic methods than the others if we compare the time taken for the test, the cost associated with each test, and their capability of detecting SARS-CoV-2 in the clinical samples. It may expect that the promising CRISPR-based methods would facilitate point-of-care (POC) applications in the CRISPR-built next-generation novel coronavirus diagnostics.

In vive effects of gliclazide and metformin on the plasma concentration of caffeine in healthy rats.

The in vivo effects of gliclazide and metformin HCl on plasma concentration of caffeine have been studied in rats. The plasma concentration of caffeine was determined by UV spectrophotometry after oral single administration of caffeine alone and with gliclazide and metformin HCl. The in vivo study for determination of plasma concentration of caffeine showed that concurrent administration of caffeine and gliclazide have not made noticeable changes in plasma concentration of caffeine. But administration of caffeine and metformin HCl has showed a significant change in plasma concentration of caffeine. So, a competitive inhibition of the binding to plasma protein by metformin HCI increases the plasma concentration of caffeine. Thus any change in plasma concentration may affect the pharmacological or toxic effects of the drug.