# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 5826 | 0 | 0.9028 | 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 |
| 5068 | 1 | 0.9026 | Ultrasensitive 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. | 2020 | 33086716 |
| 5194 | 2 | 0.9001 | 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 |
| 9076 | 3 | 0.8997 | ResiDB: An automated database manager for sequence data. The amount of publicly available DNA sequence data is drastically increasing, making it a tedious task to create sequence databases necessary for the design of diagnostic assays. The selection of appropriate sequences is especially challenging in genes affected by frequent point mutations such as antibiotic resistance genes. To overcome this issue, we have designed the webtool resiDB, a rapid and user-friendly sequence database manager for bacteria, fungi, viruses, protozoa, invertebrates, plants, archaea, environmental and whole genome shotgun sequence data. It automatically identifies and curates sequence clusters to create custom sequence databases based on user-defined input sequences. A collection of helpful visualization tools gives the user the opportunity to easily access, evaluate, edit, and download the newly created database. Consequently, researchers do no longer have to manually manage sequence data retrieval, deal with hardware limitations, and run multiple independent software tools, each having its own requirements, input and output formats. Our tool was developed within the H2020 project FAPIC aiming to develop a single diagnostic assay targeting all sepsis-relevant pathogens and antibiotic resistance mechanisms. ResiDB is freely accessible to all users through https://residb.ait.ac.at/. | 2021 | 33495705 |
| 5380 | 4 | 0.8995 | In Vitro Screening of a 1280 FDA-Approved Drugs Library against Multidrug-Resistant and Extensively Drug-Resistant Bacteria. Alternative strategies against multidrug-resistant (MDR) bacterial infections are suggested to clinicians, such as drug repurposing, which uses rapidly available and marketed drugs. We gathered a collection of MDR bacteria from our hospital and performed a phenotypic high-throughput screening with a 1280 FDA-approved drug library. We used two Gram positive (Enterococcus faecium P5014 and Staphylococcus aureus P1943) and six Gram negative (Acinetobacter baumannii P1887, Klebsiella pneumoniae P9495, Pseudomonas aeruginosa P6540, Burkholderia multivorans P6539, Pandoraea nosoerga P8103, and Escherichia coli DSM105182 as the reference and control strain). The selected MDR strain panel carried resistance genes or displayed phenotypic resistance to last-line therapies such as carbapenems, vancomycin, or colistin. A total of 107 compounds from nine therapeutic classes inhibited >90% of the growth of the selected Gram negative and Gram positive bacteria at a drug concentration set at 10 µmol/L, and 7.5% were anticancer drugs. The common hit was the antiseptic chlorhexidine. The activity of niclosamide, carmofur, and auranofin was found against the selected methicillin-resistant S. aureus. Zidovudine was effective against colistin-resistant E. coli and carbapenem-resistant K. pneumoniae. Trifluridine, an antiviral, was effective against E. faecium. Deferoxamine mesylate inhibited the growth of XDR P. nosoerga. Drug repurposing by an in vitro screening of a drug library is a promising approach to identify effective drugs for specific bacteria. | 2022 | 35326755 |
| 5828 | 5 | 0.8993 | 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 |
| 8161 | 6 | 0.8991 | Integrative strategies against multidrug-resistant bacteria: Synthesizing novel antimicrobial frontiers for global health. Concerningly, multidrug-resistant bacteria have emerged as a prime worldwide trouble, obstructing the treatment of infectious diseases and causing doubts about the therapeutic accidentalness of presently existing drugs. Novel antimicrobial interventions deserve development as conventional antibiotics are incapable of keeping pace with bacteria evolution. Various promising approaches to combat MDR infections are discussed in this review. Antimicrobial peptides are examined for their broad-spectrum efficacy and reduced ability to develop resistance, while phage therapy may be used under extreme situations when antibiotics fail. In addition, the possibility of CRISPR-Cas systems for specifically targeting and eradicating resistance genes from bacterial populations will be explored. Nanotechnology has opened up the route to improve the delivery system of the drug itself, increasing the efficacy and specificity of antimicrobial action while protecting its host. Discovering potential antimicrobial agents is an exciting prospect through developments in synthetic biology and the rediscovery of natural product-based medicines. Moreover, host-directed therapies are now becoming popular as an adjunct to the main strategies of therapeutics without specifically targeting pathogens. Although these developments appear impressive, questions about production scaling, regulatory approvals, safety, and efficacy for clinical employment still loom large. Thus, tackling the MDR burden requires a multi-pronged plan, integrating newer treatment modalities with existing antibiotic regimens, enforcing robust stewardship initiatives, and effecting policy changes at the global level. The international health community can gird itself against the growing menace of antibiotic resistance if collaboration between interdisciplinary bodies and sustained research endeavours is encouraged. In this study, we evaluate the synergistic potential of combining various medicines in addition to summarizing recent advancements. To rethink antimicrobial stewardship in the future, we provide a multi-tiered paradigm that combines pathogen-focused and host-directed strategies. | 2025 | 40914328 |
| 9742 | 7 | 0.8988 | BOCS: 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. | 2019 | 31173943 |
| 9741 | 8 | 0.8987 | ARGai 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. | 2025 | 39813877 |
| 828 | 9 | 0.8986 | Screening for Resistant Bacteria, Antimicrobial Resistance Genes, Sexually Transmitted Infections and Schistosoma spp. in Tissue Samples from Predominantly Vaginally Delivered Placentae in Ivory Coast and Ghana. Medical complications during pregnancy have been frequently reported from Western Africa with a particular importance of infectious complications. Placental tissue can either become the target of infectious agents itself, such as, e.g., in the case of urogenital schistosomiasis, or be subjected to contamination with colonizing or infection-associated microorganisms of the cervix or the vagina during vaginal delivery. In the retrospective cross-sectional assessment presented here, the quantitative dimension of infection or colonization with selected resistant or pathogenic bacteria and parasites was regionally assessed. To do so, 274 collected placental tissues from Ivory Coastal and Ghanaian women were subjected to selective growth of resistant bacteria, as well as to molecular screening for beta-lactamase genes, Schistosoma spp. and selected bacterial causative agents of sexually transmitted infections (STI). Panton-Valentine-negative methicillin-resistant Staphylococcus aureus (MRSA) was grown from 1.8% of the tissue samples, comprising the spa types t008 and t688, as well as the newly detected ones, t12101 (n = 2) and t12102. While the culture-based recovery of resistant Enterobacterales and nonfermentative rod-shaped Gram-negative bacteria failed, molecular assessments confirmed beta-lactamase genes in 31.0% of the samples with multiple detections of up to four resistance genes per sample and bla(CTX-M), bla(IMP), bla(GES), bla(VIM), bla(OXA-58)-like, bla(NDM), bla(OXA-23)-like, bla(OXA-48)-like and bla(KPC) occurring in descending order of frequency. The beta-lactamase genes bla(OXA-40/24)-like, bla(NMC_A/IMI), bla(BIC), bla(SME), bla(GIM) and bla(DIM) were not detected. DNA of the urogenital schistosomiasis-associated Schistosoma haematobium complex was recorded in 18.6% of the samples, but only a single positive signal for S. mansoni with a high cycle-threshold value in real-time PCR was found. Of note, higher rates of schistosomiasis were observed in Ghana (54.9% vs. 10.3% in Ivory Coast) and Cesarean section was much more frequent in schistosomiasis patients (61.9% vs. 14.8% in women without Schistosoma spp. DNA in the placenta). Nucleic acid sequences of nonlymphogranuloma-venereum-associated Chlamydia trachomatis and of Neisseria gonorrhoeae were recorded in 1.1% and 1.9% of the samples, respectively, while molecular attempts to diagnose Treponema pallidum and Mycoplasma genitalium did not lead to positive results. Molecular detection of Schistosoma spp. or STI-associated pathogens was only exceptionally associated with multiple resistance gene detections in the same sample, suggesting epidemiological distinctness. In conclusion, the assessment confirmed considerable prevalence of urogenital schistosomiasis and resistant bacterial colonization, as well as a regionally expected abundance of STI-associated pathogens. Continuous screening offers seem advisable to minimize the risks for the pregnant women and their newborns. | 2023 | 37623959 |
| 1477 | 10 | 0.8986 | Multicenter Evaluation of the BIOFIRE Blood Culture Identification 2 Panel for Detection of Bacteria, Yeasts, and Antimicrobial Resistance Genes in Positive Blood Culture Samples. Diagnostic tools that can rapidly identify and characterize microbes growing in blood cultures are important components of clinical microbiology practice because they help to provide timely information that can be used to optimize patient management. This publication describes the bioMérieux BIOFIRE Blood Culture Identification 2 (BCID2) Panel clinical study that was submitted to the U.S. Food & Drug Administration. Results obtained with the BIOFIRE BCID2 Panel were compared to standard-of-care (SoC) results, sequencing results, PCR results, and reference laboratory antimicrobial susceptibility testing results to evaluate the accuracy of its performance. Results for 1,093 retrospectively and prospectively collected positive blood culture samples were initially enrolled, and 1,074 samples met the study criteria and were included in the final analyses. The BIOFIRE BCID2 Panel demonstrated an overall sensitivity of 98.9% (1,712/1,731) and an overall specificity of 99.6% (33,592/33,711) for Gram-positive bacteria, Gram-negative bacteria and yeast targets which the panel is designed to detect. One hundred eighteen off-panel organisms, which the BIOFIRE BCID2 Panel is not designed to detect, were identified by SoC in 10.6% (114/1,074) of samples. The BIOFIRE BCID2 Panel also demonstrated an overall positive percent agreement (PPA) of 97.9% (325/332) and an overall negative percent agreement (NPA) of 99.9% (2,465/2,767) for antimicrobial resistance determinants which the panel is designed to detect. The presence or absence of resistance markers in Enterobacterales correlated closely with phenotypic susceptibility and resistance. We conclude that the BIOFIRE BCID2 Panel produced accurate results in this clinical trial. | 2023 | 37227281 |
| 9075 | 11 | 0.8984 | CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter. BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype . | 2023 | 37474912 |
| 1475 | 12 | 0.8983 | Evaluation 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. | 2020 | 32983057 |
| 5829 | 13 | 0.8982 | 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 |
| 5819 | 14 | 0.8982 | 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 |
| 5827 | 15 | 0.8982 | 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 |
| 9746 | 16 | 0.8979 | Fluoroamphiphilic polymers exterminate multidrug-resistant Gram-negative ESKAPE pathogens while attenuating drug resistance. ESKAPE pathogens are a panel of most recalcitrant bacteria that could "escape" the treatment of antibiotics and exhibit high incidence of drug resistance. The emergence of multidrug-resistant (MDR) ESKAPE pathogens (particularly Gram-negative bacteria) accounts for high risk of mortality and increased resource utilization in health care. Worse still, there has been no new class of antibiotics approved for exterminating the Gram-negative bacteria for more than 50 years. Therefore, it is urgent to develop novel antibacterial agents with low resistance and potent killing efficacy against Gram-negative ESKAPE pathogens. Herein, we present a class of fluoropolymers by mimicking the amphiphilicity of cationic antimicrobial peptides. Our optimal fluoroamphiphilic polymer (PD(45)HF(5)) displayed selective antimicrobial ability for all MDR Gram-negative ESAKPE pathogens, low resistance, high in vitro cell selectivity, and in vivo curative efficacy. These findings implied great potential of fluoroamphiphilic cationic polymers as promising antibacterial agents against MDR Gram-negative ESKAPE bacteria and alleviating antibiotic resistance. | 2024 | 39196947 |
| 9743 | 17 | 0.8977 | Simultaneous 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. | 2018 | 29323478 |
| 5116 | 18 | 0.8977 | Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. BACKGROUND: Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. METHODS AND FINDINGS: We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. CONCLUSION: Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/. | 2020 | 32528441 |
| 2510 | 19 | 0.8977 | Diagnosis of Multidrug-Resistant Pathogens of Pneumonia. Hospital-acquired pneumonia and ventilator-associated pneumonia that are caused by multidrug resistant (MDR) pathogens represent a common and severe problem with increased mortality. Accurate diagnosis is essential to initiate appropriate antimicrobial therapy promptly while simultaneously avoiding antibiotic overuse and subsequent antibiotic resistance. Here, we discuss the main conventional phenotypic diagnostic tests and the advanced molecular tests that are currently available to diagnose the primary MDR pathogens and the resistance genes causing pneumonia. | 2021 | 34943524 |