Investigation of Microbes and Blood Plasma Towards Implications in Biomedicine: A Raman Spectroscopic Study

Abstract

Raman spectroscopy, combined with artificial intelligence, is becoming increasingly popular in biology because of its sensitivity to molecular changes. This thesis discuss the applications of Raman spectroscopy in analysing bacterial cells, spores, and the blood plasma of patients with infectious viral diseases. Healthcare professionals face a tough challenge dealing with recurring outbreaks of viral diseases like flu, COVID-19, and dengue because these illnesses have similar symptoms. In our research, we have used Raman spectroscopy and generative AI model to create a quick and accurate diagnostic model for identifying viral diseases using blood plasma. A big problem in managing Tuberculosis, the second-leading infectious cause of death after COVID-19, is the lack of fast techniques to assess the viability of bacterial cells. This leads to the wrong use of antibiotics and, consequently, antibiotic resistance. To tackle this, we developed a culture-free diagnostic method that is sensitive to viable cells, using Raman spectroscopy along with machine learning. In our efforts to understand how bacterial cells resist antibiotics and host response, we investigated a common dormancy mechanism called sporulation. We identified markers that can help in rapid detection of spores, even in a mixture of strains. Additionally, we studied the relation between the Raman spectral signatures of spores and their ability to germinate