Lateral Flow Immunoassay Coupled with Copper Enhancement for Rapid and Sensitive SARS-CoV-2 Nucleocapsid Protein Detection.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2) is still raging all over the world. Hence, the rapid and sensitive screening of the suspected population is in high demand. The nucleocapsid protein (NP) of SARS-CoV-2 has been selected as an ideal marker for viral antigen detection. This study describes a lateral flow immunoassay (LFIA) based on colloidal gold nanoparticles for rapid NP antigen detection, in which sensitivity was improved through copper deposition-induced signal amplification. The detection sensitivity of the developed LFIA for NP antigen detection (using certified reference materials) under the optimized parameters was 0.01 µg/mL and was promoted by three orders of magnitude to 10 pg/mL after copper deposition signal amplification. The LFIA coupled with the copper enhancement technique has many merits such as low cost, high efficiency, and high sensitivity. It provides an effective approach to the rapid screening, diagnosis, and monitoring of the suspected population in the COVID-19 outbreak.

Clinical characteristics and molecular epidemiology of human metapneumovirus in children with acute lower respiratory tract infections in China, 2017 to 2019: A multicentre prospective observational study.

Human metapneumovirus (HMPV) infection is one of the leading causes of hospitalization in young children with acute respiratory illness. In this study, we prospectively collected respiratory tract samples from children who were hospitalized with acute lower respiratory tract infection in six hospitals in China from 2017 to 2019. HMPV was detected in 145 out of 2733 samples (5.3%) from the hospitalized children. The majority of HMPV-positive children were under the age of two (67.6%), with a median age of one year. HMPV can independently cause acute lower respiratory tract infection in young children, while all patients showed mild clinical symptoms. Of all the co-infected patients, HMPV was most commonly detected with enterovirus (EV) or rhinovirus (RhV) (38.0%, followed by respiratory syncytial virus (RSV) (32.0%). The highest detection rate occurred from March to May in both northern and southern China. Out of 145 HMPV positive samples, 48 were successfully typed, of which 36 strains were subgrouped into subtypes A2c (75%), eight strains were included in subtype B1 (16.7%), and four strains were included in subtype B2 (8.3%). Moreover, 16 A2c strains contained 111-nucleotide duplications in the G gene. Twenty-seven complete HMPV genomes were successfully obtained, and 25 (92.6%) strains belonged to subtype A2c, whereas one strain was included in subgroup B1 and another was included in subgroup B2. A total of 277 mutations were observed in the complete genomes of 25 A2c strains. All results presented here improve our understanding of clinical characteristics and molecular epidemiology of HMPV infection in children.

A multicentre study on the incidence of respiratory viruses in children with community-acquired pneumonia requiring hospitalization in the setting of the zero-COVID policy in China.

BACKGROUND: Stringent nonpharmaceutical interventions (NPIs) have been implemented worldwide to combat the COVID-19 pandemic, and the circulation and seasonality of common respiratory viruses have subsequently changed. There have been few multicentre studies or comparisons of the prevalence of respiratory viruses accounting for community-acquired pneumonia (CAP) in hospitalized children between the pre-COVID period and the period after community and school reopening in the setting of the zero-COVID policy. METHODS: We included 1543 children with CAP who required hospitalization from November 1, 2020 to April 30, 2021 (period 1), and 629 children with the same conditions from November 1, 2018, to April 30, 2019 (period 2), in our study. All respiratory samples from these patients were screened for six respiratory viruses (respiratory syncytial virus [RSV], adenovirus [ADV], influenza A virus [Flu A], influenza B virus [Flu B], parainfluenza virus type 1 [PIV1], and parainfluenza virus type 3 [PIV3]) using a multiplex real-time PCR assay. RESULTS AND CONCLUSIONS: The median ages of the enrolled patients at the time of diagnosis were 1.5 years and 1.0 years for period 1 and period 2, respectively. In period 1, viral pathogens were detected in 50.3% (776/1543) of the enrolled patients. The most frequently identified viral pathogen was RSV (35.9%, 554/1543), followed by PIV3 (9.6%, 148/1543), PIV1 (3.6%, 56/1543), ADV (3.4%, 52/1543), Flu A (1.0%, 16/1543), and Flu B (0.8%, 13/1543). The total detection rates of these six viruses in the peak season of CAP were at the pre-COVID level. The prevalence of Flu A decreased dramatically, and circulation activity was low compared to pre-COVID levels, while the incidence of PIV3 increased significantly. There were no significant differences in the detection rates of RSV, ADV, Flu B, and PIV1 between the two periods. Our results showed that respiratory viruses accounted for CAP in hospitalized children at pre-COVID levels as communities and schools reopened within the zero-COVID policy, although the prevalence aetiology spectrum varied.

COVID-19: Short term prediction model using daily incidence data.

BACKGROUND: Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. METHODS: Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. RESULTS: We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. CONCLUSION: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

On-Chip Optical Detection of Viruses: A Review.

The current outbreak of the coronavirus disease-19 (COVID-19) pandemic worldwide has caused millions of fatalities and imposed a severe impact on our daily lives. Thus, the global healthcare system urgently calls for rapid, affordable, and reliable detection toolkits. Although the gold-standard nucleic acid amplification tests have been widely accepted and utilized, they are time-consuming and labor-intensive, which exceedingly hinder the mass detection in low-income populations, especially in developing countries. Recently, due to the blooming development of photonics, various optical chips have been developed to detect single viruses with the advantages of fast, label-free, affordable, and point of care deployment. Herein, optical approaches especially in three perspectives, e.g., flow-free optical methods, optofluidics, and surface-modification-assisted approaches, are summarized. The future development of on-chip optical-detection methods in the wave of emerging new ideas in nanophotonics is also briefly discussed.