Antimicrobial resistance and distribution of sul genes and integron-associated intl genes among uropathogenic Escherichia coli in Queensland, Australia

We studied 137 uropathogenic Escherichia coli (UPEC) isolates from hospitalized adult patients (Queensland, Australia) for their resistance to 17 antimicrobial agents using the calibrated dichotomous sensitivity method and the presence of class I, II and III integron-associated integrase (intl) genes, including functional class II intl2, as well as the presence of suit, sul2 and sul3 genes, using PCR. Randomly amplified polymorphic DNA PCR, a high-resolution biochemical-fingerprinting method (PhP) and phylogenetic grouping were also used to identify the clonality of the sulphonamide-resistant isolates. One hundred and twenty (87.6%) isolates were resistant to one or more of the tested antimicrobial drugs, with the highest resistance (70.1%) observed against sulphafurazole (96 isolates). Of these, 84 (87.5%) contained one or more sul alleles, with sul1 being the most common allele [occurring in 69 (72%) isolates]. Only 38 of 69 (55.1%) strains carrying the sul1 gene were positive for class I integrase. Our results indicate a high prevalence of sulphafurazole-resistant UPEC strains belonging to different clones among patients with urinary tract infection in Queensland, Australia. We also conclude that these strains carry predominantly a sul1 gene that is not commonly associated with the presence of class I integrase, indicating that it may be carried on either a bacterial chromosome or other genetic elements

Antibiotic resistant Staphylococcus aureus in hospital wastewaters and sewage treatment plants with special reference to methicillin-resistant Staphylococcus aureus (MRSA)

Aims To investigate the presence of methicillin-resistant Staphylococcus aureus (MRSA) in untreated hospital wastewaters (UHWW), their transmission into the receiving sewage treatment plant (STP) and survival through the STP treatment. Methods and Results Over eight consecutive weeks of sampling, we isolated 224 Staph. aureus strains from UHWW-1, UHWW-2 and its receiving STP inlet (SI) and post-treatment outlet (SO). These strains were typed using the PhP typing method and RAPD-PCR and tested for their antibiotic resistance patterns. Resistance to cefoxitin and the presence of mecA gene identified MRSA isolates. In all, 11 common (C) and 156 single (S) PhP-RAPD types were found among isolates, with two multidrug resistant (MDR) C-types found in H2, SI and SO. These C-type strains also showed resistance to cefoxitin and vancomycin. The mean number of antibiotics to which the strains from UHWW were resistant (5.14 +/- 2) was significantly higher than the STP isolates (2.9 +/- 1.9) (P < 0.0001). Among the 131 (68%) MRSA strains, 24 were also vancomycin resistant. MDR strains (including MRSA) were more prevalent in hospital wastewaters than in the STP. Conclusion This study provides evidence of the survival of MRSA strains in UHWWs and their transit to the STP and then through to the final treated effluent and chlorination stage. Significance and Impact of the Study This preliminary study identifies the need to further investigate the load of MRSA in hospitals' wastewaters and possible their survival in STPs. From a public health point of view, this potential route of hospital MRSA dissemination is of great importance

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.Peer reviewe