(293h) Invited Speaker: Efforts Toward Measurement Assurance for Microbial Biofilm Characterization | AIChE

(293h) Invited Speaker: Efforts Toward Measurement Assurance for Microbial Biofilm Characterization

Authors 

Da Silva, S. M., National Institute of Standards and Technology
Parratt, K., National Institute of Standards and Technology
Dunkers, J., National Institute of Standards and Technology
Detrimental microbial biofilms (e.g., in human infections, medical device infections, water line contamination, etc.) and beneficial microbial biofilms (e.g., for bioremediation, probiotic effects, etc.) are often considered separately, even having completely different conferences devoted to each category. However, these two broad categories of biofilms, in addition to being united by their focus on the biofilm growth state, are also united by their need for well-characterized, robust, reproducible methods to quantify biofilm properties and response to their environment.

At NIST, our biofilm efforts focus on developing the underpinning measurement science and technologies to enable quantitative characterization of biofilms. This measurement infrastructure could include tools such as protocols, best practices, uncertainty analyses, measurement methods, documentary standards, reference datasets, and reference materials. Proper usage of these tools can help decision-makers understand the level of confidence they should have in results from a measurement. In addition, these tools should also increase the overall level of confidence in biofilm measurements. Ultimately, biofilm characterization results will better inform decision-making, ranging from decisions made during research and development of new antimicrobial technologies and microbial diagnostics and therapeutics, to decisions related to regulatory oversight and medical diagnosis/treatment.

This presentation will highlight ongoing efforts at NIST focused on improved methods to characterize microbial biofilms, including cell enumeration methods and development of cell-based reference materials, fluorescence lifetime imaging coupled with machine learning to characterize cell state, and evaluation of sources of measurement uncertainty in common biofilm characterization methods.