# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 9076 | 0 | 0.9643 | 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 |
| 9742 | 1 | 0.9633 | 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 |
| 5068 | 2 | 0.9621 | 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 |
| 5826 | 3 | 0.9616 | 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 |
| 5194 | 4 | 0.9616 | 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 |
| 5099 | 5 | 0.9615 | A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria. Microorganisms, crucial for environmental equilibrium, could be destructive, resulting in detrimental pathophysiology to the human host. Moreover, with the emergence of antibiotic resistance (ABR), the microbial communities pose the century's largest public health challenges in terms of effective treatment strategies. Furthermore, given the large diversity and number of known bacterial strains, describing treatment choices for infected patients using experimental methodologies is time-consuming. An alternative technique, gaining popularity as sequencing prices fall and technology advances, is to use bacterial genotype rather than phenotype to determine ABR. Complementing machine learning into clinical practice provides a data-driven platform for categorization and interpretation of bacterial datasets. In the present study, k-mers were generated from nucleotide sequences of pathogenic bacteria resistant to antibiotics. Subsequently, they were clustered into groups of bacteria sharing similar genomic features using the Affinity propagation algorithm with a Silhouette coefficient of 0.82. Thereafter, a prediction model based on Random Forest algorithm was developed to explore the prediction capability of the k-mers. It yielded an overall specificity of 0.99 and a sensitivity of 0.98. Additionally, the genes and ABR drivers related to the k-mers were identified to explore their biological relevance. Furthermore, a multilayer perceptron model with a hamming loss of 0.05 was built to classify the bacterial strains into resistant and non-resistant strains against various antibiotics. Segregating pathogenic bacteria based on genomic similarities could be a valuable approach for assessing the severity of diseases caused by new bacterial strains. Utilization of this strategy could aid in enhancing our understanding of ABR patterns, paving the way for more informed and effective treatment options. | 2024 | 39816256 |
| 9741 | 6 | 0.9613 | 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 |
| 9744 | 7 | 0.9613 | PARGT: a software tool for predicting antimicrobial resistance in bacteria. With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies. | 2020 | 32620856 |
| 5119 | 8 | 0.9613 | ROCker models for reliable detection and typing of short-read sequences carrying mcr, erm, mph, and lnu antibiotic resistance genes. Quantitative monitoring of emerging antimicrobial resistance genes (ARGs) using short-read sequences remains challenging due to the high frequency of amino acid functional domains and motifs shared with related but functionally distinct (non-target) proteins. To facilitate ARG monitoring efforts using unassembled short reads, we present novel ROCker models for mcr, mph, erm, and lnu ARG families, as well as models for variants of special public health concern within these families, including mcr-1, mphA, ermB, lnuF, lnuB, and lnuG genes. For this, we curated target gene sequence sets for model training and built these models using the recently updated ROCker V2 pipeline (Gerhardt et al., in review). To validate our models, we simulated reads from the whole genome of ARG-carrying isolates spanning a range of common read lengths and used them to challenge the filtering efficacy of ROCker versus common static filtering approaches, such as similarity searches using BLASTx with various e-value thresholds or hidden Markov models. ROCker models consistently showed F1 scores up to 10× higher (31% higher on average) and lower false-positive (by 30%, on average) and false-negative (by 16%, on average) rates based on 250 bp reads compared to alternative methods. The ROCker models and all related reference materials and data are freely available through http://enve-omics.ce.gatech.edu/rocker/models, further expanding the available model collection previously developed for other genes. Their application to short-read metagenomes, metatranscriptomes, and PCR amplicon data should facilitate more accurate classification and quantification of unassembled short-read sequences for these ARG families and specific genes.IMPORTANCEAntimicrobial resistance gene families encoding erm and mph genes confer resistance to the macrolide class of antimicrobials, which are used to treat a wide range of infections. Similarly, the mcr gene family confers resistance to polymyxin E (colistin), a drug of last resort for many serious drug-resistant bacterial infections, and the lnu gene family confers resistance to lincomycin, which is reserved for patients allergic to penicillin or where bacteria have developed resistance to other antimicrobials. Assessing the prevalence of these genes in clinical or environmental samples and monitoring their spread to new pathogens are thus important for quantifying the associated public health risk. However, detecting these and other resistance genes in short-read sequence data is technically challenging. Our ROCker bioinformatic pipeline achieves reliable detection and typing of broad-range target gene sequences in complex data sets, thus contributing toward solving an important problem in ongoing surveillance efforts of antimicrobial resistance. | 2025 | 41143534 |
| 9392 | 9 | 0.9610 | CNproScan: Hybrid CNV detection for bacterial genomes. Discovering copy number variation (CNV) in bacteria is not in the spotlight compared to the attention focused on CNV detection in eukaryotes. However, challenges arising from bacterial drug resistance bring further interest to the topic of CNV and its role in drug resistance. General CNV detection methods do not consider bacteria's features and there is space to improve detection accuracy. Here, we present a CNV detection method called CNproScan focused on bacterial genomes. CNproScan implements a hybrid approach and other bacteria-focused features and depends only on NGS data. We benchmarked our method and compared it to the previously published methods and we can resolve to achieve a higher detection rate together with providing other beneficial features, such as CNV classification. Compared with other methods, CNproScan can detect much shorter CNV events. | 2021 | 34224809 |
| 8161 | 10 | 0.9609 | 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 |
| 5118 | 11 | 0.9607 | Automated extraction of genes associated with antibiotic resistance from the biomedical literature. The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extraction. | 2022 | 35134132 |
| 4777 | 12 | 0.9605 | Identification of Bacterial Strains and Development of anmRNA-Based Vaccine to Combat Antibiotic Resistance in Staphylococcus aureus via In Vitro and In Silico Approaches. The emergence of antibiotic-resistant microorganisms is a significant concern in global health. Antibiotic resistance is attributed to various virulent factors and genetic elements. This study investigated the virulence factors of Staphylococcus aureus to create an mRNA-based vaccine that could help prevent antibiotic resistance. Distinct strains of the bacteria were selected for molecular identification of virulence genes, such as spa, fmhA, lukD, and hla-D, which were performed utilizing PCR techniques. DNA extraction from samples of Staphylococcus aureus was conducted using the Cetyl Trimethyl Ammonium Bromide (CTAB) method, which was confirmed and visualized using a gel doc; 16S rRNA was utilized to identify the bacterial strains, and primers of spa, lukD, fmhA, and hla-D genes were employed to identify the specific genes. Sequencing was carried out at Applied Bioscience International (ABI) in Malaysia. Phylogenetic analysis and alignment of the strains were subsequently constructed. We also performed an in silico analysis of the spa, fmhA, lukD, and hla-D genes to generate an antigen-specific vaccine. The virulence genes were translated into proteins, and a chimera was created using various linkers. The mRNA vaccine candidate was produced utilizing 18 epitopes, linkers, and an adjuvant, known as RpfE, to target the immune system. Testing determined that this design covered 90% of the population conservancy. An in silico immunological vaccine simulation was conducted to verify the hypothesis, including validating and predicting secondary and tertiary structures and molecular dynamics simulations to evaluate the vaccine's long-term viability. This vaccine design may be further evaluated through in vivo and in vitro testing to assess its efficacy. | 2023 | 37189657 |
| 5116 | 13 | 0.9605 | 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 |
| 8185 | 14 | 0.9605 | RNA-cleaving DNAzymes as a diagnostic and therapeutic agent against antimicrobial resistant bacteria. The development of nucleic-acid-based antimicrobials such as RNA-cleaving DNAzyme (RCD), a short catalytically active nucleic acid, is a promising alternative to the current antibiotics. The current rapid spread of antimicrobial resistance (AMR) in bacteria renders some antibiotics useless against bacterial infection, thus creating the need for alternative antimicrobials such as DNAzymes. This review summarizes recent advances in the use of RCD as a diagnostic and therapeutic agent against AMR. Firstly, the recent diagnostic application of RCD for the detection of bacterial cells and the associated resistant gene(s) is discussed. The next section summarises the therapeutic application of RCD in AMR bacterial infections which includes direct targeting of the resistant genes and indirect targeting of AMR-associated genes. Finally, this review extends the discussion to challenges of utilizing RCD in real-life applications, and the potential of combining both diagnostic and therapeutic applications of RCD into a single agent as a theranostic agent. | 2022 | 34505182 |
| 5121 | 15 | 0.9604 | Rapid Nanopore Whole-Genome Sequencing for Anthrax Emergency Preparedness. Human anthrax cases necessitate rapid response. We completed Bacillus anthracis nanopore whole-genome sequencing in our high-containment laboratory from a human anthrax isolate hours after receipt. The de novo assembled genome showed no evidence of known antimicrobial resistance genes or introduced plasmid(s). Same-day genomic characterization enhances public health emergency response. | 2020 | 31961318 |
| 9074 | 16 | 0.9602 | BacAnt: A Combination Annotation Server for Bacterial DNA Sequences to Identify Antibiotic Resistance Genes, Integrons, and Transposable Elements. Whole genome sequencing (WGS) of bacteria has become a routine method in diagnostic laboratories. One of the clinically most useful advantages of WGS is the ability to predict antimicrobial resistance genes (ARGs) and mobile genetic elements (MGEs) in bacterial sequences. This allows comprehensive investigations of such genetic features but can also be used for epidemiological studies. A plethora of software programs have been developed for the detailed annotation of bacterial DNA sequences, such as rapid annotation using subsystem technology (RAST), Resfinder, ISfinder, INTEGRALL and The Transposon Registry. Unfortunately, to this day, a reliable annotation tool of the combination of ARGs and MGEs is not available, and the generation of genbank files requires much manual input. Here, we present a new webserver which allows the annotation of ARGs, integrons and transposable elements at the same time. The pipeline generates genbank files automatically, which are compatible with Easyfig for comparative genomic analysis. Our BacAnt code and standalone software package are available at https://github.com/xthua/bacant with an accompanying web application at http://bacant.net. | 2021 | 34367079 |
| 8437 | 17 | 0.9601 | Tocopherol polyethylene glycol succinate-modified hollow silver nanoparticles for combating bacteria-resistance. Multiple drug resistance and the increase in the appearance of superbugs together with the exceedingly scant development of new potent antibiotic drugs pose an urgent global medical threat and imminent public security crisis. In the present study, we fabricated well-dispersed tocopherol polyethylene glycol succinate (TPGS)-capped silver nanoparticles (AgNPs) of about 10 nm in size. The hollow structure of the TPGS-capped AgNPs (TPGS/AgNPs) was confirmed and applied to load antibiotics. The TPGS/AgNPs proved to be able to cross the bacterial cell wall and penetrate into bacteria, thereby delivering more of the antibiotic to the interior of bacteria and thus enhancing the in vitro antibacterial effect of the antibiotic, even overcoming the drug-resistance in drug-resistant E. coli and Acinetobacter baumannii. It was found that the TPGS modification in the TPGS/AgNPs could decrease the activity of the efflux pumps AdeABC and AdeIJK in drug-resistant Acinetobacter baumannii via inhibiting the efflux pump genes adeB and adeJ, thus increasing the accumulation of the delivered antibiotic and overcoming the drug-resistance. Tigecycline delivered by TPGS/AgNPs could effectively antagonize drug-resistance in an acute peritonitis model mice, thereby increasing the survival rate and alleviating the inflammatory response. TPGS/AgNPs were developed as a novel and effective antibiotic delivery system and TPGS was demonstrated to have great potential as a pharmaceutical excipient for use in drug-resistant infection therapy. | 2019 | 30968093 |
| 9815 | 18 | 0.9601 | Prospecting gene therapy of implant infections. Infection still represents one of the most serious and ravaging complications associated with prosthetic devices. Staphylococci and enterococci, the bacteria most frequently responsible for orthopedic postsurgical and implant-related infections, express clinically relevant antibiotic resistance. The emergence of antibiotic-resistant bacteria and the slow progress in identifying new classes of antimicrobial agents have encouraged research into novel therapeutic strategies. The adoption of antisense or "antigene" molecules able to silence or knock-out bacterial genes responsible for their virulence is one possible innovative approach. Peptide nucleic acids (PNAs) are potential drug candidates for gene therapy in infections, by silencing a basic gene of bacterial growth or by tackling the antibiotic resistance or virulence factors of a pathogen. An efficacious contrast to bacterial genes should be set up in the first stages of infection in order to prevent colonization of periprosthesis tissues. Genes encoding bacterial factors for adhesion and colonization (biofilm and/or adhesins) would be the best candidates for gene therapy. But after initial enthusiasm for direct antisense knock-out or silencing of essential or virulence bacterial genes, difficulties have emerged; consequently, new approaches are now being attempted. One of these, interference with the regulating system of virulence factors, such as agr, appears particularly promising. | 2009 | 19882546 |
| 9075 | 19 | 0.9601 | 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 |