Antimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of mass spectral data generated by VITEK^® MS instruments for predicting antibiotic resistance in Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae using machine learning algorithms. Additionally, the potential of pre-trained models was assessed through transfer learning analysis. A dataset comprising 2229 mass spectra was collected, and classification algorithms, including Support Vector Machines, Random Forest, Logistic Regression, and CatBoost, were applied to predict resistance. CatBoost demonstrated a clear advantage over the other models, effectively handling complex non-linear relationships within the spectra and achieving an AUROC of 0.91 and an F1 score of 0.78 for E. coli. In contrast, transfer learning yielded suboptimal results. These findings highlight the potential of gradient-boosting techniques to enhance resistance prediction, particularly with data from less conventional platforms like VITEK^® MS. Furthermore, the identification of specific biomarkers using SHAP values indicates promising potential for clinical applications in early diagnosis. Future efforts focused on standardizing data and refining algorithms could expand the utility of these approaches across diverse clinical environments, supporting the global fight against AMR.

The breast microbiome presents a diverse microbial community that could affects health and disease states, in the context of breast cancer. Sequencing technologies have allowed describing the diversity and abundance of microbial communities among individuals. The complex tumoral microenvironment that includes the microbial composition could influence tumor growth. The imbalance of diversity and abundance inside the microbial community, known as dysbiosis plays a crucial role in this context. One the most prevalent bacterial genera described in breast invasive carcinoma are Bacillus, Pseudomonas, Brevibacillus, Mycobacterium, Thermoviga, Acinetobacter, Corynebacterium, Paenibacillus, Ensifer, and Bacteroides. Paenibacills genus shows a relation with patient survival. When the Paenibacills genus increases its abundance in patients with breast cancer, the survival probability decreases. Within this dysbiotic environment, various bacterial metabolites could play a pivotal role in the progression and modulation of breast cancer. Key bacterial metabolites, such as cadaverine, lipopolysaccharides (LPS), and trimethylamine N-oxide (TMAO), have been found to exhibit potential interactions within breast tissue microenvironments. Understanding the intricate relationships between dysbiosis and these metabolites in breast cancer may open new avenues for diagnostic biomarkers and therapeutic targets. Further research is essential to unravel the specific roles and mechanisms of these microbial metabolites in breast cancer progression.

Carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a serious threat to public health. Globally, carbapenemases-producing CRPA isolates mainly belong to 'high-risk' clones; however, the molecular epidemiology of CRPA isolates circulating in Chile are scarce, where this pathogen is the main aetiological agent of ventilator-associated pneumonia. To characterize the phylogenomics and molecular features of ST654 CRPA isolates collected in Chile between 2016 - 2022. 89 CRPA isolates collected in different Chilean hospitals from clinical specimens between 2005 and 2022 were analyzed. Antibiotic susceptibility tests and carbapenemases production were carried out on the CRPA ST654 isolates. Also, they were subjected to whole-genome sequencing (WGS) from which in silico analyses were performed. Thirty-four strains (38.2%) belonged to the ST654 'high risk' clone, being the most predominant lineage of the collection. Most of these isolates belonged to a sub-clade including KPC-producers that also clustered with strains from Argentina and the USA, whereas few VIM and NDM co-producers clustered in two different smaller sub-clades. The isolates exhibited a broad resistome encompassing genes mediating resistance to several other clinically relevant drugs. Additionally, all the 34 ST654 isolates were ExoS+ as a virulence factor and associated to the O4-serotype. Our report represents the most comprehensive phylogenomic study of CRPA 'high risk' clone ST654 to date. Our analyses suggest that this lineage is undergoing a divergent evolutionary path in Chile, since most of the isolates were KPC-producers and were O4-serotype, differing from previous descriptions, which underline the relevance of performing molecular surveillance on this pathogen.

The pyelonephritis-associated fimbria (P fimbria) is one of the most recognized adhesion determinants of extraintestinal pathogenic Escherichia coli strains (ExPECs). Twelve variants have been described for the gene encoding the P fimbria major structural subunit PapA and three variants for the gene encoding the adhesin subunit PapG. However, their distribution among the ExPEC diversity has not been comprehensively addressed. A complete landscape of that distribution might be valuable for delineating basic studies about the pathogenicity mechanisms of ExPECs and following up on the evolution of ExPEC lineages, particularly those most epidemiologically relevant. Therefore, we performed a massive descriptive study to detect the papA and papG variants along different E. coli genotypes represented by genomic sequences contained in the NCBI Assembly Refseq database. The most common papA variants were F11, F10, F48, F16, F12, and F7-2, which were found in significant association with the most relevant ExPEC genotypes, the phylogroups B2 and D, and the sequence types ST95, ST131, ST127, ST69, ST12, and ST73. On the other hand, the papGII variant was by far the most common followed by papGIII, and both were also found to have a significant association with common ExPEC genotypes. We noticed the presence of genomes, mainly belonging to the sequence type ST12, harboring two or three papA variants and two papG variants. Furthermore, the most common papA and papG variants were also detected in records representing strains isolated from humans and animals such as poultry, bovine, and dogs, supporting previous hypotheses of potential cross-transmission. Finally, we characterized a set of 17 genomes from Chilean uropathogenic E. coli strains and found that ST12 and ST73 were the predominant sequence types. Variants F7-1, F7-2, F8, F9, F11, F13, F14, F16, and F48 were detected for papA, and papGII and papGIII variants were detected for papG. Significant associations with the sequence types observed in the analysis of genomes contained in the NCBI Assembly Refseq database were also found in this collection in 16 of 19 cases for papA variants and 7 of 9 cases for the papG variants. This comprehensive characterization might support future basic studies about P fimbria-mediated ExPEC adherence and future typing or epidemiological studies to monitor the evolution of ExPECs producing P fimbria.