Threshold-Responsive Colorimetric Sensing System for the Continuous Monitoring of Gases.

Colorimetric sensors are widely used because of their inherent advantages including accuracy, rapid response, ease-of-use, and low costs; however, they usually lack reusability, which precludes the continuous use of a single sensor. We have developed a threshold-responsive colorimetric system that enables repeated analyte measurements by a single colorimetric sensor. The threshold responsive algorithm automatically adjusts the sensor exposure time to the analyte and measurement frequency according to the sensor response. The system registers the colorimetric sensor signal change rate, prevents the colorimetric sensor from reaching saturation, and allows the sensor to fully regenerate before the next measurement is started. The system also addresses issues common to colorimetric sensors, including the response time and range of detection. We demonstrate the benefits and feasibility of this novel system, using colorimetric sensors for ammonia and carbon dioxide gases for continuous monitoring of up to (at least) 60 detection cycles without signs of analytical performance degradation of the sensors.

Rapid Antimicrobial Susceptibility Testing on Clinical Urine Samples by Video-Based Object Scattering Intensity Detection.

To combat the ongoing public health threat of antibiotic-resistant infections, a technology that can quickly identify infecting bacterial pathogens and concurrently perform antimicrobial susceptibility testing (AST) in point-of-care settings is needed. Here, we develop a technology for point-of-care AST with a low-magnification solution scattering imaging system and a real-time video-based object scattering intensity detection method. The low magnification (1-2×) optics provides sufficient volume for direct imaging of bacteria in urine samples, avoiding the time-consuming process of culture-based bacterial isolation and enrichment. Scattering intensity from moving bacteria and particles in the sample is obtained by subtracting both spatial and temporal background from a short video. The time profile of scattering intensity is correlated with the bacterial growth rate and bacterial response to antibiotic exposure. Compared to the image-based bacterial tracking and counting method we previously developed, this simple image processing algorithm accommodates a wider range of bacterial concentrations, simplifies sample preparation, and greatly reduces the computational cost of signal processing. Furthermore, development of this simplified processing algorithm eases implementation of multiplexed detection and allows real-time signal readout, which are essential for point-of-care AST applications. To establish the method, 130 clinical urine samples were tested, and the results demonstrated an accuracy of ∼92% within 60-90 min for UTI diagnosis. Rapid AST of 55 positive clinical samples revealed 98% categorical agreement with both the clinical culture results and the on-site parallel AST validation results. This technology provides opportunities for prompt infection diagnosis and accurate antibiotic prescriptions in point-of-care settings.

Direct Antimicrobial Susceptibility Testing on Clinical Urine Samples by Optical Tracking of Single Cell Division Events.

With the increasing prevalence of antibiotic resistance, the need to develop antimicrobial susceptibility testing (AST) technologies is urgent. The current challenge has been to perform the antibiotic susceptibility testing in short time, directly with clinical samples, and with antibiotics over a broad dynamic range of clinically relevant concentrations. Here, a technology for point-of-care diagnosis of antimicrobial-resistant bacteria in urinary tract infections, by imaging the clinical urine samples directly with an innovative large volume solution scattering imaging (LVSi) system and analyzing the image sequences with a single-cell division tracking method is developed. The high sensitivity of single-cell division tracking associated with large volume imaging enables rapid antibiotic susceptibility testing directly on the clinical urine samples. The results demonstrate direct detection of bacterial infections in 60 clinical urine samples with a 60 min LVSi video, and digital AST of 30 positive clinical samples with 100% categorical agreement with both the clinical culture results and the on-site agar plating validation results. This technology provides opportunities for precise antibiotic prescription and proper treatment of the patient within a single clinic visit.

Rapid antibiotic susceptibility testing based on bacterial motion patterns with long short-term memory neural networks.

Antibiotic resistance is an increasing public health threat. To combat it, a fast method to determine the antibiotic susceptibility of infecting pathogens is required. Here we present an optical imaging-based method to track the motion of single bacterial cells and generate a model to classify active and inactive cells based on the motion patterns of the individual cells. The model includes an image-processing algorithm to segment individual bacterial cells and track the motion of the cells over time, and a deep learning algorithm (Long Short-Term Memory network) to learn and determine if a bacterial cell is active or inactive. By applying the model to human urine specimens spiked with an Escherichia coli lab strain, we show that the method can accurately perform antibiotic susceptibility testing as fast as 30 minutes for five commonly used antibiotics.

Rapid Antimicrobial Susceptibility Testing of Patient Urine Samples Using Large Volume Free-Solution Light Scattering Microscopy.

The emergence of antibiotic resistance has prompted the development of rapid antimicrobial susceptibility testing (AST) technologies that will enable evidence-based treatment and promote antimicrobial stewardship. To date, many rapid AST methods have been developed, but few are able to be performed on clinical samples directly. Here we developed a large volume light scattering microscopy technique that tracks phenotypic features of single bacterial cells directly in clinical urine samples without sample enrichment or culturing. The technique demonstrated rapid (90 min) detection of Escherichia coli in 24 clinical urine samples with 100% sensitivity and 83% specificity and rapid (90 min) AST in 12 urine samples with 87.5% categorical agreement with two antibiotics, ampicillin and ciprofloxacin.

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from human urine specimens spiked with lab strain E. coli (ATCC 43888) and an E. coli strain isolated from a clinical urine sample for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections.

Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.

Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel large-volume microscopy (LVM) system for rapid, point-of-care bacterial detection. This system, using low magnification (1-2×), visualizes sufficient sample volumes, eliminating the need for culture-based enrichment. Employing deep neural networks, our model demonstrates superior accuracy in detecting uropathogenic Escherichia coli compared to traditional machine learning methods. Future endeavors will focus on enriching our datasets with mixed samples and a broader spectrum of uropathogens, aiming to extend the applicability of our model to clinical samples.

Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy.

Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.

Real-time detection of antibiotic activity by measuring nanometer-scale bacterial deformation.

Diagnosing antibiotic-resistant bacteria currently requires sensitive detection of phenotypic changes associated with antibiotic action on bacteria. Here, we present an optical imaging-based approach to quantify bacterial membrane deformation as a phenotypic feature in real-time with a nanometer scale (∼9 nm) detection limit. Using this approach, we found two types of antibiotic-induced membrane deformations in different bacterial strains: polymyxin B induced relatively uniform spatial deformation of Escherichia coli O157:H7 cells leading to change in cellular volume and ampicillin-induced localized spatial deformation leading to the formation of bulges or protrusions on uropathogenic E. coli CFT073 cells. We anticipate that the approach will contribute to understanding of antibiotic phenotypic effects on bacteria with a potential for applications in rapid antibiotic susceptibility testing.

Current and emerging techniques for antibiotic susceptibility tests.

Infectious diseases caused by bacterial pathogens are a worldwide burden. Serious bacterial infection-related complications, such as sepsis, affect over a million people every year with mortality rates ranging from 30% to 50%. Crucial clinical microbiology laboratory responsibilities associated with patient management and treatment include isolating and identifying the causative bacterium and performing antibiotic susceptibility tests (ASTs), which are labor-intensive, complex, imprecise, and slow (taking days, depending on the growth rate of the pathogen). Considering the life-threatening condition of a septic patient and the increasing prevalence of antibiotic-resistant bacteria in hospitals, rapid and automated diagnostic tools are needed. This review summarizes the existing commercial AST methods and discusses some of the promising emerging AST tools that will empower humans to win the evolutionary war between microbial genes and human wits.

A repeatable assembling and disassembling electrochemical aptamer cytosensor for ultrasensitive and highly selective detection of human liver cancer cells.

In this work, a repeatable assembling and disassembling electrochemical aptamer cytosensor was proposed for the sensitive detection of human liver hepatocellular carcinoma cells (HepG2) based on a dual recognition and signal amplification strategy. A high-affinity thiolated TLS11a aptamer, covalently attached to a gold electrode through Au-thiol interactions, was adopted to recognize and capture the target HepG2 cells. Meanwhile, the G-quadruplex/hemin/aptamer and horseradish peroxidase (HRP) modified gold nanoparticles (G-quadruplex/hemin/aptamer-AuNPs-HRP) nanoprobe was designed. It could be used for electrochemical cytosensing with specific recognition and enzymatic signal amplification of HRP and G-quadruplex/hemin HRP-mimicking DNAzyme. With the nanoprobes as recognizing probes, the HepG2 cancer cells were captured to fabricate an aptamer-cell-nanoprobes sandwich-like superstructure on a gold electrode surface. The proposed electrochemical cytosensor delivered a wide detection range from 1×10(2) to 1×10(7) cells mL(-1) and high sensitivity with a low detection limit of 30 cells mL(-1). Furthermore, after the electrochemical detection, the activation potential of -0.9 to -1.7V was performed to break Au-thiol bond and regenerate a bare gold electrode surface, while maintaining the good characteristic of being used repeatedly. The changes of gold electrode behavior after assembling and desorption processes were investigated by electrochemical impedance spectroscopy and cyclic voltammetry techniques. These results indicate that the cytosensor has great potential in disease diagnostic of cancers and opens new insight into the reusable gold electrode with repeatable assembling and disassembling in the electrochemical sensing.