Recent Trends in Lateral Flow Immunoassays with Optical Nanoparticles.

Rapid, accurate, and convenient diagnosis is essential for effective disease management. Various detection methods, such as enzyme-linked immunosorbent assay, have been extensively used, with lateral flow immunoassay (LFIA) recently emerging as a major diagnostic tool. Nanoparticles (NPs) with characteristic optical properties are used as probes for LFIA, and researchers have presented various types of optical NPs with modified optical properties. Herein, we review the literature on LFIA with optical NPs for the detection of specific targets in the context of diagnostics.

In vitro metabolic characterization of the SARS-CoV-2 papain-like protease inhibitors GRL0617 and HY-17542

The SARS-CoV-2 pandemic requires a new therapeutic target for viral infection, and papain-like protease (Plpro) has been suggested as a druggable target. This in-vitro study was conducted to examine the drug metabolism of the GRL0617 and HY-17542, Plpro inhibitors. Metabolism of these inhibitors was studied to predict the pharmacokinetics in human liver microsomes. The hepatic cytochrome P450 (CYP) isoforms responsible for their metabolism were identified using recombinant enzymes. The drug–drug interaction potential mediated by cytochrome P450 inhibition was estimated. In human liver microsomes, the Plpro inhibitors had phase I and phase I + II metabolism with half-lives of 26.35 and 29.53 min, respectively. Hydroxylation (M1) and desaturation (-H2, M3) of the para-amino toluene side chain were the predominant reactions mediated with CYP3A4 and CYP3A5. CYP2D6 is responsible for the hydroxylation of the naphthalene side ring. GRL0617 inhibits major drug-metabolizing enzymes, including CYP2C9 and CYP3A4. HY-17542 is structural analog of GRL0617 and it is metabolized to GRL0617 through non-cytochrome P450 reactions in human liver microsomes without NADPH. Like GRL0617 and HY-17542 undergoes additional hepatic metabolism. The in-vitro hepatic metabolism of the Plpro inhibitors featured short half-lives;preclinical metabolism studies are needed to determine therapeutic doses for these inhibitors.

In vitro metabolic characterization of the SARS-CoV-2 papain-like protease inhibitors GRL0617 and HY-17542.

The SARS-CoV-2 pandemic requires a new therapeutic target for viral infection, and papain-like protease (Plpro) has been suggested as a druggable target. This in-vitro study was conducted to examine the drug metabolism of the GRL0617 and HY-17542, Plpro inhibitors. Metabolism of these inhibitors was studied to predict the pharmacokinetics in human liver microsomes. The hepatic cytochrome P450 (CYP) isoforms responsible for their metabolism were identified using recombinant enzymes. The drug-drug interaction potential mediated by cytochrome P450 inhibition was estimated. In human liver microsomes, the Plpro inhibitors had phase I and phase I + II metabolism with half-lives of 26.35 and 29.53 min, respectively. Hydroxylation (M1) and desaturation (-H2, M3) of the para-amino toluene side chain were the predominant reactions mediated with CYP3A4 and CYP3A5. CYP2D6 is responsible for the hydroxylation of the naphthalene side ring. GRL0617 inhibits major drug-metabolizing enzymes, including CYP2C9 and CYP3A4. HY-17542 is structural analog of GRL0617 and it is metabolized to GRL0617 through non-cytochrome P450 reactions in human liver microsomes without NADPH. Like GRL0617 and HY-17542 undergoes additional hepatic metabolism. The in-vitro hepatic metabolism of the Plpro inhibitors featured short half-lives; preclinical metabolism studies are needed to determine therapeutic doses for these inhibitors.

Association of Vitamin C Treatment with Clinical Outcomes for COVID-19 Patients: A Systematic Review and Meta-Analysis.

Background: Vitamin C is an essential nutrient that serves as an antioxidant and is known to reduce the inflammatory response associated with pneumonia and acute respiratory distress syndrome in patients with the coronavirus disease (COVID-19), but its clinical effects remain controversial. Methods: This study aimed to investigate the therapeutic effect of vitamin C administration on the clinical outcomes of COVID-19 patients through a systematic review and meta-analysis. Results: Nineteen studies were selected, of which 949 participants administered vitamin C were in the intervention group, and 1816 participants were in the control group. All-cause mortality, hospitalization duration, length of intensive care unit stay, and ventilation incidence in COVID-19 patients were analyzed. The intervention group tends to have a lower risk ratio (RR = 0.81, 95% CI: 0.62 to 1.07; I2 = 58%; Q = 40.95; p < 0.01) in all-cause mortality than the control group. However, there were no significant differences in ventilation incidence, hospitalization duration, and length of ICU stay between the two groups. In the subgroup analysis for all-cause mortality, the risk ratio for RCT as study design, combination therapy, of vitamin C was lower than that of the combination therapy with other agents. A moderate dosage showed a lower RR than a higher dose. Conclusion: The results suggest that vitamin C may lower mortality in COVID-19 patients, but further large-scale studies are required to assess the role of vitamin C in the treatment of COVID-19.

Single-Nanoparticle-Based Digital SERS Sensing Platform for the Accurate Quantitative Detection of SARS-CoV-2.

To prevent the ongoing spread of the highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), accurate and early detection based on a rapid, ultrasensitive, and highly reliable sensing method is crucially important. Here, we present a bumpy core-shell surface-enhanced Raman spectroscopy (SERS) nanoprobe-based sensing platform with single-nanoparticle (SNP)-based digital SERS analysis. The tailorable bumpy core-shell SERS nanoprobe with an internal self-assembled monolayer of 4-nitrobenzenethiol Raman reporters, synthesized using HEPES biological buffer, generates a strong, uniform, and reproducible SERS signal with an SNP-level sensitive and narrowly distributed enhancement factor (2.1 × 108 to 2.2 × 109). We also propose an SNP-based digital SERS analysis method that provides direct visualization of SNP detection at ultralow concentrations and reliable quantification over a wide range of concentrations. The bumpy core-shell SERS nanoprobe-based sensing platform with SNP-based digital SERS analysis achieves the ultrasensitive and quantitative detection of the SARS-CoV-2 spike protein with a limit of detection of 7.1 × 10-16 M over a wide dynamic range from 3.7 × 10-15 to 3.7 × 10-8 M, far outperforming the conventional enzyme-linked immunosorbent assay method for the target protein. Furthermore, it can detect mutated spike proteins from the SARS-CoV-2 variants, representing the key mutations of Alpha, Beta, Gamma, Delta, and Omicron variants. Therefore, this sensing platform can be effectively and efficiently used for the accurate and early detection of SARS-CoV-2 and be adapted for the ultrasensitive and reliable detection of other highly infectious diseases.

Virus blocking textile for SARS-CoV-2 using human body triboelectric energy harvesting.

Effective mitigation technology to prevent the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is required before achieving population immunity through vaccines. Here we demonstrate a virus-blocking textile (VBT) that repulses SARS-CoV-2 by applying repulsive Coulomb force to respiratory particles, powered by human body triboelectric energy harvesting. We show that SARS-CoV-2 has negative charges, and a human body generates high output current of which peak-to-peak value reaches 259.6 µA at most, based on triboelectric effect. Thereby, the human body can sustainably power a VBT to have negative electrical potential, and the VBT highly blocks SARS-CoV-2 by repulsion. In an acrylic chamber study, we found that the VBT blocks SARS-CoV-2 by 99.95%, and SARS-CoV-2 in the VBT is 13-fold reduced. Our work provides technology that may prevent the spread of virus based on repulsive Coulomb force and triboelectric energy harvesting.

COVID-19 through Adverse Outcome Pathways: Building networks to better understand the disease - 3rd CIAO AOP Design Workshop.

On April 28-29, 2021, 50 scientists from different fields of expertise met for the 3rd online CIAO workshop. The CIAO project "Modelling the Pathogenesis of COVID-19 using the Adverse Outcome Pathway (AOP) framework" aims at building a holistic assembly of the available scientific knowledge on COVID-19 using the AOP framework. An individual AOP depicts the disease progression from the initial contact with the SARS-CoV-2 virus through biological key events (KE) toward an adverse outcome such as respiratory distress, anosmia or multiorgan failure. Assembling the individual AOPs into a network highlights shared KEs as central biological nodes involved in multiple outcomes observed in COVID-19 patients. During the workshop, the KEs and AOPs established so far by the CIAO members were presented and posi­tioned on a timeline of the disease course. Modulating factors influencing the progression and severity of the disease were also addressed as well as factors beyond purely biological phenomena. CIAO relies on an interdisciplinary crowd­sourcing effort, therefore, approaches to expand the CIAO network by widening the crowd and reaching stakeholders were also discussed. To conclude the workshop, it was decided that the AOPs/KEs will be further consolidated, inte­grating virus variants and long COVID when relevant, while an outreach campaign will be launched to broaden the CIAO scientific crowd.

Simultaneous Nosocomial SARS-CoV-2 Outbreaks with Different Patterns of Occurrence in Two General Wards; Experience in a Tertiary Care Hospital in Korea.

A nosocomial outbreak of severe acute respiratory syndrome-coronavirus-2 occurred in two general wards. One outbreak had pre-symptomatic transmission and was linked epidemiologically with an index case. The other outbreak was associated with early detection failure in a crowded room. Notably, adherence to appropriate mask wearing was not confirmed in the hospital outbreak. Confirmed cases were moved into isolation rooms, and contacts were quarantined. Quarantined cases were tested regularly and facilitated early termination of the hospital outbreak.

Recycling respirator masks to a high-value product: From COVID-19 prevention to highly efficient battery separator.

COVID-19 is a pandemic that has caused serious disruption in almost every day-to-day life around the world, and wearing a mask is essential for human safety from this virus. However, masks are non-recyclable materials, and the accumulation of masks used every day causes serious environmental issues. In this study, we investigate the recycling of mask materials for addressing the environmental problems and transforming as a high value-added material through chemical modification of masks. The recycled mask is applied as a separator for aqueous rechargeable batteries, and shows outstanding safety and electrochemical performance than the existing separator. This approach will lead to an advanced energy technology considering nature after overcoming COVID-19.

Importance of wearing a mask continuously and appropriately regardless of the COVID 19 symptoms. Lessons from different mask-wearing styles among two healthcare personnel.

The anecdotal report describes two examples of COVID-19-positive healthcare professionals (HCPs) who had worked as nurses during pre-symptomatic period and subsequently presented a mild clinical course of COVID-19. The nurses' responsibilities were almost the same and worked in the general ward with no aerosol generating medical procedures. Two HCPs were expected to have similar infectiousness, but the number of secondary transmission by each HCP were different. Eleven close contacts from HCP1 were notified, and all of them tested negative for SARS-CoV-2. However, 13 of 35 close contacts of HCP2 tested positive for SARS-CoV-2. While working, mask-wearing style differed between the two HCPs. HCP1 wore a KF94 mask appropriately and kept wearing it while working. HCP2 wore a surgical mask while working, but often pulled it down to her chin or removed it. It was strongly suspected that the difference of mask wearing contributed to the SARS-CoV-2 transmission. However, other factors such as talkative behavior, exposure time, ventilation in rooms, and hand hygiene performance rates, could also have affected the transmission of COVID-19. It is crucial that healthcare workers wear a mask adequately and continuously, and they maintain proper hand hygiene while working during the COVID-19 pandemic.

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation.

BACKGROUND: Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE: We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non-COVID-19 pneumonia and nonpneumonia diseases. METHODS: A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS: Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS: FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.