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582700.9381Duplex dPCR System for Rapid Identification of Gram-Negative Pathogens in the Blood of Patients with Bloodstream Infection: A Culture-Independent Approach. Early and accurate detection of pathogens is important to improve clinical outcomes of bloodstream infections (BSI), especially in the case of drug-resistant pathogens. In this study, we aimed to develop a culture-independent digital PCR (dPCR) system for multiplex detection of major sepsiscausing gram-negative pathogens and antimicrobial resistance genes using plasma DNA from BSI patients. Our duplex dPCR system successfully detected nine targets (five bacteria-specific targets and four antimicrobial resistance genes) through five reactions within 3 hours. The minimum detection limit was 50 ag of bacterial DNA, suggesting that 1 CFU/ml of bacteria in the blood can be detected. To validate the clinical applicability, cell-free DNA samples from febrile patients were tested with our system and confirmed high consistency with conventional blood culture. This system can support early identification of some drug-resistant gram-negative pathogens, which can help improving treatment outcomes of BSI.202134528911
847610.9381Identification of a Major QTL (qRRs-10.1) That Confers Resistance to Ralstonia solanacearum in Pepper (Capsicum annuum) Using SLAF-BSA and QTL Mapping. The soilborne pathogen Ralstonia solanacearum is the causal agent of bacterial wilt (BW), a major disease of pepper (Capsicum annuum). The genetic basis of resistance to this disease in pepper is not well known. This study aimed to identify BW resistance markers in pepper. Analysis of the dynamics of bioluminescent R. solanacearum colonization in reciprocal grafts of a resistant (BVRC 1) line and a susceptible (BVRC 25) line revealed that the resistant rootstock effectively suppressed the spreading of bacteria into the scion. The two clear-cut phenotypic distributions of the disease severity index in 440 F2 plants derived from BVRC 25 × BVRC 1 indicated that a major genetic factor as well as a few minor factors that control BW resistance. By specific-locus amplified fragment sequencing combined with bulked segregant analysis, two adjacent resistance-associated regions on chromosome 10 were identified. Quantitative trait (QTL) mapping revealed that these two regions belong to a single QTL, qRRs-10.1. The marker ID10-194305124, which reached a maximum log-likelihood value at 9.79 and accounted for 19.01% of the phenotypic variation, was located the closest to the QTL peak. A cluster of five predicted R genes and three defense-related genes, which are located in close proximity to the significant markers ID10-194305124 or ID10-196208712, are important candidate genes that may confer BW resistance in pepper.201931771239
524320.9375Multiplex Hybrid Capture Improves the Deep Detection of Antimicrobial Resistance Genes from Wastewater Treatment Plant Effluents to Assess Environmental Issues. Metagenomic sequencing (mDNA-seq) is one of the best approaches to address antimicrobial resistance (AMR) issues and characterize AMR genes (ARGs) and their host bacteria (ARB); however, the sensitivity provided is insufficient for the overall detection in wastewater treatment plant (WWTP) effluents because the effluent is well treated. This study investigated the multiplex hybrid capture (xHYB) method (QIAseq × HYB AMR Panel) and its potential to increase AMR assessment sensitivity. The mDNA-Seq analysis suggested that the WWTP effluents had an average of 104 reads per kilobase of gene per million (RPKM) for the detection of all targeted ARGs, whereas xHYB significantly improved detection at 601,576 RPKM, indicating an average 5,805-fold increase in sensitivity. For instance, sul1 was detected at 15 and 114,229 RPKM using mDNA-seq and xHYB, respectively. The bla(CTX-M), bla(KPC), and mcr gene variants were not detected by mDNA-Seq but were detected by xHYB at 67, 20, and 1,010 RPKM, respectively. This study demonstrates that the multiplex xHYB method could be a suitable evaluation standard with high sensitivity and specificity for deep-dive detection, highlighting a broader illustration of ongoing dissemination in the entire community.202337433210
506830.9374Ultrasensitive Label-Free Detection of Unamplified Multidrug-Resistance Bacteria Genes with a Bimodal Waveguide Interferometric Biosensor. Infections by multidrug-resistant bacteria are becoming a major healthcare emergence with millions of reported cases every year and an increasing incidence of deaths. An advanced diagnostic platform able to directly detect and identify antimicrobial resistance in a faster way than conventional techniques could help in the adoption of early and accurate therapeutic interventions, limiting the actual negative impact on patient outcomes. With this objective, we have developed a new biosensor methodology using an ultrasensitive nanophotonic bimodal waveguide interferometer (BiMW), which allows a rapid and direct detection, without amplification, of two prevalent and clinically relevant Gram-negative antimicrobial resistance encoding sequences: the extended-spectrum betalactamase-encoding gene blaCTX-M-15 and the carbapenemase-encoding gene blaNDM-5 We demonstrate the extreme sensitivity and specificity of our biosensor methodology for the detection of both gene sequences. Our results show that the BiMW biosensor can be employed as an ultrasensitive (attomolar level) and specific diagnostic tool for rapidly (less than 30 min) identifying drug resistance. The BiMW nanobiosensor holds great promise as a powerful tool for the control and management of healthcare-associated infections by multidrug-resistant bacteria.202033086716
582840.9373Target-enriched sequencing enables accurate identification of bloodstream infections in whole blood. Bloodstream infections are within the top ten causes of death globally, with a mortality rate of up to 70%. Gold standard blood culture testing is time-consuming, resulting in delayed, but accurate, treatment. Molecular methods, such as RT-qPCR, have limited targets in one run. We present a new Ampliseq detection system (ADS) combining target amplification and next-generation sequencing for accurate identification of bacteria, fungi, and antimicrobial resistance determinants directly from blood samples. In this study, we included removal of human genomic DNA during nucleic acid extraction, optimized the target sequence set and drug resistance genes, performed antimicrobial resistance profiling of clinical isolates, and evaluated mock specimens and clinical samples by ADS. ADS successfully identified pathogens at the species-level in 36 h, from nucleic acid extraction to results. Besides pathogen identification, ADS can also present drug resistance profiles. ADS enabled detection of all bacteria and accurate identification of 47 pathogens. In 20 spiked samples and 8 clinical specimens, ADS detected at least 92.81% of reads mapped to pathogens. ADS also showed consistency with the three culture-negative samples, and correctly identified pathogens in four of five culture-positive clinical blood specimens. This Ampliseq-based technology promises broad coverage and accurate pathogen identification, helping clinicians to accurately diagnose and treat bloodstream infections.202234915067
908350.9368ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.202438725076
507160.9367Versatile and Portable Cas12a-mediated Detection of Antibiotic Resistance Markers. Antibiotic-resistant bacteria are spreading in clinical, industrial, and environmental ecosystems. The spreading dynamics to and from the environment are unknown, largely due to the lack of appropriate (robust, fast, low-cost) analytical assays. In this study, we developed C12a, a versatile molecular toolbox to detect genetic markers of antibiotic resistance using CRISPR/Cas12a. Biochemical characterization show that the C12a toolbox can detect less than 100 attoMolar of pure DNA fragments from the blaCTX-M15 and floR genes, conferring resistance to b-lactams and amphenicols, respectively important for human and veterinary uses. In microbiological assays, C12a detected less than 10(2) CFU/mL and high concordance was observed if compared to antibiotic susceptibility tests, PCR, or to whole genome sequencing. Additionally, C12a confirmed a high prevalence of the integrase/integron system in E. coli isolates containing multiple antibiotic resistance genes (ARGs). The C12a toolbox shows equivalent detection performance in diverse laboratory settings, results redout (Fluorescence vs FLA) or input sample. Altogether, this work presents a comprehensive proof-of-concept, development description, and biochemical characterization of a collection of molecular tools to detect antibiotic resistance markers in a one health setup.202539605319
147570.9366Evaluation of the FilmArray(®) Pneumonia Plus Panel for Rapid Diagnosis of Hospital-Acquired Pneumonia in Intensive Care Unit Patients. The FilmArray(®) Pneumonia plus Panel (FAPP) is a new multiplex molecular test for hospital-acquired pneumonia (HAP), which can rapidly detect 18 bacteria, 9 viruses, and 7 resistance genes. We aimed to compare the diagnosis performance of FAPP with conventional testing in 100 intensive care unit (ICU) patients who required mechanical ventilation, with clinically suspected HAP. A total of 237 samples [76 bronchoalveolar lavages (BAL(DS)) and 82 endotracheal aspirates (ETA(DS)) obtained at HAP diagnosis, and 79 ETA obtained during follow-up (ETA(TT))], were analyzed independently by routine microbiology testing and FAPP. 58 patients had paired BAL(DS) and ETA(DS). The positivity thresholds of semi-quantified bacteria were 10(3)-10(4) CFUs/mL or 10(4) copies/mL for BAL, and 10(5) CFUs/mL or copies/mL for ETA. Respiratory commensals (H. influenzae, S. aureus, E. coli, S. pneumoniae) were the most common pathogens. Discordant results for bacterial identification were observed in 33/76 (43.4%) BAL(DS) and 36/82 (43.9%) ETA(DS), and in most cases, FAPP identified one supplemental bacteria (23/33 BAL(DS) and 21/36 ETA(DS)). An absence of growth, or polybacterial cultures, explained almost equally the majority of the non-detections in culture. No linear relationship was observed between bin and CFUs/mL variables. Concordant results between paired BAL(DS) and ETA(DS) were obtained in 46/58 (79.3%) patients with FAPP. One of the 17 resistance genes detected with FAPP (mecA/C and MREJ) was not confirmed by conventional testing. Overall, FAPP enhanced the positivity rate of diagnostic testing, with increased recognition of coinfections. Implementing this strategy may allow clinicians to make more timely and informed decisions.202032983057
503980.9364Analytical validation of a novel high multiplexing real-time PCR array for the identification of key pathogens causative of bacterial ventilator-associated pneumonia and their associated resistance genes. OBJECTIVES: Rapid diagnosis and appropriate empirical antimicrobial therapy before the availability of conventional microbiological results is of pivotal importance for the clinical outcome of ventilator-associated pneumonia (VAP). We evaluated the VAPChip, a novel, closed cartridge molecular tool aiming to identify directly from clinical samples and within a working day the principal bacteria causative of VAP as well as clinically relevant β-lactam resistance genes. METHODS: The Real-time Array PCR for Infectious Diseases (RAP-ID) is a novel technology that combines multiplex PCR with real-time microarray detection. The VAPChip is a closed cartridge kit adapted to the RAP-ID instrument that targets 13 key respiratory pathogens causative of VAP and 24 relevant antimicrobial resistance genes that mediate resistance to β-lactam agents, including extended-spectrum cephalosporins and carbapenems. Analytical validation of the VAPChip was carried out blindly on a collection of 292 genotypically characterized bacterial reference and clinical isolates, including 225 isolates selected on the basis of their species identification and antimicrobial resistance profiles and 67 bacterial isolates belonging to the oropharyngeal flora not targeted by the array. RESULTS: The limit of detection of the assay lies between 10 and 100 genome copies/PCR and the dynamic range is five orders of magnitude permitting at least semi-quantitative reporting of the results. Sensitivity, specificity and negative and positive predictive values ranged from 95.8% to 100% for species identification and detection of resistance genes. CONCLUSIONS: VAPChip is a novel diagnostic tool able to identify resistant bacterial isolates by RAP-ID technology. The results of this analytical validation have to be confirmed on clinical specimens.201323065698
999590.9363Direct fluorescence in situ hybridization (FISH) in Escherichia coli with a target-specific quantum dot-based molecular beacon. Quantum dots (QDs) are inorganic fluorescent nanocrystals with excellent properties such as tunable emission spectra and photo-bleaching resistance compared with organic dyes, which make them appropriate for applications in molecular beacons. In this work, quantum dot-based molecular beacons (QD-based MBs) were fabricated to specifically detect β-lactamase genes located in pUC18 which were responsible for antibiotic resistance in bacteria Escherichia coli (E. coli) DH5α. QD-based MBs were constructed by conjugating mercaptoacetic acid-quantum dots (MAA-QDs) with black hole quencher 2 (BHQ2) labeled thiol DNA vial metal-thiol bonds. Two types of molecular beacons, double-strands beacons and hairpin beacons, were observed in product characterization by gel electrophoresis. Using QD-based MBs, one-step FISH in tiny bacteria DH5α was realized for the first time. QD-based MBs retained their bioactivity when hybridizing with complementary target DNA, which showed excellent advantages of eliminating background noise caused by adsorption of non-specific bioprobes and achieving clearer focus of genes in plasmids pUC18, and capability of bacterial cell penetration and signal specificity in one-step in situ hybridization.201020729070
9981100.9363High-contrast imaging of cellular non-repetitive drug-resistant genes via in situ dead Cas12a-labeled PCR. In situ imaging of genes of pathogenic bacteria can profile cellular heterogeneity, such as the emergence of drug resistance. Fluorescence in situ hybridization (FISH) serves as a classic approach to image mRNAs inside cells, but it remains challenging to elucidate genomic DNAs and relies on multiple fluorescently labeled probes. Herein, we present a dead Cas12a (dCas12a)-labeled polymerase chain reaction (CasPCR) assay for high-contrast imaging of cellular drug-resistant genes. We employed a syncretic dCas12a-green fluorescent protein (dCas12a-GFP) to tag the amplicons, thereby enabling high-contrast imaging and avoiding multiple fluorescently labeled probes. The CasPCR assay can quantify quinolone-resistant Salmonella enterica in mixed populations and identify them isolated from poultry farms.202439229640
5069110.9362MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples. Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene bla(NDM) and bla(KPC), colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-enhanced recombinase polymerase amplification (RPA) and a refined Chelex-100 lysis method with HRP-catalyzed lateral flow immunoassay (LFIA), we developed an MCL-PRPA-HLFIA cascade assay system for detecting these genes in UTI samples. The refined Chelex-100 lysis method extracts target DNA from UTI samples in 20 min without high-speed centrifugation or pre-incubation of urine samples. Following optimization, the cascade detection system achieved an LOD of 10(2) CFU/mL with satisfactory specificity and could detect these genes in both simulated and actual UTI samples. It takes less than an hour to complete the process without the use of high-speed centrifuges or other specialized equipment, such as PCR amplifiers. The MCL-PRPA-HLFIA cascade assay system provides new ideas for the construction of rapid detection methods for pan-drug-resistant genes in clinical UTI samples and provides the necessary medication guidance for UTI treatment.202337047757
7281120.9362City-scale monitoring of antibiotic resistance genes by digital PCR and metagenomics. BACKGROUND: Anthropogenic activities significantly contribute to the dissemination of antibiotic resistance genes (ARGs), posing a substantial threat to humankind. The development of methods that allow robust ARG surveillance is a long-standing challenge. Here, we use city-scale monitoring of ARGs by using two of the most promising cutting-edge technologies, digital PCR (dPCR) and metagenomics. METHODS: ARG hot-spots were sampled from the urban water and wastewater distribution systems. Metagenomics was used to provide a broad view of ARG relative abundance and richness in the prokaryotic and viral fractions. From the city-core ARGs in all samples, the worldwide dispersed sul2 and tetW conferring resistance to sulfonamide and tetracycline, respectively, were monitored by dPCR and metagenomics. RESULTS: The largest relative overall ARG abundance and richness were detected in the hospital wastewater and the WWTP inlet (up to ≈6,000 ARGs/Gb metagenome) with a large fraction of unclassified resistant bacteria. The abundance of ARGs in DNA and RNA contigs classified as viruses was notably lower, demonstrating a reduction of up to three orders of magnitude compared to contigs associated to prokaryotes. By metagenomics and dPCR, a similar abundance tendency of sul2 and tetW was obtained, with higher abundances in hospital wastewater and WWTP input (≈125-225 ARGs/Gb metagenome). dPCR absolute abundances were between 6,000 and 18,600 copies per ng of sewage DNA (≈10(5-7) copies/mL) and 6.8 copies/mL in seawater near the WWTP discharging point. CONCLUSIONS: dPCR was more sensitive and accurate, while metagenomics provided broader coverage of ARG detection. While desirable, a reliable correlation of dPCR absolute abundance units into metagenomic relative abundance units was not obtained here (r(2) < 0.4) suggesting methodological factors that introduce variability. Evolutionary pressure does not significantly select the targeted ARGs in natural aquatic environments.202438491508
5826130.9361Rapid and accurate sepsis diagnostics via a novel probe-based multiplex real-time PCR system. Sepsis is a critical clinical emergency that requires prompt diagnosis and intervention. Its prevalence has increased due to the aging population and increased antibiotic resistance. Early identification and the use of innovative technologies are crucial for improving patient outcomes. Modern methodologies are needed to minimize the turnaround time for diagnosis and improve outcomes. Rapid diagnostic tests and multiplex PCR are effective but have limitations in identifying a range of pathogens and target genes. Our study evaluated two novel probe-based multiplex real-time PCR systems: the SEPSI ID and SEPSI DR panels. These systems can quickly identify bacterial and fungal pathogens, alongside antibiotic resistance genes. The assays cover 29 microorganisms (gram-negative bacteria, gram-positive bacteria, yeast, and mold species), alongside 23 resistance genes and four virulence factors. A streamlined workflow uses 2 µL of broth from positive blood cultures (BCs) without nucleic acid extraction and provides results in approximately 1 h. We present the results from an evaluation of 228 BCs and 22 isolates previously characterized by whole-genome sequencing. In comparison to the reference methods, the SEPSI ID panel demonstrated a sensitivity of 96.88%, a specificity of 100%, and a PPV of 100%, whereas the SEPSI DR panel showed a sensitivity of 97.8%, a PPV of 89.7%, and a specificity of 96.7%. Both panels also identified additional pathogens and resistance-related targets not detected by conventional methods. This assay shows promise for rapidly and accurately diagnosing sepsis. Future studies should validate its performance in various clinical settings to enhance sepsis management and improve patient outcomes.IMPORTANCEWe present a new diagnostic method that enables the quick and precise identification of pathogens and resistance genes from positive blood cultures, eliminating the need for nucleic acid extraction. This technique can also be used on fresh pathogen cultures. It has the potential to greatly improve treatment protocols, leading to better patient outcomes, more responsible antibiotic use, and more efficient management of healthcare resources.202541025980
9743140.9361Simultaneous Detection of Antibiotic Resistance Genes on Paper-Based Chip Using [Ru(phen)(2)dppz](2+) Turn-on Fluorescence Probe. Antibiotic resistance, the ability of some bacteria to resist antibiotic drugs, has been a major global health burden due to the extensive use of antibiotic agents. Antibiotic resistance is encoded via particular genes; hence the specific detection of these genes is necessary for diagnosis and treatment of antibiotic resistant cases. Conventional methods for monitoring antibiotic resistance genes require the sample to be transported to a central laboratory for tedious and sophisticated tests, which is grueling and time-consuming. We developed a paper-based chip, integrated with loop-mediated isothermal amplification (LAMP) and the "light switch" molecule [Ru(phen)(2)dppz](2+), to conduct turn-on fluorescent detection of antibiotic resistance genes. In this assay, the amplification reagents can be embedded into test spots of the chip in advance, thus simplifying the detection procedure. [Ru(phen)(2)dppz](2+) was applied to intercalate into amplicons for product analysis, enabling this assay to be operated in a wash-free format. The paper-based detection device exhibited a limit of detection (LOD) as few as 100 copies for antibiotic resistance genes. Meanwhile, it could detect antibiotic resistance genes from various bacteria. Noticeably, the approach can be applied to other genes besides antibiotic resistance genes by simply changing the LAMP primers. Therefore, this paper-based chip has the potential for point-of-care (POC) applications to detect various gene samples, especially in resource-limited conditions.201829323478
9742150.9361BOCS: DNA k-mer content and scoring for rapid genetic biomarker identification at low coverage. A single, inexpensive diagnostic test capable of rapidly identifying a wide range of genetic biomarkers would prove invaluable in precision medicine. Previous work has demonstrated the potential for high-throughput, label-free detection of A-G-C-T content in DNA k-mers, providing an alternative to single-letter sequencing while also having inherent lossy data compression and massively parallel data acquisition. Here, we apply a new bioinformatics algorithm - block optical content scoring (BOCS) - capable of using the high-throughput content k-mers for rapid, broad-spectrum identification of genetic biomarkers. BOCS uses content-based sequence alignment for probabilistic mapping of k-mer contents to gene sequences within a biomarker database, resulting in a probability ranking of genes on a content score. Simulations of the BOCS algorithm reveal high accuracy for identification of single antibiotic resistance genes, even in the presence of significant sequencing errors (100% accuracy for no sequencing errors, and >90% accuracy for sequencing errors at 20%), and at well below full coverage of the genes. Simulations for detecting multiple resistance genes within a methicillin-resistant Staphylococcus aureus (MRSA) strain showed 100% accuracy at an average gene coverage of merely 0.515, when the k-mer lengths were variable and with 4% sequencing error within the k-mer blocks. Extension of BOCS to cancer and other genetic diseases met or exceeded the results for resistance genes. Combined with a high-throughput content-based sequencing technique, the BOCS algorithm potentiates a test capable of rapid diagnosis and profiling of genetic biomarkers ranging from antibiotic resistance to cancer and other genetic diseases.201931173943
9741160.9360ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer. The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying resistant strains and multi-drug patterns in E. coli is a major challenge with whole genome sequencing (WGS) and next-generation sequencing (NGS) data. To address this issue, we suggest ARGai 1.0 a deep learning architecture enhanced with generative adversarial networks (GANs). We mitigate data scarcity difficulties by augmenting limited experimental datasets with synthetic data generated by GANs. Our in-silico method (augmentation with feature selection) improves the identification of resistance genes in E. coli by using feature extraction techniques to identify valuable features from actual and GAN-generated data. Employing comprehensive validation, we exhibit the effectiveness of our ARGai 1.0 in precisely identifying the informative and resistant genes. In addition, our ARGai 1.0 identifies the resistant strains with a classification accuracy of 98.96 % on Deep Convolutional Generative Adversarial Network (DCGAN) augmented data. Additionally, ARGai 1.0 achieves more than 98 % of sensitivity and specificity. We also benchmark our ARGai 1.0 with several state-of-the-art AI models for resistant strain classification. In the fight against antibiotic resistance, ARGai 1.0 offers a promising avenue for computational genomics. With implications for research and clinical practice, this work shows the potential of deep networks with GAN augmentation as a practical and successful method for gene identification in E. coli.202539813877
2236170.9360Development of a Multiplex PCR Platform for the Rapid Detection of Bacteria, Antibiotic Resistance, and Candida in Human Blood Samples. The diagnosis of bloodstream infections (BSIs) still relies on blood culture (BC), but low turnaround times may hinder the early initiation of an appropriate antimicrobial therapy, thus increasing the risk of infection-related death. We describe a direct and rapid multiplex PCR-based assay capable of detecting and identifying 16 bacterial and four Candida species, as well as three antibiotic-resistance determinants, in uncultured samples. Using whole-blood samples spiked with microorganisms at low densities, we found that the MicrobScan assay had a mean limit of detection of 15.1 ± 3.3 CFU of bacteria/Candida per ml of blood. When applied to positive BC samples, the assay allowed the sensitive and specific detection of BSI pathogens, including bla(KPC)-, mecA-, or vanA/vanB-positive bacteria. We evaluated the assay using prospectively collected blood samples from patients with suspected BSI. The sensitivity and specificity were 86.4 and 97.0%, respectively, among patients with positive BCs for the microorganisms targeted by the assay or patients fulfilling the criteria for infection. The mean times to positive or negative assay results were 5.3 ± 0.2 and 5.1 ± 0.1 h, respectively. Fifteen of 20 patients with MicrobScan assay-positive/BC-negative samples were receiving antimicrobial therapy. In conclusion, the MicrobScan assay is well suited to complement current diagnostic methods for BSIs.201931799215
9739180.9359Au-Fe(3)O(4) nanozyme coupled with CRISPR-Cas12a for sensitive and visual antibiotic resistance diagnosing. The accumulation and spread of antibiotic resistance bacteria (ARB) in the environment may accelerate the formation of superbugs and seriously threaten the health of all living beings. The timeliness and accurate diagnosing of antibiotic resistance is essential to controlling the propagation of superbugs in the environment and formulating effective public health management programs. Herein, we developed a speedy, sensitive, accurate, and user-friendly colorimetric assay for antibiotic resistance, via a synergistic combination of the peroxidase-like property of the Au-Fe(3)O(4) nanozyme and the specific gene identification capability of the CRISPR-Cas12a. Once the CRISPR-Cas12a system recognizes a target resistance gene, it activates its trans-cleavage activity and subsequently releases the Au-Fe(3)O(4) nanozymes, which oxidizes the 3,3,5,5-tetramethylbenzidine (TMB) with color change from transparent to blue. The diagnosing signals could be captured and analyzed by a smartphone. This method detected kanamycin-resistance genes, ampicillin-resistance genes, and chloramphenicol-resistance genes by simple operation steps with high sensitivity (<0.1 CFU μL(-1)) and speediness (<1 h). This approach may prove easy for the accurate and sensitive diagnosis of the ARGs or ARB in the field, thus surveilling and controlling the microbial water quality flexibly and efficiently.202336925313
5120190.9359ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods.201729177089