Detection Of Antimicrobial Resistance Research Articles

Performance of a hybrid capture-based target enrichment next-generation sequencing for the identification of respiratory pathogens and resistance-associated genes in patients with severe pneumonia.

Severe pneumonia remains the leading infectious cause of death worldwide. The time-consuming nature and suboptimal sensitivity of sputum cultures hamper prompt pathogen detection for tailored treatments. Advanced techniques such as polymerase chain reaction (PCR) and next-generation sequencing (NGS) offer rapid genetic pathogen detection and identification of antimicrobial resistance (AMR) genes. However, the performance of hybrid capture-based target enrichment NGS, e.g., Respiratory Pathogen ID/AMR Enrichment Panel (RPIP), for pathogen detection in patients with severe pneumonia remains uncertain. A prospective study involving adults with severe pneumonia was conducted. Respiratory samples from the lower respiratory tract were collected via bronchoalveolar lavage, bronchial washing, or endotracheal tube suction. The performance of RPIP in pathogen and AMR-associated gene detection was compared to that of conventional culture methods and the multiplex PCR-based FilmArray Pneumonia Panel (FilmArray-PN). A total of 83 subjects were enrolled. The most prevalent pathogens detected by RPIP were Rothia mucilaginosa, Stenotrophomonas maltophilia, Pseudomonas aeruginosa; herpes simplex virus-1, cytomegalovirus, and Epstein-Barr virus, and Pneumocystis jirovecii. Overall, the positive and negative agreement rates for bacterial detection were 63.6% and 97.5% between RPIP and culture methods, respectively, and 55.8% and 99.4% between FilmArray-PN and culture methods, respectively. Compared to FilmArray-PN, RPIP exhibited significantly better detection rates for bacteria (P = 0.029), viruses (P < 0.001), and fungi (P < 0.001) and identified additional blaOXA, blaCMY as extended-spectrum β-lactamase genes and blaOXA, blaSHV as carbapenemase genes. In conclusion, RPIP can sensitively profile respiratory pathogens and is a promising tool for detecting multiple microorganisms and AMR-associated genes in patients with severe pneumonia.IMPORTANCESensitive pathogen detection is pivotal for timely treatment by tailoring adequate antimicrobial agents. Unlike conventional phenotypic approach, novel measures using molecular interrogation appear promising. This study aimed to elucidate the efficacy of a hybrid capture-based target enrichment next-generation sequencing technique (Respiratory Pathogen ID/AMR Enrichment Panel, RPIP) as exemplified in a cohort with severe pneumonia. Pathogen landscape in the population was illustrated by these three methodologies. As compared with multiplex polymerase chain reaction-based FilmArray Pneumonia Panel and conventional culture, RPIP demonstrated significantly improved sensitivity in identifying bacteria, viruses, and fungi. The RPIP also exhibited better performance in identifying different pathogens in patients co-infected with multiple microorganisms. Additionally, the genotypes contributing to antimicrobial resistance were determined by RPIP. The study facilitated the implementation of molecular diagnosis by presenting real-world data, whereas future studies are mandated to generalize such an approach toward different clinical settings.

Bridging Classical Methodologies in Salmonella Investigation with Modern Technologies: A Comprehensive Review.

Advancements in genomics and machine learning have significantly enhanced the study of Salmonella epidemiology. Whole-genome sequencing has revolutionized bacterial genomics, allowing for detailed analysis of genetic variation and aiding in outbreak investigations and source tracking. Short-read sequencing technologies, such as those provided by Illumina, have been instrumental in generating draft genomes that facilitate serotyping and the detection of antimicrobial resistance. Long-read sequencing technologies, including those from Pacific Biosciences and Oxford Nanopore Technologies, offer the potential for more complete genome assemblies and better insights into genetic diversity. In addition to these sequencing approaches, machine learning techniques like decision trees and random forests provide powerful tools for pattern recognition and predictive modeling. Importantly, the study of bacteriophages, which interact with Salmonella, offers additional layers of understanding. Phages can impact Salmonella population dynamics and evolution, and their integration into Salmonella genomics research holds promise for novel insights into pathogen control and epidemiology. This review revisits the history of Salmonella and its pathogenesis and highlights the integration of these modern methodologies in advancing our understanding of Salmonella.

Factors impacting target-enriched long-read sequencing of resistomes and mobilomes.

We investigated the efficiency of target-enriched long-read sequencing (TELSeq) for detecting antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) within complex matrices. We aimed to overcome limitations associated with traditional antimicrobial resistance (AMR) detection methods, including short-read shotgun metagenomics, which can lack sensitivity, specificity, and the ability to provide detailed genomic context. By combining biotinylated probe-based enrichment with long-read sequencing, we facilitated the amplification and sequencing of ARGs, eliminating the need for bioinformatic reconstruction. Our experimental design included replicates of human fecal microbiota transplant material, bovine feces, pristine prairie soil, and a mock human gut microbial community, allowing us to examine variables including genomic DNA input and probe set composition. Our findings demonstrated that TELSeq markedly improves the detection rates of ARGs and MGEs compared to traditional sequencing methods, underlining its potential for accurate AMR monitoring. A key insight from our research is the importance of incorporating mobilome profiles to better predict the transferability of ARGs within microbial communities, prompting a recommendation for the use of combined ARG-MGE probe sets for future studies. We also reveal limitations for ARG detection from low-input workflows, and describe the next steps for ongoing protocol refinement to minimize technical variability and expand utility in clinical and public health settings. This effort is part of our broader commitment to advancing methodologies that address the global challenge of AMR.

A magnetic epitope-imprinted microsphere used for selective separation and rapid detection of SHV-type β-lactamases in bacteria: a novel strategy of antimicrobial resistance detection

BackgroundThe production of β-lactamases is the most prevalent resistance mechanism for β-lactam antibiotics in Gram-negative bacteria. Presently, over 4900 β-lactamases have been discovered, and they are categorized into hundreds of families. In each enzyme family, amino acid substitutions result in subtle changes to enzyme hydrolysis profiles; in contrast, certain conserved sequences retained by all of the family members can serve as important markers for enzyme family identification.ResultsThe SHV family was chosen as the study object. First, a unique 10-mer peptide was identified as SHV family's epitope by an approach of protein fingerprint analysis. Then, an SHV-specific magnetic epitope-imprinted gel polymer (MEI-GP) was prepared by an epitope surface imprinting technique, and its sorption behavior and recognition mechanism for template epitope and SHV were both elaborated. Finally, the MEI-GP was successfully applied to selectively extract SHV from bacteria, and the extracted SHV was submitted to MALDI-TOF MS for specific determination. By following this strategy, other β-lactamase families can also be specifically detected. According to the molecular weight displayed in mass spectra, the kind of β-lactamase and its associated hydrolysis profile on β-lactams can be easily identified. Based on this, an initial drug option scheme can be quickly formulated for antimicrobial therapy. From protein extraction to medication guidance reporting, the mean time to detection (MTTD) was less than 2 h, which is much faster than conventional phenotype-based methods (at least 16–20 h) and gene-based techniques (usually about 8 h).ConclusionsThis enzyme-specific detection strategy combined the specificity of epitope imprinting with the sensitivity of mass spectrometry, enabling β-lactamase to be selectively extracted from bacteria and clearly presented in mass spectra. Compared with other drug resistance detection methods, this technique has good specificity, high sensitivity (≤ 15 mg of bacteria), a short MTTD (less than 2 h), and simple operation, and therefore has a broad application prospect in clinical medicine.Graphical

Role of migratory birds as a risk factor for the transmission of multidrug resistant Salmonella enterica and Escherichia coli to broiler poultry farms and its surrounding environment

Multidrug resistance (MDR) considered as global crisis facing poultry industry. Migratory birds play very important role in the dissemination of antimicrobial resistant pathogen during their fly way specially to poultry farms. Therefore, 750 samples from migratory birds and 300 samples from broiler chicken farms and its environment were collected during the winter seasons of five years (2019 to 2023). The samples were subjected to the isolation of Salmonella enterica and Escherichia coli with the detection of antimicrobial resistance (phenotypic and genotypic) with insight to the genetic similarity between the isolates from migratory birds and broiler chickens’ farms. Different members of Enterobacteriaceae were isolated; Salmonella enterica, Escherichia coli, Citrobacter, Enterobacter, Klebsiella, Proteus, Providencia, Serratia, Hafnia. 298 (28.4%) of S. enterica strains belonging to 27 serovars. S. Typhimurium, S. Kentucky, S. Enteritidis and S. Shangani were the common 4 serotypes between migratory birds and farms. Meanwhile, we found 489 (46.6%) isolates of E. coli belonging to 24 serogroups and O91, O128, O26, O125, O55, O103 and O159 were the common 7serogroups between migratory birds and farms samples. The majority of Salmonella (91.6%; 274 out of 298) and E. coli (92%; 450 out of 489) were MDR. The MDRI range of Salmonella and E. coli was 0.08- 1.The genetic similarity between the isolates of migratory birds and broiler chicken farms were detected by ERICPCR and hot map. This study suggests the continuous applications of surveillance programs for migratory birds and biosecurity measures in poultry farms.

Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data.

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT's super-high accuracy model. ONT's superior performance is attributed to its ability to overcome Illumina's errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT's super-high accuracy data mitigates ONT's traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.

Can flow cytometry be an alternative method for the detection of antimicrobial resistance?

Background: The inappropriate use of antibiotics may cause antibiotic resistance, cause side effects, and eventually cause an increase in healthcare costs. This study aimed to determine a flow cytometry-based test to detect ESBL E. coli and MDR p. aeruginosa in a short time. Methods: The study included 25 E.coli isolates and 25 P.aeruginosa isolates identified by Phoenix TM 100 automated system [Becton Dickinson, USA]. In the flow cytometric method, the percentages of death cells exposed to cephalosporin including ceftazidime [CAZ] or cefotaxime [CTX] and clavulanic acid [CLA] combination, were compared with death cells exposed only to a cephalosporin [CAZ or CTX]. CLA index values [CAZ-CLA and CTX-CLA indices] were obtained for CTX and CAZ. Index values that were higher than 1.5 just for one cephalosporin were accepted as positive. Results: Cell viability was performed after 1-hour exposure to the drugs. When PI staining was applied to ESBL-treated and MDR-treated bacteria, 65% and 85% had nonviable membranes, indicating the method efficiently identified only cells with damaged membranes. Conclusion: Flow cytometry is a rapid and reliable method for the detection of ESBL and MDR in clinical microbiology laboratories Keywords: Antibiotic resistance; ESBL; flow cytometry; MDR.

Effect of pneumococcal conjugate vaccination on pneumococcal carriage in hospitalised children aged 2–59 months in Mongolia: an active pneumonia surveillance programme

Data on changes in pneumococcal serotypes in hospitalised children following the introduction of the pneumococcal conjugate vaccine (PCV) in low-income and middle-income countries are scarce. In 2016, Mongolia introduced the 13-valent PCV (PCV13) into the national immunisation programme. We aimed to describe the trend and impact of PCV13 introduction on pneumococcal carriage in hospitalised children aged 2-59months with pneumonia in Mongolia over a 6-year period. In this active surveillance programme, children aged 2-59months with pneumonia who met the study case definition (cough or difficulty breathing with either respiratory rate ≥50beats per min, oxygen saturation <90%, or clinical diagnosis of severe pneumonia) were enrolled between April 1, 2015, and June 30, 2021, from four districts in Ulaanbaatar. We tested nasopharyngeal samples collected at enrolment for pneumococci using lytA real-time quantitative PCR and conducted molecular serotyping and detection of antimicrobial resistance (AMR) genes with DNA microarray. We used log-binomial regression to estimate prevalence ratios (PRs) of pneumococcal carriage, comparing prevalence in the periods before and after the introduction of PCV13 and between vaccinated and unvaccinated children for three outcomes: overall, PCV13 vaccine-type, and non-PCV13 vaccine-type carriage. PRswere adjusted with covariates that were identified by use of a directed acyclic graph, informed by relevant literature. A total of 17 688 children were enrolled, of whom 17 607 (99·5%) met the study case criteria. 6545 (42·5%) of 15 411collected nasopharyngeal swabs were tested for pneumococci. In all age groups, a similar prevalence of pneumococcal carriage was shown between the pre-PCV13 period and post-PCV13 period (882 [48·0%] of 1837 vs 2174[46·2%] of 4708; adjusted PR 0·98 [95% CI 0·92-1·04]; p=0·60). Overall, vaccine-type carriage reduced by43·6% after the introduction of PCV13 (adjusted PR 0·56 [95% CI 0·51-0·62]; p<0·0001). Younger children (aged 2-23months) showed a 47·7% reduction in vaccine-type carriage (95% CI 41·2-53·5; adjusted PR 0·52 [95% CI 0·46-0·59]; p<0·0001), whereas children aged 24-59months had a 29·3% reduction (12·6-42·8; 0·71[0·57-0·87]; p=0·0014). Prevalence of 6A, 6B, 14, 19F, and 23F decreased following the introduction of PCV13; however, 19F and 6A remained common (5·8% and 2·9%). Non-vaccine-type carriage increased (adjusted PR 1·49[95% CI 1·32-1·67]), with 15A, NT2, and 15B/C being the most prevalent serotypes. Overall, 1761 (89·3%) of 1978analysed samples contained at least one AMR gene. The percentage of samples with any AMR gene decreased with vaccine introduction (92·3% in the pre-PCV13 period vs 85·3% in the post-PCV13 period; adjusted odds ratio 0·49 [95% CI 0·34-0·70]), with similar decreases for samples with at least three AMR genes (46·8% vs 27·6%; 0·44 [0·36-0·55]). 6 years after the introduction of PCV13 in Mongolia, the prevalence of vaccine-type carriage and AMR genes showed a reduction among young hospitalised children with pneumonia. Reductions in vaccine-type carriage are likely to result in reductions in pneumococcal pneumonia. GAVI, the Vaccine Alliance.

Prediction of Antibiotic Susceptibility in E. coli Isolates Using Machine Learning.

Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment of infections caused by various microorganisms, including the gram-negative bacterium Escherichia coli. Traditional culture-based methods for detecting AMR are slow and imprecise, hindering timely clinical decision-making. In contrast, whole genome sequencing offers a faster, more accurate alternative for AMR detection. A novel machine learning study leveraging whole genomic sequencing data to predict the phenotypic susceptibility of Escherichia coli to ciprofloxacin is presented. Using a novel dataset of 256 bacterial genomes and related susceptibility data, features were generated based on AMRFinderPlus findings and k-mer frequencies. The machine learning models, Random Forest and XGBoost, were evaluated using a five-fold cross-validation approach. Results showed that combining AMRFinderPlus and k-mer frequency features could achieve more than 90% accuracy using the XGBoost gradient boosting model. These findings suggest that the best results may be achieved using reference-free features combined with known gene markers.

Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern β-lactam/β-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.IMPORTANCEPseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

Contributions of Long-Read Sequencing for the Detection of Antimicrobial Resistance.

In the context of increasing antimicrobial resistance (AMR), whole-genome sequencing (WGS) of bacteria is considered a highly accurate and comprehensive surveillance method for detecting and tracking the spread of resistant pathogens. Two primary sequencing technologies exist: short-read sequencing (50-300 base pairs) and long-read sequencing (thousands of base pairs). The former, based on Illumina sequencing platforms (ISPs), provides extensive coverage and high accuracy for detecting single nucleotide polymorphisms (SNPs) and small insertions/deletions, but is limited by its read length. The latter, based on platforms such as Oxford Nanopore Technologies (ONT), enables the assembly of genomes, particularly those with repetitive regions and structural variants, although its accuracy has historically been lower. We performed a head-to-head comparison of these techniques to sequence the K. pneumoniae VS17 isolate, focusing on blaNDM resistance gene alleles in the context of a surveillance program. Discrepancies between the ISP (blaNDM-4 allele identified) and ONT (blaNDM-1 and blaNDM-5 alleles identified) were observed. Conjugation assays and Sanger sequencing, used as the gold standard, confirmed the validity of ONT results. This study demonstrates the importance of long-read or hybrid assemblies for accurate carbapenemase resistance gene identification and highlights the limitations of short reads in the context of gene duplications or multiple alleles. In this proof-of-concept study, we conclude that recent long-read sequencing technology may outperform standard short-read sequencing for the accurate identification of carbapenemase alleles. Such information is crucial given the rising prevalence of strains producing multiple carbapenemases, especially as WGS is increasingly used for epidemiological surveillance and infection control.

Prediction of A. Baumannii Amikacin Resistance in Clinical Metagenomics.

Respiratory tract infections are a serious threat to health, especially in the presence of antimicrobial resistance (AMR). Existing AMR detection methods are limited by slow turnaround times and low accuracy due to the presence of false positives and negatives. In this study, we simulate 1,116 clinical metagenomics samples on both Illumina and Nanopore sequencing from curated, real-world sequencing of A. baumannii respiratory infections and build AI models to predict resistance to amikacin. The best performance is achieved by XGBoost on Illumina sequencing (area under the ROC curve = 0.7993 on 5-fold cross-validation).

Antimicrobial Resistance and Phenotypic Detection of Extended Spectrum Beta-Lactamase in Escherichia coli from Children with Cases of Diarrhea in Nasarawa-South, Nasarawa State, Nigeria

Antimicrobial Resistance and Phenotypic Detection of Extended Spectrum Beta-Lactamase in Escherichia coli from Children with Cases of Diarrhea in Nasarawa-South, Nasarawa State, Nigeria

Antimicrobial resistance, biofilm formation, and molecular detection of efflux pump and biofilm genes among Klebsiella pneumoniae clinical isolates from Northern Jordan

The current study aimed to investigate the antimicrobial susceptibility profiles, biofilm production capabilities, and the prevalence of efflux pump and biofilm-associated genes among Klebsiella pneumoniae clinical isolates. One hundred sixty-seven K. pneumoniae isolates were collected from microbiology laboratories in Northern Jordan hospitals. Antimicrobial susceptibility was tested using the Kirby-Bauer method. The double-disk synergy test was used to detect the extended-spectrum beta-lactamase (ESBL) phenotype. PCR was used to detect the frequency of acrAB,tolC, and mdtk efflux pump genes and fimH-1,mrkA, and mrkD biofilm-associated genes among the isolates. The highest nonsusceptibility was observed against azithromycin (87.4 %) and nitrofurantoin (85.0 %). Among the isolates, 75.4 % and 92.2 % were multidrug resistant and produced biofilms, respectively. Efflux pump genes acrAB, tolC, and mdtK were found in 96.4 %, 95.2 %, and 90.4 % of the isolates, respectively. Biofilm-associated genes mrkD, mrkA, and fimH-1 were found in 92.2 %, 89.2 %, and 88.6 % of the isolates, respectively. The presence of the mrkA was significantly associated with biofilm formation. Overall, high percentages of multi-drug resistance, efflux pump, and biofilm-associated genes were observed among the isolates. Subsequent studies are recommended to monitor changes in the prevalence of resistance phenotypes and genotypes of isolates.

Changes in Antibiotic Use and Disruptions to Antimicrobial Resistance Detection in South Africa and Uganda, 2019 – 2020

Background: The COVID-19 pandemic disrupted routine health services worldwide, including systems to detect antimicrobial resistance (AR). AR is a mounting global health threat with some studies showing the highest mortality rate from AR infection is in Sub-Saharan Africa (SSA). Antibiotic use is a major contributor to AR. We sought to characterize COVID-19-related changes to antibiotic use and AR detection capacity in two countries in SSA from 2019 to 2020. Methods: Health facilities (HF) in South Africa and Uganda were surveyed as part of a larger study assessing disruptions to essential health services in SSA in the context of COVID-19. Modified stratified random sampling of HF by facility level was conducted in regions with high COVID-19 cumulative prevalence. Hospital pharmacists were surveyed to identify perceived changes in antibiotic use. Among facilities with the capacity to detect AR, surveys were conducted with AR laboratory managers to identify perceived changes in staff, equipment, training, and supplies. Descriptive data analysis was conducted using frequencies and proportions. Results: A total of 39 HFs in South Africa and 45 HFs in Uganda responded to the antibiotic use survey. Increases in total antibiotic use from 2019 to 2020 were reported by 82% (23/28) of HF in South Africa and 68% (27/40) in Uganda. Increased use of antibiotics for multi-drug resistant bacteria (per World Health Organization Reserve classification) was reported by 36% (9/25) and 38% (8/21) of HFs in South Africa and Uganda, respectively. 19 HFs in South Africa and 12 HFs in Uganda responded to the AR detection capacity survey. HFs in both countries reported decreases in laboratory staff responsible for AR (33% [13/40] in South Africa and 31% [11/35] in Uganda). Decreased availability of reagents and consumables for bacteriology and antimicrobial susceptibility testing was reported by 50% (8/16) and 33% (4/12) of HFs, and decreased availability of specimen collection supplies for bacterial cultures was reported by 41% (7/17) and 42% (5/12) of HFs in South Africa and Uganda, respectively. Diversion of laboratory supplies was reported in both countries (32% [6/19] in South Africa and 25% [3/12] of HF in Uganda). Conclusions: HFs in South Africa and Uganda reported increases in antibiotic prescribing, a risk factor for increased AR, concurrently with disruptions in AR detection capacity during the early phases of the COVID-19 pandemic. These findings emphasize the importance of investing in bacteriology and AR testing in SSA and maintaining support during infectious disease pandemics.

Keeping up with the pathogens: improved antimicrobial resistance detection and prediction from Pseudomonas aeruginosa genomes

BackgroundAntimicrobial resistance (AMR) is an intensifying threat that requires urgent mitigation to avoid a post-antibiotic era. Pseudomonas aeruginosa represents one of the greatest AMR concerns due to increasing multi- and pan-drug resistance rates. Shotgun sequencing is gaining traction for in silico AMR profiling due to its unambiguity and transferability; however, accurate and comprehensive AMR prediction from P. aeruginosa genomes remains an unsolved problem.MethodsWe first curated the most comprehensive database yet of known P. aeruginosa AMR variants. Next, we performed comparative genomics and microbial genome-wide association study analysis across a Global isolate Dataset (n = 1877) with paired antimicrobial phenotype and genomic data to identify novel AMR variants. Finally, the performance of our P. aeruginosa AMR database, implemented in our AMR detection and prediction tool, ARDaP, was compared with three previously published in silico AMR gene detection or phenotype prediction tools—abritAMR, AMRFinderPlus, ResFinder—across both the Global Dataset and an analysis-naïve Validation Dataset (n = 102).ResultsOur AMR database comprises 3639 mobile AMR genes and 728 chromosomal variants, including 75 previously unreported chromosomal AMR variants, 10 variants associated with unusual antimicrobial susceptibility, and 281 chromosomal variants that we show are unlikely to confer AMR. Our pipeline achieved a genotype-phenotype balanced accuracy (bACC) of 85% and 81% across 10 clinically relevant antibiotics when tested against the Global and Validation Datasets, respectively, vs. just 56% and 54% with abritAMR, 58% and 54% with AMRFinderPlus, and 60% and 53% with ResFinder. ARDaP’s superior performance was predominantly due to the inclusion of chromosomal AMR variants, which are generally not identified with most AMR identification tools.ConclusionsOur ARDaP software and associated AMR variant database provides an accurate tool for predicting AMR phenotypes in P. aeruginosa, far surpassing the performance of current tools. Implementation of ARDaP for routine AMR prediction from P. aeruginosa genomes and metagenomes will improve AMR identification, addressing a critical facet in combatting this treatment-refractory pathogen. However, knowledge gaps remain in our understanding of the P. aeruginosa resistome, particularly the basis of colistin AMR.

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis.

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

The real-world impact of the BioFire FilmArray blood culture identification 2 panel on antimicrobial stewardship among patients with bloodstream infections in intensive care units with a high burden of drug-resistant pathogens

BackgroundThe increasing prevalence of drug-resistant pathogens leads to delays in adequate antimicrobial treatment in intensive care units (ICU). The real-world influence of the BioFire FilmArray Blood Culture Identification 2 (BCID2) panel on pathogen identification, diagnostic concordance with conventional culture methods, and antimicrobial stewardship in the ICU remains unexplored. MethodsThis retrospective observational study, conducted from July 2021 to August 2023, involved adult ICU patients with positive blood cultures who underwent BCID2 testing. The concordance between BCID2 and conventional culture results was examined, and its impact on antimicrobial stewardship was assessed through a comprehensive retrospective review of patient records by intensivists. ResultsA total of 129 blood specimens from 113 patients were analysed. Among these patients, a high proportion of drug-resistant strains were noted, including carbapenem-resistant Klebsiella pneumoniae (CRKP) (57.1%), carbapenem-resistant Acinetobacter calcoaceticus-baumannii complex (100%), methicillin-resistant Staphylococcus aureus (MRSA) (70%), and vancomycin-resistant Enterococcus faecium (VRE) (100%). The time from blood culture collection to obtaining BCID2 results was significantly shorter than conventional culture (46.2 h vs. 86.9 h, p < 0.001). BCID2 demonstrated 100% concordance in genotype–phenotype correlation in antimicrobial resistance (AMR) for CRKP, carbapenem-resistant Escherichia coli, MRSA, and VRE. A total of 40.5% of patients received inadequate empirical antimicrobial treatment. The antimicrobial regimen was adjusted or confirmed in 55.4% of patients following the BCID2 results. ConclusionsIn the context of a high burden of drug-resistant pathogens, BCID2 demonstrated rapid pathogen and AMR detection, with a noticeable impact on antimicrobial stewardship in BSI in the ICU.

FRI-112 Rapid infection diagnostics in liver disease- evaluation of a multiplex PCR platform to improve infection and antimicrobial resistance detection

FRI-112 Rapid infection diagnostics in liver disease- evaluation of a multiplex PCR platform to improve infection and antimicrobial resistance detection

Antimicrobial resistance, enterotoxin and biofilm production genes in Staphylococcus spp. isolated from facilities and fomites in veterinary hospital in the Caatinga biome.

The Caatinga biome occurs only in Brazil and offers epidemiological conditions that should be assessed differently from other regions of Brazil and the world. Thus, the aim of this survey was to identify antimicrobial resistance, enterotoxin and biofilm production genes in Staphylococcus spp. isolated from facilities and fomites in a veterinary hospital in Caatinga biome. Samples were collected from surfaces of small animal clinical care tables (n =8), cages in the dog and cat hospitalisation sector and animals with infectious diseases (n = 21), small animal surgical centre (n =8), sterilisation sector (n =7) and stethoscopes (n = 32) by using sterile swabs. Bacterial isolation and identification, antimicrobial resistance phenotypic test and molecular detection of antimicrobial resistance, biofilm formation and enterotoxin genes were carried out. Ninety-five bacterial isolates were obtained, and 29 (30.5%) were identified as Staphylococcus spp. Overall, 13 isolates (44.8%) of six species of Staphylococcus spp. showed antimicrobial resistance profile, as well as S. haemolyticus expressed phenotypic profile of multidrug resistance. The antimicrobials with the highest resistance rates were penicillin and tetracycline. The most frequent resistance genes were blaZ and tetM, both detected in 10 (76.9%) isolates. The mecA, tetL and tetK genes had frequencies of 38.5% (5/13), 23.1% (3/13) and 15.4% (2/13), respectively. The biofilm production marker, icaD gene, was detected in one S. sciuri strain. SEE gene, which encodes enterotoxins, was detected in 15.4% (2/13) of the strains (S. pseudintermedius and S. intermedius). The occurrence of Staphylococcus spp. carrying resistance genes to diferent classes of antimicrobials, presenting MDR phenotypic pattern and carrying enterotoxins and biofim encoding genes recovered from veterinary hospital facilities and fomites in the Caatinga biome reinforce the need to implement prevention cares in veterinary practices to avoid One Health-concerning conditions.