矽尘暴露人群血清生物标志物的筛选及其相关功能的初步研究
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摘要
矽肺(silicosis)是由于长期吸入大量游离二氧化硅(SiO2)粉尘引起的以肺间质纤维化为主的全身性疾病,在我国是危害最严重的职业病之一。在我国平均每例尘肺病人每年经济损失约为3.41万元,每年仅因尘肺病造成的直接经济损失达140多亿元,每年新增尘肺病例造成的经济损失达6亿元。近年来矽肺病的流行呈现出群发性、低龄化以及致残率高的新特点,这给矽肺的防治工作带来了新的挑战。目前已发现许多对矽肺的诊断有价值的血清生物标志物,如肺特异性的克拉拉细胞蛋白(CC16)、肺表面活性物质D(SP-D)作为诊断指标仍存在敏感性、特异性不足的缺点,不同类型的肺间质病之间这些指标重叠较大;而支气管肺泡灌洗液的化学及细胞学检查仅能作为影像学诊断的补充。运用新技术手段筛选高特异性、敏感性的标志物将对矽肺的早期防治起到重要的推动。液体芯片飞行时间质谱技术是一种基于磁珠分选与基质辅助激光解吸离子化飞行时间质谱(MALDI-TOF-MS)联合应用的蛋白质鉴定技术,可直接检测临床未经特殊处理的血清、尿液、脑脊液或浆膜腔积液等标本,对疾病(特别是肿瘤)的诊断有较高的敏感性和特异性。本研究采用液体芯片飞行时间质谱技术检测矽尘暴露人群血清蛋白的表达,筛选出矽肺发病早期的生物标志物建立矽肺早期诊断的人工神经网络诊断模型并对鉴定出的标志物进行相关功能的初步研究。
研究目的
应用液体芯片飞行时间质谱技术筛选矽尘暴露人群早期诊断血清生物标志物,并对鉴定出的生物标志物进行初步功能研究。
研究方法
矽尘暴露人群85例为山西省阳泉市某耐火材料厂接触游离二氧化硅粉尘的工人,统一摄高千伏X射线后前位胸片(120-150KV、100mA、0.03-0.05s),由具有诊断资格的诊断专家组按照《尘肺病诊断标准GBZ70-2002》进行诊断,分为矽尘暴露组(无尘肺0期)、可疑矽肺组(无尘肺0+期)和I期矽肺组;30例非暴露正常对照组为某单位年度体检人员,均无矽尘暴露史。
本研究首先应用invitrogen公司生产的Dynabeads RPC18磁珠,对矽尘暴露人群和非暴露正常对照组的血清进行蛋白质(多肽)的分选,然后对分选后的蛋白(多肽)采用MALDI-TOF-MS的一级质谱技术和ClinProTools分析软件进行差异蛋白峰的筛选,同时应用Matlab分析软件建立人工神经网络诊断模型,最后对筛选出的差异蛋白质峰采用MALDI-TOF-MS二级质谱技术进行氨基酸序列鉴定。
按照二级质谱鉴定出来的氨基酸序列人工合成差异蛋白,并用不同浓度的蛋白溶液作为刺激物,观察其对矽肺形成过程中的靶细胞人胚肺成纤维细胞(MRC-5)的影响。
研究结果
1.一级质谱寻找差异显著峰结果:利用神经网络算法(SNN)建立模型进行各组间两两比较,发现0期与0+期、0期与I期、0+期与I期比较,其识别率分别为64.35%、84.38%和73.49%,其交叉验证能力分别为63.14%、52.82%和69.07%;而正常对照组(control)与0期、正常对照组与0+期、正常对照组与I期比较,其识别率分别为89.23%、98.52%和98.96%交叉验证能力分别为87.66%、94.20%和85.53%。
由上可知,0期与0+期、0期与I期、0+期与I期比较,其识别率和交叉验证能力都较差;而正常对照组(control)与0期、正常对照组与0+期、正常对照组与I期比较,其识别率和交叉验证能力都较好。因此,仅对0期、0+期、I期与正常对照组的差异显著峰进行提取,共得到5个差异峰(P<0.01),其中5081Da和5066Da为表达上调的蛋白峰,2021da、3954da和1777da为表达下调的蛋白峰。
2.二级质谱鉴定差异显著峰氨基酸序列结果:根据二级质谱仪器要求,选择分子量<3KDa的峰2021Da和1777Da进行二级质谱鉴定其氨基酸序列结果两蛋白质峰同为补体C3的一个片段C3f。
3.矽尘暴露人群接尘量与差异峰强度相关性分析:接尘量与5081Da、5066Da、3954Da、2021Da和1777Da 5个差异显著峰的峰强的相关系数依次为r5081=0.039(P=0.614)、r5066=0.104(P=0.180)、r3954=-0.047(P=0.543)、r2021=-0.028(P=0.714)、r1777=-0.016 (P=0.853),5个差异蛋白峰强度与接尘量均不存在相关性。
4.矽尘暴露人群人工神经网络诊断模型的建立:筛选出峰效果较好的2021Da、2554Da、5066Da、8600Da 4个P<0.001的共同差异蛋白质峰建立矽尘暴露人群诊断模型,其区分矽尘暴露人群与正常对照组的特异性为100%,敏感性为90%,准确率为92.3%;该模型诊断0期、0+期和I期的识别率分别为100%、93%和96%。
5.不同浓度C3f刺激对MRC-5细胞上清液中Ⅰ、Ⅲ型胶原及TGF-β_1表达量变化的影响:随着C3f浓度的增加,Ⅰ、Ⅲ型胶原及TGF-β_1的表达量均呈进行性降低。与对照组相比,各浓度点均有显著差异(P<0.05)。
6.不同浓度C3f刺激对MRC-5胞质中TGF-β_1表达量变化的影响:随着C3f浓度的增加,MRC-5胞质中TGF-β_1的积分光密度值呈进行性降低,各浓度点均有显著差异(P<0.05)
研究结论
1.运用液体芯片飞行时间质谱技术的一级质谱寻找矽尘暴露人群差异蛋白,并成功建立了矽尘暴露人群人工神经网络诊断模型。
2.运用液体飞行时间质谱技术的二级质谱技术鉴定出1777Da和2021Da同为C3f,后续的细胞功能研究证明C3f能够减少MRC-5细胞中Ⅰ、Ⅲ型胶原和TGF-β_1的形成。
综上所述,本研究成功建立的矽尘暴露人群人工神经网络诊断模型为矽肺的早期诊断提供了一种全新而有效的方法,后续对差异蛋白C3f功能的初步研究结果也为进一步探讨矽肺发病的免疫机制提供了研究线索。
Slilicosis, with the characteristics of pulmonary interstitial fibrosis, is caused by long-term inhalation of a large number of free slilica dust .It is one of the most serious occupational diseases in China.The average annual economic losses of each pneumoconiosis case was about 34.1 thousand yuan and the direct economic losses caused by silicosis alone were more than 140 billion yuan each year.An annual increase of cases of pneumoconiosis could cause economic losses of 600 million yuan.In recent years,the prevalence of silicosis showing new features of group incidence, lower-aged tendency and high-disabled rate,which brings new chanllenges for prevetion and treatment of silicosis.Recently,many of the valuable serum biomarkers have been found for the diagnosis of silicosis, such as the lung-specific Clara cell protein(CC16) and pulmonary surfactant D(SP-D).As the diagnostic indicators, they still exist disadvantages of sentisitivity and specificity and different types of interstitial lung disease exist large overlap of these indicators.However,bronchoalveolar lavage fluid chemistry and cytology can only add or exclude from imaging diagnosis.The use of new technology to screen high specific and sensitive biomarkers will play an important force in prevention and treatment of silicosis.Liquid-chip time of flight mass spectrometry is one of the protein identification techniques that bases on magnetic beads separation combined with matrix-assisted laser desorption ionization time of flight mass spectrometry(MALDI-TOF-MS).It can directly detect clinical specimens without special treatment,such as serum,urine fluid,cerebrospinal fluid, serous effusion or others but with high sensitivity and specificity for diagnosis.The aim of this study is to screen serum biomarkers of silica-exposed population by Liquid-chip time of flight mass spectrometry,then estabilish an artifical neural network model for early diagnosis and identify some associated functions of the markers which may contribute to a further study of the pathogenesis of silicosis mechanisms.
Objectives
To screen serum biomarkers of silica-exposed population for early diagnosis by liquid-chip time of flight mass spectrometry,and identify some associated functions of the markers.
Methods
Eighty-five workers were selected from refractory plant of YangQuan City,ShanXi Province. All the silica-exposed population were diagnosed as having phase 0,phase 0+,or phase I of silicosis using radiograph(120-150KV, 100mA, 0.03-0.05s), following the national diagnostic standard(GBZ70-2002) of China for pneumoconsis.Thirty healthy people without silica exposure history were chosen as the control population.
Serum proteins(peptides) from silica-exposed population and control population was separated by a kind of magnetic beads produced by Invitrogen Company called Dynabeads RPC18, using MALDI-TOF-MS and an analysis software called ClinProTools to screen differences in protein peaks.Then,Matlab analysis software was applied to estabilish an artificial neural network diagnosis model.Finally, the amino acid sequences of the selected protein peaks were identified by MALDI-TOF-TOF-MS.
According to the amino acid sequence identified by MALDI-TOF-TOF-MS to synthesize the selected protein. Using different concentrations of protein solutions as stimulus to observe the effects of the target cells in the formation of silicosis called human embryo lung fibroblast(MRC-5) cells.
Results
1. Differentially expressed protein peaks found by MALDI-TOF-MS
Using neural network algorithm(SNN) to build pairwise comparison between each two groups, we found that the recognition rates between phase 0 and 0+, phase 0 and I , phase 0+ and I were respectively 64.35%,84.38% and 73.49%, similarly the cross-validations were 63.14%,52.82% and 69.07%; while between control group and phase 0, control group and phase 0+, control group and phase I, the recognition rates were 89.23%,98.52% and 98.96% respectively, similarly the cross-validations were 87.66%,94.20% and 85.53%.
Clear from the foregoing, the recognition rates and cross-validation capabilities between phase 0 and 0+, phase 0 and I, phase 0+ and I are all poor; while between control group and phase 0, control group and phase 0+, control group and phase I are all better. Thus, we just study differentially expressed protein peaks between control group and phase 0, control group and phase 0+, control group and phase I. A total of 5 peaks (P<0.01)have been found, in which the expressions of 5081Da and 5066Da are up-regulated while the expressions of 3954Da, 2021Da and 1777Da are down-regulated.
2. Amino acid sequence identified by MALDI-TOF-TOF-MS
2021Da and 1777Da peaks with molecular weight <3KD are selected for MALDI-TOF-TOF-MS. The amino acid sequences of the two peaks identified are both a fragment of complement C3 called complement C3f.
3. the correlation between the exprosure dose of dust and the intensitiy of peaks in silica-exposed population
The correlation coefficient is respectively r5081=0.039(P=0.614)、 r5066=0.104(P=0.180)、r3954=-0.047(P=0.543)、r2021=-0.028(P=0.714)、r1777=-0.016(P=0.853),and there were no correlation between each group.
4.Establishment of artificial neural network diagnosis model of silica-exposed population
Peaks of 2021Da, 2554Da, 5066Da, 8600Da(P<0.01) are chosen to estabilish the diagnosis model. The specificity, sensitivity and accuracy rate of the model to distinguish between silica-exposed population and control group are 100%, 90% and 92.3%. While the recognition rates of phase 0,phase 0+ and phase I are respectively 100%,93% and 96%.
5.Expression differences of typeⅠ,Ⅰcollagen and TGF-β_1in the supernatant of MRC-5 stimulated by C3f with different concentrations
With the increasing concentrations of C3f, the expressions of typeⅠ,Ⅰcollagen and TGF-β_1 show progressive reductions.Compared with control group, any concentration points are significantly different(P<0.05). 6.Expression differences of TGF-β_1 in the cytoplasm of MRC-5 stimulated by C3f with different concentrations
With the increasing concentrations of C3f, the expressions of TGF-β_1 show progressive reductions.Compared with control group, any concentration points are significantly different(P<0.05).
Conclusions
1.Using MALDI-TOF-MS of liquid-chip time of flight mass spectrometry to search differentially expressed protein peaks and successfully estabilish artificial neural network diagnosis model of silica-exposed population. 2.Using MALDI-TOF-TOF-MS of liquid-chip time of flight mass spectrometry to identify the peaks of 1777Da and 2021Da are both complement C3f. The following functional studies have show that C3f can reduce the formation of typeⅠ,Ⅰcollagen and TGF-β_1of MRC-5.
In summary, this study successfully estabilishs artificial neural network diagnosis model of silica-exposed population which can offer a new and effective way for the early diagnosis of silica-exposed population,while the following functional study of C3f provides a clue for further study of the immune mechanisms in the pathogenesis of silicosis.
研究目的
应用液体芯片飞行时间质谱技术筛选矽尘暴露人群早期诊断血清生物标志物,并对鉴定出的生物标志物进行初步功能研究。
研究方法
矽尘暴露人群85例为山西省阳泉市某耐火材料厂接触游离二氧化硅粉尘的工人,统一摄高千伏X射线后前位胸片(120-150KV、100mA、0.03-0.05s),由具有诊断资格的诊断专家组按照《尘肺病诊断标准GBZ70-2002》进行诊断,分为矽尘暴露组(无尘肺0期)、可疑矽肺组(无尘肺0+期)和I期矽肺组;30例非暴露正常对照组为某单位年度体检人员,均无矽尘暴露史。
本研究首先应用invitrogen公司生产的Dynabeads RPC18磁珠,对矽尘暴露人群和非暴露正常对照组的血清进行蛋白质(多肽)的分选,然后对分选后的蛋白(多肽)采用MALDI-TOF-MS的一级质谱技术和ClinProTools分析软件进行差异蛋白峰的筛选,同时应用Matlab分析软件建立人工神经网络诊断模型,最后对筛选出的差异蛋白质峰采用MALDI-TOF-MS二级质谱技术进行氨基酸序列鉴定。
按照二级质谱鉴定出来的氨基酸序列人工合成差异蛋白,并用不同浓度的蛋白溶液作为刺激物,观察其对矽肺形成过程中的靶细胞人胚肺成纤维细胞(MRC-5)的影响。
研究结果
1.一级质谱寻找差异显著峰结果:利用神经网络算法(SNN)建立模型进行各组间两两比较,发现0期与0+期、0期与I期、0+期与I期比较,其识别率分别为64.35%、84.38%和73.49%,其交叉验证能力分别为63.14%、52.82%和69.07%;而正常对照组(control)与0期、正常对照组与0+期、正常对照组与I期比较,其识别率分别为89.23%、98.52%和98.96%交叉验证能力分别为87.66%、94.20%和85.53%。
由上可知,0期与0+期、0期与I期、0+期与I期比较,其识别率和交叉验证能力都较差;而正常对照组(control)与0期、正常对照组与0+期、正常对照组与I期比较,其识别率和交叉验证能力都较好。因此,仅对0期、0+期、I期与正常对照组的差异显著峰进行提取,共得到5个差异峰(P<0.01),其中5081Da和5066Da为表达上调的蛋白峰,2021da、3954da和1777da为表达下调的蛋白峰。
2.二级质谱鉴定差异显著峰氨基酸序列结果:根据二级质谱仪器要求,选择分子量<3KDa的峰2021Da和1777Da进行二级质谱鉴定其氨基酸序列结果两蛋白质峰同为补体C3的一个片段C3f。
3.矽尘暴露人群接尘量与差异峰强度相关性分析:接尘量与5081Da、5066Da、3954Da、2021Da和1777Da 5个差异显著峰的峰强的相关系数依次为r5081=0.039(P=0.614)、r5066=0.104(P=0.180)、r3954=-0.047(P=0.543)、r2021=-0.028(P=0.714)、r1777=-0.016 (P=0.853),5个差异蛋白峰强度与接尘量均不存在相关性。
4.矽尘暴露人群人工神经网络诊断模型的建立:筛选出峰效果较好的2021Da、2554Da、5066Da、8600Da 4个P<0.001的共同差异蛋白质峰建立矽尘暴露人群诊断模型,其区分矽尘暴露人群与正常对照组的特异性为100%,敏感性为90%,准确率为92.3%;该模型诊断0期、0+期和I期的识别率分别为100%、93%和96%。
5.不同浓度C3f刺激对MRC-5细胞上清液中Ⅰ、Ⅲ型胶原及TGF-β_1表达量变化的影响:随着C3f浓度的增加,Ⅰ、Ⅲ型胶原及TGF-β_1的表达量均呈进行性降低。与对照组相比,各浓度点均有显著差异(P<0.05)。
6.不同浓度C3f刺激对MRC-5胞质中TGF-β_1表达量变化的影响:随着C3f浓度的增加,MRC-5胞质中TGF-β_1的积分光密度值呈进行性降低,各浓度点均有显著差异(P<0.05)
研究结论
1.运用液体芯片飞行时间质谱技术的一级质谱寻找矽尘暴露人群差异蛋白,并成功建立了矽尘暴露人群人工神经网络诊断模型。
2.运用液体飞行时间质谱技术的二级质谱技术鉴定出1777Da和2021Da同为C3f,后续的细胞功能研究证明C3f能够减少MRC-5细胞中Ⅰ、Ⅲ型胶原和TGF-β_1的形成。
综上所述,本研究成功建立的矽尘暴露人群人工神经网络诊断模型为矽肺的早期诊断提供了一种全新而有效的方法,后续对差异蛋白C3f功能的初步研究结果也为进一步探讨矽肺发病的免疫机制提供了研究线索。
Slilicosis, with the characteristics of pulmonary interstitial fibrosis, is caused by long-term inhalation of a large number of free slilica dust .It is one of the most serious occupational diseases in China.The average annual economic losses of each pneumoconiosis case was about 34.1 thousand yuan and the direct economic losses caused by silicosis alone were more than 140 billion yuan each year.An annual increase of cases of pneumoconiosis could cause economic losses of 600 million yuan.In recent years,the prevalence of silicosis showing new features of group incidence, lower-aged tendency and high-disabled rate,which brings new chanllenges for prevetion and treatment of silicosis.Recently,many of the valuable serum biomarkers have been found for the diagnosis of silicosis, such as the lung-specific Clara cell protein(CC16) and pulmonary surfactant D(SP-D).As the diagnostic indicators, they still exist disadvantages of sentisitivity and specificity and different types of interstitial lung disease exist large overlap of these indicators.However,bronchoalveolar lavage fluid chemistry and cytology can only add or exclude from imaging diagnosis.The use of new technology to screen high specific and sensitive biomarkers will play an important force in prevention and treatment of silicosis.Liquid-chip time of flight mass spectrometry is one of the protein identification techniques that bases on magnetic beads separation combined with matrix-assisted laser desorption ionization time of flight mass spectrometry(MALDI-TOF-MS).It can directly detect clinical specimens without special treatment,such as serum,urine fluid,cerebrospinal fluid, serous effusion or others but with high sensitivity and specificity for diagnosis.The aim of this study is to screen serum biomarkers of silica-exposed population by Liquid-chip time of flight mass spectrometry,then estabilish an artifical neural network model for early diagnosis and identify some associated functions of the markers which may contribute to a further study of the pathogenesis of silicosis mechanisms.
Objectives
To screen serum biomarkers of silica-exposed population for early diagnosis by liquid-chip time of flight mass spectrometry,and identify some associated functions of the markers.
Methods
Eighty-five workers were selected from refractory plant of YangQuan City,ShanXi Province. All the silica-exposed population were diagnosed as having phase 0,phase 0+,or phase I of silicosis using radiograph(120-150KV, 100mA, 0.03-0.05s), following the national diagnostic standard(GBZ70-2002) of China for pneumoconsis.Thirty healthy people without silica exposure history were chosen as the control population.
Serum proteins(peptides) from silica-exposed population and control population was separated by a kind of magnetic beads produced by Invitrogen Company called Dynabeads RPC18, using MALDI-TOF-MS and an analysis software called ClinProTools to screen differences in protein peaks.Then,Matlab analysis software was applied to estabilish an artificial neural network diagnosis model.Finally, the amino acid sequences of the selected protein peaks were identified by MALDI-TOF-TOF-MS.
According to the amino acid sequence identified by MALDI-TOF-TOF-MS to synthesize the selected protein. Using different concentrations of protein solutions as stimulus to observe the effects of the target cells in the formation of silicosis called human embryo lung fibroblast(MRC-5) cells.
Results
1. Differentially expressed protein peaks found by MALDI-TOF-MS
Using neural network algorithm(SNN) to build pairwise comparison between each two groups, we found that the recognition rates between phase 0 and 0+, phase 0 and I , phase 0+ and I were respectively 64.35%,84.38% and 73.49%, similarly the cross-validations were 63.14%,52.82% and 69.07%; while between control group and phase 0, control group and phase 0+, control group and phase I, the recognition rates were 89.23%,98.52% and 98.96% respectively, similarly the cross-validations were 87.66%,94.20% and 85.53%.
Clear from the foregoing, the recognition rates and cross-validation capabilities between phase 0 and 0+, phase 0 and I, phase 0+ and I are all poor; while between control group and phase 0, control group and phase 0+, control group and phase I are all better. Thus, we just study differentially expressed protein peaks between control group and phase 0, control group and phase 0+, control group and phase I. A total of 5 peaks (P<0.01)have been found, in which the expressions of 5081Da and 5066Da are up-regulated while the expressions of 3954Da, 2021Da and 1777Da are down-regulated.
2. Amino acid sequence identified by MALDI-TOF-TOF-MS
2021Da and 1777Da peaks with molecular weight <3KD are selected for MALDI-TOF-TOF-MS. The amino acid sequences of the two peaks identified are both a fragment of complement C3 called complement C3f.
3. the correlation between the exprosure dose of dust and the intensitiy of peaks in silica-exposed population
The correlation coefficient is respectively r5081=0.039(P=0.614)、 r5066=0.104(P=0.180)、r3954=-0.047(P=0.543)、r2021=-0.028(P=0.714)、r1777=-0.016(P=0.853),and there were no correlation between each group.
4.Establishment of artificial neural network diagnosis model of silica-exposed population
Peaks of 2021Da, 2554Da, 5066Da, 8600Da(P<0.01) are chosen to estabilish the diagnosis model. The specificity, sensitivity and accuracy rate of the model to distinguish between silica-exposed population and control group are 100%, 90% and 92.3%. While the recognition rates of phase 0,phase 0+ and phase I are respectively 100%,93% and 96%.
5.Expression differences of typeⅠ,Ⅰcollagen and TGF-β_1in the supernatant of MRC-5 stimulated by C3f with different concentrations
With the increasing concentrations of C3f, the expressions of typeⅠ,Ⅰcollagen and TGF-β_1 show progressive reductions.Compared with control group, any concentration points are significantly different(P<0.05). 6.Expression differences of TGF-β_1 in the cytoplasm of MRC-5 stimulated by C3f with different concentrations
With the increasing concentrations of C3f, the expressions of TGF-β_1 show progressive reductions.Compared with control group, any concentration points are significantly different(P<0.05).
Conclusions
1.Using MALDI-TOF-MS of liquid-chip time of flight mass spectrometry to search differentially expressed protein peaks and successfully estabilish artificial neural network diagnosis model of silica-exposed population. 2.Using MALDI-TOF-TOF-MS of liquid-chip time of flight mass spectrometry to identify the peaks of 1777Da and 2021Da are both complement C3f. The following functional studies have show that C3f can reduce the formation of typeⅠ,Ⅰcollagen and TGF-β_1of MRC-5.
In summary, this study successfully estabilishs artificial neural network diagnosis model of silica-exposed population which can offer a new and effective way for the early diagnosis of silica-exposed population,while the following functional study of C3f provides a clue for further study of the immune mechanisms in the pathogenesis of silicosis.
引文
[1]中华人民共和国卫生部.卫生部通报2008年全国职业卫生监督管理工作情况[EB/OL].http://www.gov.cn/gzdt/2009-06/09/content_1335962.htm, 2009-06-09/2009-11-29.
[2] Davis GS,Holmes CE,Pfeiffer LM,et al. Lymphocytes, lymphokines, and silicosis[J].J Environ Pathol Toxicol Oncol, 2001,20(Suppl 1):53-65.
[3] Thakur SA, Beamer CA, Migliaccio CT,etal.Critical role of MARCO in crystalline silica-induced pulmonary inflammation[J]. Toxicol Sci,2009,108(2):462-71.
[4] Borges VM, Lopes MF, Falc?o H,etal. Apoptosis underlies immunopathogenic mechanisms in acute silicosis[J].Am J Respir Cell Mol Biol,2002,27(1):78-84.
[5] Brown JM, Swindle EJ, Kushnir-Sukhov NM,etal.Silica-directed mast cell activation is enhanced by scavenger receptors[J]. Am J Respir Cell Mol Biol,2007,36(1):43-52.
[6] Hamada H, Vallyathan V, Cool CD,etal.Mast cell basic fibroblast growth factor in silicosis[J].Am J Respir Crit Care Med,2000, 161(6):2026-34.
[7] Fubini B,Hubbard A.Reactive oxygen species(ROS)and reactive nitrogen species(RNS) generation by silica in inflammation and fibrosis[J].Free Radic Biol Med,2003,34(12):1507—1516.
[8]魏茂提,王世鑫,周蔚,等.染矽尘大鼠血浆一氧化氮、一氧化氮合酶的变化[J].中国工业医学杂志,2002,15(2):80-82.
[9] Vanhée D, Gosset P, Boitelle A, etal. Cytokines and cytokine network in silicosis and coal workers' pneumoconiosis[J].Eur Respir J, 1995 ,8(5):834-42.
[10]袁宝军,丁秀荣,刘志忠,等.矽肺患者血清白细胞介素-12和γ-干扰素水平变化[J].中国职业医学,2006,33(2):111-113.
[11] Otsuki T, Miura Y, Nishimura Y, etal. Alterations of Fas and Fas-related molecules in patients with silicosis[J]. Exp Biol Med (Maywood),2006 ,231(5):522-33.
[12] Seibert V, Ebert MP, Buschmann T.Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery[J]. BRIEFINGS INFUNCTIONAL GENOMICS AND PROTEOMICS,2005,4(1):16–26.
[13] Zhang XY, Leung SM, Morris CR ,etal. Evaluation of a Novel, Integrated Approach Using Functionalized Magnetic Beads, Bench-Top MALDI-TOF-MS with Prestructured Sample Supports, and Pattern Recognition Software for Profiling Potential Biomarkers in Human Plasma[J].Journal of Biomolecular Techniques,2004,15:167–175.
[14] Baumann S, Ceglarek U, Fiedler GM,etal. Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[J].Clinical Chemistry,2005, 51(6):973–980.
[15]曾家伟.利用双向电泳和质谱技术筛选矽肺血清差异蛋白[D].重庆医科大学图书馆:重庆医科大学,2007.9-51.
[16]赵学峰.矽肺血清生物标志物的筛选与诊断指纹研究[D].重庆医科大学图书馆:重庆医科大学,2007.10-44.
[17] Wang SX, Zhao XF, Wei MT, etal. Screening of Serum Biomarkers and Esteblishment of a Decision Tree in Slica-Exposed populations by Surface-Enhanced Laser Desporption Ionization Time-of-Fly Mass Sepctrometry [J].Occup Environ Med ,2007,49: 764-770.
[1] Fubini B,Hubbard A.Reactive oxygen species(ROS)and reactive nitrogen species(RNS) generation by silica in inflammation and fibrosis[J].Free Radic Biol Med,2003,34(12):1507—1516.
[2] Vanhée D, Gosset P, Boitelle A, etal. Cytokines and cytokine network in silicosis and coal workers' pneumoconiosis[J].Eur Respir J, 1995 ,8(5):834-42.
[3] Otsuki T, Miura Y, Nishimura Y, etal. Alterations of Fas and Fas-related molecules in patients with silicosis[J]. Exp Biol Med (Maywood), 2006 , 231 (5): 522-33.
[4]刘萍,王世鑫,陈蕾,等.矽肺患者血清克拉拉细胞蛋白和表面活性蛋白D的改变.中华劳动卫生职业病杂志[J], 2007,25(1): 18-21.
[5] Bernard AM, Gonzalez-Lorenzo JM, Siles E,etal. Early decrease of serum Clara cell protein in silica-exposed workers[J]. Eur Respir J,1994,7(11):1932-1937.
[6] Pan T, Nielsen LD, Allen MJ,etal. Serum SP-D is a marker of lung injury in rats[J].Am J Physiol Lung Cell Mol Physiol,2002,282(4):L824-832.
[7] Sato T, Takeno M, Honma K, etal.Heme oxygenase-1, a potential biomarker of chronic silicosis, attenuates silica-induced lung injury[J]. Am J Respir Crit Care Med,2006 ,174(8):906-14.
[8] Lin XY, Yang SY, Du J,etal. Detection of lung adenocarcinoma using magnetic beads based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum protein profiling[J].Chin Med J(Engl),2010,123(1):34-39.
[9] Baumann S, Ceglarek U, Fiedler GM,etal. Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[J].Clinical Chemistry,2005, 51(6):973–980.
[10] Villanueva J, Shaffer DR, Philip J,etal. Differential exoprotease activities confer tumor-specific serum peptidome patterns[J].Clin Invest, 2006,116(1):271-284.
[11] Petricoin E F, Ardekani A M, Hitt B A, etal.Use of proteomic patterns in serum to identify ovarian cancer[J].Lancet ,2002,359(9306) :572-579.
[12] Han M Y, Liu Q, Yu J K, etal.Detection and significance of serum protein markers of small-cell lung cancer[J].J Clin Lab Anal ,2008,22(2) :131-137.
[13] Lisboa P J, Taktak A F.The use of artificial neural networks in decision support in cancer:a systematic review[J].Neural Net ,2006,19(4) :408-415.
[14]韩明勇,刘奇,余捷凯,等.血清蛋白质指纹图谱与人工神经网络模型在肺癌诊断中的应用[J].山东大学学报,2008,46(6):604-607.
[15] Sahu A, Lambris JD. Structure and biology of complement protein C3, a connecting link between innate and acquired immunity[J]. Immunological Reviews,2001,180:35-48.
[16] Xiang Y, Matsui T, Matsuo K,et al.Comprehensive Investigation of Disease -Specific Short Peptides in Sera from Patients with Systemic Sclerosis(SSc): Complement C3f-des-arginine (DRC3f),Detected Dominantly in SSc, Enhances Proliferation of Vascular Endothelial Cells[J].Arthritis Rheum, 2007,56(6): 2018-2030.
[17]向阳,加藤智启.补体片段C3f、DRC3f对皮肤成纤维细胞合成和分泌转化生长因子-β_1的调节作用[J].湖北民族学院学报(医学版),2007,24(1): 10-13.
[1] Ganu VS, Muller-Eberhard HJ, Hugli TE.Factor C3f is a spasmogenic fragment released from C3b by factors I and H:the hep tadeca-peptide C3f was synthesized and characterized [ J ].Mol Immunol,1989, 26(10): 939-948.
[2] Hamada H, Vallyathan V, Cool CD,etal. Mast cell basic fibroblast growth factor in silicosis[J].Am J Respir Crit Care Med,2000, 161(6): 2026-2034.
[3] Misson P, van den Br?le S, Barbarin V,etal. Markers of macrophage differentiation in experimental silicosis[J].J Leukoc Biol,2004, 76(5):926-932.
[4] Liu H, Zhang H, Forman HJ. Silica Induces Macrophage Cytokines through Phosphatidylcholine-Specific Phospholipase C with Hydrogen Peroxide[J].Am J Respir Cell Mol Biol,2007,36(5):594-599.
[5] Pernis B. Silica and the Immune System[J].Acta Biomed,2007,76 Suppl 2:38-44.
[6] Beamer CA, Holian A. Antigen-Presenting Cell Population Dynamics during Murine Silicosis[J]. Am J Respir Cell Mol Biol,2007,36(6): 729-738.
[7] CORBETT EL, MOZZATO-CHAMAY N, BUTTERWORTH AE,etal. Polymorphisms in the Tumor Necrosis Factor-αGene Promoter May Predispose to Severe Silicosis in Black South African Miners[J].Am J Respir Care Med,2002, 165(5):690-693.
[8] SRIVASTAVA KD, ROM WN, JAGIRDAR J,etal.Crucial Role of Interleukin-1 and Nitric Oxide Synthase in Silica-induced Inflammation and Apoptosis in Mice[J].Am J Respir Care Med,2002,165(4):527-533.
[9] Davis GS, Pfeiffer LM, Hemenway DR. Interleukin-12 is not essential for silicosis in mice[J].Part Fibre Toxicol,2006,3(2):1-16.
[10] Williams AO, Flanders KC, Saffiotti U. Immunohistochemical Localization of Transforming Growth Factor-j31 in Rats with Experimental Silicosis, Alveolar TypeⅡHyperplasia, and Lung Cancer[J].Am J Pathol, 142(6):1831-1840.
[11] Nagamoto T, Eguchi G, Beebe DC. Alpha-Smooth Muscle Actin Expression inCultured Lens Epithelial Cells[J].Invest Ophthalmol Vis Sci,2000, 41(5):1122-1129.
[12] Hinz B, Celetta G, Tomasek JJ,etal. Alpha-Smooth Muscle Actin Expression Upregulates Fibroblast Contractile Activity[J].Mol Biol Cell,2001,12(9):2730-2741.
[13] Hu YB,Zeng QF,Peng JW,etal. Effects of silicon dioxide on expression of alpha-smooth muscle actin in human lung fibroblasts[J]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi,2006,24(9):523-525.
[14] Tomasek JJ, McRae J, Owens GK,etal. Regulation ofα-Smooth Muscle Actin Expression in Granulation Tissue Myofibroblasts Is Dependent on the Intronic CArG Element and the Transforming Growth Factor-β_1 Control Element[J].Am J Pathol,2005,166(5):1343-1351.
[15] van den Br?le S, Misson P, Bühling F,etal. Overexpression of cathepsin K during silica-induced lung fibrosis and control by TGF-β[J].Respir Res,2005,84(6): 1-13.
[16] Xiang Y, Matsui T, Matsuo K,et al.Comprehensive Investigation of Disease -Specific Short Peptides in Sera from Patients with Systemic Sclerosis(SSc): Complement C3f-des-arginine (DRC3f),Detected Dominantly in SSc, Enhances Proliferation of Vascular Endothelial Cells[J].Arthritis Rheum, 2007,56(6): 2018-2030.
[17]向阳,加藤智启.补体片段C3f、DRC3f对皮肤成纤维细胞合成和分泌转化生长因子-β_1的调节作用[J].湖北民族学院学报(医学版), 2007,24(1): 10-13.
[1] Porter DW, Millecchia LL, Willard P,etal. Nitric Oxide and Reactive Oxygen Species Production Causes Progressive Damage in Rats after Cessation of Silica Inhalation[J]. TOXICOLOGICAL SCIENCES,2006, 90(1):188–197.
[2] Carlsten C, de Roos AJ, Kaufman DJ, etal. Cell Markers, Cytokines, and Immune Parameters in Cement Mason Apprentices[J].Arthritis Care & Research, 2007, 57(1):147–153.
[3] Borges VM, Falc?o H, Leite-Júnior JH, etal. Fas Ligand Triggers Pulmonary Silicosis[J]. Exp. Med, 2001,194(2):155–163.
[4] Huang SH, Hubbs AF, Stanley CF, etal. Immunoglobulin Responses to Experimental Silicosis[J].TOXICOLOGICAL SCIENCES,2001, 59:108 -117.
[5] Wua P, Hyodoha F, Hatayamaa T, etal.Induction of CD69 antigen expression in peripheral blood mononuclear cells on exposure to silica, but not by asbestos/chrysotile-A[J].Immunology Letters ,2005,98:145-152.
[6] Fubini B,Hubbard A.Reactive oxygen species(ROS)and reactive nitrogen species(RNS) generation by silica in inflammation and fibrosis[J].Free Radic Biol Med,2003,34(12):1507—1516.
[7]魏茂提,王世鑫,周蔚,等.染矽尘大鼠血浆一氧化氮、一氧化氮合酶的变化[J].中国工业医学杂志,2002,15(2):80-82.
[8] Vanhée D, Gosset P, Boitelle A, etal. Cytokines and cytokine network in silicosis and coal workers' pneumoconiosis[J].Eur Respir J, 1995 ,8(5):834-42.
[9]袁宝军,丁秀荣,刘志忠,等.矽肺患者血清白细胞介素-12和γ-干扰素水平变化[J].中国职业医学,2006,33(2):111-113.
[10]李倩.手掌参醇提取物对染矽尘大鼠IL-8及ERK、JNK影响的研究[D].河北医科大学图书馆:河北医科大学,2008,37-45.
[11]Otsuki T, Miura Y, Nishimura Y, etal. Alterations of Fas and Fas-related molecules in patients with silicosis[J]. Exp Biol Med (Maywood), 2006 ,231(5): 522-33.
[12]刘萍,王世鑫,陈蕾,等.矽肺患者血清克拉拉细胞蛋白和表面活性蛋白D的改变.中华劳动卫生职业病杂志[J], 2007,25(1): 18-21.
[13] Bernard AM, Gonzalez-Lorenzo JM, Siles E,etal. Early decrease of serum Clara cell protein in silica-exposed workers[J]. Eur Respir J,1994,7(11):1932-1937.
[14] Pan T, Nielsen LD, Allen MJ,etal. Serum SP-D is a marker of lung injury in rats[J].Am J Physiol Lung Cell Mol Physiol,2002,282(4):L824-832.
[15] Sato T, Takeno M, Honma K, etal.Heme oxygenase-1, a potential biomarker of chronic silicosis, attenuates silica-induced lung injury[J]. Am J Respir Crit Care Med,2006 ,174(8):906-14.
[16] Minden J.Comparative Proteomics and Difference Gel Electrophoresis[J]. Tech Insight, 2007, 43(6):741-745.
[17] Zieske LR. A perspective on the use of iTRAQTM reagent technologyfor protein complex and profiling studies[J].Journal of Experimental Botany, 2006,57(7):1501–1508.
[18]叶雯,刘凯于,洪华珠,等.定量蛋白质组学中的同位素标记技术[J].中国生物工程杂志,2005,25(12):56—61.
[19]杨何义,钱小红.定量蛋白质组学研究技术[J].生命的化学,2002,22(4):382-385.
[20] Cannataro M, Cuda G, Gaspari M,etal.The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC-MS/MS experiments[J].BMC Bioinformatics, 2007, 8:255-268. [21 ]BouyssiéD, Gonzalez de Peredo A, Mouton E,etal.Mascot File Parsing and Quantification (MFPaQ), a New Software to Parse, Validate, and Quantify Proteomics Data Generated by ICAT and SILAC Mass Spectrometric Analyses[J]. Molecular & Cellular Proteomics,2007, 6:1621–1637.
[22] Seibert V, Ebert MP, Buschmann T.Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery[J]. BRIEFINGS IN FUNCTIONAL GENOMICS AND PROTEOMICS,2005,4(1):16–26.
[23] Zhang XY, Leung SM, Morris CR ,etal. Evaluation of a Novel, Integrated Approach Using Functionalized Magnetic Beads, Bench-Top MALDI-TOF-MS with Prestructured Sample Supports, and Pattern Recognition Software forProfiling Potential Biomarkers in Human Plasma[J].Journal of Biomolecular Techniques,2004,15:167–175.
[24] Baumann S, Ceglarek U, Fiedler GM,etal. Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[J].Clinical Chemistry,2005, 51(6):973–980.
[25]曾家伟.利用双向电泳和质谱技术筛选矽肺血清差异蛋白[D].重庆医科大学图书馆:重庆医科大学,2007.9-51.
[26]赵学峰.矽肺血清生物标志物的筛选与诊断指纹研究[D].重庆医科大学图书馆:重庆医科大学,2007.10-44.
[27] Wang SX, Zhao XF, Wei MT, etal. Screening of Serum Biomarkers and Esteblishment of a Decision Tree in Slica-Exposed populations by Surface-Enhanced Laser Desporption Ionization Time-of-Fly Mass Sepctrometry [J].Occup Environ Med ,2007,49: 764-770.
[2] Davis GS,Holmes CE,Pfeiffer LM,et al. Lymphocytes, lymphokines, and silicosis[J].J Environ Pathol Toxicol Oncol, 2001,20(Suppl 1):53-65.
[3] Thakur SA, Beamer CA, Migliaccio CT,etal.Critical role of MARCO in crystalline silica-induced pulmonary inflammation[J]. Toxicol Sci,2009,108(2):462-71.
[4] Borges VM, Lopes MF, Falc?o H,etal. Apoptosis underlies immunopathogenic mechanisms in acute silicosis[J].Am J Respir Cell Mol Biol,2002,27(1):78-84.
[5] Brown JM, Swindle EJ, Kushnir-Sukhov NM,etal.Silica-directed mast cell activation is enhanced by scavenger receptors[J]. Am J Respir Cell Mol Biol,2007,36(1):43-52.
[6] Hamada H, Vallyathan V, Cool CD,etal.Mast cell basic fibroblast growth factor in silicosis[J].Am J Respir Crit Care Med,2000, 161(6):2026-34.
[7] Fubini B,Hubbard A.Reactive oxygen species(ROS)and reactive nitrogen species(RNS) generation by silica in inflammation and fibrosis[J].Free Radic Biol Med,2003,34(12):1507—1516.
[8]魏茂提,王世鑫,周蔚,等.染矽尘大鼠血浆一氧化氮、一氧化氮合酶的变化[J].中国工业医学杂志,2002,15(2):80-82.
[9] Vanhée D, Gosset P, Boitelle A, etal. Cytokines and cytokine network in silicosis and coal workers' pneumoconiosis[J].Eur Respir J, 1995 ,8(5):834-42.
[10]袁宝军,丁秀荣,刘志忠,等.矽肺患者血清白细胞介素-12和γ-干扰素水平变化[J].中国职业医学,2006,33(2):111-113.
[11] Otsuki T, Miura Y, Nishimura Y, etal. Alterations of Fas and Fas-related molecules in patients with silicosis[J]. Exp Biol Med (Maywood),2006 ,231(5):522-33.
[12] Seibert V, Ebert MP, Buschmann T.Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery[J]. BRIEFINGS INFUNCTIONAL GENOMICS AND PROTEOMICS,2005,4(1):16–26.
[13] Zhang XY, Leung SM, Morris CR ,etal. Evaluation of a Novel, Integrated Approach Using Functionalized Magnetic Beads, Bench-Top MALDI-TOF-MS with Prestructured Sample Supports, and Pattern Recognition Software for Profiling Potential Biomarkers in Human Plasma[J].Journal of Biomolecular Techniques,2004,15:167–175.
[14] Baumann S, Ceglarek U, Fiedler GM,etal. Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[J].Clinical Chemistry,2005, 51(6):973–980.
[15]曾家伟.利用双向电泳和质谱技术筛选矽肺血清差异蛋白[D].重庆医科大学图书馆:重庆医科大学,2007.9-51.
[16]赵学峰.矽肺血清生物标志物的筛选与诊断指纹研究[D].重庆医科大学图书馆:重庆医科大学,2007.10-44.
[17] Wang SX, Zhao XF, Wei MT, etal. Screening of Serum Biomarkers and Esteblishment of a Decision Tree in Slica-Exposed populations by Surface-Enhanced Laser Desporption Ionization Time-of-Fly Mass Sepctrometry [J].Occup Environ Med ,2007,49: 764-770.
[1] Fubini B,Hubbard A.Reactive oxygen species(ROS)and reactive nitrogen species(RNS) generation by silica in inflammation and fibrosis[J].Free Radic Biol Med,2003,34(12):1507—1516.
[2] Vanhée D, Gosset P, Boitelle A, etal. Cytokines and cytokine network in silicosis and coal workers' pneumoconiosis[J].Eur Respir J, 1995 ,8(5):834-42.
[3] Otsuki T, Miura Y, Nishimura Y, etal. Alterations of Fas and Fas-related molecules in patients with silicosis[J]. Exp Biol Med (Maywood), 2006 , 231 (5): 522-33.
[4]刘萍,王世鑫,陈蕾,等.矽肺患者血清克拉拉细胞蛋白和表面活性蛋白D的改变.中华劳动卫生职业病杂志[J], 2007,25(1): 18-21.
[5] Bernard AM, Gonzalez-Lorenzo JM, Siles E,etal. Early decrease of serum Clara cell protein in silica-exposed workers[J]. Eur Respir J,1994,7(11):1932-1937.
[6] Pan T, Nielsen LD, Allen MJ,etal. Serum SP-D is a marker of lung injury in rats[J].Am J Physiol Lung Cell Mol Physiol,2002,282(4):L824-832.
[7] Sato T, Takeno M, Honma K, etal.Heme oxygenase-1, a potential biomarker of chronic silicosis, attenuates silica-induced lung injury[J]. Am J Respir Crit Care Med,2006 ,174(8):906-14.
[8] Lin XY, Yang SY, Du J,etal. Detection of lung adenocarcinoma using magnetic beads based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum protein profiling[J].Chin Med J(Engl),2010,123(1):34-39.
[9] Baumann S, Ceglarek U, Fiedler GM,etal. Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[J].Clinical Chemistry,2005, 51(6):973–980.
[10] Villanueva J, Shaffer DR, Philip J,etal. Differential exoprotease activities confer tumor-specific serum peptidome patterns[J].Clin Invest, 2006,116(1):271-284.
[11] Petricoin E F, Ardekani A M, Hitt B A, etal.Use of proteomic patterns in serum to identify ovarian cancer[J].Lancet ,2002,359(9306) :572-579.
[12] Han M Y, Liu Q, Yu J K, etal.Detection and significance of serum protein markers of small-cell lung cancer[J].J Clin Lab Anal ,2008,22(2) :131-137.
[13] Lisboa P J, Taktak A F.The use of artificial neural networks in decision support in cancer:a systematic review[J].Neural Net ,2006,19(4) :408-415.
[14]韩明勇,刘奇,余捷凯,等.血清蛋白质指纹图谱与人工神经网络模型在肺癌诊断中的应用[J].山东大学学报,2008,46(6):604-607.
[15] Sahu A, Lambris JD. Structure and biology of complement protein C3, a connecting link between innate and acquired immunity[J]. Immunological Reviews,2001,180:35-48.
[16] Xiang Y, Matsui T, Matsuo K,et al.Comprehensive Investigation of Disease -Specific Short Peptides in Sera from Patients with Systemic Sclerosis(SSc): Complement C3f-des-arginine (DRC3f),Detected Dominantly in SSc, Enhances Proliferation of Vascular Endothelial Cells[J].Arthritis Rheum, 2007,56(6): 2018-2030.
[17]向阳,加藤智启.补体片段C3f、DRC3f对皮肤成纤维细胞合成和分泌转化生长因子-β_1的调节作用[J].湖北民族学院学报(医学版),2007,24(1): 10-13.
[1] Ganu VS, Muller-Eberhard HJ, Hugli TE.Factor C3f is a spasmogenic fragment released from C3b by factors I and H:the hep tadeca-peptide C3f was synthesized and characterized [ J ].Mol Immunol,1989, 26(10): 939-948.
[2] Hamada H, Vallyathan V, Cool CD,etal. Mast cell basic fibroblast growth factor in silicosis[J].Am J Respir Crit Care Med,2000, 161(6): 2026-2034.
[3] Misson P, van den Br?le S, Barbarin V,etal. Markers of macrophage differentiation in experimental silicosis[J].J Leukoc Biol,2004, 76(5):926-932.
[4] Liu H, Zhang H, Forman HJ. Silica Induces Macrophage Cytokines through Phosphatidylcholine-Specific Phospholipase C with Hydrogen Peroxide[J].Am J Respir Cell Mol Biol,2007,36(5):594-599.
[5] Pernis B. Silica and the Immune System[J].Acta Biomed,2007,76 Suppl 2:38-44.
[6] Beamer CA, Holian A. Antigen-Presenting Cell Population Dynamics during Murine Silicosis[J]. Am J Respir Cell Mol Biol,2007,36(6): 729-738.
[7] CORBETT EL, MOZZATO-CHAMAY N, BUTTERWORTH AE,etal. Polymorphisms in the Tumor Necrosis Factor-αGene Promoter May Predispose to Severe Silicosis in Black South African Miners[J].Am J Respir Care Med,2002, 165(5):690-693.
[8] SRIVASTAVA KD, ROM WN, JAGIRDAR J,etal.Crucial Role of Interleukin-1 and Nitric Oxide Synthase in Silica-induced Inflammation and Apoptosis in Mice[J].Am J Respir Care Med,2002,165(4):527-533.
[9] Davis GS, Pfeiffer LM, Hemenway DR. Interleukin-12 is not essential for silicosis in mice[J].Part Fibre Toxicol,2006,3(2):1-16.
[10] Williams AO, Flanders KC, Saffiotti U. Immunohistochemical Localization of Transforming Growth Factor-j31 in Rats with Experimental Silicosis, Alveolar TypeⅡHyperplasia, and Lung Cancer[J].Am J Pathol, 142(6):1831-1840.
[11] Nagamoto T, Eguchi G, Beebe DC. Alpha-Smooth Muscle Actin Expression inCultured Lens Epithelial Cells[J].Invest Ophthalmol Vis Sci,2000, 41(5):1122-1129.
[12] Hinz B, Celetta G, Tomasek JJ,etal. Alpha-Smooth Muscle Actin Expression Upregulates Fibroblast Contractile Activity[J].Mol Biol Cell,2001,12(9):2730-2741.
[13] Hu YB,Zeng QF,Peng JW,etal. Effects of silicon dioxide on expression of alpha-smooth muscle actin in human lung fibroblasts[J]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi,2006,24(9):523-525.
[14] Tomasek JJ, McRae J, Owens GK,etal. Regulation ofα-Smooth Muscle Actin Expression in Granulation Tissue Myofibroblasts Is Dependent on the Intronic CArG Element and the Transforming Growth Factor-β_1 Control Element[J].Am J Pathol,2005,166(5):1343-1351.
[15] van den Br?le S, Misson P, Bühling F,etal. Overexpression of cathepsin K during silica-induced lung fibrosis and control by TGF-β[J].Respir Res,2005,84(6): 1-13.
[16] Xiang Y, Matsui T, Matsuo K,et al.Comprehensive Investigation of Disease -Specific Short Peptides in Sera from Patients with Systemic Sclerosis(SSc): Complement C3f-des-arginine (DRC3f),Detected Dominantly in SSc, Enhances Proliferation of Vascular Endothelial Cells[J].Arthritis Rheum, 2007,56(6): 2018-2030.
[17]向阳,加藤智启.补体片段C3f、DRC3f对皮肤成纤维细胞合成和分泌转化生长因子-β_1的调节作用[J].湖北民族学院学报(医学版), 2007,24(1): 10-13.
[1] Porter DW, Millecchia LL, Willard P,etal. Nitric Oxide and Reactive Oxygen Species Production Causes Progressive Damage in Rats after Cessation of Silica Inhalation[J]. TOXICOLOGICAL SCIENCES,2006, 90(1):188–197.
[2] Carlsten C, de Roos AJ, Kaufman DJ, etal. Cell Markers, Cytokines, and Immune Parameters in Cement Mason Apprentices[J].Arthritis Care & Research, 2007, 57(1):147–153.
[3] Borges VM, Falc?o H, Leite-Júnior JH, etal. Fas Ligand Triggers Pulmonary Silicosis[J]. Exp. Med, 2001,194(2):155–163.
[4] Huang SH, Hubbs AF, Stanley CF, etal. Immunoglobulin Responses to Experimental Silicosis[J].TOXICOLOGICAL SCIENCES,2001, 59:108 -117.
[5] Wua P, Hyodoha F, Hatayamaa T, etal.Induction of CD69 antigen expression in peripheral blood mononuclear cells on exposure to silica, but not by asbestos/chrysotile-A[J].Immunology Letters ,2005,98:145-152.
[6] Fubini B,Hubbard A.Reactive oxygen species(ROS)and reactive nitrogen species(RNS) generation by silica in inflammation and fibrosis[J].Free Radic Biol Med,2003,34(12):1507—1516.
[7]魏茂提,王世鑫,周蔚,等.染矽尘大鼠血浆一氧化氮、一氧化氮合酶的变化[J].中国工业医学杂志,2002,15(2):80-82.
[8] Vanhée D, Gosset P, Boitelle A, etal. Cytokines and cytokine network in silicosis and coal workers' pneumoconiosis[J].Eur Respir J, 1995 ,8(5):834-42.
[9]袁宝军,丁秀荣,刘志忠,等.矽肺患者血清白细胞介素-12和γ-干扰素水平变化[J].中国职业医学,2006,33(2):111-113.
[10]李倩.手掌参醇提取物对染矽尘大鼠IL-8及ERK、JNK影响的研究[D].河北医科大学图书馆:河北医科大学,2008,37-45.
[11]Otsuki T, Miura Y, Nishimura Y, etal. Alterations of Fas and Fas-related molecules in patients with silicosis[J]. Exp Biol Med (Maywood), 2006 ,231(5): 522-33.
[12]刘萍,王世鑫,陈蕾,等.矽肺患者血清克拉拉细胞蛋白和表面活性蛋白D的改变.中华劳动卫生职业病杂志[J], 2007,25(1): 18-21.
[13] Bernard AM, Gonzalez-Lorenzo JM, Siles E,etal. Early decrease of serum Clara cell protein in silica-exposed workers[J]. Eur Respir J,1994,7(11):1932-1937.
[14] Pan T, Nielsen LD, Allen MJ,etal. Serum SP-D is a marker of lung injury in rats[J].Am J Physiol Lung Cell Mol Physiol,2002,282(4):L824-832.
[15] Sato T, Takeno M, Honma K, etal.Heme oxygenase-1, a potential biomarker of chronic silicosis, attenuates silica-induced lung injury[J]. Am J Respir Crit Care Med,2006 ,174(8):906-14.
[16] Minden J.Comparative Proteomics and Difference Gel Electrophoresis[J]. Tech Insight, 2007, 43(6):741-745.
[17] Zieske LR. A perspective on the use of iTRAQTM reagent technologyfor protein complex and profiling studies[J].Journal of Experimental Botany, 2006,57(7):1501–1508.
[18]叶雯,刘凯于,洪华珠,等.定量蛋白质组学中的同位素标记技术[J].中国生物工程杂志,2005,25(12):56—61.
[19]杨何义,钱小红.定量蛋白质组学研究技术[J].生命的化学,2002,22(4):382-385.
[20] Cannataro M, Cuda G, Gaspari M,etal.The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC-MS/MS experiments[J].BMC Bioinformatics, 2007, 8:255-268. [21 ]BouyssiéD, Gonzalez de Peredo A, Mouton E,etal.Mascot File Parsing and Quantification (MFPaQ), a New Software to Parse, Validate, and Quantify Proteomics Data Generated by ICAT and SILAC Mass Spectrometric Analyses[J]. Molecular & Cellular Proteomics,2007, 6:1621–1637.
[22] Seibert V, Ebert MP, Buschmann T.Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery[J]. BRIEFINGS IN FUNCTIONAL GENOMICS AND PROTEOMICS,2005,4(1):16–26.
[23] Zhang XY, Leung SM, Morris CR ,etal. Evaluation of a Novel, Integrated Approach Using Functionalized Magnetic Beads, Bench-Top MALDI-TOF-MS with Prestructured Sample Supports, and Pattern Recognition Software forProfiling Potential Biomarkers in Human Plasma[J].Journal of Biomolecular Techniques,2004,15:167–175.
[24] Baumann S, Ceglarek U, Fiedler GM,etal. Standardized Approach to Proteome Profiling of Human Serum Based on Magnetic Bead Separation and Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry[J].Clinical Chemistry,2005, 51(6):973–980.
[25]曾家伟.利用双向电泳和质谱技术筛选矽肺血清差异蛋白[D].重庆医科大学图书馆:重庆医科大学,2007.9-51.
[26]赵学峰.矽肺血清生物标志物的筛选与诊断指纹研究[D].重庆医科大学图书馆:重庆医科大学,2007.10-44.
[27] Wang SX, Zhao XF, Wei MT, etal. Screening of Serum Biomarkers and Esteblishment of a Decision Tree in Slica-Exposed populations by Surface-Enhanced Laser Desporption Ionization Time-of-Fly Mass Sepctrometry [J].Occup Environ Med ,2007,49: 764-770.