Multirresistencia, resistencia extendida y panresistencia a antibacterianos en el norte de México.

OBJETIVO: Describir el perfil de multirresistencia (MDR), resistencia extendida (XDR) y panresistencia (PDR) a antibacterianos en aislados de muestras de pacientes de un hospital privado de tercer nivel en el norte de México. MÉTODO: Se realizó un estudio retrospectivo durante el periodo comprendido de febrero de 2016 a abril de 2019. A partir de 156 muestras clínicas de orina, heridas, sangre, expectoración y otros fluidos se aislaron 166 bacterias; 10 de las muestras incluyeron dos microorganismos. Los microrganismos aislados se clasificaron en MDR, XDR o PDR. RESULTADOS: El 78% de los aislados gramnegativos y el 69% de los aislados grampositivos mostraron MDR, XDR o PDR. Staphylococcus epidermidis fue la bacteria grampositiva con multirresistencia más frecuentemente aislada. Escherichia coli y Klebsiella sp. se encontraron entre los gramnegativos MDR más frecuentes. En dos casos, los aislados clínicos de Pseudomonas aeruginosa procedentes de la unidad de cuidados intensivos neonatales mostraron PDR. CONCLUSIÓN: Los servicios de terapia intensiva, cirugía y unidad de cuidados intensivos neonatales merecen especial atención por la alta proporción de aislados MDR y la presencia de PDR a causa de P. aeruginosa. OBJECTIVE: To describe the profile of multidrug-resistance (MDR), extensively resistance (XDR) and pandrug-resistance (PDR) to antibacterial drugs in isolates from patient samples from a third level private hospital in the North of Mexico. METHOD: A retrospective study was carried out during the period from February 2016 to April 2019. From 156 clinical samples of urine, wounds, blood, expectoration and other fluids, 166 bacteria were isolated; 10 samples included two microorganisms. Isolated microorganisms were classified into MDR, XDR or PDR. RESULTS: 78% of the Gram negative and 69% of the Gram positive isolates showed MDR, XDR or PDR. Staphylococcus epidermidis was the most frequently isolated MDR Gram positive bacteria. Escherichia coli and Klebsiella sp. were among the most frequent MDR Gram negative. In two cases, the clinical isolates of Pseudomonas aeruginosa from the neonatal intensive care unit showed PDR. CONCLUSIONS: The intensive care, surgery and neonatal intensive care unit services deserve special attention due to the high proportion of MDR isolates and the presence of PDR due to P. aeruginosa.

Similarity corpus on microbial transcriptional regulation.

BACKGROUND: The ability to express the same meaning in different ways is a well-known property of natural language. This amazing property is the source of major difficulties in natural language processing. Given the constant increase in published literature, its curation and information extraction would strongly benefit from efficient automatic processes, for which corpora of sentences evaluated by experts are a valuable resource. RESULTS: Given our interest in applying such approaches to the benefit of curation of the biomedical literature, specifically that about gene regulation in microbial organisms, we decided to build a corpus with graded textual similarity evaluated by curators and that was designed specifically oriented to our purposes. Based on the predefined statistical power of future analyses, we defined features of the design, including sampling, selection criteria, balance, and size, among others. A non-fully crossed study design was applied. Each pair of sentences was evaluated by 3 annotators from a total of 7; the scale used in the semantic similarity assessment task within the Semantic Evaluation workshop (SEMEVAL) was adapted to our goals in four successive iterative sessions with clear improvements in the agreed guidelines and interrater reliability results. Alternatives for such a corpus evaluation have been widely discussed. CONCLUSIONS: To the best of our knowledge, this is the first similarity corpus-a dataset of pairs of sentences for which human experts rate the semantic similarity of each pair-in this domain of knowledge. We have initiated its incorporation in our research towards high-throughput curation strategies based on natural language processing.

RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12.

RegulonDB, first published 20 years ago, is a comprehensive electronic resource about regulation of transcription initiation of Escherichia coli K-12 with decades of knowledge from classic molecular biology experiments, and recently also from high-throughput genomic methodologies. We curated the literature to keep RegulonDB up to date, and initiated curation of ChIP and gSELEX experiments. We estimate that current knowledge describes between 10% and 30% of the expected total number of transcription factor- gene regulatory interactions in E. coli. RegulonDB provides datasets for interactions for which there is no evidence that they affect expression, as well as expression datasets. We developed a proof of concept pipeline to merge binding and expression evidence to identify regulatory interactions. These datasets can be visualized in the RegulonDB JBrowse. We developed the Microbial Conditions Ontology with a controlled vocabulary for the minimal properties to reproduce an experiment, which contributes to integrate data from high throughput and classic literature. At a higher level of integration, we report Genetic Sensory-Response Units for 200 transcription factors, including their regulation at the metabolic level, and include summaries for 70 of them. Finally, we summarize our research with Natural language processing strategies to enhance our biocuration work.

Strategies towards digital and semi-automated curation in RegulonDB.

Experimentally generated biological information needs to be organized and structured in order to become meaningful knowledge. However, the rate at which new information is being published makes manual curation increasingly unable to cope. Devising new curation strategies that leverage upon data mining and text analysis is, therefore, a promising avenue to help life science databases to cope with the deluge of novel information. In this article, we describe the integration of text mining technologies in the curation pipeline of the RegulonDB database, and discuss how the process can enhance the productivity of the curators. Specifically, a named entity recognition approach is used to pre-annotate terms referring to a set of domain entities which are potentially relevant for the curation process. The annotated documents are presented to the curator, who, thanks to a custom-designed interface, can select sentences containing specific types of entities, thus restricting the amount of text that needs to be inspected. Additionally, a module capable of computing semantic similarity between sentences across the entire collection of articles to be curated is being integrated in the system. We tested the module using three sets of scientific articles and six domain experts. All these improvements are gradually enabling us to obtain a high throughput curation process with the same quality as manual curation.