RESUMEN
Se estudiaron 100 aislados consecutivos y no epidemiológicamente relacionados de Acinetobacter baumannii resistentes a los carbapenems, recuperados entre enero y agosto de 2016 de muestras clínicas en 11 hospitales de 10 provincias de la Argentina, ubicadas en distintas regiones del país. Los genes que codifican las carbapenemasas de Ambler clase D y clase B se investigaron mediante la técnica de PCR utilizando cebadores específicos. Todos los aislados se agruparon mediante las técnicas de 3-locus sequence typing y la secuenciación del gen blaOXA-51-like. El gen blaOXA-23 se recuperó en todos los aislados estudiados. La población de A. baumannii resistente a carbapenems en Argentina estuvo asociada, principalmente, con ST1 (45%), ST25 (34%) y ST79 (15%). ST25 se recuperó en todas las regiones estudiadas y no se detectó CC2.
One hundred sequential, epidemiologically unrelated carbapenem-resistant- Acinetobacter baumannii isolates from 11 hospitals in 10 Argentine provinces were collected between January and August 2016. Genes coding for Ambler class D and B carbapenemases were investigated by PCR using specific primers. All isolates were typed using the 3-locus sequence typing and b/aOXA-51-like sequence-based typing techniques. The blaOXA-23 gene was recovered in all isolates studied. The population of carbapenem-resistant- A. baumannii in Argentina was principally associated with ST1 (45%), ST25 (34%) and ST79 (15%). ST25 was recovered in all the regions studied and CC2 was not detected.
Asunto(s)
Humanos , Proteínas Bacterianas/genética , beta-Lactamasas/genética , Infecciones por Acinetobacter/microbiología , Carbapenémicos/farmacología , Infección Hospitalaria/microbiología , Resistencia betalactámica , Acinetobacter baumannii/aislamiento & purificación , Argentina/epidemiología , Infecciones por Acinetobacter/epidemiología , Infección Hospitalaria/epidemiología , Acinetobacter baumannii/efectos de los fármacos , Acinetobacter baumannii/enzimología , Acinetobacter baumannii/genéticaRESUMEN
One hundred sequential, epidemiologically unrelated carbapenem-resistant- Acinetobacter baumannii isolates from 11 hospitals in 10 Argentine provinces were collected between January and August 2016. Genes coding for Ambler class D and B carbapenemases were investigated by PCR using specific primers. All isolates were typed using the 3-locus sequence typing and blaOXA-51-like sequence-based typing techniques. The blaOXA-23 gene was recovered in all isolates studied. The population of carbapenem-resistant- A. baumannii in Argentina was principally associated with ST1 (45%), ST25 (34%) and ST79 (15%). ST25 was recovered in all the regions studied and CC2 was not detected.
Asunto(s)
Infecciones por Acinetobacter/microbiología , Acinetobacter baumannii/aislamiento & purificación , Proteínas Bacterianas/genética , Carbapenémicos/farmacología , Infección Hospitalaria/microbiología , Resistencia betalactámica , beta-Lactamasas/genética , Infecciones por Acinetobacter/epidemiología , Acinetobacter baumannii/efectos de los fármacos , Acinetobacter baumannii/enzimología , Acinetobacter baumannii/genética , Argentina/epidemiología , Infección Hospitalaria/epidemiología , HumanosRESUMEN
Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms.
