The SARS-CoV-2 virus is known as the causal agent for the current COVID-19 global pandemic. The majority of COVID-19 patients develop acute respiratory distress syndrome (ARDS), while some experience a cytokine storm effect, which is considered as one of the leading causes of patient mortality. Lipids are known to be involved in the various stages of the lifecycle of a virus functioning as receptors or co-receptors that controls viral propagation inside the host cell. Therefore, lipid-related metabolomics aims to provide insight into the immune response of the novel coronavirus. Our study has focused on determination of the potential metabolomic biomarkers utilizing a Teslin Substrate in paper spray mass spectrometry (PS-MS) for the development of a rapid detection test within 60 seconds of analysis time. In this study, results were correlated with PCR tests to reflect that the systemic responses of the cells were affected by the COVID-19 virus.
The main purpose of this study was to develop an identification methodology that could greatly aid in detecting viral infection. It has been made clear that this method can be utilized as a sensitive and rapid technique to obtain vastly different lipid information for the chemical changes in lipid metabolites and small molecules present in the human body fluids with a minimum effort in sample preparation. Although thirty-one metabolites were (nine downregulated, twenty-two upregulated) detected with the PS-MS, only eleven of the metabolites were eligible (m/z 92, 93, 96, 98, 108, 140, 175, 257, 296, 307, and 539) in implementing significant statistical dataset with the integration of symptomatic PCR analysis. It is because individuals may express a high degree of heterogeneity in metabolomic responses under different symptomatic conditions. Overall, based on the statistical analysis PS-MS has proved a 93.3% correlation to the PCR classification. Nonetheless, consideration needs to be given to the potential uncertainty with respect to the PCR study. Because the accuracy of the predictive PCR value depends on the patient’s period of exposure and on the onset of symptoms, it has been shown that 39% of false-negatives and 16.7% of false-positives can be reported with PCR analysis without any other clinical support. Despite the current uncertainties, we had to correlate our dataset with the PCR because it is the viral identification “gold standard” currently in operation.That being said, considering follow up clinical and epidemiological character assays as standard confirmatory tests may minimize the impact of false identity. Imp and external clinical features will make it easier to mitigate the risk and the false impression of diagnosis. However, PS-MS is not the ultimate method for the detection of COVID-19, but we believe the demonstrative results urge new methodologies to be implemented along with machine learning. This experiment can be further beneficial for expanding and developing for better and fast real-time monitoring of the diseased stage using biofluid samples over time-consuming extensive laboratory testing methods.ortantly, a combination of PCR
Reference & Source Information: https://pubs.rsc.org/
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