Developmental roadmap for antimicrobial susceptibility testing systems

Challenges and opportunities for AMR research in the ASEAN following the One Health approach

Background

Antimicrobial resistance (AMR) has emerged as a significant global challenge and Southeast Asia with rapid economic and population growth faces substantial challenge in dealing with emerging infectious diseases and antimicrobial resistance. Here we present the recommendations of a workshop that explored the challenges and opportunities for One Health approach towards AMR research in three countries of AEAN, namely, Cambodia, Laos, and Vietnam.

Methods

A workshop was organised in Hanoi, Vietnam in August 2023, involving participants involved in AMR research across varied sectors from three participating countries to prioritise the strategies that can be implemented in the region to fructify the One Health approach to tackle AMR. A modified Delphi approach was used to prioritise the top 10 Global Priority Research Questions for the region as developed by the Quadripartite (FAO, WHO, WOAH and UNEP). An iterative process was adopted to map priorities according to their impact and feasibility of application.

Results

Collaborative initiatives, such as a common platform for listing the research goals, a web-based surveillance mechanism, and an enhanced AMR awareness curricula were identified as the steps forward. A consensus statement highlighting the critical needs for improved technical and infrastructure capacity, collaboration between sectors, increased funding, and systematic data analysis was drafted.

Discussion

The participating countries have National Action Plans guided by the World Health Organization's Global Action Plan on AMR, but limited collaboration between human health and other sectors has impeded the benefits that One Health approach may achieve in the region. The recommendations include the need for improved technical and infrastructure capacity, and data collection across One Health sectors, besides increasing awareness at multiple levels.

Conclusion

A collaborative and coordinated effort to apply One Health initiatives for tackling AMR in the ASEAN region is imperative. The workshop formulated a roadmap for future direction by identifying priorities aimed at enhancing collaboration, addressing infrastructure gaps, and contributing to an effective intervention in the fight against AMR in the region.

Rapid diagnosis of urinary tract infection with miniaturised point-of-care cultivation on a dipstick

PURPOSE

Urinary Tract InfectionAQ1 (UTI) affects over 400 million people annually and globally and is a major reason for empiric antibiotic prescription by general practitioners (GPs).

BACKGROUND

A problem related to microbiological UTI diagnosis is the current lack of point of care (POC) diagnostics. In addition, remote settings, including low and middle income countries (LMIC), are hard to service. Compliance with requirements posed by the In Vitro Diagnostic Regulation (IVDR) and adherence to guidelines as defined by professional user groups are mandatory to pursue. In addition, the World Health Organisation (WHO) promotes optimization of antimicrobial use and more adequate microbiological diagnostics to cure UTI and combat antimicrobial resistance (AMR).

METHODS

Miniaturised chromogenic bacterial cultivation including rapid antimicrobial susceptibility testing (RAST) at the POC can be successfully used for the diagnosis of UTI. Using small and cost-effective dipsticks containing chromogenic cultivation media, UTI-causing bacteria can be detected, quantified and identified with good sensitivity and specificity.

CONCLUSION

Access to such trustworthy, easy-to-use and cost-efficient diagnostic tools at the POC would offer more timely results for optimised antibiotic treatment. This will improve UTI therapy and prevent AMR.

Genomic nano-biosensor for rapid detection of the carbapenem-resistant gene bla NDM-1 in carbapenemase-producing bacteria

Antimicrobial resistance (AMR) has become one of the major public health concerns causing serious obstacles to the successful prevention and treatment of infectious diseases. To curb the spread of AMR, well-equipped laboratories for the early detection of disease-causing pathogens and resistant genes are crucial, something that remains unmet in developing countries due to resource constraints and inadequate infrastructure. This paper presents an affordable and simple nanoparticle-based biosensor for rapidly detecting the bla NDM-1 gene in carbapenemase-producing (CP) bacteria. The biosensor employs thiol-ligand surface functionalized gold nanoparticles (GNPs) conjugated with an oligonucleotide probe specific for detecting the bla NDM-1 gene. The biosensor was evaluated using DNA extracted from CP bacteria having the target bla NDM-1 gene, two non-NDM-1 CP bacteria, and five susceptible bacterial strains. Tuning of the localized surface plasmon resonance (LSPR) of the GNPs was achieved by reducing the surrounding pH of the GNPs, hence inducing aggregation. With the binding of GNPs-probe-target DNA, the stability of GNPs was enhanced, as confirmed by the retention of the red colour when an optimized amount of 0.1 M HCl was added to induce aggregation. The absence of target DNA was indicated by the aggregation of GNPs after the addition of acid, which resulted in a colour change from red to blue/purple and a shift in the LSPR band to a longer wavelength, averaging 620 nm. The biosensor visual detection results were quantified with absorbance spectra measurements and the results were achieved within 30 minutes. The biosensor successfully detected the target DNA from bla NDM-1 positive bacteria and distinguished the non-targets. The analytical sensitivity achieved was 2.5 ng μL-1 which corresponds to approximately 103 colony-forming units per milliliter. These findings were confirmed through PCR amplification. This nano-biosensor offers an inexpensive, simple, rapid, and sensitive method for detecting the bla NDM-1 gene in carbapenemase producers, and is readily implementable in resource-limited settings.

Rapid and sensitive antimicrobial susceptibility testing of biofilm-forming bacteria using scalable paper-based organic transistors

A scalable, cost-effective paper-based organic field-effect transistor platform has been developed for rapid antimicrobial susceptibility testing (AST) of biofilm-forming pathogens. Traditional AST methods are costly, labor-intensive, and slow, with a lack of standardized biofilm models. This system directly tracks protons generated by biofilms, which serve as key indicators of bacterial metabolism under antibiotic exposure. A proton-sensitive PEDOT:PSS channel is employed, where metabolic proton activity de-dopes the transistor, reducing conductivity. The engineered paper substrate facilitates rapid, high-quality biofilm formation, improving assay reliability. The platform was validated on three clinically significant pathogens against frontline antibiotics, providing real-time, quantitative antibiotic efficacy profiles. Integrated with a microcontroller and machine learning algorithm, results are displayed on a liquid crystal display (LCD), classifying antibiotic concentration relative to the minimum inhibitory concentration with over 85% accuracy. This clinically translatable system offers a high-throughput, point-of-care solution for efficient infection management and antibiotic stewardship.

Utilizing machine learning for predicting drug release from polymeric drug delivery systems

Polymeric drug delivery systems (PDDS) play a crucial role in controlled drug release, providing improved therapeutic outcomes. However, formulating PDDS and predicting their release profiles remain challenging due to their complex structures and the numerous variables that influence their behavior. Traditional mathematical and empirical prediction methods are limited in capturing these complexities. Recent studies have unveiled the potential of Machine Learning (ML) in revolutionizing drug delivery, particularly in formulating complex PDDS. This article provides an overview of the significant and fundamental principles of various ML strategies in estimating PDDS drug release behavior. Our focus extends to the accomplishments and pivotal discoveries in current research, spanning seven distinct sustained-release drug delivery systems: matrix tablets, microspheres, implants, hydrogels, films, 3D-printed dosage forms, and other innovations. Furthermore, it addresses the challenges associated with ML-based drug release prediction and presents current solutions while delving into future perspectives. Our investigation underscores the significance of Artificial Neural Networks in ML-based PDDS release profile prediction, surpassing both traditional and alternative ML-based methods. These extensive datasets can be drawn from literature-based resources or enhanced through specific algorithms. Moreover, ensemble-based models have proven advantageous in scenarios involving intricate relationships, such as a high number of output parameters. ML-based drug release prediction notably exhibits substantial promise in 3D-printed dosage forms, presenting a frontier for personalized medicine and precise drug delivery.

A Self-Calibrating Chemiluminescence Sensor for Rapid and Precise Antibiotic Prescribing Guidelines on Urinary Tract Infections

Traditional antimicrobial-susceptibility testing methodologies, including the isolation and culture of bacteria from urine samples and antibiotic-susceptibility test (AST), are expensive and time-consuming. Therefore, a rapid, user-friendly phenotypic AST is urgently needed to guide treatment strategies. Several novel phenotypic AST platforms based on the physiological characteristics of bacteria obtained directly from clinical urine samples have been proposed as promising methods as rapid AST and appropriate antibiotic treatments. However, inaccurate bacterial quantification can lead to false results when high-accuracy quantitative assays are required using these procedures. Coupling the expression of catalase by pathogens with a chemiluminescence-based analytical method enables a convenient and low-cost operation. Herein, we demonstrate a rapid self-calibrating chemiluminescence sensor that can measure bacterial viability through the variation in catalase activity and its response to hydrogen peroxide after treatment with antibiotics. This rapid nanosensor platform can be utilized to determine the antibiotic susceptibility of uropathogenic Escherichia coli and Klebsiella pneumoniae, which account for 80% of all urinary tract infections, directly from clinical urine samples within 40 min without bacterial quantification. The proposed ultrafast and highly accurate AST can enable the precise guidance of antibiotic prescriptions and shorten the time required for clinical decision-making.

R, Baldaniya L, kaur J, Rani B, Beig M. Insights into Haemophilus macrolide resistance: A comprehensive systematic review and meta-analysis

BACKGROUND

Haemophilus spp., particularly Haemophilus influenzae, are major global pathogens causing various infections. Macrolides are crucial in treating these infections, but rising resistance to macrolides in Haemophilus spp. highlights the growing threat of antimicrobial resistance (AMR).

OBJECTIVE

This study aims to assess the prevalence of macrolide resistance in Haemophilus spp, across different global regions.

METHODS

A systematic literature search was conducted across PubMed, Embase, Web of Science, and Scopus databases from May 2015 to December 2023 to identify studies on macrolide resistance in Haemophilus spp. The review included English-language full-text articles that reported resistance proportions and sample sizes. Study quality was assessed using the JBI Critical Appraisal Tool. Statistical analysis was performed using a random-effects model using the metafor package in R.

RESULTS

A total of 10,114 articles were retrieved, and after a comprehensive evaluation, 15 studies (from 19 reports) met the eligibility criteria for inclusion in this systematic review and meta-analysis. Most studies (eight reports from three countries) focused on clarithromycin susceptibility, revealing a pooled prevalence of 7.2%. High heterogeneity was observed for azithromycin (I² = 96.31%, p < 0.001). Azithromycin resistance was higher than clarithromycin, with a resistance rate of 9.3% (nine reports), while erythromycin resistance was significantly higher at 79% (four reports). Subgroup analysis revealed significant variations in resistance prevalence based on geographic location and continent for azithromycin, erythromycin, and clarithromycin. Additionally, notable differences were observed in resistance rates depending on antimicrobial susceptibility testing (AST) methods and AST guidelines for both azithromycin and erythromycin. Clarithromycin resistance increased from 0.7% (2015-2019) to 12.6% (2020-2023).

CONCLUSION

The study underscores the significant challenges of macrolide resistance in treating Haemophilus spp. infections. Additionally, ongoing surveillance of resistance patterns and exploring contributing factors are crucial to enhancing treatment effectiveness.

Integrating machine learning and multitargeted drug design to combat antimicrobial resistance: a systematic review

Antimicrobial resistance (AMR) is a critical global health challenge, undermining the efficacy of antimicrobial drugs against microorganisms like bacteria, fungi and viruses. Multidrug resistance (MDR) arises when microorganisms become resistant to multiple antimicrobial agents. The World Health Organisation classifies AMR bacteria into priority list - I (critical), II (high) and III (medium), prompting action from nearly 170 countries. Six priority bacterial strains account for over 70% of AMR-related fatalities, contributing to more than 1.3 million direct deaths annually and linked to over 5 million deaths globally. Enterobacteriaceae, including Escherichia coli, Salmonella enterica and Klebsiella pneumoniae, significantly contribute to AMR fatalities. This systematic literature review explores how machine learning (ML) and multitargeted drug design (MTDD) can combat AMR in Enterobacteriaceae. We followed PRISMA guidelines and comprehensively analysed current prospects and limitations by mining PubMed and Scopus literature databases. Innovative strategies integrating AI algorithms with advanced computational techniques allow for the analysis of vast datasets, identification of novel drug targets, prediction of resistance mechanisms, and optimisation of drug molecules to overcome resistance. Leveraging ML and MTDD is crucial for both advancing our fight against AMR in Enterobacteriaceae, and developing combination therapies that target multiple bacterial survival pathways, reducing the risk of resistance development.

Ribosome phenotypes for rapid classification of antibiotic-susceptible and resistant strains of Escherichia coli

Rapid antibiotic susceptibility tests (ASTs) are an increasingly important part of clinical care as antimicrobial resistance (AMR) becomes more common in bacterial infections. Here, we use the spatial distribution of fluorescently labelled ribosomes to detect intracellular changes associated with antibiotic susceptibility in E. coli cells using a convolutional neural network (CNN). By using ribosome-targeting probes, one fluorescence image provides data for cell segmentation and susceptibility phenotyping. Using 60,382 cells from an antibiotic-susceptible laboratory strain of E. coli, we showed that antibiotics with different mechanisms of action result in distinct ribosome phenotypes, which can be identified by a CNN with high accuracy (99%, 98%, 95%, and 99% for ciprofloxacin, gentamicin, chloramphenicol, and carbenicillin). With 6 E. coli strains isolated from bloodstream infections, we used 34,205 images of ribosome phenotypes to train a CNN that could classify susceptible cells with 91% accuracy and resistant cells with 99% accuracy. Such accuracies correspond to the ability to differentiate susceptible and resistant samples with 99% confidence with just 2 cells, meaning that this method could eliminate lengthy culturing steps and could determine susceptibility with 30 min of antibiotic treatment. The ribosome phenotype method should also be able to identify phenotypes in other strains and species.

Detection of antimicrobial resistance via state-of-the-art technologies versus conventional methods

Antimicrobial resistance (AMR) is recognized as one of the foremost global health challenges, complicating the treatment of infectious diseases and contributing to increased morbidity and mortality rates. Traditionally, microbiological culture and susceptibility testing methods, such as disk diffusion and minimum inhibitory concentration (MIC) assays, have been employed to identify AMR bacteria. However, these conventional techniques are often labor intensive and time consuming and lack the requisite sensitivity for the early detection of resistance. Recent advancements in molecular and genomic technologies-such as next-generation sequencing (NGS), matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), lateral flow immunoassays (LFIAs), PCR-based diagnostic methods, and CRISPR-based diagnostics-have revolutionized the diagnosis of AMR. These innovative approaches provide increased sensitivity, reduced turnaround times, and the ability to identify genetic resistance mechanisms. This review seeks to examine the advantages and disadvantages of both emerging technologies and traditional methods for detecting AMR, emphasizing the potential benefits and limitations inherent to each. By understanding the strengths and limitations of these technologies, stakeholders, including researchers, healthcare professionals, regulatory agencies, health authorities, financial managers, and patients, can make informed decisions aimed at preventing the emergence and dissemination of antibiotic-resistant strains, thereby ultimately increasing patient safety.

Parallel Detection of the Unamplified Carbapenem Resistance Genes blaNDM-1 and blaOXA-1 Using a Plasmonic Nano-Biosensor with a Field-Portable DNA Extraction Method

Antimicrobial resistance (AMR) is a rapidly growing global concern resulting from the overuse of antibiotics in agricultural and clinical settings. The challenge is exacerbated by the lack of rapid surveillance for resistant bacteria in clinical, environmental, and food supply settings. The increasing resistance to carbapenems, an important sub-class of beta-lactam antibiotics, is a major concern in the healthcare community. Carbapenem resistance (CR) has been found in the environment and food supply chain, where it has the potential to spread to pathogens, animals, and humans through direct or indirect contact. Rapid detection for preventative and control measures should be developed. This study utilized a gold nanoparticle-based plasmonic biosensor for the parallel detection of the CR genes blaNDM-1 and blaOXA-1. To explore the field portability, DNA was extracted using two methods: a commercial extraction kit and a boiling method. The results were compared between the two methods using a spectrophotometer and a cellphone application for RGB values to quantify the visual results. The results showed that the boiling method of extraction was more effective than extraction with a commercial kit for this analysis. The parallel detection of unamplified genes extracted via the boiling method is novel. When combined with other portable testing equipment, the approach has the potential to be an inexpensive, rapid, and simple on-site CR gene detection protocol.

Rapid antimicrobial susceptibility testing using carbon screen printed electrodes in a microfluidic device

The development of rapid, sensitive, and affordable antimicrobial susceptibility testing (AST) is essential for controlling antibiotic overuse, thereby creating a critical checkpoint for the emerging antimicrobial resistance threat. Here, we introduce a novel method of electrochemical monitoring of bacterial growth in a diluted low-conductivity nutrient medium for rapid susceptibility testing using impedance spectroscopy. The method works on the change in charge transfer resistance exhibited by bacteria in response to antibiotics. The proposed Electrochemical Microfluidic device (ε-µD) employs low-cost carbon screen-printed electrodes and uses a simple microfluidic geometry. We explored the utilisation of a diluted nutrient medium as an electrolyte since it provides a higher charge transfer baseline signal for better sensitivity and supports the growth of the bacteria required for detection. The method enables sensitive detection of bacteria even at a low density of 84/mm2 in three hours of incubation time. For proof of concept, bacteria such as Escherichia coli and Bacillus subtilis were used, and the efficacy of the ampicillin and tetracycline drugs were tested. The experiments were done with the spiked urine samples, which correlated well with the controlled sample. The proposed system enhances the accessibility and affordability of rapid susceptibility testing, enabling its widespread use.

Click chemistry-enabled gold nanorods for sensitive detection and viability evaluation of copper(II)-reducing bacteria

The rise of antibiotic resistance poses a significant and ongoing challenge to public health, with pathogenic bacteria remaining a persistent threat. Traditional culture methods, while considered the gold standard for bacterial detection and viability assessment, are time-consuming and labor-intensive. To address this limitation, we developed a novel point-of-care (POC) detection method leveraging citrate- and alkyne-modified gold nanorods (AuNRs) synthesized with click chemistry properties. These AuNRs exhibit superior biocompatibility and enhanced quantitative performance compared to conventional surfactant-modified AuNRs. Our method, termed AuNRs-bacteria-initiated click chemistry (AuNRs-BICC), detects CuII-reducing bacteria by quantifying AuNRs bound to a biosensing interface via bacteria-mediated CuII reduction to CuI and subsequent click chemistry with biosensing interface of azide modifications. Using dark-field microscopy (DFM), we demonstrated a strong linear correlation between AuNR counts and the logarithm of bacterial concentration for both Gram-negative Escherichia coli (including KPC-2-expressing antibiotic-resistant strains) and Gram-positive Staphylococcus aureus across a range of 101 to 107 cells, achieving a remarkable detection limit of 101 cells. The AuNRs-BICC biosensor exhibits high selectivity for target bacterial strains and provides rapid detection within 3 h. Furthermore, it can assess bacterial viability in the presence of various antibiotics, including meropenem, ceftriaxone and tetracycline, suggesting its potential for rapid antibiotic susceptibility testing and facilitating timely clinical intervention for infectious diseases.

Antimicrobial Susceptibility of Escherichia coli Isolates Causing Community-Acquired Urinary Tract Infections: Comparison of Methods

Due to bacterial resistance to antimicrobials, antibiotic therapy for urinary tract infections (UTIs) has become a major challenge for clinicians. The present work aimed to compare the antimicrobial susceptibility profiles of 53 uropathogenic Escherichia coli (UPEC) isolates, assessed using the disk diffusion method and two automated systems (PHOENIX BD™ and VITEK2), with interpretations based on CLSI and BrCAST guidelines. Twenty-five antibiotics were tested to assess differences in susceptibility profiles. Statistical tools, including Kappa coefficient analysis and chi-square tests, were applied to assess concordance and significance between methods. Among the main discrepancies found, BrCAST has classified a greater number of UPEC isolates as resistant to more than half of the antibiotics tested by the disk diffusion method, when compared to CLSI. Although faster, the PHOENIX BD and VITEK2 automated systems exhibited significant discrepancies, with divergences observed for half of the antimicrobials tested. Both automated methods showed discrepancies compared to the disk diffusion method under CLSI and BrCAST guidelines. PHOENIX BD classified some isolates resistant by DD/CLSI as susceptible, while VITEK2 misclassified 25% to 50% of the antimicrobials tested. Conversely, VITEK2 also classified some isolates susceptible to DD/CLSI as resistant to 25% of the antimicrobials tested. Regarding DD/BrCAST, PHOENIX BD classified resistant isolates as susceptible (to 50% of the antimicrobials tested). In comparison, VITEK2 classified resistant isolates as susceptible and susceptible isolates as resistant (25% of the antimicrobials for both). These findings highlight the need for careful selection of susceptibility testing methods, as variations in interpretive criteria between CLSI and BrCAST could impact clinical decision-making. This study underscores the importance of methodological consistency in accurately informing antibiotic therapy in UTI management, especially in the face of rising resistance.

Measurement of Bacterial Headspaces by FT-IR Spectroscopy Reveals Distinct Volatile Organic Compound Signatures

Ensuring prompt and precise identification of bacterial pathogens is essential for initiating appropriate antibiotic therapy and combating severe bacterial infections effectively. Traditional microbiological diagnostics, involving initial culturing and subsequent pathogen detection, are often laborious and time-consuming. Even though modern techniques such as Raman spectroscopy, MALDI-TOF, and 16S rRNA PCR have significantly expedited this process, new methods are required for the accurate and fast detection of bacterial pathogens. In this context, using bacterial metabolites for detection is promising as a future diagnostic approach. Fourier-transform infrared spectroscopy was employed in our study to analyze the biochemical composition of gas phases of bacterial isolates. We can characterize individual bacterial strains and identify specific bacteria within mixtures by utilizing volatile-metabolite-based infrared detection techniques. This approach enables rapid identification by discerning distinctive spectral features and intensities for different bacteria, offering new perspectives for bacterial pathogen diagnostics. This technique holds innovative potential to accelerate progress in the field, providing a faster and potentially more precise alternative to conventional diagnostic methods.

A Four-Year Study of Antibiotic Resistance, Prevalence and Biofilm-Forming Ability of Uropathogens Isolated from Community- and Hospital-Acquired Urinary Tract Infections in Southern Italy

The aim of this study was to investigate the differences between nosocomial and community microorganisms isolated from patients with UTI by determining their bacterial profile, antibiotic resistance and ability to produce biofilms. A retrospective study, based on bacterial isolates from consecutive urine samples collected between January 2019 and December 2023, was conducted at a university hospital. The main pathogens isolated from both community and hospital samples were the same, but their frequency of isolation differed. Compared with community-associated cases, hospital-associated infections have more isolates of Acinetobacter baumanii complex. In contrast, Proteus mirabilis isolates were more prevalent in community than in hospital infections. In both hospital and community isolates, gram-positive bacteria showed a lower overall antimicrobial resistance (22%) compared to gram-negative bacteria (30%). The data demonstrated that individual strains exhibited disparate degrees of capacity for biofilm formation. Additionally, the data indicate an inverse correlation between biofilm production and antibiotic resistance. Isolates from community patients exhibited lower capacities for biofilm production in comparison to the capacities demonstrated by microorganisms isolated from nosocomial patients (29% and 35%, respectively). Area-specific surveillance studies can provide valuable information on UTI pathogens and antimicrobial resistance patterns, which can be useful in guiding empirical treatment.

Multi-label classification to predict antibiotic resistance from raw clinical MALDI-TOF mass spectrometry data

Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S. aureus, K. pneumoniae, and P. aeruginosa. Using multiple datasets from the DRIAMS repository, we evaluated the performance of four algorithms - Multi-Layer Perceptron, Support Vector Classifier, Random Forest, and Extreme Gradient Boosting - under both single-label and multi-label frameworks. Our results demonstrate that the multi-label approach delivers competitive performance compared to traditional single-label models, with no statistically significant differences in most cases. The multi-label framework naturally captures the complex, interconnected nature of AMR data, reflecting real-world scenarios more accurately. We further validated the models on external datasets (DRIAMS B and C), confirming their generalizability and robustness. Additionally, we investigated the impact of oversampling techniques and provided a reproducible methodology for handling MALDI-TOF data, ensuring scalability for future studies. These findings underscore the potential of multi-label classification to enhance predictive accuracy in AMR research, offering valuable insights for developing diagnostic tools and guiding clinical interventions.

The volatile organic compounds detection in MDR Gram-negatives antimicrobial susceptibility testing: Results from a four-month laboratory experience

Systemic bacterial infections represent a significant clinical challenge due to the increasing resistance rate towards antimicrobials. An essential key to controlling antimicrobial resistance spread is to administer targeted therapy after a precise minimum inhibitory concentration reporting. Among the available fast technologies for antimicrobial susceptibility testing (AST), the VITEKⓇ REVEAL™ (Biomerieux, Florence, Italy) proposes volatile organic compounds (VOC) colourimetric arrays to discriminate between susceptible and resistant Gram-negative isolates directly from positive blood cultures. We evaluated this methodology during a four-month laboratory experience on 40 positive blood culture samples, reporting a comparison to standard culture-based methods. The protocol revealed an essential agreement of 100 % between the conventional and the experimental procedures, while the categorical agreement resulted in 97.5 % due to one very major error (VME) for meropenem/vaborbactam in K. pneumoniae. Although further studies will be necessary to investigate its performance on rare microorganisms, the VITEKⓇ REVEAL™ demonstrated an optimal sensitivity in defining MIC values for multi-drug resistant (MDR) microorganisms. These results encourage the application of the method in all high-risk epidemiological areas, confirming the effectiveness of VOC detection in monitoring bacterial susceptibility profiles.

Evaluation of DNA extraction kits for long-read shotgun metagenomics using Oxford Nanopore sequencing for rapid taxonomic and antimicrobial resistance detection

During a bacterial infection or colonization, the detection of antimicrobial resistance (AMR) is critical, but slow due to culture-based approaches for clinical and screening samples. Culture-based phenotypic AMR detection and confirmation require up to 72 hours (h) or even weeks for slow-growing bacteria. Direct shotgun metagenomics by long-read sequencing using Oxford Nanopore Technologies (ONT) may reduce the time for bacterial species and AMR gene identification. However, screening swabs for metagenomics is complex due to the range of Gram-negative and -positive bacteria, diverse AMR genes, and host DNA present in the samples. Therefore, DNA extraction is a critical initial step. We aimed to compare the performance of different DNA extraction protocols for ONT applications to reliably identify species and AMR genes using a shotgun long-read metagenomic approach. We included three different sample types: ZymoBIOMICS Microbial Community Standard, an in-house mock community of ESKAPE pathogens including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE Mock), and anonymized clinical swab samples. We processed all sample types with four different DNA extraction kits utilizing different lysis (enzymatic vs. mechanical) and purification (spin-column vs. magnetic beads) methods. We used kits from Qiagen (QIAamp DNA Mini and QIAamp PowerFecal Pro DNA) and Promega (Maxwell RSC Cultured Cells and Maxwell RSC Buccal Swab DNA). After extraction, samples were subject to the Rapid Barcoding Kit (RBK004) for library preparation followed by sequencing on the GridION with R9.4.1 flow cells. The fast5 files were base called to fastq files using Guppy in High Accuracy (HAC) mode with the inbuilt MinKNOW software. Raw read quality was assessed using NanoPlot and human reads were removed using Minimap2 alignment against the Hg38 genome. Taxonomy identification was performed on the raw reads using Kraken2 and on assembled contigs using Minimap2. The AMR genes were identified using Minimap2 with alignment against the CARD database on both the raw reads and assembled contigs. We identified all bacterial species present in the Zymo Mock Community (8/8) and ESKAPE Mock (6/6) with Qiagen PowerFecal Pro DNA kit (chemical and mechanical lysis) at read and assembly levels. Enzymatic lysis retrieved fewer aligned bases for the Gram-positive species (Staphylococcus aureus and Enterococcus faecium) from the ESKAPE Mock on the assembly level compared to the mechanical lysis. We detected the AMR genes from Gram-negative and -positive species in the ESKAPE Mock with the QIAamp PowerFecal Pro DNA kit on reads level with a maximum median time of 1.9 h of sequencing. Long-read metagenomics with ONT may reduce the turnaround time in screening for AMR genes. Currently, the QIAamp PowerFecal Pro DNA kit (chemical and mechanical lysis) for DNA extraction along with the Rapid Barcoding Kit for the ONT sequencing captured the best taxonomy and AMR identification for our specific use case.

Progress on implementing the WHO-GLASS recommendations on priority pathogen-antibiotic sensitivity testing in Africa: A scoping review

Introduction

The World Health Organization global antimicrobial resistance surveillance system (GLASS) was rolled out in 2015 to guide antimicrobial resistance (AMR) surveillance. However, its implementation in Africa has not been fully evaluated. We conducted a scoping review to establish the progress of implementing the WHO 2015 GLASS manual in Africa.

Methods

We used MeSH terms to comprehensively search electronic databases (MEDLINE and Embase) for articles from Africa published in English between January 2016 and December 2023. The Arksey and O'Malley's methodological framework for scoping reviews was employed. Data were collected on compliance with WHO GLASS recommendations for AMR surveillance-priority samples, pathogens, and pathogen-antibiotic combinations and analysed using Microsoft Excel.

Results

Overall, 13,185 articles were identified. 7,409 were duplicates, and 5,141 articles were excluded based on titles and abstracts. 609 full-text articles were reviewed, and 147 were selected for data extraction. Of the 147 selected articles, 78.9% had been published between 2020 and 2023; 57.8% were from Eastern Africa. 93.9% of articles were on cross-sectional studies. 96.6% included only one priority sample type; blood (n=56), urine (n=64), and stool (n=22). Of the 60 articles that focused on blood as a priority sample type, 71.7%, 68.3%, 68.3%, 36.7%, 30%, and 10% reported recovery of Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Salmonella species and Streptococcus pneumoniae, respectively. Salmonella and Shigella species were reported to have been recovered from 91.3% and 73.9% of the 23 articles that focused on stool. E. coli and K. pneumoniae recoveries were also reported from 94.2% and 68.1% of the 69 articles that focused on urine. No article in this review reported having tested all the recommended WHO GLASS pathogen-antibiotic combinations for specific pathogens.

Conclusion

Progress has been made in implementing the GLASS recommendations in Africa, but adoption varies across countries limiting standardisation and comparability of data.

Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa

Antimicrobial resistance (AMR) remains a critical global health threat, with significant impacts on individuals and healthcare systems, particularly in low-income countries. By 2019, AMR was responsible for >4.9 million fatalities globally, and projections suggest this could rise to 10 million annually by 2050 without effective interventions. Sub-Saharan Africa (SSA) faces considerable challenges in managing AMR due to insufficient surveillance systems, resulting in fragmented data. Technological advancements, notably artificial intelligence (AI), offer promising avenues to enhance AMR biosurveillance. AI can improve the detection, tracking and prediction of resistant strains through advanced machine learning and deep learning algorithms, which analyze large datasets to identify resistance patterns and develop predictive models. AI's role in genomic analysis can pinpoint genetic markers and AMR determinants, aiding in precise treatment strategies. Despite the potential, SSA's implementation of AI in AMR surveillance is hindered by data scarcity, infrastructural limitations and ethical concerns. This review explores what is known about the integration and applicability of AI-enhanced biosurveillance methodologies in SSA, emphasizing the need for comprehensive data collection, interdisciplinary collaboration and the establishment of ethical frameworks. By leveraging AI, SSA can significantly enhance its AMR surveillance capabilities, ultimately improving public health outcomes.

Application of the Electrical Microbial Growth Analyzer Method for Efficiently Quantifying Viable Bacteria in Ready-to-Eat Sea Cucumber Products

Accurate and efficient quantification of viable bacteria in ready-to-eat food products is crucial for food safety and public health. The rapid and accurate assessment of foodborne bacteria in complex food matrices remains a significant challenge. Herein a culture-based approach was established for easily quantifying viable bacteria in ready-to-eat sea cucumber (RSC) products. Samples of the liquid companion within the package were directly transferred into test tubes to determine bacterial growth curves and growth rate curves, utilizing the electrical microbial growth analyzer. Viable bacteria in the samples were then quantified based on the time required to attain the maximum growth rate indicated on the growth rate curve. At a concentration of 5.0 × 103 CFU/mL of viable bacteria in the liquid companion, the recovery rates were 108.85-112.77% for Escherichia coli (E. coli) and 107.01-130.54% for Staphylococcus aureus (S. aureus), with standard deviations of 1.60 and 3.92, respectively. For the solid content in the package, the quantification was performed using the same methodology following an additional homogenization step. At a concentration of 5.0 × 103 CFU/mL of viable bacteria in the sample, the recovery rates were 91.94-102.24% for E. coli and 81.43-104.46% for S. aureus, with standard deviations of 2.34 and 2.38, respectively. In instances where the viable bacterial concentration was 5.0 × 103 CFU/mL in RSC products, the total time required for the quantification did not exceed 10.5 h. This method demonstrated advantages over traditional plate counting and PCR methods regarding simplicity and efficiency, representing a promising alternative for the quantification of viable bacteria in food like RSC products.

Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions

Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing strategies. Methods: We analyzed AMR gene data in the PanRes dataset by applying unsupervised learning techniques, namely K-means clustering and Principal Component Analysis (PCA). These techniques were applied to identify clusters based on gene length and distribution according to resistance class, offering insights into the resistance genes' structural and functional properties. Data preprocessing, such as filtering and normalization, was conducted prior to applying machine learning methods to ensure consistency and accuracy. Our methodology included the preprocessing of data and reduction of dimensionality to ensure that our models were both accurate and interpretable. Results: The unsupervised learning models highlighted distinct clusters of AMR genes, with significant patterns in gene length, including their associated resistance classes. Further dimensionality reduction by PCA allows for clearer visualizations of relationships among gene groupings. These patterns provide novel insights into the potential mechanisms of resistance, particularly the role of gene length in different resistance pathways. Conclusions: This study demonstrates the potential of ML, specifically unsupervised approaches, to enhance the understanding of AMR. The identified patterns in resistance genes could support clinical decision-making and inform public health interventions. However, challenges remain, particularly in integrating genomic data and ensuring model interpretability. Further research is needed to advance ML applications in AMR prediction and management.

A comparative study of a rapid phenotypic antimicrobial susceptibility testing system directly from positive blood cultures to the disk diffusion and VITEK 2 methods

BACKGROUND

Sepsis is a life-threatening condition that impacts 49 million people annually and causes 11 million deaths worldwide. Surviving bloodstream infections (BSIs) depends on the rapid administration of effective antimicrobial treatment, underscoring a need for rapid antimicrobial susceptibility testing (AST).

AIM

To evaluate the performance of Quantamatrix's dRAST v2.5 system (Seoul, South Korea) for AST directly from positive blood cultures as compared to the Disk-Diffusion (DD) and VITEK 2 methods.

METHODS

The study included 191 positive blood cultures from clinical samples and spiked blood culture bottles. Following Gram staining and species-level identification, AST was performed by VITEK 2 and standard DD methods using CLSI (2021) interpretation.

RESULTS

dRAST demonstrated very good AST performance for a Gram-negative isolate, and good performance for Gram-positive isolates, meeting CLSI criteria for the acceptance of a new method. Antimicrobials that were not considered verified compared to VITEK 2 and DD were cefazolin, ceftazidime, meropenem, and trimethoprim/sulfamethoxazole for Gram-negatives and clindamycin, erythromycin, penicillin, and oxacillin for Gram-positives. dRAST ESBL detection results were strongly correlated with the ESBL phenotypes obtained with other methods. Additional resistance mechanisms were in concordance with traditional tests.

CONCLUSIONS

dRAST demonstrated good AST performance, meeting CLSI criteria for most relevant antibiotics. dRAST was associated with a significant reduction in time-to-results, labor, and the subjectivity of result analyses, making it a valuable addition to efforts supporting the treatment of patients with bacteremia. AST (antimicrobial susceptibility test), blood culture, dRAST, rapid methods, sepsis, turnaround time (TAT).

Fluorometric determination of mecA gene in MRSA with a graphene-oxide based bioassay using flap endonuclease 1-assisted target recycling and Klenow fragment-triggered signal amplification

Methicillin-resistant Staphylococcus aureus (MRSA) is a multidrug-resistant bacterium that causes a wide range of illnesses, necessitating the development of new technologies for its detection. Herein, we propose a graphene oxide (GO)-based sensing platform for the detection of mecA gene in MRSA using flap endonuclease 1 (FEN1)-assisted target recycling and Klenow fragment (KF)-triggered signal amplification. Without the target, all the DNA probes were adsorbed onto GO, resulting in fluorescence quenching of the dye. Upon the addition of the target, a triple complex was formed that triggered FEN1-assisted target recycling and initiated two polymerization reactions with the assistance of KF polymerase, generating numerous dsDNA that were repelled by GO. These dsDNAs triggered fluorescence enhancement when SYBR Green I was added. Therefore, the target DNA was quantified by measuring the fluorescence at excitation and emission wavelengths of 480/526 nm. This mecA gene assay showed a good linear range from 1 to 50 nM with a lower limit of detection of 0.26 nM, and displayed good applicability to the analysis of real samples. Thus, a new method for monitoring MRSA has been developed that has great potential for early clinical diagnosis and treatment.

Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.

Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning

There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.

Rapid and Simple Morphological Assay for Determination of Susceptibility/Resistance to Combined Ciprofloxacin and Ampicillin, Independently, in Escherichia coli

Current antibiograms cannot discern the particular effect of a specific antibiotic when the bacteria are incubated with a mixture of antibiotics. To prove that this task is achievable, Escherichia coli strains were treated with ciprofloxacin for 45 min, immobilized on a slide and stained with SYBR Gold. In susceptible strains, the nucleoid relative surface started to decrease near the MIC, being progressively condensed as the dose increased. The shrinkage level correlated with the DNA fragmentation degree. Ciprofloxacin-resistant bacilli showed no change. Additionally, E. coli strains were incubated with ampicillin for 45 min and processed similarly. The ampicillin-susceptible strain revealed intercellular DNA fragments that increased with dose, unlike the resistant strain. Co-incubation with both antibiotics revealed that ampicillin did not modify the nucleoid condensation effect of ciprofloxacin, whereas the quinolone partially decreased the background of DNA fragments induced by ampicillin. Sixty clinical isolates, with different combinations of susceptibility-resistance to each antibiotic, were co-incubated with the EUCAST breakpoints of susceptibility of ciprofloxacin and ampicillin. The morphological assay correctly categorized all the strains for each antibiotic in 60 min, demonstrating the feasible independent evaluation of a mixture of quinolone and beta-lactam. The rapid phenotypic assay may shorten the incubation times and necessary microbial mass currently required for evaluation.

Development of Rapid Disk Diffusion Device Using Laser Speckle Formation Technology for Rapid Antimicrobial Susceptibility Testing

The escalation of antimicrobial resistance (AMR) due to the excessive and inappropriate use of antimicrobials has prompted the urgent need for more rapid and effective antimicrobial susceptibility testing (AST) methods. Conventional AST techniques often take 16-24 h, leading to empirical prescription practices and the potential emergence of AMR. The study aimed to develop a rapid disk diffusion (RDD) method utilizing laser speckle formation (LSF) technology to expedite AST results. The study aimed to evaluate the performance of LSF technology in determining antimicrobial susceptibility. In this study, preclinical and clinical settings were established to compare the LSF technology with conventional disk diffusion (DD) methods to measure the inhibition zones. Preclinical experiments with different bacterial strains demonstrated more than 70% categorical agreement (CA) against most antimicrobials. Further, clinical experiments with multiple strains and antibiotics revealed CA ranging from 40 to 79%, while major and minor discrepancies were observed around 30% and 11%, respectively. These observations revealed high concordance between RDD and DD for multiple antimicrobials in multiple species. The results underscore the potential of RDD-based LSF technology for hastening AST procedures. The current study is marked by a unique equipment setup and analysis approach. Collectively, the suggested laser-based RDD showed greater potential than previously developed comparable methods. The proposed method and design have a higher application potential than formerly developed similar technologies. Together, the study contributes to the ongoing development of rapid AST methods.

Limitations of current techniques in clinical antimicrobial resistance diagnosis: examples and future prospects

Antimicrobial resistance is a global threat to public health. Without proactive intervention, common infections may become untreatable, restricting the types of clinical intervention that can be undertaken and reversing improvements in mortality rates. Effective antimicrobial stewardship represents one approach to restrict the spread of antimicrobial resistance but relies on rapid and accurate diagnostics that minimise the unnecessary use of antibiotics. This is increasingly a key unmet clinical need. In this paper, we describe existing techniques for the detection of antimicrobial resistance, while examining their drawbacks and limitations. We also discuss emerging diagnostic technologies in the field, and the need for standardisation to allow for swifter and more widespread clinical adoption.

Rapid Phenotypic Antibiotics Susceptibility Analysis by a 3D Printed Prototype

One of the most important public health concerns is the increase in antibiotic-resistant pathogens and corresponding treatment of associated infections. Addressing this challenge requires more efficient use of antibiotics, achievable by the use of evidence-based, effective antibiotics identified by antibiotic susceptibility testing (AST). However, the current standard method of phenotypic AST used for this purpose requires 48 h or more from sample collection to result. Until results are available, broad-spectrum antibiotics are used to avoid delaying treatment. The turnaround time must therefore be shortened in order for the results to be available before the second administration of antibiotics. The phenotypic electrochemical AST method presented here identifies effective antibiotics within 5-10 h after sampling. Spiked serum samples, including polymicrobial samples, with clinically relevant pathogens and respective concentrations commonly found in bloodstream infections (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Pseudomonas aeruginosa) are used. Direct loading of the test with diluted serum eliminates the need for a pre-culture, as required by existing methods. Furthermore, by combining several electrochemical measurement procedures with computational analysis, allowing the method to be used both online and offline, the AST achieves a sensitivity of 94.44% and a specificity of 95.83% considering each replicate individually.

Predicting S. aureus antimicrobial resistance with interpretable genomic space maps

Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non-linear dimensionality reduction method - generative topographic mapping (GTM). This approach, applied to AMR data of >5000 S. aureus isolates retrieved from the PATRIC database, yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The Generative Topographic Maps (GTMs) represent data in the form of illustrative maps of the genomic space and allow for antibiotic-wise comparison of resistant phenotypes. The maps were also found to be useful for the analysis of genetic determinants responsible for drug resistance. Overall, the GTM-based methodology is a useful tool for both the illustrative exploration of the genomic sequence space and AMR prediction.

A capillary-based centrifugal indicator equipped with in situ pathogenic bacteria culture for fast antimicrobial susceptibility testing

Antimicrobial resistance has become a major global health threat due to the misuse and overuse of antibiotics. Rapid, affordable, and high-efficiency antimicrobial susceptibility testing (AST) is among the effective means to solve this problem. Herein, we developed a capillary-based centrifugal indicator (CBCI) equipped with an in situ culture of pathogenic bacteria for fast AST. The bacterial incubation and growth were performed by macro-incubation, which seamlessly integrated the capillary indicator. Through simple centrifugation, all the bacterial cells were confined at the nanoliter-level capillary column. The packed capillary column height could linearly reflect the bacterial count, and the minimum inhibitory concentration (MIC) was determined based on the difference in the column height between the drug-added groups and the control group. The AST results could easily be determined by the naked eye or smartphone imaging. Thus, the CBCI realized the combination of macro-bacterial incubation and early micro assessment, which accelerated the phenotypic AST without complex microscopic counting or fluorescent labelling. The whole operation process was simple and easy to use. AST results could be determined for E. coli ATCC strains within 3.5 h, and the output results for clinical samples were consistent with the hospital reports. We expect this AST platform to become a useful tool in limiting antimicrobial resistance, especially in remote/resource-limited areas or in underdeveloped countries.

In-depth analysis of the treatment effect and synergistic mechanism of TanReQing injection on clinical multi-drug resistant Pseudomonas aeruginosa

Antibiotic resistance is a recognized and concerning public health issue. Gram-negative bacilli, such as Pseudomonas aeruginosa (P. aeruginosa), are notorious for their rapid development of drug resistance, leading to treatment failures. TanReQing injection (TRQ) was chosen to explore its pharmacological mechanisms against clinical multidrug-resistant P. aeruginosa (MDR-PA), given its antibacterial and anti-inflammatory properties. We revealed the expression of proteins and genes in P. aeruginosa after co-culture with TRQ. This study developed an assessment method to evaluate clinical resistance of P. aeruginosa using MALDI-TOF MS identification and Biotyper database searching techniques. Additionally, it combined MIC determination to investigate changes in MDR-PA treated by TRQ. TRQ effectively reduced the MICs of ceftazidime and cefoperazone and enhanced the confidence scores of MDR-PA as identified by mass spectrometry. Using this evaluation method, the fingerprints of standard P. aeruginosa and MDR-PA were compared, and the characteristic peptide sequence (Seq-PA No. 1) associated with flagellum was found. The phenotypic experiments were conducted to confirm the effect of TRQ on the motility and adhesion of P. aeruginosa. A combination of co-immunoprecipitation and proteome analysis was employed, and 16 proteins were significantly differentially expressed and identified as potential candidates for investigating the mechanism of inhibiting resistance in P. aeruginosa treated by TRQ. The candidates were verified by quantitative real-time PCR analysis, and TRQ may affect these core proteins (MexA, MexB, OprM, OprF, OTCase, IDH, and ASL) that influence resistance of P. aeruginosa. The combination of multiple methods helps elucidate the synergistic mechanism of TRQ in overcoming resistance of P. aeruginosa.IMPORTANCEPseudomonas aeruginosa is an opportunistic pathogen closely associated with various life-threatening acute and chronic infections. The presence of antimicrobial resistance and multidrug resistance in P. aeruginosa infections significantly complicates antibiotic treatment. The expression of β-lactamase, efflux systems such as MexAB-OprM, and outer membrane permeability are considered to have the greatest impact on the sensitivity of P. aeruginosa. The study used a method to assess the clinical resistance of P. aeruginosa using matrix-assisted laser desorption ionization time of flight mass spectrometry identification and Biotyper database search techniques. TanReQing injection (TRQ) effectively reduced the MICs of ceftazidime and cefoperazone in multidrug-resistant P. aeruginosa (MDR-PA) and improved the confidence scores for co-cultured MDR-PA. The study found a characteristic peptide sequence for distinguishing whether P. aeruginosa is resistant. Through co-immunoprecipitation and proteome analysis, we explored the mechanism of TRQ overcoming resistance of P. aeruginosa.

Microfluidic systems for infectious disease diagnostics

Microorganisms, encompassing both uni- and multicellular entities, exhibit remarkable diversity as omnipresent life forms in nature. They play a pivotal role by supplying essential components for sustaining biological processes across diverse ecosystems, including higher host organisms. The complex interactions within the human gut microbiota are crucial for metabolic functions, immune responses, and biochemical signalling, particularly through the gut-brain axis. Viruses also play important roles in biological processes, for example by increasing genetic diversity through horizontal gene transfer when replicating inside living cells. On the other hand, infection of the human body by microbiological agents may lead to severe physiological disorders and diseases. Infectious diseases pose a significant burden on global healthcare systems, characterized by substantial variations in the epidemiological landscape. Fast spreading antibiotic resistance or uncontrolled outbreaks of communicable diseases are major challenges at present. Furthermore, delivering field-proven point-of-care diagnostic tools to the most severely affected populations in low-resource settings is particularly important and challenging. New paradigms and technological approaches enabling rapid and informed disease management need to be implemented. In this respect, infectious disease diagnostics taking advantage of microfluidic systems combined with integrated biosensor-based pathogen detection offers a host of innovative and promising solutions. In this review, we aim to outline recent activities and progress in the development of microfluidic diagnostic tools. Our literature research mainly covers the last 5 years. We will follow a classification scheme based on the human body systems primarily involved at the clinical level or on specific pathogen transmission modes. Important diseases, such as tuberculosis and malaria, will be addressed more extensively.

Antimicrobial Agent Functional Gold Nanocluster-Mediated Multichannel Sensor Array for Bacteria Sensing

Urinary tract infections (UTIs) have greatly affected human health in recent years. Accurate and rapid diagnosis of UTIs can enable a more effective treatment. Herein, we developed a multichannel sensor array for efficient identification of bacteria based on three antimicrobial agents (vancomycin, lysozyme, and bacitracin) functional gold nanoclusters (AuNCs). In this sensor, the fluorescence intensity of the three AuNCs was quenched to varying degrees by the bacterial species, providing a unique fingerprint for different bacteria. With this sensing platform, seven pathogenic bacteria, different concentrations of the same bacteria, and even bacterial mixtures were successfully differentiated. Furthermore, UTIs can be accurately identified with our sensors in ∼30 min with 100% classification accuracy. The proposed sensing systems offer a rapid, high-throughput, and reliable sensing platform for the diagnosis of UTIs.

Sentinel laboratory compliance with best practices in Burkina Faso's antimicrobial resistance surveillance network

Background

Standardising procedures is the best way to harmonise and strengthen the quality of laboratory-based antimicrobial resistance surveillance. Since 2018, Burkina Faso has developed and disseminated the national manual of procedures for performing antibiotic susceptibility tests in sentinel laboratories within its national antimicrobial resistance surveillance network.

Objective

Our study aimed to assess these sentinel laboratories' compliance with good practices for antibiotics susceptibility tests.

Methods

Four teams evaluated the antimicrobial resistance sentinel sites laboratories throughout Burkina Faso from 19 to 28 September 2022. Eighteen out of 19 sentinel laboratories were evaluated. A four-member technical committee designed and validated the evaluation tool composed of three Microsoft Excel sheets. The evaluation emphasised quality controls for culture media, antibiotic discs and compliance with antimicrobial susceptibility testing procedures by the laboratories. Excel software was used for data recording and graphs and table design. The free R software version 4.2.0 was used for descriptive statistics. An overall score below 80% was considered noncompliance.

Results

Most (83.33%) of the sentinel laboratories conducted at least one quality control activity for culture media, and 66.67% conducted at least one quality control activity for antibiotic discs. Over three-quarters (76.47%) of the laboratories were more than 80% compliant with the modified Kirby Bauer antimicrobial susceptibility testing method.

Conclusion

The evaluation revealed the noncompliance of sentinel laboratories with the national procedure manual, particularly in the quality control component.

What this study adds

This study has provided baseline data on the sentinel laboratories' compliance with the national antimicrobial susceptibility testing procedures manual, particularly in areas performing quality control checks or meeting quality indicators for culture media and antibiotic discs.

Metabolism-Triggered Plasmonic Nanosensor for Bacterial Detection and Antimicrobial Susceptibility Testing of Clinical Isolates

Antimicrobial resistance (AMR) is predicted to become the leading cause of death worldwide in the coming decades. Rapid and on-site antibiotic susceptibility testing (AST) is crucial for guiding appropriate antibiotic choices to combat AMR. With this in mind, we have designed a simple and efficient plasmonic nanosensor consisting of Cu2+ and cysteine-modified AuNP (Au/Cys) that utilizes the metabolic activity of bacteria toward Cu2+ for bacterial detection and AST. When Cu2+ is present, it induces the aggregation of Au/Cys. However, in the presence of bacteria, Cu2+ is metabolized to varying extents, resulting in distinct levels of aggregation. Moreover, the metabolic activity of bacteria can be influenced by their antibiotic susceptibility, allowing us to differentiate between susceptible and resistant strains through direct color changes from the Cu2+-Au/Cys platform over approximately 3 h. These color changes can be easily detected using naked-eye observation, smartphone analysis, or absorption readout. We have validated the platform using four clinical isolates and six types of antibiotics, demonstrating a clinical sensitivity and specificity of 95.8%. Given its simplicity, low cost, high speed, and high accuracy, the plasmonic nanosensor holds great potential for point-of-care detection of antibiotic susceptibility across various settings.

Antimicrobial resistance in multistate outbreaks of nontyphoidal Salmonella infections linked to animal contact-United States, 2015-2018

Animal contact is an established risk factor for nontyphoidal Salmonella infections and outbreaks. During 2015-2018, the U.S. Centers for Disease Control and Prevention (CDC) and other U.S. public health laboratories began implementing whole-genome sequencing (WGS) of Salmonella isolates. WGS was used to supplement the traditional methods of pulsed-field gel electrophoresis for isolate subtyping, outbreak detection, and antimicrobial susceptibility testing (AST) for the detection of resistance. We characterized the epidemiology and antimicrobial resistance (AMR) of multistate salmonellosis outbreaks linked to animal contact during this time period. An isolate was considered resistant if AST yielded a resistant (or intermediate, for ciprofloxacin) interpretation to any antimicrobial tested by the CDC or if WGS showed a resistance determinant in its genome for one of these agents. We identified 31 outbreaks linked to contact with poultry (n = 23), reptiles (n = 6), dairy calves (n = 1), and guinea pigs (n = 1). Of the 26 outbreaks with resistance data available, we identified antimicrobial resistance in at least one isolate from 20 outbreaks (77%). Of 1,309 isolates with resistance information, 247 (19%) were resistant to ≥1 antimicrobial, and 134 (10%) were multidrug-resistant to antimicrobials from ≥3 antimicrobial classes. The use of resistance data predicted from WGS increased the number of isolates with resistance information available fivefold compared with AST, and 28 of 43 total resistance patterns were identified exclusively by WGS; concordance was high (>99%) for resistance determined by AST and WGS. The use of predicted resistance from WGS enhanced the characterization of the resistance profiles of outbreaks linked to animal contact by providing resistance information for more isolates.

Antimicrobial susceptibility testing using infrared attenuated total reflection (IR-ATR) spectroscopy to monitor metabolic activity

Fast and accurate detection of antimicrobial resistance in pathogens remains a challenge, and with the increase in antimicrobial resistance due to mis- and overuse of antibiotics, it has become an urgent public health problem. We demonstrate how infrared attenuated total reflection (IR-ATR) can be used as a simple method for assessment of bacterial susceptibility to antibiotics. This is achieved by monitoring the metabolic activities of bacterial cells via nutrient consumption and using this as an indicator of bacterial viability. Principal component analysis of the obtained spectra provides a tool for fast and simple discrimination of antimicrobial resistance in the acquired data. We demonstrate this concept using four bacterial strains and four different antibiotics, showing that the change in glucose concentration in the growth medium after 2 h, as monitored by IR-ATR, can be used as a spectroscopic diagnostic technique, to reduce detection time and to improve quality in the assessment of antimicrobial resistance in pathogens.

Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety

Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.

Rapid Drug Susceptibility Testing to Preserve Antibiotics

Antibiotic resistance is a global challenge likely to cost trillions of dollars in excess costs in the health system and more importantly, millions of lives every year. A major driver of resistance is the absence of susceptibility testing at the time a healthcare worker needs to prescribe an antimicrobial. The effect is that many prescriptions are unintentionally wasted and expose mutable organisms to antibiotics increasing the risk of resistance emerging. Often simplistic solutions are applied to this growing issue, such as a naïve drive to increase the speed of drug susceptibility testing. This puts a spotlight on a technological solution and there is a multiplicity of such candidate DST tests in development. Yet, if we do not define the necessary information and the speed at which it needs to be available in the clinical decision-making progress as well as the necessary integration into clinical pathways, then little progress will be made. In this chapter, we place the technological challenge in a clinical and systems context. Further, we will review the landscape of some promising technologies that are emerging and attempt to place them in the clinic where they will have to succeed.

Expert guidance on target product profile development for AMR diagnostic tests

Diagnostics are widely considered crucial in the fight against antimicrobial resistance (AMR), which is expected to kill 10 million people annually by 2030. Nevertheless, there remains a substantial gap between the need for AMR diagnostics versus their development and implementation. To help address this problem, target product profiles (TPP) have been developed to focus developers' attention on the key aspects of AMR diagnostic tests. However, during discussion between a multisectoral working group of 51 international experts from industry, academia and healthcare, it was noted that specific AMR-related TPPs could be extended by incorporating the interdependencies between the key characteristics associated with the development of such TPPs. Subsequently, the working group identified 46 characteristics associated with six main categories (ie, Intended Use, Diagnostic Question, Test Description, Assay Protocol, Performance and Commercial). The interdependencies of these characteristics were then identified and mapped against each other to generate new insights for use by stakeholders. Specifically, it may not be possible for diagnostics developers to achieve all of the recommendations in every category of a TPP and this publication indicates how prioritising specific TPP characteristics during diagnostics development may influence (or not) a range of other TPP characteristics associated with the diagnostic. The use of such guidance, in conjunction with specific TPPs, could lead to more efficient AMR diagnostics development.

Application of diagnostic network optimization in Kenya and Nepal to design integrated, sustainable and efficient bacteriology and antimicrobial resistance surveillance networks

Antimicrobial resistance (AMR) is a major global public health concern, particularly in low- and middle-income countries, which experience the highest burden of AMR. Critical to combatting AMR is ensuring there are effective, accessible diagnostic networks in place to diagnose, monitor and prevent AMR, but many low- and middle-income countries lack such networks. Consequently, there is substantial need for approaches that can inform the design of efficient AMR laboratory networks and sample referral systems in lower-resource countries. Diagnostic network optimization (DNO) is a geospatial network analytics approach to plan diagnostic networks and ensure greatest access to and coverage of services, while maximizing the overall efficiency of the system. In this intervention, DNO was applied to strengthen bacteriology and AMR surveillance network design in Kenya and Nepal for human and animal health, by informing linkages between health facilities and bacteriology testing services and sample referral routes between farms, health facilities and laboratories. Data collected from the target settings in each country were entered into the open-access DNO tool OptiDx, to generate baseline scenarios, which depicted the current state of AMR laboratory networks and sample referral systems in the countries. Subsequently, baselines were adjusted to evaluate changing factors such as samples flows, transport frequency, transport costs, and service distances. Country stakeholders then compared resulting future scenarios to identify the most feasible solution for their context. The DNO analyses enabled a wealth of insights that will facilitate strengthening of AMR laboratory and surveillance networks in both countries. Overall, the project highlights the benefits of using a data-driven approach for designing efficient diagnostic networks, to ensure better health resource allocation while maximizing the impact and equity of health interventions. Given the critical need to strengthen AMR laboratory and surveillance capacity, DNO should be considered an integral part of diagnostic strategic planning in the future.

Multiplex Real-Time PCR for the Detection of Tetracycline, Ciprofloxacin, and Erythromycin Resistance Determinants from Human and Foodborne Campylobacter jejuni and Campylobacter coli

Campylobacter jejuni and Campylobacter coli are the predominant thermophilic species responsible for foodborne gastroenteritis worldwide. Elevated resistance to certain antibiotics was observed due to antimicrobial therapy in farm animals and humans, while reduced antimicrobial usage partially reduced antibiotic resistance. Monitoring the antimicrobial resistance demonstrated a substantial fraction of multi-resistant isolates, indicating the necessity of reliable tools for their detection. In this study, resistance determinants in 129 German and 21 Vietnamese isolates were selected to establish a novel multiplex real-time PCR (qPCR), facilitating the simultaneous detection of four resistance determinants. These comprised tet(O) gene variants associated with tetracycline resistance, point mutations GyrA_T86I and GyrA_T86V associated with ciprofloxacin resistance, and the erm(B) gene together with the point mutation A2075G in the 23S rRNA gene, associated with erythromycin resistance. Moreover, the performance of the qPCR assay was evaluated by comparing the results of qPCR to phenotypic antimicrobial resistance profiles, obtained with standardized EUCAMP3 microdilution panel, which showed 100% similarity (inclusivity and exclusivity). Variation in measurement methods, including qPCR machines and master mixes showed robustness, essential for laboratories. The assay can be used for the rapid detection of resistance determinants, and is beneficial for monitoring the spread of antibiotic resistance in C. jejuni and C. coli.

Augmented surveillance of antimicrobial resistance with high-throughput robotics detects transnational flow of fluoroquinolone-resistant Escherichia coli strain into poultry

BACKGROUND

Food animal AMR surveillance programs assess only small numbers of Escherichia coli (from 100 to 600 per animal class) nationally each year, severely limiting the evaluation of public health risk(s). Here we demonstrate an affordable approach for early detection of emerging resistance on a broad scale that can also accurately characterize spatial and temporal changes in resistance.

METHODS

Caecal samples (n = 295) obtained from 10 meat poultry were screened using high-throughput robotics. Initial screening via agar dilution (5310 plates) quantified AMR carriage (cfu/g) for each sample. Ciprofloxacin-resistant isolates (n = 91) proceeded to downstream broth microdilution susceptibility testing. A subset of 28 ciprofloxacin-resistant isolates underwent WGS and phylogenetic analysis.

RESULTS

Intra- and inter-flock carriage of resistance varied with drug class. Ampicillin and tetracycline resistance was ubiquitous to most birds in all flocks with an average carriage rate of 5.8 log10 cfu/g. Gentamicin and ciprofloxacin-resistant E. coli colonized fewer birds, and had an average carriage rate of 1.2 log10 cfu/g and 1.0 log10 cfu/g of faeces, respectively. Resistance to extended-spectrum cephalosporins was absent. ST354 was the dominant ST among the WGS isolates, but they demonstrated markedly lower resistance gene carriage than their international counterparts.

CONCLUSIONS

These data amply demonstrate the ineffectiveness of commonly relied-on approaches to AMR surveillance for achieving early detection of emergence, or for measuring spatial and temporal resistance trends. Genetic analysis suggested there has been transnational flow of a ciprofloxacin-resistant strain into Australian poultry flocks, explaining their detection in a nation that prohibits fluoroquinolone use in poultry.

Dissolvable alginate hydrogel-based biofilm microreactors for antibiotic susceptibility assays

Biofilms are found in many infections in the forms of surface-adhering aggregates on medical devices, small clumps in tissues, or even in synovial fluid. Although antibiotic resistance genes are studied and monitored in the clinic, the structural and phenotypic changes that take place in biofilms can also lead to significant changes in how bacteria respond to antibiotics. Therefore, it is important to better understand the relationship between biofilm phenotypes and resistance and develop approaches that are compatible with clinical testing. Current methods for studying antimicrobial susceptibility are mostly planktonic or planar biofilm reactors. In this work, we develop a new type of biofilm reactor-three-dimensional (3D) microreactors-to recreate biofilms in a microenvironment that better mimics those in vivo where bacteria tend to form surface-independent biofilms in living tissues. The microreactors are formed on microplates, treated with antibiotics of 1000 times of the corresponding minimal inhibitory concentrations (1000 × MIC), and monitored spectroscopically with a microplate reader in a high-throughput manner. The hydrogels are dissolvable on demand without the need for manual scraping, thus enabling measurements of phenotypic changes. Bacteria inside the biofilm microreactors are found to survive exposure to 1000 × MIC of antibiotics, and subsequent comparison with plating results reveals no antibiotic resistance-associated phenotypes. The presented microreactor offers an attractive platform to study the tolerance and antibiotic resistance of surface-independent biofilms such as those found in tissues.

WHY STOP? A prospective observational vignette-based study to determine the cognitive-behavioural effects of rapid diagnostic PCR-based point-of-care test results on antibiotic cessation in ICU infections

OBJECTIVES

Point-of-care tests (POCTs) for infection offer accurate rapid diagnostics but do not consistently improve antibiotic stewardship (ASP) of suspected ventilator-associated pneumonia. We aimed to measure the effect of a negative PCR-POCT result on intensive care unit (ICU) clinicians' antibiotic decisions and the additional effects of patient trajectory and cognitive-behavioural factors (clinician intuition, dis/interest in POCT, risk averseness).

DESIGN

Observational cohort simulation study.

SETTING

ICU.

PARTICIPANTS

70 ICU consultants/trainees working in UK-based teaching hospitals.

METHODS

Clinicians saw four case vignettes describing patients who had completed a course of antibiotics for respiratory infection. Vignettes comprised clinical and biological data (ie, white cell count, C reactive protein), varied to create four trajectories: clinico-biological improvement (the 'improvement' case), clinico-biological worsening ('worsening'), clinical improvement/biological worsening ('discordant clin better'), clinical worsening/biological improvement ('discordant clin worse'). Based on this, clinicians made an initial antibiotics decision (stop/continue) and rated confidence (6-point Likert scale). A PCR-based POCT was then offered, which clinicians could accept or decline. All clinicians (including those who declined) were shown the result, which was negative. Clinicians updated their antibiotics decision and confidence.

MEASURES

Antibiotics decisions and confidence were compared pre-POCT versus post-POCT, per vignette.

RESULTS

A negative POCT result increased the proportion of stop decisions (54% pre-POCT vs 70% post-POCT, χ2(1)=25.82, p<0.001, w=0.32) in all vignettes except improvement (already high), most notably in discordant clin worse (49% pre-POCT vs 74% post-POCT). In a linear regression, factors that significantly reduced clinicians' inclination to stop antibiotics were a worsening trajectory (b=-0.73 (-1.33, -0.14), p=0.015), initial confidence in continuing (b=0.66 (0.56, 0.76), p<0.001) and involuntary receipt of POCT results (clinicians who accepted the POCT were more inclined to stop than clinicians who declined it, b=1.30 (0.58, 2.02), p<0.001). Clinician risk averseness was not found to influence antibiotic decisions (b=-0.01 (-0.12, 0.10), p=0.872).

CONCLUSIONS

A negative PCR-POCT result can encourage antibiotic cessation in ICU, notably in cases of clinical worsening (where the inclination might otherwise be to continue). This effect may be reduced by high clinician confidence to continue and/or disinterest in POCT, perhaps due to low trust/perceived utility. Such cognitive-behavioural and trajectorial factors warrant greater consideration in future ASP study design.

Microwell-enhanced optical rapid antibiotic susceptibility testing of single bacteria

Bacteria that are resistant to antibiotics present an increasing burden on healthcare. To address this emerging crisis, novel rapid antibiotic susceptibility testing (AST) methods are eagerly needed. Here, we present an optical AST technique that can determine the bacterial viability within 1 h down to a resolution of single bacteria. The method is based on measuring intensity fluctuations of a reflected laser focused on a bacterium in reflective microwells. Using numerical simulations, we show that both refraction and absorption of light by the bacterium contribute to the observed signal. By administering antibiotics that kill the bacteria, we show that the variance of the detected fluctuations vanishes within 1 h, indicating the potential of this technique for rapid sensing of bacterial antibiotic susceptibility. We envisage the use of this method for massively parallelizable AST tests and fast detection of drug-resistant pathogens.

Portable Colorimetric Hydrogel Beads for Point-of-Care Antimicrobial Susceptibility Testing

Sepsis is a life-threatening condition with systemic inflammatory responses caused by bacterial infections. Considering the emergence of antibiotic-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), sepsis is a great threat to public health. The gold standard methods for antimicrobial susceptibility testing (AST), however, take at least approximately 3 days to implement the entire blood culture, pure culture, and AST processes. To overcome the time-consuming nature of conventional AST, a method employing a chromatic biosensor composed of poly(diacetylene), alginate, and LB broth (PAL) is introduced in this study. Compared to the gold standards, AST with PAL biosensors can be completed within a time frame as short as 16 h. Such a significant reduction in time is possible because the consecutive cultures and AST are carried out simultaneously by encapsulating the bacterial nutrients and detection molecules into a single component. The bead-like hydrogel sensors were used in their freeze-dried form, which endows them with portability and stability, thus making them adequate for point-of-care testing. The PAL biosensor yields minimum inhibitory concentrations comparable to those from the Clinical and Laboratory Standards Institute, and the applicability of the biosensor is further shown in MRSA-infected mice.