# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 5194 | 0 | 1.0000 | Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis. We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development. | 2019 | 30429253 |
| 5828 | 1 | 0.9992 | Target-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. | 2022 | 34915067 |
| 5826 | 2 | 0.9992 | Rapid 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. | 2025 | 41025980 |
| 5467 | 3 | 0.9991 | Whole genome sequencing-based classification of human-related Haemophilus species and detection of antimicrobial resistance genes. BACKGROUND: Bacteria belonging to the genus Haemophilus cause a wide range of diseases in humans. Recently, H. influenzae was classified by the WHO as priority pathogen due to the wide spread of ampicillin resistant strains. However, other Haemophilus spp. are often misclassified as H. influenzae. Therefore, we established an accurate and rapid whole genome sequencing (WGS) based classification and serotyping algorithm and combined it with the detection of resistance genes. METHODS: A gene presence/absence-based classification algorithm was developed, which employs the open-source gene-detection tool SRST2 and a new classification database comprising 36 genes, including capsule loci for serotyping. These genes were identified using a comparative genome analysis of 215 strains belonging to ten human-related Haemophilus (sub)species (training dataset). The algorithm was evaluated on 1329 public short read datasets (evaluation dataset) and used to reclassify 262 clinical Haemophilus spp. isolates from 250 patients (German cohort). In addition, the presence of antibiotic resistance genes within the German dataset was evaluated with SRST2 and correlated with results of traditional phenotyping assays. RESULTS: The newly developed algorithm can differentiate between clinically relevant Haemophilus species including, but not limited to, H. influenzae, H. haemolyticus, and H. parainfluenzae. It can also identify putative haemin-independent H. haemolyticus strains and determine the serotype of typeable Haemophilus strains. The algorithm performed excellently in the evaluation dataset (99.6% concordance with reported species classification and 99.5% with reported serotype) and revealed several misclassifications. Additionally, 83 out of 262 (31.7%) suspected H. influenzae strains from the German cohort were in fact H. haemolyticus strains, some of which associated with mouth abscesses and lower respiratory tract infections. Resistance genes were detected in 16 out of 262 datasets from the German cohort. Prediction of ampicillin resistance, associated with bla(TEM-1D), and tetracycline resistance, associated with tetB, correlated well with available phenotypic data. CONCLUSIONS: Our new classification database and algorithm have the potential to improve diagnosis and surveillance of Haemophilus spp. and can easily be coupled with other public genotyping and antimicrobial resistance databases. Our data also point towards a possible pathogenic role of H. haemolyticus strains, which needs to be further investigated. | 2022 | 35139905 |
| 5796 | 4 | 0.9991 | Antibiotic treatment algorithm development based on a microarray nucleic acid assay for rapid bacterial identification and resistance determination from positive blood cultures. Rapid diagnosis of bloodstream infections remains a challenge for the early targeting of an antibiotic therapy in sepsis patients. In recent studies, the reliability of the Nanosphere Verigene Gram-positive and Gram-negative blood culture (BC-GP and BC-GN) assays for the rapid identification of bacteria and resistance genes directly from positive BCs has been demonstrated. In this work, we have developed a model to define treatment recommendations by combining Verigene test results with knowledge on local antibiotic resistance patterns of bacterial pathogens. The data of 275 positive BCs were analyzed. Two hundred sixty-three isolates (95.6%) were included in the Verigene assay panels, and 257 isolates (93.5%) were correctly identified. The agreement of the detection of resistance genes with subsequent phenotypic susceptibility testing was 100%. The hospital antibiogram was used to develop a treatment algorithm on the basis of Verigene results that may contribute to a faster patient management. | 2016 | 26712265 |
| 2247 | 5 | 0.9991 | Metagenomic identification of pathogens and antimicrobial-resistant genes in bacterial positive blood cultures by nanopore sequencing. Nanopore sequencing workflows have attracted increasing attention owing to their fast, real-time, and convenient portability. Positive blood culture samples were collected from patients with bacterial bloodstream infection and tested by nanopore sequencing. This study compared the sequencing results for pathogen taxonomic profiling and antimicrobial resistance genes to those of species identification and phenotypic drug susceptibility using traditional microbiology testing. A total of 37 bacterial positive blood culture results of strain genotyping by nanopore sequencing were consistent with those of mass spectrometry. Among them, one mixed infection of bacteria and fungi was identified using nanopore sequencing and confirmatory quantitative polymerase chain reaction. The amount of sequencing data was 21.89 ± 8.46 MB for species identification, and 1.0 MB microbial strain data enabled accurate determination. Data volumes greater than or equal to 94.6 MB nearly covered all the antimicrobial resistance genes of the bacteria in our study. In addition, the results of the antimicrobial resistance genes were compared with those of phenotypic drug susceptibility testing for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus. Therefore, the nanopore sequencing platform for rapid identification of causing pathogens and relevant antimicrobial resistance genes complementary to conventional blood culture outcomes may optimize antimicrobial stewardship management for patients with bacterial bloodstream infection. | 2023 | 38192400 |
| 5833 | 6 | 0.9990 | Rapid identification, virulence analysis and resistance profiling of Staphylococcus aureus by gene segment-based DNA microarrays: application to blood culture post-processing. Up to now, blood culturing systems are the method of choice to diagnose bacteremia. However, definitive pathogen identification from positive blood cultures is a time-consuming procedure, requiring subculture and biochemical analysis. We developed a microarray for the identification of Staphylococcus aureus comprising PCR generated gene-segments, which can reduce the blood culture post-processing time to a single day. Moreover, it allows concomitant identification of virulence factors and antibiotic resistance determinants directly from positive blood cultures without previous amplification by PCR. The assay unambiguously identifies most of the important virulence genes such as tsst-1, sea, seb, eta and antibiotic resistance genes such as mecA, aacA-aphD, blaZ and ermA. To obtain positive signals, 20 ng of purified genomic S. aureus DNA or 2 microg of total DNA extracted from blood culture was required. The microarray specifically distinguished S. aureus from gram-negative bacteria as well as from closely related coagulase negative staphylococci (CoNS). The microarray-based identification of S. aureus can be accomplished on the same day blood cultures become positive in the Bactec. The results of our study demonstrate the feasibility of microarray-based systems for the direct identification and characterization of bacteria from cultured clinical specimens. | 2007 | 17141897 |
| 5823 | 7 | 0.9990 | Comparing Patient Risk Factor-, Sequence Type-, and Resistance Locus Identification-Based Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections. Rapid diagnostic tests for antibiotic resistance that identify the presence or absence of antibiotic resistance genes/loci are increasingly being developed. However, these approaches usually neglect other sources of predictive information which could be identified over shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage. Using a data set of 414 Escherichia coli isolates recovered from separate episodes of bacteremia at a single academic institution in Toronto, Ontario, Canada, between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic risk factor-, pathogen sequence type [ST]-, and resistance gene identification-based approaches) for classifying phenotypic resistance to three antibiotics representing classes of broad-spectrum antimicrobial therapy (ceftriaxone [a 3rd-generation cephalosporin], ciprofloxacin [a fluoroquinolone], and gentamicin [an aminoglycoside]). We used logistic regression models to generate model receiver operating characteristic (ROC) curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) areas under the curves (AUCs). Epidemiologic risk factor-based models based on two simple risk factors (prior antibiotic exposure and recent prior susceptibility of Gram-negative bacteria) provided a modest predictive discrimination, with AUCs ranging from 0.65 to 0.74. Sequence type-based models demonstrated strong discrimination (AUCs, 0.83 to 0.94) across all three antibiotic classes. The addition of epidemiologic risk factors to sequence type significantly improved the ability to predict resistance for all antibiotics (P < 0.05). Resistance gene identification-based approaches provided the highest degree of discrimination (AUCs, 0.88 to 0.99), with no statistically significant benefit being achieved by adding the patient epidemiologic predictors. In summary, sequence type or other lineage-based approaches could produce an excellent discrimination of antibiotic resistance and may be improved by incorporating readily available patient epidemiologic predictors but are less discriminatory than identification of the presence of known resistance loci. | 2019 | 30894438 |
| 4939 | 8 | 0.9990 | Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. OBJECTIVES: The introduction of metagenomic sequencing to diagnostic microbiology has been hampered by slowness, cost and complexity. We explored whether MinION nanopore sequencing could accelerate diagnosis and resistance profiling, using complicated urinary tract infections as an exemplar. METHODS: Bacterial DNA was enriched from clinical urines (n = 10) and from healthy urines 'spiked' with multiresistant Escherichia coli (n = 5), then sequenced by MinION. Sequences were analysed using external databases and bioinformatic pipelines or, ultimately, using integrated real-time analysis applications. Results were compared with Illumina data and resistance phenotypes. RESULTS: MinION correctly identified pathogens without culture and, among 55 acquired resistance genes detected in the cultivated bacteria by Illumina sequencing, 51 were found by MinION sequencing directly from the urines; with three of the four failures in an early run with low genome coverage. Resistance-conferring mutations and allelic variants were not reliably identified. CONCLUSIONS: MinION sequencing comprehensively identified pathogens and acquired resistance genes from urine in a timeframe similar to PCR (4 h from sample to result). Bioinformatic pipeline optimization is needed to better detect resistances conferred by point mutations. Metagenomic-sequencing-based diagnosis will enable clinicians to adjust antimicrobial therapy before the second dose of a typical (i.e. every 8 h) antibiotic. | 2017 | 27667325 |
| 5092 | 9 | 0.9990 | Rapid detection of antibiotic resistance genes in lactic acid bacteria using PMMA-based microreactor arrays. The emergence of lactic acid bacteria (LABs) resistant to existing antimicrobial drugs is a growing health crisis. To decrease the overuse of antibiotics, molecular diagnostic systems that can rapidly determine the presence of antibiotic resistance (AR) genes in LABs from yogurt samples are needed. This paper describes a fully integrated, miniaturized plastic chip and closed-tube detection chemistry that performs multiplex nucleic acid amplification. High-throughput identification of AR genes was achieved through this approach, and six AR genes were analyzed simultaneously in < 2 h. This time-to-result included the time required for the extraction of DNA. The detection limit of the chip was 10(3) CFU mL(-1), which was consistent with that of tube LAMP. We detected and identified multiple DNAs, including streptomycin, tetracycline, and vancomycin resistance-associated genes, with complete concordance to the Kirby-Bauer disk diffusion method.Key Points• A miniaturized chip was presented, and multiplex nucleic acid amplification was performed.• The device can be integrated with LAMP for rapid detection of antibiotic resistance genes.• The approach had a high throughput of AR gene analysis in lactic acid bacteria. | 2020 | 32488313 |
| 5797 | 10 | 0.9990 | PCR-reverse blot hybridization assay for screening and identification of pathogens in sepsis. Rapid and accurate identification of the pathogens involved in bloodstream infections is crucial for the prompt initiation of appropriate therapy, as this can decrease morbidity and mortality rates. A PCR-reverse blot hybridization assay for sepsis, the reverse blot hybridization assay (REBA) Sepsis-ID test, was developed; it uses pan-probes to distinguish Gram-positive and -negative bacteria and fungi. In addition, the assay was designed to identify bacteria and fungi using six genus-specific and 13 species-specific probes; it uses additional probes for antibiotic resistance genes, i.e., the mecA gene of methicillin-resistant Staphylococcus aureus (MRSA) and the vanA and vanB genes of vancomycin-resistant enterococci (VRE). The REBA Sepsis-ID test successfully identified clinical isolates and blood culture samples as containing Gram-positive bacteria, Gram-negative bacteria, or fungi. The results matched those obtained with conventional microbiological methods. For the REBA Sepsis-ID test, of the 115 blood culture samples tested, 47 (40.8%) and 49 (42.6%) samples were identified to the species and genus levels, respectively, and the remaining 19 samples (16.5%), which included five Gram-positive rods, were identified as Gram-positive bacteria, Gram-negative bacteria, or fungi. The antibiotic resistances of the MRSA and VRE strains were identified using both conventional microbiological methods and the REBA Sepsis-ID test. In conclusion, the REBA Sepsis-ID test developed for this study is a fast and reliable test for the identification of Gram-positive bacteria, Gram-negative bacteria, fungi, and antibiotic resistance genes (including mecA for MRSA and the vanA and vanB genes for VRE) in bloodstream infections. | 2013 | 23447637 |
| 5694 | 11 | 0.9990 | Multiplex characterization of human pathogens including species and antibiotic-resistance gene identification. The efficient medical treatment of infections requires detailed information about the pathogens involved and potential antibiotic-resistance mechanisms. The dramatically increasing incidence of multidrug-resistant bacteria especially highlights the importance of sophisticated diagnostic tests enabling a fast patient-customized therapy. However, the current molecular detection methods are limited to either the detection of species or only a few antibiotic-resistance genes.In this work, we present a human pathogen characterization assay using a rRNA gene microarray identifying 75 species comprising bacteria and fungi. A statistical classifier was developed to facilitate the automated species identification. Additionally, the clinically most important β-lactamases were identified simultaneously in a 100-plex reaction using padlock probes and the same microarray. The specificity and sensitivity of the combined assay was determined using clinical isolates. The detection limit was 10(5) c.f.u. ml(-1), recovering 89 % of the detectable β-lactamase-encoding genes specifically. The total assay time was less than 7 hand the modular character of the antibiotic-resistance detection allows the easy integration of further genetic targets. In summary, we present a fast, highly specific and sensitive multiplex pathogen characterization assay. | 2016 | 26489938 |
| 5819 | 12 | 0.9990 | Application of mNGS in the Etiological Analysis of Lower Respiratory Tract Infections and the Prediction of Drug Resistance. Lower respiratory tract infections (LRTIs) have high morbidity and mortality rates. However, traditional etiological detection methods have not been able to meet the needs for the clinical diagnosis and prognosis of LRTIs. The rapid development of metagenomic next-generation sequencing (mNGS) provides new insights for the diagnosis and treatment of LRTIs; however, little is known about how to interpret the application of mNGS results in LRTIs. In this study, lower respiratory tract specimens from 46 patients with suspected LRTIs were tested simultaneously using conventional microbiological detection methods and mNGS. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the logarithm of reads per kilobase per million mapped reads [lg(RPKM)], genomic coverage, and relative abundance of the organism in predicting the true-positive pathogenic bacteria. True-positive viruses were identified according to the lg(RPKM) threshold of bacteria. We also evaluated the ability to predict drug resistance genes using mNGS. Compared to that using conventional detection methods, the false-positive detection rate of pathogenic bacteria was significantly higher using mNGS. It was concluded from the ROC curves that the lg(RPKM) and genomic coverage contributed to the identification of pathogenic bacteria, with the performance of lg(RPKM) being the best (area under the curve [AUC] = 0.99). The corresponding lg(RPKM) threshold for identifying the pathogenic bacteria was -1.35. Thirty-five strains of true-positive virus were identified based on the lg(RPKM) threshold of bacteria, with the detection of human gammaherpesvirus 4 being the highest and prone to coinfection with Pseudomonas aeruginosa, Acinetobacter baumannii, and Stenotrophomonas maltophilia. Antimicrobial susceptibility tests (AST) revealed the resistance of bacteria containing drug resistance genes (detected by mNGS). However, the drug resistance genes of some multidrug-resistant bacteria were not detected. As an emerging technology, mNGS has shown many advantages for the unbiased etiological detection and the prediction of antibiotic resistance. However, a correct understanding of mNGS results is a prerequisite for its clinical application, especially for LRTIs. IMPORTANCE LRTIs are caused by hundreds of pathogens, and they have become a great threat to human health due to the limitations of traditional etiological detection methods. As an unbiased approach to detect pathogens, mNGS overcomes such etiological diagnostic challenges. However, there is no unified standard on how to use mNGS indicators (the sequencing reads, genomic coverage, and relative abundance of each organism) to distinguish between pathogens and colonizing microorganisms or contaminant microorganisms. Here, we selected the mNGS indicator with the best identification performance and established a cutoff value for the identification of pathogens in LRTIs using ROC curves. In addition, we also evaluated the accuracy of antibiotic resistance prediction using mNGS. | 2022 | 35171007 |
| 5087 | 13 | 0.9990 | Sensitive colorimetric detection of antibiotic resistant Staphylococcus aureus on dairy farms using LAMP with pH-responsive polydiacetylene. Rapidly and accurately detecting antibiotic-resistant pathogens in agriculture and husbandry is important since these represent a major threat to public health. While much attention has been dedicated to detecting now-common resistant bacteria, such as methicillin-resistant Staphylococcus aureus, fewer methods have been developed to assess resistance against macrolides in Staphylococcus aureus (SA). Here, we report a visual on-site detection system for macrolide resistant SA in dairy products. First, metagenomic sequencing in raw milk, cow manure, water and aerosol deposit collected from dairy farms around Tianjin was used to identify the most abundant macrolide resistance gene, which was found to be the macB gene. In parallel, SA housekeeping genes were screened to allow selective identification of SA, which resulted in the selection of the SAOUHSC_01275 gene. Next, LAMP assays targeting the above-mentioned genes were developed and interpreted by agarose gel electrophoresis. For on-site application, different pH-sensitive colorimetric LAMP indicators were compared, which resulted in selection of polydiacetylene (PDA) as the most sensitive candidate. Additionally, a semi-quantitative detection could be realized by analyzing the RGB information via smartphone with a LOD of 1.344 × 10(-7) ng/μL of genomic DNA from a milk sample. Finally, the proposed method was successfully carried out at a real farm within 1 h from sample to result by using freeze-dried reagents and portable devices. This is the first instance in which PDA is used to detect LAMP products, and this generic read-out system can be expanded to other antibiotic resistant genes and bacteria. | 2023 | 36327562 |
| 5777 | 14 | 0.9990 | Rapid Detection of Antimicrobial Resistance Genes in Critically Ill Children Using a Custom TaqMan Array Card. Bacteria are identified in only 22% of critically ill children with respiratory infections treated with antimicrobial therapy. Once an organism is isolated, antimicrobial susceptibility results (phenotypic testing) can take another day. A rapid diagnostic test identifying antimicrobial resistance (AMR) genes could help clinicians make earlier, informed antimicrobial decisions. Here we aimed to validate a custom AMR gene TaqMan Array Card (AMR-TAC) for the first time and assess its feasibility as a screening tool in critically ill children. An AMR-TAC was developed using a combination of commercial and bespoke targets capable of detecting 23 AMR genes. This was validated using isolates with known phenotypic resistance. The card was then tested on lower respiratory tract and faecal samples obtained from mechanically ventilated children in a single-centre observational study of respiratory infection. There were 82 children with samples available, with a median age of 1.2 years. Major comorbidity was present in 29 (35%) children. A bacterial respiratory pathogen was identified in 13/82 (16%) of children, of which 4/13 (31%) had phenotypic AMR. One AMR gene was detected in 49/82 (60%), and multiple AMR genes were detected in 14/82 (17%) children. Most AMR gene detections were not associated with the identification of phenotypic AMR. AMR genes are commonly detected in samples collected from mechanically ventilated children with suspected respiratory infections. AMR-TAC may have a role as an adjunct test in selected children in whom there is a high suspicion of antimicrobial treatment failure. | 2023 | 38136735 |
| 5971 | 15 | 0.9990 | Detection of antibiotic resistance genes in different Salmonella serovars by oligonucleotide microarray analysis. In this study the feasibility of 50- and 60-mer oligonucleotides in microarray analysis for the detection and identification of antibiotic resistance genes in various Salmonella strains was assessed. The specificity of the designed oligonucleotides was evaluated, furthermore the optimal spotting concentration was determined. The oligonucleotide microarray was used to screen two sets of Salmonella strains for the presence of several antibiotic resistance genes. Set 1 consisted of strains with variant Salmonella Genomic Island 1 (SGI1) multidrug resistance (MDR) regions of which the antibiotic resistance profiles and genotypes were known. The second set contained strains of which initially only phenotypic data were available. The microarray results of the first set of Salmonella strains perfectly matched with the phenotypic and genotypic information. The microarray data of the second set were almost completely in concordance with the available phenotypic data. It was concluded that the microarray technique in combination with random primed genomic labeling and 50- or 60-mer oligonucleotides is a powerful tool for the detection of antibiotic resistance genes in bacteria. | 2005 | 15823391 |
| 5829 | 16 | 0.9990 | Diagnosing Antibiotic Resistance Using Nucleic Acid Enzymes and Gold Nanoparticles. The rapid and accurate detection of antimicrobial resistance is critical to limiting the spread of infections and delivering effective treatments. Here, we developed a rapid, sensitive, and simple colorimetric nanodiagnostic platform to identify disease-causing pathogens and their associated antibiotic resistance genes within 2 h. The platform can detect bacteria from different biological samples (i.e., blood, wound swabs) with or without culturing. We validated the multicomponent nucleic acid enzyme-gold nanoparticle (MNAzyme-GNP) platform by screening patients with central line associated bloodstream infections and achieved a clinical sensitivity and specificity of 86% and 100%, respectively. We detected antibiotic resistance in methicillin-resistant Staphylococcus aureus (MRSA) in patient swabs with 90% clinical sensitivity and 95% clinical specificity. Finally, we identified mecA resistance genes in uncultured nasal, groin, axilla, and wound swabs from patients with 90% clinical sensitivity and 95% clinical specificity. The simplicity and versatility for detecting bacteria and antibiotic resistance markers make our platform attractive for the broad screening of microbial pathogens. | 2021 | 33970612 |
| 5827 | 17 | 0.9990 | Duplex 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. | 2021 | 34528911 |
| 5795 | 18 | 0.9990 | Direct identification of Gram-positive bacteria and resistance determinants from blood cultures using a microarray-based nucleic acid assay: in-depth analysis of microarray data for undetermined results. BACKGROUND: The Verigene Gram-Positive Blood Culture (BC-GP) nucleic acid assay (Nanosphere, Inc., Northbrook, IL, USA) is a newly developed microarray-based test with which 12 Gram-positive bacterial genes and three resistance determinants can be detected using blood culture broths. We evaluated the performance of this assay and investigated the signal characteristics of the microarray images. METHODS: At the evaluation stage, we tested 80 blood cultures that were positive for various bacteria (68 bacteria covered and 12 not covered by the BC-GP panel) collected from the blood of 36 patients and 44 spiked samples. In instances where the automated system failed and errors were called, we manually inspected microarray images, measured the signal intensities of target spots, and reclassified the results. RESULTS: With the manual analysis of the microarray images of 14 samples for which error calls were reported, we could obtain correct identification results for 12 samples without the need for retesting, because strong signals in the target spots were clearly discriminable from background noise. With our interpretation strategy, we could obtain 97.1% sensitivity and 100% specificity for bacterial identification by using the BC-GP assay. The two unidentified bacteria were viridans group streptococci, which produced weaker target signals. During the application stage, among 25 consecutive samples positive for Gram-positive bacteria, we identified two specimens with error calls as Streptococcus spp. by using manual analysis. CONCLUSIONS: With help of the manual review of the microarray images, the BC-GP assay could successfully identify species and resistance markers for many clinically important Gram-positive bacteria. | 2015 | 25536666 |
| 5779 | 19 | 0.9990 | Development of a One-Step Multiplex qPCR Assay for Detection of Methicillin and Vancomycin Drug Resistance Genes in Antibiotic-Resistant Bacteria. The most common antibiotic-resistant bacteria in Korea are methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE). Pathogen identification in clinical laboratories can be divided into traditional phenotype- and genotype-based methods, both of which are complementary to each other. The genotype-based method using multiplex real-time polymerase chain reaction (PCR) is a rapid and accurate technique that analyzes material at the genetic level by targeting genes simultaneously. Accordingly, we aimed to develop a rapid method for studying the genetic characteristics of antibiotic-resistant bacteria and to provide an experimental guide for the efficient antibiotic resistance gene analysis of mecA detection for MRSA and vanA or vanB detection for VRE using a one-step multiplex qPCR assay at an early stage of infection. As a result, the sensitivity and specificity of the mecA gene for clinical S. aureus isolates, including MRSA and methicillin-susceptible S. aureus, were 97.44% (95% CI, 86.82-99.87%) and 96.15% (95% CI, 87.02-99.32%), respectively. The receiver operating characteristic area under the curve for the diagnosis of MRSA was 0.9798 (*** p < 0.0001). Therefore, the molecular diagnostic method using this newly developed one-step multiplex qPCR assay can provide accurate and rapid results for the treatment of patients with MRSA and VRE infections. | 2024 | 39452724 |