ABSTRACT
Coronavirus disease 2019 (COVID-19) and influenza A are two respiratory infectious diseases with similar clinical manifestations. Because of the complex global epidemic situation of COVID-19, the distinction and diagnosis of COVID-19 and influenza A infected persons is crucial for epidemic prevention and control. In this study, tetrahedral DNA framework (TDF) was combined with a rotational paper-based analytical device, and the color change generated by the reaction between horseradish peroxidase (HRP) and 3,3'5,5'-tetramethylbenzidine (TMB)-H2O2 was used for grayscale signal analysis by ImageJ software. The quantitative detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A H1N1 virus were realized simultaneously. Under the optimal conditions, the paper-based analytical device showed a good linear relationship between the two viruses in the range of 10-14-10-8g/mL, and the two viruses were not affected by cross reaction. This sensor provides a convenient and reliable method for clinical rapid differentiation and diagnosis of COVID-19 and influenza A.
ABSTRACT
Background: The present study aims to better understand the efficacy and safety of mesenchymal stromal cells (MSCs) in treating severe/critical patients with COVID-19. Methods: PubMed, the Cochrane Library, and the Chinese electronic database CNKI were searched from inception up to Dec 19, 2021. Original comparative studies for MSC treatment + standard treatment for severe/critical patients with COVID-19, with placebo or standard treatment as the control group, were included. The primary outcomes were in-hospital mortality and adverse events (AEs). A meta-analysis was performed to compare the mortality rates between the two groups. Then, a subgroup analysis was performed according to the category of the disease (severe or critical) and MSC dose. Afterwards, a descriptive analysis was performed for AEs and secondary outcomes. The funnel plot and Egger's test were used for the publication bias assessment. Findings: Compared to placebo or standard care, MSCs provide significant benefit in the treatment of patients with severe/critical COVID-19, in terms of in-hospital mortality rate (odds ratio: 0.52, 95% CI 0.32-0.84), with very low heterogeneity (P=0.998 [Q test], I 2=0.0%) and less AEs. No significant difference was found in mortality rate due to the different disease categories or MSC doses. Furthermore, no publication bias was found. Interpretation: The present study demonstrates that MSCs are highly likely to reduce mortality and are safe to use for patients with severe or critical COVID-19, regardless of whether 1-3 doses are applied. However, due to the small sample size of the included studies, further high-quality, large-scale trials are needed to confirm this statement in the future. Funding: The National Key Research and Development Program of China (No. 2020YFC0860900), the Science and Technology Project of Wuhan (No. 2020020602012112), the Tianjin Science and Technology Research Program (18PTSYJC00070 and 16PTWYHZ00030), Haihe Laboratory of Cell Ecosystem Innovation Fund (HH22KYZX0046), and the Tianjin Free Trade Zone Innovation Development Project (ZMCY-03-2021002-01) funded the study. We are also grateful for the support from the 3551 Talent Plan of China Optics Valley.
ABSTRACT
Background Social distancing has been implemented by many countries to curb the COVID-19 pandemic. Understanding public support for this policy calls for effective and efficient methods of monitoring public opinion on social distancing. Twitter analysis has been suggested as a cheaper and faster-responding alternative to traditional survey methods. The current empirical evidence is mixed in terms of the correspondence between the two methods. Objective We aim to compare the two methods in the context of monitoring the Dutch public's opinion on social distancing. For this comparison, we quantified the temporal and spatial variations in public opinion and their sensitivities to critical events using data from both Dutch Twitter users and respondents from a longitudinal survey. Methods A longitudinal survey on a representative Dutch sample (n = 1,200) was conducted between July and November 2020 to measure opinions on social distancing weekly. From the same period, near 100,000 Dutch tweets were categorized as supporting or rejecting social distancing based on a model trained with annotated data. Average stances for the 12 Dutch provinces and over the 20 weeks were computed from the two data sources and were compared through visualizations and statistical analyses. Results Both data sources suggested strong support for social distancing, but public opinion was much more varied among tweets than survey responses. Both data sources showed an increase in public support for social distancing over time, and a strong temporal correspondence between them was found for most of the provinces. In addition, the survey but not Twitter data revealed structured differences among the 12 provinces, while the two data sources did not correspond much spatially. Finally, stances estimated from tweets were more sensitive to critical events happened during the study period. Conclusions Our findings indicate consistencies between Twitter data analysis and survey methods in describing the overall stance on social distancing and temporal trends. The lack of spatial correspondence may imply limitations in the data collections and calls for surveys with larger regional samples. For public health management, Twitter analysis can be used to complement survey methods, especially for capturing public's reactivities to critical events amid the current pandemic.
ABSTRACT
The spread of Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), resulting in a global pandemic with around four million deaths. Although there are a variety of nucleic acid-based tests for detecting SARS-CoV-2, these methods have a relatively high cost and require expensive supporting equipment. To overcome these limitations and improve the efficiency of SARS-CoV-2 diagnosis, we developed a microfluidic platform that collected serum by a pulling-force spinning top and paper-based microfluidic enzyme-linked immunosorbent assay (ELISA) for quantitative IgA/IgM/IgG measurements in an instrument-free way. We further validated the paper-based microfluidic ELISA analysis of SARS-CoV-2 receptor-binding domain (RBD)-specific IgA/IgM/IgG antibodies from human blood samples as a good measurement with higher sensitivity compared with traditional IgM/IgG detection (99.7% vs 95.6%) for early illness onset patients. In conclusion, we provide an alternative solution for the diagnosis of SARS-CoV-2 in a portable manner by this smart integration of pulling-force spinning top and paper-based microfluidic immunoassay.