Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patienttailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/ or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?

With global antimicrobial resistance becoming increasingly detrimental to society, improving current clinical antimicrobial susceptibility testing (AST) is crucial to allow physicians to initiate appropriate antibiotic treatment as early as possible, reducing not only mortality rates but also the emergence of resistant pathogens. In this work, we tackle the main bottlenecks in clinical AST by designing biofunctionalized silicon micropillar arrays to provide both a preferable solid-liquid interface for bacteria networking and a simultaneous transducing element that monitors the response of bacteria when exposed to chosen antibiotics in real time. We harness the intrinsic ability of the micropillar architectures to relay optical phase-shift reflectometric interference spectroscopic measurements (referred to as PRISM) and employ it as a platform for culture-free, label-free phenotypic AST. The responses of E. coli to various concentrations of five clinically relevant antibiotics are optically tracked by PRISM, allowing for the minimum inhibitory concentration (MIC) values to be determined and compared to both standard broth microdilution testing and clinic-based automated AST system readouts. Capture of bacteria within these microtopologies, followed by incubation of the cells with the appropriate antibiotic solution, yields rapid determinations of antibiotic susceptibility. This platform not only provides accurate MIC determinations in a rapid manner (total assay time of 2-3 h versus 8 h with automated AST systems) but can also be employed as an advantageous method to differentiate bacteriostatic and bactericidal antibiotics.

There is a demonstrated and paramount need for rapid, reliable infectious disease diagnostics, particularly those for invasive fungal infections. Current clinical determinations for an appropriate antifungal therapy can take up to 3 days using current antifungal susceptibility testing methods, a time-to-readout that can prove detrimental for immunocompromised patients and promote the spread of antifungal resistant pathogens. Herein, we demonstrate the application of intensity-based reflectometric interference spectroscopic measurements (termed iPRISM) on microstructured silicon sensors for use as a rapid, phenotypic antifungal susceptibility test. This diagnostic platform optically tracks morphological changes of fungi corresponding to conidia growth and hyphal colonization at a solid–liquid interface in real time. Using Aspergillus niger as a model fungal pathogen, we can determine the minimal inhibitory concentration of clinically relevant antifungals within 12 h. This assay allows for expedited detection of fungal growth and provides a label-free alternative to broth microdilution and agar diffusion methods, with the potential to be used for point-of-care diagnostics.

With new advances in infectious disease, antifouling surfaces, and environmental microbiology research comes the need to understand and control the accumulation and attachment of bacterial cells on a surface. Thus,...