Yang Yong-Chang, Yu Hua, Xiao Dai-Wen, Liu Hua, Hu Qi, Huang Bo, Liao Wei-Jin, Huang Wen-Fang
Clinical Laboratory Department, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, China.
J Microbiol Methods. 2009 May;77(2):202-6. doi: 10.1016/j.mimet.2009.02.004. Epub 2009 Feb 20.
Staphylococcus aureus (S. aureus), a vital nosocomial pathogen, is responsible for several diseases. With the increasing isolation rate in clinical specimens, rapid identification of this bacterial species is required. But present identification via conventional methods is time-consuming and lacks accuracy. The purpose of the current study was to evaluate the use of surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) for rapid identification of S. aureus. A total of 120 clinical isolates of S. aureus and 153 non-S. aureus species were identified by conventional methods, and the species nature of all staphylococci was further confirmed by 16S rDNA sequencing. All strains observed were analyzed by SELDI-TOF MS. An identification model for S. aureus was developed and validated by an artificial neural network. The model based on 6 protein peaks exhibited a sensitivity of 98.4% and specificity of 98.6%. This strategy has the potential for rapid identification of S. aureus.
金黄色葡萄球菌是一种重要的医院病原体，可引发多种疾病。随着临床标本中其分离率的不断增加，需要对该细菌物种进行快速鉴定。但目前通过传统方法进行鉴定耗时且缺乏准确性。本研究的目的是评估表面增强激光解吸电离飞行时间质谱（SELDI-TOF MS）用于快速鉴定金黄色葡萄球菌的效用。通过传统方法共鉴定出120株金黄色葡萄球菌临床分离株和153株非金黄色葡萄球菌菌株，所有葡萄球菌的物种性质均通过16S rDNA测序进一步确认。对所有观察到的菌株进行SELDI-TOF MS分析。通过人工神经网络开发并验证了金黄色葡萄球菌的鉴定模型。基于6个蛋白峰的模型灵敏度为98.4%，特异性为98.6%。该策略具有快速鉴定金黄色葡萄球菌的潜力。
