CAJ | 학술논문

目的采用基于HPLC/MS的代谢组学技术构建脓毒症预后的早期预测模型.方法采用盲肠结扎穿孔法(CLP)复制脓毒症大鼠模型.根据6 d生存状况,45只CLP大鼠分为生存组(n=23)和死亡组(n:22).术后12 h断尾法采集CLP组和假手术组(n=25)大鼠外周血0.5mL,静置、离心后收集血清.采用HPLC/MS分析大鼠血清代谢特征,进而采用径向基函数神经网络算法(RBFNN)构建脓毒症的预后判别模型.结果主成分分析(PCA)可完全区分三组大鼠的生理特征.8个与脓毒症预后相关的代谢物被结构性鉴定,它们是棕榈油酸、棕榈酸、亚麻酸、亚油酸、软脂酸、二十二碳六烯酸和二十二碳五烯醇。采用RBFNN构建的模型预测效能优于k-最近邻算法,其敏感性为(96.1±3.6)%,特异性为(91.0±4.3)%.结论脓毒症后,脂肪酸代谢明显异常,基于HPLC/MS的代谢组学技术可全面了解脂肪酸代谢的变化规律,采用所有代谢信息构建的模型可早期、快速、有效地预测脓毒症大鼠的预后.
목적 채용기우HPLC/MS적대사조학기술구건농독증예후적조기예측모형.방법 채용맹장결찰천공법(CLP)복제농독증대서모형.근거6 d생존상황,45지CLP대서분위생존조(n=23)화사망조(n:22).술후12 h단미법채집CLP조화가수술조(n=25)대서외주혈0.5mL,정치、리심후수집혈청.채용HPLC/MS분석대서혈청대사특정,진이채용경향기함수신경망락산법(RBFNN)구건농독증적예후판별모형.결과 주성분분석(PCA)가완전구분삼조대서적생리특정.8개여농독증예후상관적대사물피결구성감정,타문시종려유산、종려산、아마산、아유산、연지산、이십이탄륙희산화이십이탄오희순。채용RBFNN구건적모형예측효능우우k-최근린산법,기민감성위(96.1±3.6)%,특이성위(91.0±4.3)%.결론 농독증후,지방산대사명현이상,기우HPLC/MS적대사조학기술가전면료해지방산대사적변화규률,채용소유대사신식구건적모형가조기、쾌속、유효지예측농독증대서적예후.
Objective To innovate an early, rapid and efficient approach to the pmgnestic evaluation of sep-sis in order to lower the mortality. Method Forty-five septic rats, induced by cecal ligation and puncture, were divided into surviving group (n=23) and non-survival group (n=22) on six days after onset of sepsis. Serum samples were taken from septic and sham-operated rats (n=25) at 12 hours after surgery. HPLC/MS assays were performed to acquire the serum metabolic profiles, and radial basis function neural network (RBFNN) was em-ployed to build predictive model for prognostic evaluation of sepsis. Results The principal component analysis al-lows differentiating the rots of survive,non-survive and sham-operated from one another in respect of the pathologic characteristics. Six metabolites, linolenic acid, linoleic acid, oleic acid, stearic acid, docosahexaenoic acid and do-cosapentaenoic acid, related to the outcomes of septic rats were then structurally identified. A RBFNN model for outcome predication was built based upon the metabolic profile data from rat sera with the sensitivity of (96.1 ±3.6)% (n=10) and specificity of (91.0±4.3)% (n=10). Condusions HPLC/MS-based metabonomic approach combined with pattern recognition permits accurate outcome prediction of septic rats in the early stage. The proposed approach has advantages of rapid, low-cost and efficiency, and is isph-ing to be applied in clinical prognostic evaluation of septic patients.