Doctor of Philosophy (PhD)
Bacterial infections are the leading cause of mortality worldwide due to the lack of accurate diagnosis of the pathogens causing infections. Currently, microbiological and molecular techniques are used to identify pathogenic bacteria. However, these techniques have limitations as they are time-consuming, cost-inefficient, and require trained personnel. Therefore, timely and precise identification of pathogenic bacteria is crucial for public safety. Our lab previously synthesized a fluorescent sensor array (3-hydroxyflavone derivatives) encapsulated in hyaluronic acid-based nanoparticles to identify bacterial species and recognize their Gram status. Though promising, this technique was relatively time-consuming and required high sample and volumes.
To overcome these limitations, this research work focuses on the development of a paper-based sensing platform with the integration of machine learning. Paper microzone plates fabricated by photolithography significantly reduced sample volumes and provided a stable platform for immobilizing the sensor array. The interaction of the paper-based sensor array with whole bacterial cells generated unique responses that were analyzed using linear discriminant analysis (LDA) and other machine learning algorithms. The sensing platform could rapidly and accurately differentiate between 16 bacterial species, as well as identify their Gram status using LDA and neural networks. Paper-based fluorescent sensor array differentiated between Staphylococcus aureus strains as well as their clinical isolates that were resistant and susceptible to antibiotics with an accuracy of >80%. Additionally, biofilms associated with drug-resistant and sensitive S. aureus were identified with >90% accuracy. Lastly, leveraging the differences in the gut microbiome compositions associated with gastrointestinal disease, fecal culture was tested as an alternate biological source. This sensing platform precisely identified three different diseases associated with gut dysbiosis.
Overall, this paper-based sensing platform shows excellent potential as a point-of-care diagnostic tool. This state-of-the-art tool can be easily manufactured on a large scale, has a long shelf life, and can rapidly identify a wide range of pathogenic bacterial species. The clinical applicability and prospects for diagnosing diseases make it a promising diagnostic tool in various healthcare and limited-resource settings.
Laliwala, Aayushi, "Paper-based Ratiometric Sensor Array for Pathogenic Bacterial Species Identification" (2023). Theses & Dissertations. 780.
Available for download on Friday, November 28, 2025