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Background/Objectives: Urinary tract infections (UTIs) pose an increasing risk of antimicrobial resistance, and novel diagnostic tests have been developed to address the limitations of standard urine culture in these cases. It is important that these novel tests be validated for agreement and error rates against the standard antibiotic susceptibility testing (AST) methods. Methods: Polymicrobial (≥two non-fastidious microorganisms) consecutive clinical urine specimens submitted for UTI diagnostic testing were included in this analysis. Specimens were tested with Pooled Antibiotic Susceptibility Testing (P-AST) and with broth microdilution/disk diffusion (BMD/DD) in parallel. Performance characteristics, such as essential agreement (EA%), very major errors (VMEs), and major errors (MEs), were assessed using Clinical and Laboratory Standards Institute (CLSI) standards. Specimens with P-AST-resistant and BMD/DD consensus-sensitive results were assessed for heteroresistance. Real-world clinical sample data were used to assess associations between increasing organism counts and average “sensitive” antibiotic count per sample. Results: The essential agreement between P-AST and standard isolate AST was ≥90%, VMEs were <2.0%, and MEs were <3.0%, meeting the CLSI guidelines for AST verification and validation studies. When heteroresistance was accounted for, overall VMEs and MEs were both <1.5%. The presence of additional non-fastidious organisms dropped the number of average “sensitive” antibiotics from 9.8 with one organism to 2.5 with five or more organisms. The presence of fastidious organisms did not have any meaningful impact. Conclusions: P-AST, a component of the Guidance® UTI assay (Pathnostics, Irvine, CA, USA), performed within CLSI standards for AST in polymicrobial UTI diagnostic urine specimens.

Antibiotic resistance is a major global health concern that requires action across all sectors of society. In particular, to allow conservative and effective use of antibiotics clinical settings require better diagnostic tools that provide rapid determination of antimicrobial susceptibility. We present a method for rapid and scalable antimicrobial susceptibility testing using stationary nanoliter droplet arrays that is capable of delivering results in approximately half the time of conventional methods, allowing its results to be used the same working day. In addition, we present an algorithm for automated data analysis and a multiplexing system promoting practicality and translatability for clinical settings. We test the efficacy of our approach on numerous clinical isolates and demonstrate a 2-d reduction in diagnostic time when testing bacteria isolated directly from urine samples.

The lack of rapid antibiotic susceptibility tests adversely affects the treatment of bacterial infections and contributes to increased prevalence of multidrug-resistant bacteria. Here, we describe an all-electrical approach that allows for ultrasensitive measurement of growth signals from only tens of bacteria in a microfluidic device. Our device is essentially a set of microfluidic channels, each with a nanoconstriction at one end and cross-sectional dimensions close to that of a single bacterium. Flowing a liquid bacteria sample (e.g., urine) through the microchannels rapidly traps the bacteria in the device, allowing for subsequent incubation in drugs. We measure the electrical resistance of the microchannels, which increases (or decreases) in proportion to the number of bacteria in the microchannels. The method and device allow for rapid antibiotic susceptibility tests in about 2 h. Further, the short-time fluctuations in the electrical resistance during an antibiotic susceptibility test are correlated with the morphological changes of bacteria caused by the antibiotic. In contrast to other electrical approaches, the underlying geometric blockage effect provides a robust and sensitive signal, which is straightforward to interpret without electrical models. The approach also obviates the need for a high-resolution microscope and other complex equipment, making it potentially usable in resource-limited settings.

Mass spectrometry (MS) is a type of analysis used to determine what molecules make up a sample, based on the mass spectrum that are created by the ions. Mass spectrometers are able to perform traditional target analyte identification and quantitation; however, they may also be used within a clinical setting for the rapid identification of bacteria. The causative agent in sepsis is changed over time, and clinical decisions affecting the management of infections are often based on the outcomes of bacterial identification. Therefore, it is essential that such identifications are performed quickly and interpreted correctly. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometer is one of the most popular MS instruments used in biology, due to its rapid and precise identification of genus and species of an extensive range of Gram-negative and -positive bacteria. Microorganism identification by Mass spectrometry is based on identifying a characteristic spectrum of each species and then matched with a large database within the instrument. The present review gives a contemporary perspective on the challenges and opportunities for bacterial identification as well as a written report of how technological innovation has advanced MS. Future clinical applications will also be addressed, particularly the use of MALDI-TOF MS in the field of microbiology for the identification and the analysis of antibiotic resistance. [Display omitted] •During this decade, MALDI-TOF MS has become an indispensable tool for clinical microbiology laboratories.•The speed of identification, accuracy, low cost and waste reduction are characteristics of this technology.•There is also immense potential for analysis of antibiotic susceptibility testing (AST) and subspecies.•MALDI-TOF MS will play a more significant role in the laboratory in the future.

The emergence of antibiotic-resistant pathogens has threatened not only our ability to deal with common infectious diseases but also the management of life-threatening complications. Antimicrobial resistance (AMR) remains a significant threat in both industrialized and developing countries alike. In Africa, though, poor clinical care, indiscriminate antibiotic use, lack of robust AMR surveillance programs, lack of proper regulations and the burden of communicable diseases are factors aggravating the problem of AMR. In order to effectively address the challenge of AMR, antimicrobial stewardship programs, solid AMR surveillance systems to monitor the trend of resistance, as well as robust, affordable and rapid diagnostic tools which generate data that informs decision-making, have been demonstrated to be effective. However, we have identified a significant knowledge gap in the area of the application of fast and affordable diagnostic tools, surveillance, and stewardship programs in Africa. Therefore, we set out to provide up-to-date information in these areas. We discussed available hospital-based stewardship initiatives in addition to the role of governmental and non-governmental organizations. Finally, we have reviewed the application of various phenotypic and molecular AMR detection tools in both research and routine laboratory settings in Africa, deployment challenges and the efficiency of these methods.

Antimicrobial resistance (AMR) is one of the most challenging threats in public health; thus, there is a growing demand for methods and technologies that enable rapid antimicrobial susceptibility testing (AST). The conventional methods and technologies addressing AMR diagnostics and AST employed in clinical microbiology are tedious, with high turnaround times (TAT), and are usually expensive. As a result, empirical antimicrobial therapies are prescribed leading to AMR spread, which in turn causes higher mortality rates and increased healthcare costs. This review describes the developments in current cutting-edge methods and technologies, organized by key enabling research domains, towards fighting the looming AMR menace by employing recent advances in AMR diagnostic tools. First, we summarize the conventional methods addressing AMR detection, surveillance, and AST. Thereafter, we examine more recent non-conventional methods and the advancements in each field, including whole genome sequencing (WGS), matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectrometry, Fourier transform infrared (FTIR) spectroscopy, and microfluidics technology. Following, we provide examples of commercially available diagnostic platforms for AST. Finally, perspectives on the implementation of emerging concepts towards developing paradigm-changing technologies and methodologies for AMR diagnostics are discussed.

Surveillance involves the collection and analysis of data for the detection and monitoring of threats to public health. Surveillance should also inform as to the epidemiology of the threat and its burden in the population. A further key component of surveillance is the timely feedback of data to stakeholders with a view to generating action aimed at reducing or preventing the public health threat being monitored. Surveillance of antibiotic resistance involves the collection of antibiotic susceptibility test results undertaken by microbiology laboratories on bacteria isolated from clinical samples sent for investigation. Correlation of these data with demographic and clinical data for the patient populations from whom the pathogens were isolated gives insight into the underlying epidemiology and facilitates the formulation of rational interventions aimed at reducing the burden of resistance. This article describes a range of surveillance activities that have been undertaken in the UK over a number of years, together with current interventions being implemented. These activities are not only of national importance but form part of the international response to the global threat posed by antibiotic resistance.

Improved diagnostic tools are vital for maintaining efficient treatment of antibiotic-resistant bacteria and for reducing antibiotic overconsumption. In our research, we introduce a new deep learning-based method capable of predicting untested antibiotic resistance phenotypes. The method uses transformers, a powerful artificial intelligence (AI) technique that efficiently leverages both antibiotic susceptibility tests (AST) and patient data simultaneously. The model produces predictions that can be used as time- and cost-efficient alternatives to results from cultivation-based diagnostic assays. Significantly, our study highlights the potential of AI technologies to address the increasing prevalence of antibiotic-resistant bacterial infections.

Renew interest and enthusiasm for anaerobes stem from both technological improvements (culture media, production of an adequate anaerobic atmosphere, identification methods) and greater awareness on the part of clinicians. Anaerobic infections were historically treated empirically, targeting the species known to be involved in each type of infection. Prevotella, fusobacteria, and Gram-positive cocci (GPAC) were considered responsible for infections above the diaphragm whereas for intra-abdominal infections, Bacteroides of the fragilis group (BFG), GPAC and clostridia were predominantly implicated. The antibiotic susceptibility of anaerobes was only taken into consideration by the clinician in the event of treatment failure or when faced with infections by multidrug-resistant bacteria (MDR). The evolution of antibiotic resistance together with clinical failures due to the absence of detection of hetero-resistant clones has resulted in a greater need for accessible antibiotic susceptibility testing (AST) and disc diffusion method. Improved isolation and identification of anaerobes, along with the availability of accessible and robust methods for performing AST, will ensure that treatment, whether empirical or guided by an antibiogram, will lead to better outcomes for anaerobic infections.

Antimicrobial resistance is a growing global health threat, and artificial intelligence offers a promising avenue for developing advanced tools to address this challenge. In this study, we applied various machine learning techniques to predict bacterial antibiotic resistance using the Pfizer ATLAS Antibiotics dataset. This comprehensive dataset includes patient demographic data, sample collection details, antibiotic susceptibility test results, and resistance phenotypes for 917,049 bacterial isolates. The dataset was divided into two subsets: Phenotype-Only and Phenotype + Genotype, excluding and including 589,998 isolates with genotype data, respectively. Both subsets underwent exploratory data analysis, preprocessing, machine learning model training, validation, and optimization. XGBoost consistently outperformed other models, achieving AUC values of 0.96 and 0.95 for the Phenotype-Only and Phenotype + Genotype sets, respectively. Hyperparameter tuning yielded slight accuracy improvements, while data balancing techniques notably increased recall. Across all models, the antibiotic used emerged as the most influential feature in predicting resistance outcomes. The SHAP summary plots generated provide insights into model interpretability. Our findings provide valuable insights into global AMR patterns and demonstrate the potential of AI-driven approaches for resistance prediction to help inform clinical decision-making and support the formulation of effective AMR mitigation policies, subject to the availability of highly granular datasets.