RNA Interference Approach Is a Good Strategy against SARS-CoV-2.

COVID-19, caused by SARS-CoV-2, created a devastating outbreak worldwide and consequently became a global health concern. However, no verifiable, specifically targeted treatment has been devised for COVID-19. Several emerging vaccines have been used, but protection has not been satisfactory. The complex genetic composition and high mutation frequency of SARS-CoV-2 have caused an uncertain vaccine response. Small interfering RNA (siRNA)-based therapy is an efficient strategy to control various infectious diseases employing post-transcriptional gene silencing through the silencing of target complementary mRNA. Here, we designed two highly effective shRNAs targeting the conserved region of RNA-dependent RNA polymerase (RdRP) and spike proteins capable of significant SARS-CoV-2 replication suppression. The efficacy of this approach suggested that the rapid development of an shRNA-based therapeutic strategy might prove to be highly effective in treating COVID-19. However, it needs further clinical trials.

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.

Outbreak of respiratory syncytial virus subtype ON1 among children during COVID-19 pandemic in Southern Taiwan.

BACKGROUND: The regional respiratory syncytial virus (RSV) outbreak in southern Taiwan in late 2020 followed the surge of RSV cases in the national surveillance data and displayed distinct clinical features. This study investigated RSV epidemiology in the most recent five years and compared the clinical manifestations of this outbreak with non-outbreak period. METHODS: Medical records of RSV-infected children at the National Cheng Kung University Hospital from January 2016 to December 2020 were retrospectively retrieved from hospital-based electronic medical database. Cases of RSV infection were identified by RSV antigen positive and/or RSV isolated from respiratory specimens. The demographic, clinical presentations, and laboratory data were recorded. The RSV isolates in 2020 was sequenced for phylogenetic analysis. RESULTS: Overall, 442 RSV-infected cases were retrieved and 42.1% (186 cases) clustered in late 2020. The 2020 outbreak started in September, peaked in November, and lasted for 3 months. 2020 RSV-infected children were older (2.3 ± 2.2 years vs. 1.0 ± 1.0 years), more likely to be diagnosed with bronchopneumonia (57.5% vs. 31.6%), but also had a lower hospitalization rate, shorter hospital stay, less oxygen use, and less respiratory distress than those in 2016-2019 (all p value < 0.05). The RSV isolates in 2020 belonged to RSV-A subtype ON1 but were phylogenetically distinct from the ON1 strains prevalent in Taiwan previously. CONCLUSION: The 2020 RSV outbreak was led by the novel RSV-A subtype ON1 variant with clinical manifestations distinct from previous years. Continuous surveillance of new emerging variants of respiratory viruses in the post-pandemic era is warranted.

Electromagnetically-driven integrated microfluidic platform using reverse transcription loop-mediated isothermal amplification for detection of severe acute respiratory syndrome coronavirus 2.

Rapid, sensitive and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great need for effective quarantining and treatment. Real-time reverse-transcription polymerase chain reaction requiring thermocyling has been commonly used for diagnosis of SARS-CoV-2 though it may take two to 4 h before lengthy sample pretreatment process and require bulky apparatus and well-trained personnel. Since multiple reverse transcription loop-mediated isothermal amplification (multiple RT-LAMP) process without thermocycling is sensitive, specific and fast, an electromagnetically-driven microfluidic chip (EMC) was developed herein to lyse SARS-CoV-2 viruses, extract their RNAs, and perform qualitative analysis of three marker genes by on-chip multiple RT-LAMP in an automatic format within 82 min at a limit of detection of only ∼5000 copies per reaction (i.e. 200 virus/ µL). This compact EMC may be especially promising for SARS-CoV-2 diagnostics in resource-limited countries.

An integrated microfluidic platform featuring real-time reverse transcription loop-mediated isothermal amplification for detection of COVID-19.

An integrated microfluidic platform (IMP) utilizing real-time reverse-transcription loop-mediated isothermal amplification (RT-LAMP) was developed here for detection and quantification of three genes of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; i.e., coronavirus diseases 2019 (COVID-19)): RNA-dependent RNA polymerase, the envelope gene, and the nucleocapsid gene for molecular diagnosis. The IMP comprised a microfluidic chip, a temperature control module, a fluidic control module that collectively carried out viral lysis, RNA extraction, RT-LAMP, and the real-time detection within 90 min in an automatic format. A limit of detection of 5 × 103 copies/reaction for each gene was determined with three samples including synthesized RNAs, inactive viruses, and RNAs extracted from clinical samples; this compact platform could be a useful tool for COVID-19 diagnostics.

Respiratory viral infections in pragmatically selected adults in intensive care units.

Respiratory viruses can be detected in 18.3 to 48.9% of critically ill adults with severe respiratory tract infections (RTIs). The present study aims to assess the clinical significance of respiratory viruses in pragmatically selected adults in medical intensive care unit patients and to identify factors associated with viral respiratory viral tract infections (VRTIs). We conducted a prospective study on critically ill adults with suspected RTIs without recognized respiratory pathogens. Viral cultures with monoclonal antibody identification, in-house real-time polymerase chain reaction (PCR) for influenza virus, and FilmArray respiratory panel were used to detect viral pathogens. Multivariable logistic regression was applied to identify factors associated with VRTIs. Sixty-four (40.5%) of the included 158 critically ill adults had respiratory viruses detected in their respiratory specimens. The commonly detected viruses included influenza virus (20), followed by human rhinovirus/enterovirus (11), respiratory syncitial virus (9), human metapneumovirus (9), human parainfluenza viruses (8), human adenovirus (7), and human coronaviruses (2). The FilmArray respiratory panel detected respiratory viruses in 54 (34.6%) patients, but showed negative results for seven of 13 patients with influenza A/H3 infection. In the multivariable logistic regression model, patient characters associated with VRTIs included those aged < 65 years, household contact with individuals with upper RTI, the presence of fever, cough with sputum production, and sore throat. Respiratory viruses were not uncommonly detected in the pragmatically selected adults with critical illness. The application of multiplex PCR testing for respiratory viruses in selected patient population is a practical strategy, and the viral detection rate could be further improved by the patient characters recognized in this study.

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.

Application of an Artificial Intelligence Trilogy to Accelerate Processing of Suspected Patients With SARS-CoV-2 at a Smart Quarantine Station: Observational Study.

BACKGROUND: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. OBJECTIVE: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. METHODS: This observational study on the emerging COVID-19 pandemic included 643 patients. An "AI trilogy" of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. RESULTS: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001). CONCLUSIONS: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

A case of COVID-19 and pneumonia returning from Macau in Taiwan: Clinical course and anti-SARS-CoV-2 IgG dynamic.

A 46-year-old woman presented to the emergency department with 2-day fever and cough at seven days after returning from Macau. COVID-19 and pneumonia was diagnosed based on the positive real-time RT-PCR tests for oropharyngeal swab samples and the presence of anti-SARS-COV-2 IgG starting from the illness day 11 and post-exposure 18-21 days.