ABSTRACT
The recent SARS-CoV-2 pandemic has highlighted the urgent need for novel point-of-care devices to be promptly used for a rapid and reliable large screening analysis of several biomarkers like genetic sequences and antibodies. Currently, one of the main limitations of rapid tests is the high percentage of false negatives in the presence of variants and, in particular for the Omicron one. We demonstrate in this work the detection of SARS-CoV-2 and the Omicron variant with a cost-effective silicon nanosensor enabling high sensitivity, selectivity, and fast response. We have shown that a silicon (Si) nanowires (NW) platform detects both Sars-CoV-2 and its Omicron variant with a limit of detection (LoD) of four effective copies (cps), without any amplification of the genome, and with high selectivity. This ultrasensitive detection of 4 cps allows to obtain an extremely early diagnosis paving the way for efficient and widespread tracking. The sensor is made with industrially compatible techniques, which in perspective may allow easy and cost-effective industrialization.
ABSTRACT
The continuing accumulation of mutations in the RNA genome of the SARS-CoV-2 virus generates an endless succession of highly contagious variants that cause concern around the world due to their antibody resistance and the failure of current diagnostic techniques to detect them in a timely manner. Raman spectroscopy represents a promising alternative to variants detection and recognition techniques, thanks to its ability to provide a characteristic spectral fingerprint of the biological samples examined under all circumstances. In this work we exploit the surface-enhanced Raman scattering (SERS) properties of a silver dendrite layer to explore, for the first time to our knowledge, the distinctive features of the Omicron variant genome. We obtain a complex spectral signal of the Omicron variant genome where the fingerprints of nucleobases in nucleosides are clearly unveiled and assigned in detail. Furthermore, the fractal SERS layer offers the presence of confined spatial regions in which the analyte remains trapped under hydration conditions. This opens up the prospects for a prompt spectral identification of the genome in its physiological habitat and for a study on its activity and variability.
ABSTRACT
The continuing accumulation of mutations in the RNA genome of the SARS-CoV-2 virus generates an endless succession of highly contagious variants that cause concern around the world due to their antibody resistance and the failure of current diagnostic techniques to detect them in a timely manner. Raman spectroscopy represents a promising alternative to variants detection and recognition techniques, thanks to its ability to provide a characteristic spectral fingerprint of the biological samples examined under all circumstances. In this work we exploit the surface-enhanced Raman scattering (SERS) properties of a silver dendrite layer to explore, for the first time to our knowledge, the distinctive features of the Omicron variant genome. We obtain a complex spectral signal of the Omicron variant genome where the fingerprints of nucleobases in nucleosides are clearly unveiled and assigned in detail. Furthermore, the fractal SERS layer offers the presence of confined spatial regions in which the analyte remains trapped under hydration conditions. This opens up the prospects for a prompt spectral identification of the genome in its physiological habitat and for a study on its activity and variability.
ABSTRACT
We have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.