1.Pathogens of Nosocomial Infection in Intensive Care Units and Related Drug-resistance Analysis
Qiongna CHEN ; Yanbo LI ; Zhehua CHEN ; Haihong TANG ; Qian YU
Chinese Journal of Nosocomiology 1994;0(04):-
OBJECTIVE To estimate the distribution of pathogens of nosocomial infection in intensive care units,the sites where infections often taken place and the related antibiotic-resistance of bacteria.METHODS Various samples of bacteria from intensive care units have been examined by applying half-automatic analyzer ATB Expression,with bacteria inspecting cards and drug-sensitivity cards,all from Bio-Merieux,France.RESULTS Totally 153 strains of nosocomial pathogens have been found,where Acinebacter baumannii,fungi and Pseudomonas aeruginosa ranked top three,with rates of 32.0%,16.3%,and 15.0%,respectively.About 45.8% of pathogens were found in samples from the lower respiratory tract.Approximately 4.3% of A.baumannii showed drug-resistance to imipenem.However,more than 50.0% of A.baumannii presented its strong drug-resistance to rest of eight regular antibiotics.CONCLUSIONS A.baumannii is a dominating pathogen to cause nosocomial infection in intensive care units,and has shown strong multi-drug-resistance,which should incur high clinical attention.It comes to be crucial to highlight sanitization and sterilization operation,applying proper antibiotics in order to reduce appearance of bacteria with drug-resistance.
2.Frequency-domain analysis methods for single ion channel currents.
Zhenyu CHEN ; Xingpeng GUO ; Zhehua DONG ; Zhiwang LI
Journal of Biomedical Engineering 2004;21(5):753-755
The frequency domain of single channel currents is analyzed by the power distribution function (PDF) constructed by the discrete wavelet transform (DWT) and power spectral density (PSD). The result shows that the power distribution function based on DWT is an effective frequency-domain analysis method for single channel currents.
Fourier Analysis
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Ganglia, Spinal
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physiology
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Humans
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Ion Channels
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physiology
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Mathematics
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Membrane Potentials
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Models, Biological
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Signal Processing, Computer-Assisted