MXene‐Based Aptameric Fluorosensor for Sensitive and Rapid Detection of COVID‐19

MXene‐Based Aptameric FluorosensorsThe aptamer‐functionalized MXene nanosheet acts as an effective bionanosensor for fluorescence‐enhanced detection of COVID‐19 with high sensitivity and specificity. This fluosensor is capable of detecting SARS‐CoV‐2 spike protein (limit of detection: 38.9 fg mL−1) and SARS‐CoV‐2 pseudovirus (limit of detection: 7.2 copies) within 30 min, and can also detect clinical samples. More details can be found in article number 2301146 by Binwu Ying and co‐workers.

MXene-Based Aptameric Fluorosensor for Sensitive and Rapid Detection of COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-caused COVID-19 pandemic has rapidly escalated into the largest global health emergency, which pushes to develop detection kits for the detection of COVID-19 with high sensitivity, specificity, and fast analysis. Here, aptamer-functionalized MXene nanosheet is demonstrated as a novel bionanosensor that detects COVID-19. Upon binding to the spike receptor binding domain of SARS-CoV-2, the aptamer probe is released from MXene surface restoring the quenched fluorescence. The performances of the fluorosensor are evaluated using antigen protein, cultured virus, and swab specimens from COVID-19 patients. It is evidenced that this sensor can detect SARS-CoV-2 spike protein at final concentration of 38.9 fg mL-1 and SARS-CoV-2 pseudovirus (limit of detection: 7.2 copies) within 30 min. Its application for clinical samples analysis is also demonstrated successfully. This work offers an effective sensing platform for sensitive and rapid detection of COVID-19 with high specificity.

Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China.

Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer-Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.

Epidemiological surveillance of common respiratory viruses in patients with suspected COVID-19 in Southwest China.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently the peak season of common respiratory viral infections. However, the clinical symptoms of most SARS-CoV-2 infected patients are not significantly different from those of common respiratory viral infections. Therefore, knowing the epidemiological patterns of common respiratory viruses may be valuable to improve the diagnostic and therapeutic efficacy of patients with suspected COVID-19, especially in Southwest China (a mild epidemic area). METHODS: A total of 2188 patients with clinically suspected of COVID-19 in Southwest China were recruited from January 21 to February 29, 2020. Nasopharyngeal swabs, throat swabs and sputum specimens were collected to detect SARS-CoV-2 by using real-time reverse transcription-polymerase chain reaction (RT-PCR) and other 12 viruses via PCR fragment analysis combined with capillary electrophoresis. Clinical characteristics and laboratory test findings were acquired from electronic medical records. All data were analyzed to unravel the epidemiological patterns. RESULTS: Only 1.1% (24/2188) patients with suspected COVID-19 were eventually confirmed to have SARS-CoV-2 infection, and the most frequently observed symptoms were fever (75.0%, 18/24) and cough (20.8%, 5/24). The overall detection rate of other respiratory pathogens was 10.3% (226/2188). Among them, human rhinovirus (3.2%, 71/2188), human parainfluenza viruses (1.6%, 35/2188), influenza B virus (1.2%, 26/2188) and mycoplasma pneumonia (1.2%, 26/2188) were the predominantly detected pathogens in this study. Moreover, the co-infection was observed in 22 specimens. Notably, one COVID-19 case had a coexisting infection with human parainfluenza virus (4.2%, 1/24) and bocavirus was the most common virus tending to occur in co-infection with other respiratory pathogens. CONCLUSIONS: This study reveals the epidemiological features of common respiratory viruses and their clinical impact during the ongoing outbreak of COVID-19 in a mild epidemic area. The findings highlight the importance of understanding the transmission patterns of the common respiratory virus in COVID-19 regions, which can provide information support for the development of appropriate treatment plans and health policies, while eliminating unnecessary fear and tension.