ObjectiveTo investigate the distribution and drug resistance of Acinetobacter baumannii (AB) in a women and children's hospital. MethodsStrains of AB isolated from clinical specimens between January 2011 and December 2013 were identified with Vitek2-compact microbiology analyzer; antimicrobial susceptibility test was performed by Kirby-Bauer disk diffusion method. The resistant rate, intermediate rate and susceptibility rate of drugs were calculated according to the criteria in guidelines of Clinical and Laboratory Standards Institute. WHONET 5.6 software was used to analyze the data. ResultsA total of 167 strains of AB were isolated and tested. Neonatal ward had the highest detection proportion. Most strains of AB were isolated from sputum. The drug resistance rate of AB to piperacillin tazobactam, cefepime and carbapenem was<25%. ConclusionThe drug sensitivity rate of AB to piperacillin/tazobactam, cefepime and carbapenems was high, but drug resistence to antimicrobial drugs increased continuously in three years. Medical institutions should strengthen the monitoring of AB resistance, implement rational use of antibiotics, and carry out hand hygiene education, to reduce the generation and dissemination of AB resistant strains.
In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience’s emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.