纺织品中分散染料的检测研究和代谢组学预测吉非替尼对非小细胞肺癌疗效探究
发布时间：2018-08-14 20:09
【摘要】：第一部分：纺织品中十七种分散染料的检测研究纺织品与人类生活息息相关,其中的致敏分散染料的使用不当势必会影响身体健康。本课题利用新型的检测手段超高效合相色谱-质谱联用的方法对纺织品常用的17种致敏分散染料进行检测分析,主要做了以下研究内容：1.实验应用超高效合相色谱-三重四级杆串联质谱法(UPC2-TQMS),建立了纺织品中十七种致敏分散染料—分散橙1,分散黄39,分散橙37,分散橙3,分散蓝124,分散黄9,分散黄49,分散红1,分散黄3,分散蓝3,分散红11,分散蓝106,分散红17,分散棕1,分散蓝102,分散黄1,分散蓝1的分析方法。实验以超声萃取为样品前处理手段,考察了固定相、柱温、背压等仪器条件对分离度的影响,最终采用Waters ACQUITY UPC2TM BEH(1.7μm, 3×100 mm I.D.)色谱柱进行分离,柱温45℃,背压1600 psi,流动相为CO2(A)和甲醇(B),流速2 mL·min-1,流出时间5 min,通过质谱多反应监测模式(MRM),得到样品的定量限为0.1-2.0μg·mL-1,检出限是0.02～1.0μg·mL-1,线性相关系数大于0.99,加标回收率是70.55～103.03%,相对标准偏差(RSD)小于9%(n=5),方法重现性较好且用于实际样品检测。2.考察了BEH、BEH 2-Ethyl-pyridine、HSS C18 SB以及CSH Fluorophenyl四种色谱柱对不同基团的分散染料的分离检测情况。结果表明,对于HSS C18 SB柱,样品整体保留时间延长。含有蒽醌结构的染料在CSH Fluorophenyl中出峰较晚,该固定相对含有芳香结构顺反异构体具有很好的选择性及分离度。这部分内容提供了含有偶氮、葸醌、苯胺、羟基类的物质用于四种固定相适用性的参考依据。第二部分：代谢组学方法预测吉非替尼对非小细胞肺癌患者疗效的探究吉非替尼作为治疗非小细胞肺癌的主要药物之一有较好的治疗效果,同时价格不菲。但吉非替尼并不是对所有非小细胞肺癌患者都有疗效,寻找有效的生物标志物预测服用吉非替尼肺癌患者的治疗效果具有重要意义,不仅可以降低病人的经济负担,而且还帮助医生选择正确有效的治疗方案。本实验运用超高效液相色谱-高分辨飞行时间质谱联用技术(UHPLC-QTOFMS)对134个生存时间不同的肺癌患者的血清进行分析,结合统计学软件对结果进行代谢组学研究。主要内容和结果如下：1.利用甲醇溶剂对1 34个非小细胞肺癌患者的血清进行沉淀蛋白前处理,样品用UHPLC-QTOFMS上机检测。具体分析条件如下：色谱柱是Waters ACQUITY UHPLCTM BEH C,8(1.7 μm,2.1×100 mm I.D.),柱温45℃,流动相由甲醇(A)-0.2%甲酸的水溶液(B)组成,流速0.35 mL·min-1,样品洗脱时间为22 min。134个血清样品连续进样,并采集电喷雾电离源(ESI)的正、负模式下的样品信息,最终获得代谢指纹图谱。2.通过Markerlynx V4.1软件将图谱数据转为三维矩阵,进行峰对齐、峰匹配以及归一化,导入SIMCA-P软件中进行偏最小二乘-判别分析,通过SPSS软件进行Kaplan-Meier生存时间分析,Logrank检验,并运用COX回归模型进行多因素分析,最终找出22个重要的差异代谢物,从中进一步筛选出2种潜在代谢物。3.通过飞行时间质谱获得的精确分子质量,以及二极质谱碎片断裂分析,在HMDB数据库中找到匹配代谢物信息,从而推断标志物,初步解释可能的生物学意义。最终实验确定了两个代谢物为PC类磷脂化合物,为临床治疗非小细胞肺癌方面提供有效的治疗方案。
[Abstract]:The first part is about the determination of seventeen disperse dyes in textiles. There is a close relationship between textiles and human life. Improper use of sensitized disperse dyes is bound to affect human health. The main research contents are as follows: 1. The 17 sensitized disperse dyes in textiles, disperse orange 1, disperse yellow 39, disperse orange 37, disperse orange 3, disperse blue 124, disperse yellow 9, disperse yellow 49, disperse red 1, disperse yellow 3, disperse blue 3, disperse red, disperse red, disperse orange 1, disperse blue 3, disperse red, disperse orange 1, disperse orange 3, disperse blue 3, disperse red, disperse orange 1, disperse orange 1, dispers 11, Disperse Blue 106, Disperse Red 17, Disperse Brown 1, Disperse Blue 102, Disperse Yellow 1, Disperse Blue 1. The influence of the stationary phase, column temperature and back pressure on the separation was investigated by ultrasonic extraction. Finally, the separation was carried out by Waters ACQUITY UPC2TM BEH (1.7 micron, 3 *100 mm I.D.) column at 45 C. The mobile phase was CO2 (A) and methanol (B), the flow rate was 2 mL min 1, the flow time was 5 min. The quantitative limit was 0.1-2.0 UG mL 1, the detection limit was 0.02-1.0 UG mL 1, the linear correlation coefficient was over 0.99, the recovery was 70.55-103.03%, and the RSD was less than 9% (n = 5). The separation and determination of disperse dyes with different groups on BEH, BEH 2-Ethyl-pyridine, HSS C18 SB and CSH Fluorophenyl columns were investigated. The results showed that for HSS C18 SB column, the overall retention time of the sample was prolonged. Dyes containing anthraquinone structure were found in CSH Fluorophenyl. This section provides a reference for the applicability of four stationary phases containing azo, anthraquinone, aniline and hydroxyl groups. Part II: Metabonomic methods to predict the efficacy of gefitinib in patients with non-small cell lung cancer. However, gefitinib is not effective for all patients with non-small cell lung cancer. It is important to look for effective biomarkers to predict the efficacy of gefitinib in the treatment of lung cancer. Ultra-high performance liquid chromatography-high resolution time-of-flight mass spectrometry (UHPLC-QTOFMS) was used to analyze the serum of 134 lung cancer patients with different survival time, and metabonomics study was carried out with statistical software. The contents and results are as follows: 1. The serum samples of 134 patients with non-small cell lung cancer were pretreated with methanol and detected by UHPLC-QTOFMS. The specific analysis conditions are as follows: Waters ACQUITY UHPLC TM BEH C, 8 (1.7 micron, 2.1 *100 mm I.D.), column temperature 45 and mobile phase is water-soluble methanol (A) - 0.2% formic acid. The metabolic fingerprint was obtained by collecting the positive and negative patterns of the electrospray ionization source (ESI). 2. The metabolic fingerprint was transformed into a three-dimensional matrix by Markerlynx V4.1 software, and the peak alignment, peak matching and normalization were performed. Partial least squares-discriminant analysis was performed in SIMCA-P software, Kaplan-Meier survival time analysis was performed by SPSS software, Logrank test was performed, and COX regression model was used for multivariate analysis. Finally, 22 important differential metabolites were identified, and two potential metabolites were further screened out. 3. Accurate fractions were obtained by time-of-flight mass spectrometry. Quantum mass and fragmentation analysis of polar mass spectroscopy were used to identify matching metabolites in the HMDB database to infer markers and preliminarily explain the possible biological significance.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：R734.2;TS197
本文编号：2183984
[Abstract]:The first part is about the determination of seventeen disperse dyes in textiles. There is a close relationship between textiles and human life. Improper use of sensitized disperse dyes is bound to affect human health. The main research contents are as follows: 1. The 17 sensitized disperse dyes in textiles, disperse orange 1, disperse yellow 39, disperse orange 37, disperse orange 3, disperse blue 124, disperse yellow 9, disperse yellow 49, disperse red 1, disperse yellow 3, disperse blue 3, disperse red, disperse red, disperse orange 1, disperse blue 3, disperse red, disperse orange 1, disperse orange 3, disperse blue 3, disperse red, disperse orange 1, disperse orange 1, dispers 11, Disperse Blue 106, Disperse Red 17, Disperse Brown 1, Disperse Blue 102, Disperse Yellow 1, Disperse Blue 1. The influence of the stationary phase, column temperature and back pressure on the separation was investigated by ultrasonic extraction. Finally, the separation was carried out by Waters ACQUITY UPC2TM BEH (1.7 micron, 3 *100 mm I.D.) column at 45 C. The mobile phase was CO2 (A) and methanol (B), the flow rate was 2 mL min 1, the flow time was 5 min. The quantitative limit was 0.1-2.0 UG mL 1, the detection limit was 0.02-1.0 UG mL 1, the linear correlation coefficient was over 0.99, the recovery was 70.55-103.03%, and the RSD was less than 9% (n = 5). The separation and determination of disperse dyes with different groups on BEH, BEH 2-Ethyl-pyridine, HSS C18 SB and CSH Fluorophenyl columns were investigated. The results showed that for HSS C18 SB column, the overall retention time of the sample was prolonged. Dyes containing anthraquinone structure were found in CSH Fluorophenyl. This section provides a reference for the applicability of four stationary phases containing azo, anthraquinone, aniline and hydroxyl groups. Part II: Metabonomic methods to predict the efficacy of gefitinib in patients with non-small cell lung cancer. However, gefitinib is not effective for all patients with non-small cell lung cancer. It is important to look for effective biomarkers to predict the efficacy of gefitinib in the treatment of lung cancer. Ultra-high performance liquid chromatography-high resolution time-of-flight mass spectrometry (UHPLC-QTOFMS) was used to analyze the serum of 134 lung cancer patients with different survival time, and metabonomics study was carried out with statistical software. The contents and results are as follows: 1. The serum samples of 134 patients with non-small cell lung cancer were pretreated with methanol and detected by UHPLC-QTOFMS. The specific analysis conditions are as follows: Waters ACQUITY UHPLC TM BEH C, 8 (1.7 micron, 2.1 *100 mm I.D.), column temperature 45 and mobile phase is water-soluble methanol (A) - 0.2% formic acid. The metabolic fingerprint was obtained by collecting the positive and negative patterns of the electrospray ionization source (ESI). 2. The metabolic fingerprint was transformed into a three-dimensional matrix by Markerlynx V4.1 software, and the peak alignment, peak matching and normalization were performed. Partial least squares-discriminant analysis was performed in SIMCA-P software, Kaplan-Meier survival time analysis was performed by SPSS software, Logrank test was performed, and COX regression model was used for multivariate analysis. Finally, 22 important differential metabolites were identified, and two potential metabolites were further screened out. 3. Accurate fractions were obtained by time-of-flight mass spectrometry. Quantum mass and fragmentation analysis of polar mass spectroscopy were used to identify matching metabolites in the HMDB database to infer markers and preliminarily explain the possible biological significance.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：R734.2;TS197
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