RESUMO
Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.
Assuntos
COVID-19 , COVID-19/diagnóstico , Humanos , Análise dos Mínimos Quadrados , Redes Neurais de Computação , Espectroscopia de Infravermelho com Transformada de Fourier/métodosRESUMO
In this study, the agricultural traits, alkaloids content and Fourier transform infrared spectroscopy (FT-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR) analysis of the tobacco after Berberine Bridge Enzyme-Like Proteins (BBLs) knockout were investigated. The knockout of BBLs has limited effect on tobacco agricultural traits. After the BBLs knockout, nicotine and most alkaloids are significantly reduced, but the content of myosmine and its derivatives increases dramatically. In order to identify the gene editing of tobacco, principal component analysis (PCA) was performed on the FT-IR and 2DCOS-IR spectroscopy data. The results showed that FT-IR can distinguish between tobacco roots and leaves but cannot classify the gene mutation tobacco from the wild one. 2DCOS-IR can enhance the characteristics of the samples due to the increased apparent resolution of the spectra. Using the autopeaks in the synchronous map for PCA analysis, we successfully identified the mutants with an accuracy of over 90%.
Assuntos
Nicotiana , Nicotina , Sistemas CRISPR-Cas/genética , Espectrofotometria Infravermelho , Espectroscopia de Infravermelho com Transformada de Fourier/métodos , Nicotiana/genéticaRESUMO
The outbreak of coronavirus disease 2019 (COVID-19) has led to substantial infections and mortality around the world. Fast screening and diagnosis are thus crucial for quick isolation and clinical intervention. In this work, we showed that attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FT-IR) can be a primary diagnostic tool for COVID-19 as a supplement to in-use techniques. It requires only a small volume (â¼3 µL) of the serum sample and a shorter detection time (several minutes). The distinct spectral differences and the separability between normal control and COVID-19 were investigated using multivariate and statistical analysis. Results showed that ATR-FT-IR coupled with partial least squares discriminant analysis was effective to differentiate COVID-19 from normal controls and some common respiratory viral infections or inflammation, with the area under the receiver operating characteristic curve (AUROC) of 0.9561 (95% CI: 0.9071-0.9774). Several serum constituents including, but not just, antibodies and serum phospholipids could be reflected on the infrared spectra, serving as "chemical fingerprints" and accounting for good model performances.
Assuntos
COVID-19/diagnóstico , Espectroscopia de Infravermelho com Transformada de Fourier/métodos , Estudos de Casos e Controles , Diagnóstico Diferencial , Análise Discriminante , Estudos de Viabilidade , HumanosRESUMO
In the context of spreading Coronavirus disease 2019 (COVID-19), the combination of heating, ventilation, and air-conditioning (HVAC) system with air disinfection device is an effective way to reduce transmissible infections. Atmospheric-pressure non-equilibrium plasma is an emerging technique for fast pathogen aerosol abatement. In this work, in-duct disinfectors based on grating-like dielectric barrier discharge (DBD) plasmas with varied electrode arrangements were established and evaluated. The highest airborne bacterial inactivation efficiency was achieved by 'vertical' structure, namely when aerosol was in direct contact with the discharge region, at a given discharge power. For all reactors, the efficiency was linearly correlated to the discharge power (R2 =0.929-0.994). The effects of environmental factors were examined. Decreased airflow rates boosted the efficiency, which reached 99.8% at the velocity of 0.5 m/s with an aerosol residence time of ~3.6 ms. Increasing humidity (relative humidity (RH)=20-60%) contributed to inactivation efficacy, while high humidity (RH=70%-90%) led to a saturated efficiency, possibly due to the disruption of discharge uniformity. As suggested by the plasma effluent treatment and scavenger experiments, gaseous short-lived chemical species or charged particles were concluded as the major agents accounting for bacterial inactivation. This research provides new hints for air disinfection by DBD plasmas.