Preparation of Highly Stable and Cost-Efficient Antiviral Materials for Reducing Infections and Avoiding the Transmission of Viruses such as SARS-CoV-2.

The current global pandemic due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has demonstrated the necessity to develop novel materials with antimicrobial and antiviral activities to prevent the infection. One significant route for the spread of diseases is by the transmission of the virus through contact with contaminated surfaces. Antiviral surface treatments can help to reduce or even avoid these hazards. In particular, the development of active-virucidal fabrics or paints represents a very important challenge with multiple applications in hospitals, public transports, or schools. Modern, cutting-edge methods for creating antiviral surface coatings use either materials with a metal base or sophisticated synthetic polymers. Even if these methods are effective, they will still face significant obstacles in terms of large-scale applicability. Here, we describe the preparation of fabrics and paints treated with a scaled-up novel nanostructured biohybrid material composed of very small crystalline phosphate copper(II) nanoparticles, synthesized based on a technology that employs the use of a small amount of biological agent for its formation at room temperature in aqueous media. We demonstrate the efficient inactivation of the human coronavirus 229E (HCoV-229E), the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and non-enveloped human rhinovirus 14 (HRV-14) (>99.9%) using an inexpensive, ecologically friendly coating agent. The reactive oxygen species produced during the oxidation of water or the more intensive reaction with hydrogen peroxide are believed to be the cause of the antiviral mechanism of the nanostructured material. In contrast to the release of a specific antiviral drug, this process does not consume the surface coating and does not need regeneration. A 12-month aging research that revealed no decline in antiviral activity is proof that the coating is durable in ambient circumstances. Also, the coated fabric can be reused after different washing cycles, even at moderate to high temperatures.

Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI

Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.

An Evaluation of Serological Tests to Determine Postvaccinal Immunity to SARS-CoV-2 by mRNA Vaccines.

BACKGROUND: The duration of the protective efficacy of vaccines against SARS-CoV-2 is unknown. Thus, an evaluation of the clinical performance of available tests is required. OBJECTIVES: To evaluate the clinical performance of LFIA immunoassay compared to ELIA and CLIA immunoassays available in Europe for the detection of IgG antibodies generated by mRNA vaccines against SARS-CoV-2. METHODS: Two automated immunoassays (the EUROIMMUN anti-SARS-CoV-2 IgG S1 ELISA and the LIAISON de Diasorin anti-SARS-CoV-2 IgG S1/S2 test) and a lateral flow immunoassay (the Livzon LFIA anti-SARS-CoV-2 IgG S test) were tested. We analyzed 300 samples distributed in three groups: 100 subjects aged over 18 years and under 45 years, 100 subjects aged between 45 and 65 years, and 100 subjects aged over 65 years. The samples were collected before vaccination; at 21 days; and then at 1, 2, 3, and 6 months after vaccination. The sensitivity, specificity, positive predictive value, negative predictive value, positive probability quotient, negative probability quotient, and concordance (kappa index) were calculated for each serological test. RESULTS: The maximum sensitivity values for IgG were 98.7%, 98.1%, and 97.8% for the EUROIMMUN ELISA, Abbott CLIA, and Livzon LFIA tests, respectively, and the maximum specificity values for IgG were 99.4%, 99.9%%, and 98.4% for the ELISA, CLIA, and LFIA tests, respectively, at the third month after vaccination, representing a decrease in the antibody levels after the sixth month. The best agreement was observed between the ELISA and CLIA tests at 100% (k = 1.00). The agreement between the ELIA, CLIA, and LFIA tests was 99% (k = 0.964) at the second and third month after vaccination. Seroconversion was faster and more durable in the younger age groups. CONCLUSION: Our study examined the equivalent and homogeneous clinical performance for IgG of three immunoassays after vaccination and found LFIA to be the most cost-effective, reliable, and accurate for routine use in population seroconversion and seroprevalence studies.

Predicting the evolution of COVID-19 mortality risk: A Recurrent Neural Network approach.

Background: In December 2020, the COVID-19 disease was confirmed in 1,665,775 patients and caused 45,784 deaths in Spain. At that time, health decision support systems were identified as crucial against the pandemic. Methods: This study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information were used. They included 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospitals. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient, comparing different data representation methods. Next, we used the sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted an extensive hyperparameter search and cross-validation. Finally, we ensembled the resulting RNNs to enhance sensitivity. Results: We assessed the performance of our models by averaging the performance across all the days in the sequences. Additionally, we evaluated day-by-day predictions starting from both the hospital admission day and the outcome day. We compared our models with two strong baselines, Support Vector Classifier and Random Forest, and in all cases our models were superior. Furthermore, we implemented an ensemble model that substantially increased the system's sensitivity while producing more stable predictions. Conclusions: We have shown the feasibility of our approach to predicting the clinical outcome of patients. The result is an RNN-based model that can support decision-making in healthcare systems aiming at interpretability. The system is robust enough to deal with real-world data and can overcome the problems derived from the sparsity and heterogeneity of data.

A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection and advanced artificial intelligence

The paper presents a framework to forecast futures prices of stocks listed on the National Stock Exchange (NSE) in India during normal (unaffected by the COVID-19 pandemic) and new normal times (affected by COVID-19 and a macroeconomic slowdown). The model leverages a structural model that determines the relevance of the explanatory features used in the study;namely, spot prices, market sentiment, sectoral outlook, historic and implied volatility, crude price volatility, and exchange rate volatility. The proposed Ensemble Feature Selection (EFS) methodology comprising Boruta and Regularized Random Forest (RRF) algorithms is used to screen the explanatory features. Two advanced Artificial Intelligence techniques—Regularized Greedy Forest (RGF) and Deep Neural Network (DNN)—are used in conjunction with Kernel Principal Component Analysis (KPCA) and Autoencoder (AE) for forecasting. To understand the extent and nature of the influence of the explanatory variables, the Explainable Artificial Intelligence (AI) approach has been used. Statistical checks confirm that our hybrid framework is effective. The results indicate that the relative importance of the explanatory variables in forecasting futures prices differs depending on the company concerned and the period under consideration.

Glyconanomaterials for Human Virus Detection and Inhibition.

Viruses are among the most infectious pathogens, responsible for the highest death toll around the world. Lack of effective clinical drugs for most viral diseases emphasizes the need for speedy and accurate diagnosis at early stages of infection to prevent rapid spread of the pathogens. Glycans are important molecules which are involved in different biological recognition processes, especially in the spread of infection by mediating virus interaction with endothelial cells. Thus, novel strategies based on nanotechnology have been developed for identifying and inhibiting viruses in a fast, selective, and precise way. The nanosized nature of nanomaterials and their exclusive optical, electronic, magnetic, and mechanical features can improve patient care through using sensors with minimal invasiveness and extreme sensitivity. This review provides an overview of the latest advances of functionalized glyconanomaterials, for rapid and selective biosensing detection of molecules as biomarkers or specific glycoproteins and as novel promising antiviral agents for different kinds of serious viruses, such as the Dengue virus, Ebola virus, influenza virus, human immunodeficiency virus (HIV), influenza virus, Zika virus, or coronavirus SARS-CoV-2 (COVID-19).

Use of EHRs in a Tertiary Hospital During COVID-19 Pandemic: A Multi-Purpose Approach Based on Standards.

This work aims to describe how EHRs have been used to meet the needs of healthcare providers and researchers in a 1,300-beds tertiary Hospital during COVID-19 pandemic. For this purpose, essential clinical concepts were identified and standardized with LOINC and SNOMED CT. After that, these concepts were implemented in EHR systems and based on them, data tools, such as clinical alerts, dynamic patient lists and a clinical follow-up dashboard, were developed for healthcare support. In addition, these data were incorporated into standardized repositories and COVID-19 databases to improve clinical research on this new disease. In conclusion, standardized EHRs allowed implementation of useful multi- purpose data resources in a major Hospital in the course of the pandemic.