Detection of live SARS-CoV-2 virus and its variants by specially designed SERS-active substrates and spectroscopic analyses.

A method using label-free surface enhanced Raman spectroscopy (SERS) based on substrate design is provided for an early detection and differentiation of spike glycoprotein mutation sites in live SARS-CoV-2 variants. Two SERS-active substrates, Au nanocavities (Au NCs) and Au NPs on porous ZrO2 (Au NPs/pZrO2), were used to identify specific peaks of A.3, Alpha, and Delta variants at different concentrations and demonstrated the ability to provide their SERS spectra with detection limits of 0.1-1.0% (or 104-5 copies/mL). Variant identification can be achieved by cross-examining reference spectra and analyzing the substrate-analyte relationship between the suitability of the analyte upon the hotspot(s) formed at high concentrations and the effective detection distance at low concentrations. Mutation sites on the S1 chain of the spike glycoprotein for each variant may be related and distinguishable. This method does not require sample preprocessing and therefore allows for fast screening, which is of high value for more comprehensive and specific studies to distinguish upcoming variants.

Fast screening framework for infection control scenario identification.

Due to the emergence of the novel coronavirus disease, many recent studies have investigated prediction methods for infectious disease transmission. This paper proposes a framework to quickly screen infection control scenarios and identify the most effective scheme for reducing the number of infected individuals. Analytical methods, as typified by the SIR model, can conduct trial-and-error verification with low computational costs; however, they must be reformulated to introduce additional constraints, and thus are inappropriate for case studies considering detailed constraint parameters. In contrast, multi-agent system (MAS) simulators introduce detailed parameters but incur high computation costs per simulation, making them unsuitable for extracting effective measures. Therefore, we propose a framework that implements an MAS for constructing a training dataset, and then trains a support vector regression (SVR) model to obtain effective measure results. The proposed framework overcomes the weaknesses of conventional methods to produce effective control measure recommendations. The constructed SVR model was experimentally verified by comparing its performance on datasets with expected and unexpected outputs. Although datasets producing an unexpected output decreased the prediction accuracy, by removing randomness from the training dataset, the accuracy of the proposed method was still high in these cases. High-precision predictions of the MAS-based simulation output were obtained for both test datasets in under one second of the computational time. Furthermore, the experimental results establish that the proposed framework can obtain intuitively correct outputs for unknown inputs, and produces sufficiently high-precision prediction with lower computation costs than an existing method.

WE-Net: An Ensemble Deep Learning Model for Covid-19 Detection in Chest X-ray Images Using Segmentation and Classification

Amidst the increasing surge of Covid-19 infections worldwide, chest X-ray (CXR) imaging data have been found incredibly helpful for the fast screening of COVID-19 patients. This has been particularly helpful in resolving the overcapacity situation in the urgent care center and emergency department. An accurate Covid-19 detection algorithm can further aid this effort to reduce the disease burden. As part of this study, we put forward WE-Net, an ensemble deep learning (DL) framework for detecting pulmonary manifestations of COVID-19 from CXRs. We incorporated lung segmentation using U-Net to identify the thoracic Region of Interest (RoI), which was further utilized to train DL models to learn from relevant features. ImageNet based pre-trained DL models were fine-tuned, trained, and evaluated on the publicly available CXR collections. Ensemble methods like stacked generalization, voting, averaging, and the weighted average were used to combine predictions from best-performing models. The purpose of incorporating ensemble techniques is to overcome some of the challenges, such as generalization errors encountered due to noise and training on a small number of data sets. Experimental evaluations concluded on significant improvement in performance using the deep fusion neural network, i.e., the WE-Net model, which led to 99.02% accuracy and 0.989 area under the curve (AUC) in detecting COVID-19 from CXRs. The combined use of image segmentation, pre-trained DL models, and ensemble learning (EL) boosted the prediction results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Rapid Control of a SARS-CoV-2 B.1.617.2 (Delta) Variant COVID-19 Community Outbreak: The Successful Experience in Pingtung County of Taiwan.

The Severe Acute Respiratory Syndrome-associated Coronavirus 2 (SARS-CoV-2) was an outbreak in December, 2019 and rapidly spread to the world. All variants of SARS-CoV-2, including the globally and currently dominant Delta variant (Delta-SARS-CoV-2), caused severe disease and mortality. Among all variants, Delta-SARS-CoV-2 had the highest transmissibility, growth rate, and secondary attack rate than other variants except for the new variant of Omicron that still exists with many unknown effects. In Taiwan, the pandemic Delta-SARS-CoV-2 began in Pingtung from 14 June 2021 and ceased at 11 July 2021. Seventeen patients were infected by Delta-SARS-CoV-2 and 1 person died during the Pingtung outbreak. The Public Health Bureau of Pingtung County Government stopped the Delta-SARS-CoV-2 outbreak within 1 month through measures such as epidemic investigation, rapid gene sequencing, rapidly expanding isolation, expanded screening of the Delta-SARS-CoV-2 antigen for people who lived in regional villages, and indirect intervention, including rapid vaccination, short lockdown period, and travel restrictions. Indirect environmental factors, such as low levels of air pollution, tropic weather in the summer season, and rural areas might have accelerated the ability to control the Delta-SARS-CoV-2 spread. This successful experience might be recommended as a successful formula for the unvaccinated or insufficiently vaccinated regions.

Challenges of SERS technology as a non-nucleic acid or -antigen detection method for SARS-CoV-2 virus and its variants.

The COVID-19 pandemic has caused a significant burden since December 2019 that has negatively impacted the global economy owing to the fact that the SARS-CoV-2 virus is fast-transmitting and highly contagious. Efforts have been taken to minimize the impact through strict screening measures in country borders in order to isolate potential virus carriers. Effective fast-screening methods are thus needed to identify infected individuals. The standard diagnostic methods for screening SARS-CoV-2 virus have always been to perform nucleic acid-based and serological tests. However, with each having drawbacks on producing false results at very early or later stage after symptoms onset, supplementary techniques are needed to back up these tests. Surface-enhanced Raman spectroscopy (SERS) as a detection technique has continuously advanced throughout the years in terms of sensitivity and capability to detect ultralow concentration of analytes ranging from single molecule to pathogens, to present as a highly potential alternative to known sensing methods. SERS technology as a candidate for an alternative and supplementary diagnostic method for the viral envelope of SARS-CoV-2 virus is presented, comparing its pros and cons to the standard methods and what other aspects it could offer that the other methods are not capable of. Factors that contribute to the detection effectivity of SERS is also discussed to show the advantages and limitations of this technique. Despite its promising capabilities, challenges like sources of SARS-CoV-2 virus and its variations, reliable SERS spectra, mass production of SERS-active substrates, and compliance to regulations for wide-scale testing scenario are highlighted.