Dynamic Laser Speckle Imaging Meets Machine Learning to Enable Rapid Antibacterial Susceptibility Testing (DyRAST)

Low-cost, real-time detection of bacterial growth via diffraction-based sensing

The emergence of antibacterial resistance impacts healthcare networks globally, with mortality rates and linked burdens of infection disproportionately affecting the developing world. Rapid alternatives to antibiotic susceptibility testing (AST) allow for swifter, more effective treatment, though they are limited in use in low-resource settings due to significant cost barriers. Herein we demonstrate a simple, cost-effective diffraction sensing-based approach for rapidly detecting bacterial growth (a precursor to AST). Diffraction gratings (1D, lined) directly comprised of our test bacteria (Escherichia coli DH5α) were produced using soft agar-based gel templates designed to direct bacterial attachment and produce a near-zero background signal. The diffraction spot intensities from the live bacterial gratings were monitored in growth and no growth (ampicillin) conditions at room temperature, using a simple fixed laser and photodetector setup. Growth-induced differences in signal were observed within 10-20 minutes, highlighting the sensitivity of this approach and its potential to be adapted as a rapid and accessible AST alternative.

Laser Speckle Image analysis for identifying the minimum lethal concentration of ampicillin in Escherichia coli liquid cultures

Understanding a pathogen's sensitivity to antimicrobial drugs through Minimum Lethal Concentration (MLC) is crucial for effective treatment planning for bactericidal drugs. In this paper, we propose a novel approach using Laser Speckle Imaging (LSI) to determine the MLC of Escherichia coli (E. coli), a common pathogenic bacterial species. LSI enables the capture and analysis of the dynamic changes in speckle patterns caused by alterations in optical scattering and shape alterations of bacterial cells as a response to antibiotic treatments through a label-free approach. The observed speckle pattern changes are correlated with the gold standard method to determine the MLC, representing the lowest concentration at which E. coli is lethally affected. The results demonstrate the potential of LSI as a reliable and rapid method for determining the MLC of E. coli. This method has much potential for antimicrobial research since it provides a quick, non-destructive evaluation of bacterial responses to various bactericidal antibiotic doses without requiring labor-intensive processes like pour plate tests to calculate the MLC.

Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings

Antibiotic resistance has expanded as a result of the careless use of antibiotics in the medical field, the food industry, agriculture, and other industries. By means of genetic recombination between commensal and pathogenic bacteria, the microbes obtain antibiotic resistance genes (ARGs). In bacteria, horizontal gene transfer (HGT) is the main mechanism for acquiring ARGs. With the development of high-throughput sequencing, ARG sequence analysis is now feasible and widely available. Preventing the spread of AMR in the environment requires the implementation of ARGs mapping. The metagenomic technique, in particular, has helped in identifying antibiotic resistance within microbial communities. Due to the exponential growth of experimental and clinical data, significant investments in computer capacity, and advancements in algorithmic techniques, the application of machine learning (ML) algorithms to the problem of AMR has attracted increasing attention over the past five years. The review article sheds a light on the application of bioinformatics for the antibiotic resistance monitoring. The most advanced tool currently being employed to catalog the resistome of various habitats are metagenomics and metatranscriptomics. The future lies in the hands of artificial intelligence (AI) and machine learning (ML) methods, to predict and optimize the interaction of antibiotic-resistant compounds with target proteins.

Next-generation rapid phenotypic antimicrobial susceptibility testing

Slow progress towards implementation of conventional clinical bacteriology in low resource settings and strong interest in greater speed for antimicrobial susceptibility testing (AST) more generally has focused attention on next-generation rapid AST technologies. In this Review, we systematically synthesize publications and submissions to regulatory agencies describing technologies that provide phenotypic AST faster than conventional methods. We characterize over ninety technologies in terms of underlying technical innovations, technology readiness level, extent of clinical validation, and time-to-results. This work provides a guide for technology developers and clinical microbiologists to understand the rapid phenotypic AST technology landscape, current development pipeline, and AST-specific validation milestones.

Accurate estimation of the inhibition zone of antibiotics based on laser speckle imaging and multiple random speckle illumination

The antimicrobial susceptibility test (AST) plays a crucial role in selecting appropriate antibiotics for the treatment of bacterial infections in patients. The diffusion disk method is widely adopted AST method due to its simplicity, cost-effectiveness, and flexibility. It assesses antibiotic efficacy by measuring the size of the inhibition zone where bacterial growth is suppressed. Quantification of the zone diameter is typically achieved using tools such as rulers, calipers, or automated zone readers, as the inhibition zone is visually discernible. However, challenges arise due to inaccuracies stemming from human errors or image processing of intensity-based images. Here, we proposed a bacterial activity-based AST using laser speckle imaging (LSI) with multiple speckle illumination. LSI measures a speckle pattern produced by interferences of scattered light from the sample; therefore, LSI enables the detection of variation or movement within the sample such as bacterial activity. We found that LSI with multiple speckle illuminations provides consistent and uniform analysis of measured time-varying speckle images. Furthermore, our proposed method effectively identified the boundary of the inhibition zone using the k-means clustering algorithm, exploiting a result of speckle pattern analysis as features. Collectively, the proposed method offers a versatile analytical tool in the diffusion disk method.

Accurate Prediction of Antimicrobial Susceptibility for Point-of-Care Testing of Urine in Less than 90 Minutes via iPRISM Cassettes

The extensive and improper use of antibiotics has led to a dramatic increase in the frequency of antibiotic resistance among human pathogens, complicating infectious disease treatments. In this work, a method for rapid antimicrobial susceptibility testing (AST) is presented using microstructured silicon diffraction gratings integrated into prototype devices, which enhance bacteria-surface interactions and promote bacterial colonization. The silicon microstructures act also as optical sensors for monitoring bacterial growth upon exposure to antibiotics in a real-time and label-free manner via intensity-based phase-shift reflectometric interference spectroscopic measurements (iPRISM). Rapid AST using clinical isolates of Escherichia coli (E. coli) from urine is established and the assay is applied directly on unprocessed urine samples from urinary tract infection patients. When coupled with a machine learning algorithm trained on clinical samples, the iPRISM AST is able to predict the resistance or susceptibility of a new clinical sample with an Area Under the Receiver Operating Characteristic curve (AUC) of ∼ 0.85 in 1 h, and AUC > 0.9 in 90 min, when compared to state-of-the-art automated AST methods used in the clinic while being an order of magnitude faster.

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests

Rapid identification of effective antibiotic treatment is crucial for increasing patient survival and preventing the formation of new antibiotic-resistant bacteria due to preventative antibiotic use. Currently utilized "gold standard" methods require 16-24 h to determine the most appropriate antibiotic for the patient's treatment. The proposed technique of laser speckle imaging with subpixel correlation analysis allows for identifying dynamics and changes in the zone of inhibition, which are impossible to observe with classical methods. Furthermore, it obtains the resulting zone of inhibition diameter earlier than the disk diffusion method which is recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). These results could improve mathematical models of changes in the diameter of the zone of inhibition around the disc containing the antimicrobial agent, thereby speeding up and facilitating epidemiological analysis.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Combinatorial nanodroplet platform for screening antibiotic combinations

The emergence and spread of multidrug resistant bacterial strains and concomitant dwindling of effective antibiotics pose worldwide healthcare challenges. To address these challenges, advanced engineering tools are developed to personalize antibiotic treatments by speeding up the diagnostics that is critical to prevent antibiotic misuse and overuse and make full use of existing antibiotics. Meanwhile, it is necessary to investigate novel antibiotic strategies. Recently, repurposing mono antibiotics into combinatorial antibiotic therapies has shown great potential for treatment of bacterial infections. However, widespread adoption of drug combinations has been hindered by the complexity of screening techniques and the cost of reagent consumptions in practice. In this study, we developed a combinatorial nanodroplet platform for automated and high-throughput screening of antibiotic combinations while consuming orders of magnitude lower reagents than the standard microtiter-based screening method. In particular, the proposed platform is capable of creating nanoliter droplets with multiple reagents in an automatic manner, tuning concentrations of each component, performing biochemical assays with high flexibility (e.g., temperature and duration), and achieving detection with high sensitivity. A biochemical assay, based on the reduction of resazurin by the metabolism of bacteria, has been characterized and employed to evaluate the combinatorial effects of the antibiotics of interest. In a pilot study, we successfully screened pairwise combinations between 4 antibiotics for a model Escherichia coli strain.

Plasmonic optical fiber for bacteria manipulation-characterization and visualization of accumulation behavior under plasmo-thermal trapping

In this article, we demonstrate a plasmo-thermal bacterial accumulation effect using a miniature plasmonic optical fiber. The combined action of far-field convection and a near-field trapping force (referred to as thermophoresis)-induced by highly localized plasmonic heating-enabled the large-area accumulation of Escherichia coli. The estimated thermophoretic trapping force agreed with previous reports, and we applied speckle imaging analysis to map the in-plane bacterial velocities over large areas. This is the first time that spatial mapping of bacterial velocities has been achieved in this setting. Thus, this analysis technique provides opportunities to better understand this phenomenon and to drive it towards in vivo applications.

Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments

Food packaging encompasses the topical role of preserving food, hence, extending the shelf-life, while ensuring the highest quality and safety along the production chain as well as during storage. Intelligent food packaging further develops the functions of traditional packages by introducing the capability of continuously monitoring food quality during the whole chain to assess and reduce the insurgence of food-borne disease and food waste. To this purpose, several sensing systems based on different food quality indicators have been proposed in recent years, but commercial applications remain a challenge. This review provides a critical summary of responsive systems employed in the real-time monitoring of food quality and preservation state. First, food quality indicators are briefly presented, and subsequently, their exploitation to fabricate intelligent packaging based on responsive materials is discussed. Finally, current challenges and future trends are reviewed to highlight the importance of concentrating efforts on developing new functional solutions.

Laser speckle imaging for early detection of microbial colony forming units

In this study, an optical contactless laser speckle imaging technique for the early identification of bacterial colony-forming units was tested. The aim of this work is to compare the laser speckle imaging method for the early assessment of microbial activity with standard visual inspection under white light illumination. In presented research, the growth of Vibrio natriegens bacterial colonies on the solid medium was observed and analyzed. Both - visual examination under white light illumination and laser speckle correlation analysis were performed. Based on various experiments and comparisons with the theoretical Gompertz model, colony radius growth curves were obtained. It was shown that the Gompertz model can be used to describe both types of analysis. A comparison of the two methods shows that laser speckle contrast imaging, combined with signal processing, can detect colony growth earlier than standard CFU counting method under white light illumination.