Citizen science in action: Time-resolved immunofluorescence-based field detection of antibiotics with portable...

Citizen scientist-based environmental monitoring and public education are becoming increasingly popular. However, current technologies for antibiotic-based novel contaminant identification are still restricted to laboratory sample collection and analysis due to detection methodologies and apparatus limitations. This study developed a time-resolved immunofluorescence-based simultaneous field-based assay for ciprofloxacin (CIP) and enrofloxacin (ENR) that matches test results to geographic locations. The assay helps the public understand the potential levels of antibiotic exposures in their environments and helps them take appropriate action to reduce risk. The assay was developed using smartphones and social software in addition to rapid testing. The method uses a portable, low-cost analytical kit with a smartphone app to build a field-based detection platform for the detection and analysis of ENR and CIP in water and aquatic products. The methodological evaluation was good, with detection limits of 0.4 ng/mL and 0.5 ng/g for ENR in water and fish, and quantification limits of 1.2 ng/mL and 1.4 ng/g, with recoveries of 89.0 %-101.0 % and 78.0 %-97.0 %. For CIP in water and fish, the limits of detection were 0.3 ng/mL and 0.4 ng/g, the limits of quantification were 0.9 ng/mL and 1.2 ng/g, and the recoveries were 75.0 %-91.0 % and 72.0 %-89.0 %, both with coefficients of variation <15 %. These limits were sufficient to prevent the two antibiotics from crossing over during simultaneous detection. The assay was validated using real samples to assess the effectiveness of the assay platform in field deployments, and the results were consistent with those obtained through liquid chromatography-tandem mass spectrometry (LC-MS) and enzyme-linked immunoassay (ELISA) techniques. In addition, the TRFIA assay process requires less time, uses more portable instruments, and is less complex than traditional methods. This study provides a new scientific, accurate, and rapid detection method for antibiotic detection by citizen scientists, helping scientists to obtain a wider range of data and providing more opportunities to solve scientific problems.