Accessory genes define species-specific routes to antibiotic resistance.

A deeper understanding of the relationship between the antimicrobial resistance (AMR) gene carriage and phenotype is necessary to develop effective response strategies against this global burden. AMR phenotype is often a result of multi-gene interactions; therefore, we need approaches that go beyond current simple AMR gene identification tools. Machine-learning (ML) methods may meet this challenge and allow the development of rapid computational approaches for AMR phenotype classification. To examine this, we applied multiple ML techniques to 16,950 bacterial genomes across 28 genera, with corresponding MICs for 23 antibiotics with the aim of training models to accurately determine the AMR phenotype from sequenced genomes. This resulted in a >1.5-fold increase in AMR phenotype prediction accuracy over AMR gene identification alone. Furthermore, we revealed 528 unique (often species-specific) genomic routes to antibiotic resistance, including genes not previously linked to the AMR phenotype. Our study demonstrates the utility of ML in predicting AMR phenotypes across diverse clinically relevant organisms and antibiotics. This research proposes a rapid computational method to support laboratory-based identification of the AMR phenotype in pathogens.

Tuberculosis Pulmonar: Causas de abandono en el tratamiento / Pulmonary tuberculosis: causes of abandonment in the processing

La muestra lo constituye los 18 casos de T.B.C. pulmonar que abandonan su tratamiento de un universo de los casos nuevos diagnósticados en 1982 en el Centro de Salud Buenos Aires de Villa, Area Hospitalaria # 8 y que representa el 16 por ciento. En la presente investigación consideramos como unidad de análisis los factores que determinaron el abandono del tratamiento, para ello se trata de encontrar una explicación causal de esta situación, sistematizando la información requerida en dos elementos de análisis o variables de primer orden: 1. Relaciones con el enfermo.2. Relaciones con la organización y funcionamiento del sistema de servicios de salud. Estas variables han sido separadas en variables de tercer orden para su mejor análisis además los indicadores respectivos