Paweł Wityk

In this article we present the novel spectroscopy method supported with machine learning for real‐time detection of infectious agents in wastewater. In the case of infectious diseases, wastewater monitoring can be used to detect the presence of inflammation biomarkers, such as the proposed C‐reactive protein, for monitoring inflammatory conditions and mass screening during epidemics for early detection in communities of concern, such as hospitals, schools, and so on. The proposed spectroscopy method supported with machine learning for real‐time detection of infectious agents will eliminate the need for time‐consuming processes, which contribute to reducing costs. The spectra in range 220–750 nm were used for the study. We achieve accuracy of our prediction model up to 68% with using only absorption spectrophotometer and machine learning. The use of such a set makes the method universal, due to the possibility of using many different detectors.

In this paper, we present the design and the principle of operation of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) specific immunoglobulin G (IgG) biophotonic sensor, which is based on the single‐mode telecommunication fiber. We fabricated the sensor head at the face of the single mode fiber‐28. Due to the process of bio‐functionalization, our sensor has the ability to selectively detect the SARS‐CoV‐2 specific IgG antibodies. The results of preliminary tests allowed us to correctly determine the presence of antibodies in less than 1 min in 5 μl in a volume sample of concentration of 10 μg/ml, which according to studies, corresponds to the concentration of IgG antibodies in human serum. Additionally, the tested sample can be smaller than 5 μl in volume.