# | Rank | Similarity | Title + Abs. | Year | PMID |
|---|---|---|---|---|---|
| 0 | 1 | 2 | 3 | 4 | 5 |
| 5120 | 0 | 0.9828 | ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customizable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods. | 2017 | 29177089 |
| 9744 | 1 | 0.9823 | 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 |
| 9743 | 2 | 0.9820 | 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 |
| 5068 | 3 | 0.9820 | 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 |
| 9083 | 4 | 0.9819 | ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract. | 2024 | 38725076 |
| 9981 | 5 | 0.9819 | High-contrast imaging of cellular non-repetitive drug-resistant genes via in situ dead Cas12a-labeled PCR. In situ imaging of genes of pathogenic bacteria can profile cellular heterogeneity, such as the emergence of drug resistance. Fluorescence in situ hybridization (FISH) serves as a classic approach to image mRNAs inside cells, but it remains challenging to elucidate genomic DNAs and relies on multiple fluorescently labeled probes. Herein, we present a dead Cas12a (dCas12a)-labeled polymerase chain reaction (CasPCR) assay for high-contrast imaging of cellular drug-resistant genes. We employed a syncretic dCas12a-green fluorescent protein (dCas12a-GFP) to tag the amplicons, thereby enabling high-contrast imaging and avoiding multiple fluorescently labeled probes. The CasPCR assay can quantify quinolone-resistant Salmonella enterica in mixed populations and identify them isolated from poultry farms. | 2024 | 39229640 |
| 6695 | 6 | 0.9819 | 'Disperse abroad in the land': the role of wildlife in the dissemination of antimicrobial resistance. Antimicrobial resistance (AMR) has been detected in the microbiota of many wildlife species, including long-distance migrants. Inadequately treated wastes from humans and livestock dosed with antimicrobial drugs are often assumed to be the main sources of AMR to wildlife. While wildlife populations closely associated with human populations are more likely to harbour clinically important AMR related to that found in local humans and livestock, AMR is still common in remote wildlife populations with little direct human influence. Most reports of AMR in wildlife are survey based and/or small scale, so researchers can only speculate on possible sources and sinks of AMR or the impact of wildlife AMR on clinical resistance. This lack of quantitative data on the flow of AMR genes and AMR bacteria across the natural environment could reflect the numerous AMR sources and amplifiers in the populated world. Ecosystems with relatively simple and well-characterized potential inputs of AMR can provide tractable, but realistic, systems for studying AMR in the natural environment. New tools, such as animal tracking technologies and high-throughput sequencing of resistance genes and mobilomes, should be integrated with existing methodologies to understand how wildlife maintains and disperses AMR. | 2016 | 27531155 |
| 9074 | 7 | 0.9818 | 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 |
| 9568 | 8 | 0.9818 | Identification of virulence factors and antibiotic resistance markers using bacterial genomics. In recent years, the number of multidrug-resistant bacteria has increased rapidly and several epidemics were signaled in different regions of the world. Faced with this situation that presents a major global public health concern, the development and the use of new and rapid technologies is more than urgent. The use of the next-generation sequencing platforms by microbiologists and infectious disease specialists has allowed great progress in the medical field. Here, we review the usefulness of whole-genome sequencing for the detection of virulence and antibiotic resistance associated genes. | 2016 | 26974504 |
| 5115 | 9 | 0.9817 | Search Engine for Antimicrobial Resistance: A Cloud Compatible Pipeline and Web Interface for Rapidly Detecting Antimicrobial Resistance Genes Directly from Sequence Data. BACKGROUND: Antimicrobial resistance remains a growing and significant concern in human and veterinary medicine. Current laboratory methods for the detection and surveillance of antimicrobial resistant bacteria are limited in their effectiveness and scope. With the rapidly developing field of whole genome sequencing beginning to be utilised in clinical practice, the ability to interrogate sequencing data quickly and easily for the presence of antimicrobial resistance genes will become increasingly important and useful for informing clinical decisions. Additionally, use of such tools will provide insight into the dynamics of antimicrobial resistance genes in metagenomic samples such as those used in environmental monitoring. RESULTS: Here we present the Search Engine for Antimicrobial Resistance (SEAR), a pipeline and web interface for detection of horizontally acquired antimicrobial resistance genes in raw sequencing data. The pipeline provides gene information, abundance estimation and the reconstructed sequence of antimicrobial resistance genes; it also provides web links to additional information on each gene. The pipeline utilises clustering and read mapping to annotate full-length genes relative to a user-defined database. It also uses local alignment of annotated genes to a range of online databases to provide additional information. We demonstrate SEAR's application in the detection and abundance estimation of antimicrobial resistance genes in two novel environmental metagenomes, 32 human faecal microbiome datasets and 126 clinical isolates of Shigella sonnei. CONCLUSIONS: We have developed a pipeline that contributes to the improved capacity for antimicrobial resistance detection afforded by next generation sequencing technologies, allowing for rapid detection of antimicrobial resistance genes directly from sequencing data. SEAR uses raw sequencing data via an intuitive interface so can be run rapidly without requiring advanced bioinformatic skills or resources. Finally, we show that SEAR is effective in detecting antimicrobial resistance genes in metagenomic and isolate sequencing data from both environmental metagenomes and sequencing data from clinical isolates. | 2015 | 26197475 |
| 2525 | 10 | 0.9817 | Review of antimicrobial resistance surveillance programmes in livestock and meat in EU with focus on humans. OBJECTIVES: In this review, we describe surveillance programmes reporting antimicrobial resistance (AMR) and resistance genes in bacterial isolates from livestock and meat and compare them with those relevant for human health. METHODS: Publications on AMR in European countries were assessed. PubMed was reviewed and AMR monitoring programmes were identified from reports retrieved by Internet searches and by contacting national authorities in EU/European Economic Area (EEA) member states. RESULTS: Three types of systems were identified: EU programmes, industry-funded supranational programmes and national surveillance systems. The mandatory EU-financed programme has led to some harmonization in national monitoring and provides relevant information on AMR and extended-spectrum β-lactamase/AmpC- and carbapenemase-producing bacteria. At the national level, AMR surveillance systems in livestock apply heterogeneous sampling, testing and reporting modalities, resulting in results that cannot be compared. Most reports are not publicly available or are written in a local language. The industry-funded monitoring systems undertaken by the Centre Européen d'Etudes pour la Santé Animale (CEESA) examines AMR in bacteria in food-producing animals. CONCLUSIONS: Characterization of AMR genes in livestock is applied heterogeneously among countries. Most antibiotics of human interest are included in animal surveillance, although results are difficult to compare as a result of lack of representativeness of animal samples. We suggest that EU/EEA countries provide better uniform AMR monitoring and reporting in livestock and link them better to surveillance systems in humans. Reducing the delay between data collection and publication is also important to allow prompt identification of new resistance patterns. | 2018 | 28970159 |
| 5125 | 11 | 0.9817 | Do we still need Illumina sequencing data? Evaluating Oxford Nanopore Technologies R10.4.1 flow cells and the Rapid v14 library prep kit for Gram negative bacteria whole genome assemblies. The best whole genome assemblies are currently built from a combination of highly accurate short-read sequencing data and long-read sequencing data that can bridge repetitive and problematic regions. Oxford Nanopore Technologies (ONT) produce long-read sequencing platforms and they are continually improving their technology to obtain higher quality read data that is approaching the quality obtained from short-read platforms such as Illumina. As these innovations continue, we evaluated how much ONT read coverage produced by the Rapid Barcoding Kit v14 (SQK-RBK114) is necessary to generate high-quality hybrid and long-read-only genome assemblies for a panel of carbapenemase-producing Enterobacterales bacterial isolates. We found that 30× long-read coverage is sufficient if Illumina data are available, and that more (at least 100× long-read coverage is recommended for long-read-only assemblies. Illumina polishing is still improving single nucleotide variants (SNVs) and INDELs in long-read-only assemblies. We also examined if antimicrobial resistance genes could be accurately identified in long-read-only data, and found that Flye assemblies regardless of ONT coverage detected >96% of resistance genes at 100% identity and length. Overall, the Rapid Barcoding Kit v14 and long-read-only assemblies can be an optimal sequencing strategy (i.e., plasmid characterization and AMR detection) but finer-scale analyses (i.e., SNV) still benefit from short-read data. | 2024 | 38354391 |
| 9075 | 12 | 0.9816 | 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 |
| 9076 | 13 | 0.9815 | 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 |
| 9189 | 14 | 0.9815 | CRISPR-Cas9 System: A Prospective Pathway toward Combatting Antibiotic Resistance. Antibiotic resistance is rising to dangerously high levels throughout the world. To cope with this problem, scientists are working on CRISPR-based research so that antibiotic-resistant bacteria can be killed and attacked almost as quickly as antibiotic-sensitive bacteria. Nuclease activity is found in Cas9, which can be programmed with a specific target sequence. This mechanism will only attack pathogens in the microbiota while preserving commensal bacteria. This article portrays the delivery methods used in the CRISPR-Cas system, which are both viral and non-viral, along with its implications and challenges, such as microbial dysbiosis, off-target effects, and failure to counteract intracellular infections. CRISPR-based systems have a lot of applications, such as correcting mutations, developing diagnostics for infectious diseases, improving crops productions, improving breeding techniques, etc. In the future, CRISPR-based systems will revolutionize the world by curing diseases, improving agriculture, and repairing genetic disorders. Though all the drawbacks of the technology, CRISPR carries great potential; thus, the modification and consideration of some aspects could result in a mind-blowing technique to attain all the applications listed and present a game-changing potential. | 2023 | 37370394 |
| 9557 | 15 | 0.9814 | Antimicrobial Resistance Profile by Metagenomic and Metatranscriptomic Approach in Clinical Practice: Opportunity and Challenge. The burden of bacterial resistance to antibiotics affects several key sectors in the world, including healthcare, the government, and the economic sector. Resistant bacterial infection is associated with prolonged hospital stays, direct costs, and costs due to loss of productivity, which will cause policy makers to adjust their policies. Current widely performed procedures for the identification of antibiotic-resistant bacteria rely on culture-based methodology. However, some resistance determinants, such as free-floating DNA of resistance genes, are outside the bacterial genome, which could be potentially transferred under antibiotic exposure. Metagenomic and metatranscriptomic approaches to profiling antibiotic resistance offer several advantages to overcome the limitations of the culture-based approach. These methodologies enhance the probability of detecting resistance determinant genes inside and outside the bacterial genome and novel resistance genes yet pose inherent challenges in availability, validity, expert usability, and cost. Despite these challenges, such molecular-based and bioinformatics technologies offer an exquisite advantage in improving clinicians' diagnoses and the management of resistant infectious diseases in humans. This review provides a comprehensive overview of next-generation sequencing technologies, metagenomics, and metatranscriptomics in assessing antimicrobial resistance profiles. | 2022 | 35625299 |
| 9184 | 16 | 0.9814 | Unlocking the potential of phages: Innovative approaches to harnessing bacteriophages as diagnostic tools for human diseases. Phages, viruses that infect bacteria, have been explored as promising tools for the detection of human disease. By leveraging the specificity of phages for their bacterial hosts, phage-based diagnostic tools can rapidly and accurately detect bacterial infections in clinical samples. In recent years, advances in genetic engineering and biotechnology have enabled the development of more sophisticated phage-based diagnostic tools, including those that express reporter genes or enzymes, or target specific virulence factors or antibiotic resistance genes. However, despite these advancements, there are still challenges and limitations to the use of phage-based diagnostic tools, including concerns over phage safety and efficacy. This review aims to provide a comprehensive overview of the current state of phage-based diagnostic tools, including their advantages, limitations, and potential for future development. By addressing these issues, we hope to contribute to the ongoing efforts to develop safe and effective phage-based diagnostic tools for the detection of human disease. | 2023 | 37770168 |
| 3771 | 17 | 0.9814 | RFPlasmid: predicting plasmid sequences from short-read assembly data using machine learning. Antimicrobial-resistance (AMR) genes in bacteria are often carried on plasmids and these plasmids can transfer AMR genes between bacteria. For molecular epidemiology purposes and risk assessment, it is important to know whether the genes are located on highly transferable plasmids or in the more stable chromosomes. However, draft whole-genome sequences are fragmented, making it difficult to discriminate plasmid and chromosomal contigs. Current methods that predict plasmid sequences from draft genome sequences rely on single features, like k-mer composition, circularity of the DNA molecule, copy number or sequence identity to plasmid replication genes, all of which have their drawbacks, especially when faced with large single-copy plasmids, which often carry resistance genes. With our newly developed prediction tool RFPlasmid, we use a combination of multiple features, including k-mer composition and databases with plasmid and chromosomal marker proteins, to predict whether the likely source of a contig is plasmid or chromosomal. The tool RFPlasmid supports models for 17 different bacterial taxa, including Campylobacter, Escherichia coli and Salmonella, and has a taxon agnostic model for metagenomic assemblies or unsupported organisms. RFPlasmid is available both as a standalone tool and via a web interface. | 2021 | 34846288 |
| 9561 | 18 | 0.9814 | The resistance tsunami, antimicrobial stewardship, and the golden age of microbiology. Modern medicine is built on antibiotics. Antibiotics are something that we take for granted. We have however spent over 60 years educating bacteria to become resistant, and the global resistance tsunami has caught everyone unawares. Since bacteria have changed, we also have to change, and to change most of the practices of how we use antibiotics. Because the development of new antibiotics is so expensive, a stewardship approach may help to preserve those that we have now while we work to develop new antibiotics and to develop other approaches to controlling and treating infections. We need to adopt the ethic of Good Stewardship Practice (GSP) as an active and dynamic process of continuous improvement in antibiotic use, a process with many steps of different sizes involving everyone involved in antibiotic use. All antibiotic users have an important role to play in GSP. Although the resistance situation is pessimistic, and the future of antibiotics looks uncertain, we are fortunately entering what may be seen as the golden age of microbiology. This encompasses an astonishing array of technologies for rapid pathogen and resistance gene detection, for clone identification by genome sequencing, for identification of novel bacterial genes and for identification of the Achilles' heels of different pathogens. Future antibiotics may have to be far more targeted to the individual pathogen and the site of infection. A global tax on antibiotics might reduce their use while funding the cost of developing new antibiotics and new approaches to control of infectious diseases. | 2014 | 24646601 |
| 4177 | 19 | 0.9814 | Statement on how to interpret the QPS qualification on 'acquired antimicrobial resistance genes'. The qualified presumption of safety (QPS) approach was developed to provide a regularly updated generic pre-evaluation of the safety of microorganisms intended for use in the food or feed chains. Safety concerns identified for a taxonomic unit (TU) are, where possible, confirmed at the species/strain or product level and reflected by 'qualifications' which should be assessed at strain and/or product level by EFSA's Scientific Panels. The generic qualification 'the strains should not harbour any acquired antimicrobial resistance (AMR) genes to clinically relevant antimicrobials' applies to all QPS bacterial TUs. The different EFSA risk assessment areas use the same approach to assess the qualification related to AMR genes. In this statement, the terms 'intrinsic' and 'acquired' AMR genes were defined for the purpose of EFSA's risk assessments, and they apply to bacteria used in the food and feed chains. A bioinformatic approach is proposed for demonstrating the 'intrinsic'/'acquired' nature of an AMR gene. All AMR genes that confer resistance towards 'critically important', 'highly important' and 'important' antimicrobials, as defined by the World Health Organisation (WHO), found as hits, need to be considered as hazards (for humans, animals and environment) and need further assessment. Genes identified as responsible for 'intrinsic' resistance could be considered as being of no concern in the frame of the EFSA risk assessment. 'Acquired' AMR genes resulting in a resistant phenotype should be considered as a concern. If the presence of the 'acquired' AMR gene is not leading to phenotypic resistance, further case-by-case assessment is necessary. | 2023 | 37915981 |