Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics

There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infrared (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-IR (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-IR spectroscopy (NIRS) and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco's modified eagle medium (DMEM), were collected in long- and short-term experiments. The spectra were preprocessed and analyzed by partial least-squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus-type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on- site, and low-cost virus prescreening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.

ATR-FTIR spectroscopy and chemometrics as a quick and simple alternative for discrimination of SARS-CoV-2 infected food of animal origin.

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the "bio-fingerprint" (800 - 1900 cm-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.

A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data.

Data analysis is widely used to generate new insights into human disease mechanisms and provide better treatment methods. In this work, we used the mechanistic models of viral infection to generate synthetic data of influenza and COVID-19 patients. We then developed and validated a supervised machine learning model that can distinguish between the two infections. Influenza and COVID-19 are contagious respiratory illnesses that are caused by different pathogenic viruses but appeared with similar initial presentations. While having the same primary signs COVID-19 can produce more severe symptoms, illnesses, and higher mortality. The predictive model performance was externally evaluated by the ROC AUC metric (area under the receiver operating characteristic curve) on 100 virtual patients from each cohort and was able to achieve at least AUC = 91% using our multiclass classifier. The current investigation highlighted the ability of machine learning models to accurately identify two different diseases based on major components of viral infection and immune response. The model predicted a dominant role for viral load and productively infected cells through the feature selection process.

Metabolomics reveals sex-specific metabolic shifts and predicts the duration from positive to negative in non-severe COVID-19 patients during recovery process.

Metabolic profiling in COVID-19 patients has been associated with disease severity, but there is no report on sex-specific metabolic changes in discharged survivors. Herein we used an integrated approach of LC-MS-and GC-MS-based untargeted metabolomics to analyze plasma metabolic characteristics in men and women with non-severe COVID-19 at both acute period and 30 days after discharge. The results demonstrate that metabolic alterations in plasma of COVID-19 patients during the recovery and rehabilitation process were presented in a sex specific manner. Overall, the levels of most metabolites were increased in COVID-19 patients after the cure relative to acute period. The major plasma metabolic changes were identified including fatty acids in men and glycerophosphocholines and carbohydrates in women. In addition, we found that women had shorter length of hospitalization than men and metabolic characteristics may contribute to predict the duration from positive to negative in non-severe COVID-19 patients. Collectively, this study shed light on sex-specific metabolic shifts in non-severe COVID-19 patients during the recovery process, suggesting a sex bias in prognostic and therapeutic evaluations based on metabolic profiling.

Soybean-associated endophytic fungi as potential source for anti-COVID-19 metabolites supported by docking analysis.

AIMS: To identify the metabolites produced by the endophytic fungus, Aspergillus terreus and to explore the anti-viral activity of the identified metabolites against the pandemic disease COVID-19 in-silico. METHODS AND RESULTS: Herein, we reported the isolation of A. terreus, the endophytic fungus associated with soybean roots, which is then subcultured using OSMAC approach in five different culture media. Analytical analysis of media ethylacetate extracts using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) was carried out. Furthermore, the obtained LC-MS data were statistically processed with MetaboAnalyst 4.0. Molecular docking studies were performed for the dereplicated metabolites against COVID-19 main protease (Mpro ). Metabolomic profiling revealed the presence of 18 compounds belonging to different chemical classes. Quinones, polyketides and isocoumarins were the most abundant classes. Multivariate analysis revealed that potato dextrose broth and modified potato dextrose broth are the optimal media for metabolites production. Molecular docking studies declared that the metabolites, Aspergillide B1 and 3a-Hydroxy-3, 5-dihydromonacolin L showed the highest binding energy scores towards COVID-19 main protease (Mpro ) (-9·473) and (-9·386), respectively, and they interact strongly with the catalytic dyad (His41 and Cys145) amino acid residues of Mpro . CONCLUSIONS: A combination of metabolomics and in-silico approaches have allowed a shorter route to search for anti-COVID-19 natural products in a shorter time. The dereplicated metabolites, aspergillide B1 and 3α-Hydroxy-3, 5-dihydromonacolin L were found to be potent anti-COVID-19 drug candidates in the molecular docking study. SIGNIFICANCE AND IMPACT OF THE STUDY: This study revealed that the endophytic fungus, A. terreus can be considered as a potential source of natural bioactive products. In addition to, the possibility of developing the metabolites, aspergillide B1 and 3α-Hydroxy-3, 5-dihydromonacolin L to be used as phytopharmaceuticals for the management of COVID-19.