急性血栓性疾病血栓标志物的探索性研究
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摘要
血栓性疾病(Thrombotic diseases)是涉及临床各科的一大类疾病。动脉粥样硬化及其血栓栓塞性并发症在我国和西方国家均已成为人口死亡与致残的第一位原因，且呈逐年上升趋势。血栓的发生是一个较为长期的过程，患者就医时，血栓往往已经形成并导致血管栓塞。心脏和脑等对缺血十分敏感的器官，发生栓塞后治疗效果不佳，故血栓性疾病严重危害人类健康。如能在血栓发生的早期即进行诊断并及时采取干预措施，则能够大大降低血栓栓塞性疾病的危害。
目前，血栓性疾病的确诊主要依靠血栓形成部位的影像学证据。临床上主要以D-二聚体(d-dimer)、活化部分凝血酶原时间(activated partial thromboplastin time，APTT)、凝血酶原时间(prothrombin time，PT)、纤维蛋白原(fibrinogen，Fbg)含量等作为血栓性疾病的初步筛查项目，其敏感度和准确性尚无统一的临床结论。血栓性疾病发病机制极其复杂，在血栓形成早期，或血栓尚未形成时，上述筛查指标可能出现阴性结果，往往延误正确判断；而当阳性结果出现时，疾病往往已进展到中晚期，失去了最有效的治疗时机。因此，在血栓病高危人群中，对血栓发生危险性进行全面的评估及早期诊断血栓形成已成为当今血栓病研究领域的热点。
蛋白质-飞行时间质谱技术(Time of flight mass spectrometry，TOF-MS)是蛋白质组学中一种大规模、高通量的分子研究方法，与生物信息学和基因功能实验等相结合已经在揭示疾病发生的分子机制领域中发挥越来越重要的作用。本试验采用表面增强激光解析/电离飞行时间质谱仪(surface enhanced laser desorption/ionization time offlight mass spectrometry，SELDI-TOF-MS)及蛋白质生物芯片技术探索性研究动脉、静脉血栓形成患者的血浆蛋白质指纹图谱，旨在发现能够区分血栓病与对照组的高敏感、高特异的蛋白质生物标志物，为开发新的可用于诊断血栓形成的特异性标记物提供实验依据。同时，结合一系列与凝血、抗凝及纤溶系统相关的液态基因芯片检测平台及相关生化指标检测，通过临床研究，为发现血栓形成建立快速、准确的早期诊断综合体系。
第一部分
急性脑梗死凝血功能变化及其危险因素评估的回顾性研究
目的：已有研究表明，血液中凝血、抗凝或纤溶蛋白水平的改变与脑血管疾病的发生明显相关。但较少研究综合考虑其在脑血管疾病中的改变及作用。在本次研究中，我们系统探讨了其中的16个凝血指标、血脂水平、以及其他可能的危险因素与急性脑梗死的关系。
方法：武汉协和医院急性脑梗死(Acute Cerebral Infarction，ACI)住院患者50例，住院对照54例。所有研究对象均在入院第二天清晨空腹采取肘静脉血。血浆蛋白C(protein C，PC)、游离蛋白S(free protein S，FPS)、总蛋白S(total protein S，TPS)、凝血酶调节蛋白(thrombomodulin，TM)、活化凝血因子VII(activated factorⅦ，FⅦa)、凝血因子Ⅶ抗原(factorⅦantigen，FⅦAg)、P-选择素(p-selectin)、组织型纤溶酶原激活剂(tissue-type plasminogen activator，t-PA)及纤溶酶原激活剂抑制物-1(plasminogen activator inhibitor-1，PAI-1)水平均由ELISA试剂盒检测；组织因子活性(tissue factor activity，aTF)由发色底物法检测；全自动凝血分析仪(SysmexCA-7000，日本)检测活化蛋白C(activated protein C，APC)比率、活化部分凝血活酶时间(activated partial thromboplastin time，APTT)、凝血酶原时间(prothrombin time，PT)、凝血酶时间(thrombin time，TT)、纤维蛋白原(fibrinogen，Fbg)以及D-二聚体(d-dimer)；检验科自动生化分析仪(日立7170A，东京，日本)检测血脂水平。
结果：疾病组血浆凝血酶调节蛋白、纤维蛋白原水平及组织因子活性显著高于对照组(P＜0．001，P＜0．01，P＜0．05)；而高密度脂蛋白水平显著低于对照组(P＜0．01)。多变量逻辑回归分析表明：高血压的存在，血浆高水平的凝血酶调节蛋白、纤维蛋白原及组织因子活性与急性脑梗死的发病明显相关(比值比[odds ratio，OR]=143．74，P＜0．001；OR=2．05，P＜0．05；OR=2．09，P＜0．05；OR=1．02，P＜0．05)。
结论：高血压，血浆凝血酶调节蛋白、纤维蛋白原水平及组织因子活性的升高是急性脑梗死的独立危险因素。
第二部分
急性深静脉血栓形成危险因素的评估
目的：观察急性下肢深静脉血栓形成(deep venous thrombosis，DVT)患者凝血功能变化及评估其危险因素。
方法：全自动凝血分析仪(Sysmex CA-7000，日本)检测62例急性下肢DVT患者和70例健康对照者血浆活化部分凝血活酶时间(activated partial thromboplastin time，APTT)、凝血酶原时间(prothrombin time，PT)、凝血酶时间(thrombin time，TT)、纤维蛋白原(fibrinogen，Fbg)及D-二聚体(d-dimer)水平；检验科自动生化分析仪(日立7170A，东京，日本)检测高密度脂蛋白胆固醇(high-density lipoprotein cholesterol，HDL-C)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol，LDL-C)、总胆固醇(total cholesterol，TC)及甘油三酯(triglyceride，TG)水平；并通过二分类逻辑回归分析回顾性研究所有患者的临床资料。
结果：(1)DVT组血浆APTT、PT、TT、D-二聚体、Fbg水平及D-二聚体与Fbg比值(D/F值)都明显高于健康对照组，差异均有统计学意义(所有P＜0．01)；(2)DVT组和对照组血浆D-二聚体与Fbg水平之间均存在正相关性(r=0．475，P＜0．01；r=0．564，P＜0．01)；(3)逻辑回归分析表明：急性下肢DVT的发生与患者存在高血压，血浆纤维蛋白原水平的升高明显相关(比值比[odds ratio，OR]=24．99，P＜0．01；OR=4．346，P＜0．01)。
结论：高血压和升高的血浆纤维蛋白原是急性下肢DVT的独立危险因素。
第三部分
急性血栓性疾病血浆蛋白质指纹图谱研究
实验一急性脑梗死血浆潜在标志物的蛋白质组学研究
目的：既往研究表明，并不存在单一的生化指标可用于急性脑梗死(Acute CerebralInfarction，ACI)的常规诊断。本实验运用表面增强激光解吸/电离-飞行时间质谱(Surface-enhanced laser desorption/ionization time of flight mass spectrometry，SELDI-TOF-MS)技术筛选急性脑梗死患者血浆中潜在的特异标志物。
方法：入选急性脑梗死患者32例，健康对照者60例。采用SELDI-TOF-MS及弱阳离子交换芯片阵列技术(weak cation exchange CM10 ProteinChip arrays，CiphergenBiosystems)检测两组研究对象的血浆蛋白质质谱；并借助生物信息学工具(非线性的支持向量机，nonlinear support vector machine)提出急性脑梗死的诊断模型，并运用留一交叉验证法(Leave-one-out cross validation)来评估该模型的判别效能。
结果：由13个标志物组成的血浆蛋白质质谱诊断模型，能够最佳地区分脑梗死与正常对照组。该模型具有84．4％的敏感性和95．0％的特异性。
结论：SELDI-TOF-MS及蛋白质芯片技术在急性脑梗死的诊断中具有潜在的应用前景。急性脑梗死的诊断可能依赖于一组生物标志物的结合。
实验二
急性心肌梗死及深静脉血栓形成血浆潜在标志物的蛋白质组学研究
目的：本研究运用表面增强激光解吸/电离-飞行时间质谱(Surface enhanced laserdesorption/ionization time of flight mass spectrometry，SELDI-TOF-MS)技术来寻找血栓栓塞性疾病(动脉和静脉)的特异生物标志物。
方法：我们筛查了69例血浆样本中的潜在生物标志物，其中包括20例特发性深静脉血栓形成(Deep vein thrombosis，DVT)、20例急性心肌梗死(Acute myocardialinfarction，AMI)患者以及29例既往无血栓栓塞疾病史的健康对照者。在蛋白质生物系统Ⅱc及SELDI-TOF-MS(Ciphergen Biosystems，Fremont，CA)上分析预先处理过的血浆样品。并运用铜离子活化的固定金属鳌合亲和层析芯片阵列(immobilized metalaffinity chromatography，IMAC-3)产生蛋白质组学的波谱，并用质荷比(mass to chargeratio，m/z)来表示。
结果：质荷比分别为2667、5914及6890 Da的3个生物标志物组成一种诊断模型。该模型能够最好地区分AMI患者与正常对照者，具有100％的正确率。另外一种仅由一个生物标志物(m/z，5914 Da)组成的诊断模型，也能够完全将DVT患者与正常对照者区别开。进一步对AMI与DVT患者之间的分析表明，由质荷比分别为3418、5271、33378及68125 Da组成的判别模型具有82．5％的准确率。
结论：在区分血栓病患者与健康对照者时，SELDI-TOF-MS及蛋白质芯片技术具有很高的敏感性及特异性。对血栓栓塞性疾病的早期诊断而言，血浆中所发现的生物标志物展现出较大的应用潜能。
As a main category of illness,thrombotic diseases have close relationship with eachclinical department.Atherosclerosis and its thromboembolic complication has become thefirst top of causes for death and disability both in western countries and China.Theincidence is increasing year by year.Process of thrombogenesis is a long period of duration.When a patient visits his doctor,a thrombus has already formed leading to vascularembolism.Vital organs,including the heart and brain which are very sensitive to ischemia,have bad response to therapy after an event of embolism.Therefore,thrombotic diseasesseverely do harm to human well-being.If a diagnosis of thrombosis during the early stageof thrombogenesis is performed and interventional measures are taken in time,the harmfuleffect caused by thromboembolic diseases can be weakened to a great extent.
At present,accurate diagnosis for thrombotic diseases mainly depends on imageological evidence at the regional location of thrombosis.In clinical practice,detection of plasma d-dimer,activated partial thromboplastin time (APTT),prothrombintime (PT),and level of fibrinogen is considered to be an initial screening proceedingwithout a positive conclusion on the sensitivity and specificity.The pathogenesis ofthrombotic diseases is very complicated.At the early stage of thrombosis or prior to theformation of a thrombus,negative results might be shown about the above-mentionedscreening markers,ultimately leading to delay in precise diagnosis.When positive findingsare indicated,the illness usually develops to be at an intermediate or advanced stage duringwhen the most effective treatment couldn't be performed.Therefore,among high-riskgroup with thrombotic diseases,the popular research today in the field of thromboticdiseases is concentrated on allround assessment of risk and performance of diagnosis ofthrombosis at an early stage.
For studies on proteomics,time-of-flight mass spectrometry (TOF-MS) is an extensiveand high-throughout research technique at molecular level.Application of TOF-MScoupled with bioinformatics and experiments on gene function are playing more and moreimportant roles in the field of bringing molecular mechanisms of a disease to light.Usingsurface enhanced laser desorption / ionization time of flight mass spectrometry(SELDI-TOF-MS) and protein biochips,exploratory studies were performed to drawplasma protein fingerprints for arterial and venous thrombosis in order to discover specificprotein biomarkers which could discriminate between patients with thrombotic diseasesand control subjects with high sensitivity and specificity and to provide experimentalevidence for developing new specific biomarkers used for diagnosis of thrombosis in thefuture.Meanwhile,coupled with detection of correlated biomarkers and application of aseries of platforms for liquid-phase gene chips related with coagulation,anticoagulation,and fibrinolytic systems,a fast,precise,and comprehensive system for early diagnosisshould be set up to find the presence of a thrombus based on clinical investigation.
PartⅠ
Retrospective Study on Changes of Coagulation Function andEvaluation on Risk Factors for Acute Cerebral Infarction
Objective Several studies have indicated an association between changes ofcoagulation,anticoagulation,and fibrinolytic proteins and development of cerebrovasculardisease,but few reports referred to their roles together.In this report,we systematicallyinvestigated the relationship between sixteen coagulation markers of them and acutecerebral infarction,as well as considering the contribution of blood lipids and otherpossible risk factors.
Methods Inpatients diagnosed for acute cerebral infarction (ACI) (n=50) andhospital controls (n=54) were recruited from Union Hospital.All blood samples werecollected from ulnar vein on the mornings of 2~(nd) day of hospitalization.Plasmaconcentrations of protein C (PC),free proteinS (FPS),total protein S (TPS),thrombomodulin (TM),activated FⅦ(FⅦa),FⅦantigen (FⅦAg),p-selectin (p-sel),tissue-type plasminogen activator (t-PA),and plasminogen activator inhibitor-1 (PAI-1)were assayed by ELISA kits;Activity of tissue factor (aTF) was analyzed by chromogenicactivity assay kits;Activated protein C (APC) ratio,activated partial thromboplastin time(APTT),prothrombin time (PT),thrombin time (TI),fibrinogen (Fbg),and D-dimer weredetected in an automated coagulation analyzer (Sysmex CA-7000,Japan).Blood lipidswere detected using an automatic biochemical analyzer (Hitachi 7170A,Tokyo,Japan) at Department of Laboratory Medicine.
Results Plasma levels of thrombomodulin,fibrinogen,and activity of tissue factorwere significantly higher in cases than in control subjects (P＜0.001,P＜0.01,and P＜0.05,respectively);Concentration of high-density lipoprotein cholesterol (HDL-C) wassignificantly lower in cases than in controls (P＜0.01).Multivariate logistic regressionanalysis showed that hypertension and high plasma levels of thrombomodulin,fibrinogen,and aTF were significantly associated with presence of ACI (odds ratio [OR],143.74,P＜0.001;OR,2.05,P＜0.05;OR,2.09,P＜0.05;OR,1.02,P＜0.05,respectively).
Conclusion Our findings indicate that hypertension and elevation of plasmathrombomodulin,fibrinogen,and aTF are independent risk factors for ACI.
PartⅡ
Assessment of Risk Factors for Acute Deep Venous Thrombosis
Objective To observe the changes of coagulation function in patients with acutelower-limb deep venous thrombosis (DVT) and evaluate the risk factors for DVT.
Methods Plasma APTT,PT,TT,fibrinogen (Fbg),and D-dimer were detected by anautomated coagulation analyzer in 62 acute lower-limb DVT patients and 70 controlsubjects;And retrospective studies on the clinical data of all patients were done by binarylogistic regression analysis.
Results (1) In DVT group,plasma APTT,PT,TT,D-dimer,and fibrinogen andD-dimer / fibrinogen ratio (D/F ratio) were higher when compared with control group;allof the differences were significant (all P＜0.01);(2) There were positive correlationsbetween D-dimer and fibrinogen both in DVT and control groups (r= 0.475,P＜0.01;r=0.564,P＜0.01,respectively);(3) Logistic analysis indicated that acute lower-limb DVTwas associated with the presence of hypertension and increased plasma level of fibrinogen(odds ratio [OR],24.99,P＜0.01;OR,4.346,P＜0.01,respectively).
Conclusions Hypertension and elevated plasma level of fibrinogen are independentrisk factors for acute lower-limb DVT.
PartⅢ
Study on Plasma Protein Fingerprints for Acute Thrombotic Diseases
Experiment 1 Proteomic study on plasma potentialbiomarkers for acute cerebral infarction
Objective Previous researches indicated that no single biologic marker was used inthe routine diagnosis of acute cerebral infarction (ACI).In this experiment,surfaceenhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS)technology was used to screen potential specific biomarkers in plasma samples frompatients with ACI.
Methods Plasma samples were drawn from 32 cases with ACI and from 60 healthycontrol subjects.Plasma proteomic profiling was detected by the SELDI-TOF-MStechnology coupled with weak cation exchange arrays (CM10 ProteinChip,CiphergenBiosystems).A diagnostic pattern was introduced with the help of a bioinformatics tool(nonlinear support vector machine) and the method of leave-one-out cross validation wasused to estimate the discriminating power of this pattern.
Results A differential pattern consisting of 13 biomarkers was selected based ontheir collective contribution to the optimal separation between patients with ACI andcontrol subjects with a sensitivity of 84.4% and specificity of 95.0%,respectively.
Conclusion Plasma proteomic profiling with SELDI-TOF-MS and ProteinChiptechnologies shows potential in discriminating patients with acute cerebral infarction andcontrol subjects.Diagnosis of acute cerebral infarction should probably depend on the useof a panel of biomarkers.
Experiment 2 Proteomic study on plasma potential biomarkers foracute myocardial infarction and deep vein thrombosis
Objective The present study is concerned about searching for specific biomarkersfor thromboembolic (arterial and venous) diseases by the use of Surface-Enhanced LaserDesorption / Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS).
Methods We screened for potential biomarkers in 69 plasma samples,includingsamples from 20 patients with idiopathetic deep vein thrombosis (DVT),20 patients withacute myocardial infarction (AMI),and 29 healthy controls without a history ofthromboembolism.Pretreated plasma samples were analyzed on the Protein BiologySystem IIc plus SELDI-TOF-MS (Ciphergen Biosystems,Fremont,CA).Proteomic spectraof mass to charge ratio (m/z) were generated by the application of plasma to immobilizedmetal affinity capture (IMAC-3) ProteinChip arrays activated with copper.
Results A pattern of three biomarkers (m/z:2 667,5 914,and 6 890 Da,respectively)with a total accuracy of 100% was selected based on their collective contribution to theoptimal separation between patients with AMI and healthy controls.Another patternconsisting of only one biomarker (m/z:5 914 Da) could totally discriminate patients withDVT and control subjects.For further analysis between patients with AMI and those withDVT,a pattern of four biomarkers (m/z:3 418,5 271,33 378,and 68 125 Da,respectively)was selected with a total accuracy of 82.5%.
Conclusions Plasma proteomic profiling with SELDI-TOF-MS and ProteinChiptechnologies provides high sensitivity and specificity in discriminating patients withthrombosis and healthy subjects.The discovered biomarkers might show great potential forearly diagnosis of thromboembolic diseases.
目前，血栓性疾病的确诊主要依靠血栓形成部位的影像学证据。临床上主要以D-二聚体(d-dimer)、活化部分凝血酶原时间(activated partial thromboplastin time，APTT)、凝血酶原时间(prothrombin time，PT)、纤维蛋白原(fibrinogen，Fbg)含量等作为血栓性疾病的初步筛查项目，其敏感度和准确性尚无统一的临床结论。血栓性疾病发病机制极其复杂，在血栓形成早期，或血栓尚未形成时，上述筛查指标可能出现阴性结果，往往延误正确判断；而当阳性结果出现时，疾病往往已进展到中晚期，失去了最有效的治疗时机。因此，在血栓病高危人群中，对血栓发生危险性进行全面的评估及早期诊断血栓形成已成为当今血栓病研究领域的热点。
蛋白质-飞行时间质谱技术(Time of flight mass spectrometry，TOF-MS)是蛋白质组学中一种大规模、高通量的分子研究方法，与生物信息学和基因功能实验等相结合已经在揭示疾病发生的分子机制领域中发挥越来越重要的作用。本试验采用表面增强激光解析/电离飞行时间质谱仪(surface enhanced laser desorption/ionization time offlight mass spectrometry，SELDI-TOF-MS)及蛋白质生物芯片技术探索性研究动脉、静脉血栓形成患者的血浆蛋白质指纹图谱，旨在发现能够区分血栓病与对照组的高敏感、高特异的蛋白质生物标志物，为开发新的可用于诊断血栓形成的特异性标记物提供实验依据。同时，结合一系列与凝血、抗凝及纤溶系统相关的液态基因芯片检测平台及相关生化指标检测，通过临床研究，为发现血栓形成建立快速、准确的早期诊断综合体系。
第一部分
急性脑梗死凝血功能变化及其危险因素评估的回顾性研究
目的：已有研究表明，血液中凝血、抗凝或纤溶蛋白水平的改变与脑血管疾病的发生明显相关。但较少研究综合考虑其在脑血管疾病中的改变及作用。在本次研究中，我们系统探讨了其中的16个凝血指标、血脂水平、以及其他可能的危险因素与急性脑梗死的关系。
方法：武汉协和医院急性脑梗死(Acute Cerebral Infarction，ACI)住院患者50例，住院对照54例。所有研究对象均在入院第二天清晨空腹采取肘静脉血。血浆蛋白C(protein C，PC)、游离蛋白S(free protein S，FPS)、总蛋白S(total protein S，TPS)、凝血酶调节蛋白(thrombomodulin，TM)、活化凝血因子VII(activated factorⅦ，FⅦa)、凝血因子Ⅶ抗原(factorⅦantigen，FⅦAg)、P-选择素(p-selectin)、组织型纤溶酶原激活剂(tissue-type plasminogen activator，t-PA)及纤溶酶原激活剂抑制物-1(plasminogen activator inhibitor-1，PAI-1)水平均由ELISA试剂盒检测；组织因子活性(tissue factor activity，aTF)由发色底物法检测；全自动凝血分析仪(SysmexCA-7000，日本)检测活化蛋白C(activated protein C，APC)比率、活化部分凝血活酶时间(activated partial thromboplastin time，APTT)、凝血酶原时间(prothrombin time，PT)、凝血酶时间(thrombin time，TT)、纤维蛋白原(fibrinogen，Fbg)以及D-二聚体(d-dimer)；检验科自动生化分析仪(日立7170A，东京，日本)检测血脂水平。
结果：疾病组血浆凝血酶调节蛋白、纤维蛋白原水平及组织因子活性显著高于对照组(P＜0．001，P＜0．01，P＜0．05)；而高密度脂蛋白水平显著低于对照组(P＜0．01)。多变量逻辑回归分析表明：高血压的存在，血浆高水平的凝血酶调节蛋白、纤维蛋白原及组织因子活性与急性脑梗死的发病明显相关(比值比[odds ratio，OR]=143．74，P＜0．001；OR=2．05，P＜0．05；OR=2．09，P＜0．05；OR=1．02，P＜0．05)。
结论：高血压，血浆凝血酶调节蛋白、纤维蛋白原水平及组织因子活性的升高是急性脑梗死的独立危险因素。
第二部分
急性深静脉血栓形成危险因素的评估
目的：观察急性下肢深静脉血栓形成(deep venous thrombosis，DVT)患者凝血功能变化及评估其危险因素。
方法：全自动凝血分析仪(Sysmex CA-7000，日本)检测62例急性下肢DVT患者和70例健康对照者血浆活化部分凝血活酶时间(activated partial thromboplastin time，APTT)、凝血酶原时间(prothrombin time，PT)、凝血酶时间(thrombin time，TT)、纤维蛋白原(fibrinogen，Fbg)及D-二聚体(d-dimer)水平；检验科自动生化分析仪(日立7170A，东京，日本)检测高密度脂蛋白胆固醇(high-density lipoprotein cholesterol，HDL-C)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol，LDL-C)、总胆固醇(total cholesterol，TC)及甘油三酯(triglyceride，TG)水平；并通过二分类逻辑回归分析回顾性研究所有患者的临床资料。
结果：(1)DVT组血浆APTT、PT、TT、D-二聚体、Fbg水平及D-二聚体与Fbg比值(D/F值)都明显高于健康对照组，差异均有统计学意义(所有P＜0．01)；(2)DVT组和对照组血浆D-二聚体与Fbg水平之间均存在正相关性(r=0．475，P＜0．01；r=0．564，P＜0．01)；(3)逻辑回归分析表明：急性下肢DVT的发生与患者存在高血压，血浆纤维蛋白原水平的升高明显相关(比值比[odds ratio，OR]=24．99，P＜0．01；OR=4．346，P＜0．01)。
结论：高血压和升高的血浆纤维蛋白原是急性下肢DVT的独立危险因素。
第三部分
急性血栓性疾病血浆蛋白质指纹图谱研究
实验一急性脑梗死血浆潜在标志物的蛋白质组学研究
目的：既往研究表明，并不存在单一的生化指标可用于急性脑梗死(Acute CerebralInfarction，ACI)的常规诊断。本实验运用表面增强激光解吸/电离-飞行时间质谱(Surface-enhanced laser desorption/ionization time of flight mass spectrometry，SELDI-TOF-MS)技术筛选急性脑梗死患者血浆中潜在的特异标志物。
方法：入选急性脑梗死患者32例，健康对照者60例。采用SELDI-TOF-MS及弱阳离子交换芯片阵列技术(weak cation exchange CM10 ProteinChip arrays，CiphergenBiosystems)检测两组研究对象的血浆蛋白质质谱；并借助生物信息学工具(非线性的支持向量机，nonlinear support vector machine)提出急性脑梗死的诊断模型，并运用留一交叉验证法(Leave-one-out cross validation)来评估该模型的判别效能。
结果：由13个标志物组成的血浆蛋白质质谱诊断模型，能够最佳地区分脑梗死与正常对照组。该模型具有84．4％的敏感性和95．0％的特异性。
结论：SELDI-TOF-MS及蛋白质芯片技术在急性脑梗死的诊断中具有潜在的应用前景。急性脑梗死的诊断可能依赖于一组生物标志物的结合。
实验二
急性心肌梗死及深静脉血栓形成血浆潜在标志物的蛋白质组学研究
目的：本研究运用表面增强激光解吸/电离-飞行时间质谱(Surface enhanced laserdesorption/ionization time of flight mass spectrometry，SELDI-TOF-MS)技术来寻找血栓栓塞性疾病(动脉和静脉)的特异生物标志物。
方法：我们筛查了69例血浆样本中的潜在生物标志物，其中包括20例特发性深静脉血栓形成(Deep vein thrombosis，DVT)、20例急性心肌梗死(Acute myocardialinfarction，AMI)患者以及29例既往无血栓栓塞疾病史的健康对照者。在蛋白质生物系统Ⅱc及SELDI-TOF-MS(Ciphergen Biosystems，Fremont，CA)上分析预先处理过的血浆样品。并运用铜离子活化的固定金属鳌合亲和层析芯片阵列(immobilized metalaffinity chromatography，IMAC-3)产生蛋白质组学的波谱，并用质荷比(mass to chargeratio，m/z)来表示。
结果：质荷比分别为2667、5914及6890 Da的3个生物标志物组成一种诊断模型。该模型能够最好地区分AMI患者与正常对照者，具有100％的正确率。另外一种仅由一个生物标志物(m/z，5914 Da)组成的诊断模型，也能够完全将DVT患者与正常对照者区别开。进一步对AMI与DVT患者之间的分析表明，由质荷比分别为3418、5271、33378及68125 Da组成的判别模型具有82．5％的准确率。
结论：在区分血栓病患者与健康对照者时，SELDI-TOF-MS及蛋白质芯片技术具有很高的敏感性及特异性。对血栓栓塞性疾病的早期诊断而言，血浆中所发现的生物标志物展现出较大的应用潜能。
As a main category of illness,thrombotic diseases have close relationship with eachclinical department.Atherosclerosis and its thromboembolic complication has become thefirst top of causes for death and disability both in western countries and China.Theincidence is increasing year by year.Process of thrombogenesis is a long period of duration.When a patient visits his doctor,a thrombus has already formed leading to vascularembolism.Vital organs,including the heart and brain which are very sensitive to ischemia,have bad response to therapy after an event of embolism.Therefore,thrombotic diseasesseverely do harm to human well-being.If a diagnosis of thrombosis during the early stageof thrombogenesis is performed and interventional measures are taken in time,the harmfuleffect caused by thromboembolic diseases can be weakened to a great extent.
At present,accurate diagnosis for thrombotic diseases mainly depends on imageological evidence at the regional location of thrombosis.In clinical practice,detection of plasma d-dimer,activated partial thromboplastin time (APTT),prothrombintime (PT),and level of fibrinogen is considered to be an initial screening proceedingwithout a positive conclusion on the sensitivity and specificity.The pathogenesis ofthrombotic diseases is very complicated.At the early stage of thrombosis or prior to theformation of a thrombus,negative results might be shown about the above-mentionedscreening markers,ultimately leading to delay in precise diagnosis.When positive findingsare indicated,the illness usually develops to be at an intermediate or advanced stage duringwhen the most effective treatment couldn't be performed.Therefore,among high-riskgroup with thrombotic diseases,the popular research today in the field of thromboticdiseases is concentrated on allround assessment of risk and performance of diagnosis ofthrombosis at an early stage.
For studies on proteomics,time-of-flight mass spectrometry (TOF-MS) is an extensiveand high-throughout research technique at molecular level.Application of TOF-MScoupled with bioinformatics and experiments on gene function are playing more and moreimportant roles in the field of bringing molecular mechanisms of a disease to light.Usingsurface enhanced laser desorption / ionization time of flight mass spectrometry(SELDI-TOF-MS) and protein biochips,exploratory studies were performed to drawplasma protein fingerprints for arterial and venous thrombosis in order to discover specificprotein biomarkers which could discriminate between patients with thrombotic diseasesand control subjects with high sensitivity and specificity and to provide experimentalevidence for developing new specific biomarkers used for diagnosis of thrombosis in thefuture.Meanwhile,coupled with detection of correlated biomarkers and application of aseries of platforms for liquid-phase gene chips related with coagulation,anticoagulation,and fibrinolytic systems,a fast,precise,and comprehensive system for early diagnosisshould be set up to find the presence of a thrombus based on clinical investigation.
PartⅠ
Retrospective Study on Changes of Coagulation Function andEvaluation on Risk Factors for Acute Cerebral Infarction
Objective Several studies have indicated an association between changes ofcoagulation,anticoagulation,and fibrinolytic proteins and development of cerebrovasculardisease,but few reports referred to their roles together.In this report,we systematicallyinvestigated the relationship between sixteen coagulation markers of them and acutecerebral infarction,as well as considering the contribution of blood lipids and otherpossible risk factors.
Methods Inpatients diagnosed for acute cerebral infarction (ACI) (n=50) andhospital controls (n=54) were recruited from Union Hospital.All blood samples werecollected from ulnar vein on the mornings of 2~(nd) day of hospitalization.Plasmaconcentrations of protein C (PC),free proteinS (FPS),total protein S (TPS),thrombomodulin (TM),activated FⅦ(FⅦa),FⅦantigen (FⅦAg),p-selectin (p-sel),tissue-type plasminogen activator (t-PA),and plasminogen activator inhibitor-1 (PAI-1)were assayed by ELISA kits;Activity of tissue factor (aTF) was analyzed by chromogenicactivity assay kits;Activated protein C (APC) ratio,activated partial thromboplastin time(APTT),prothrombin time (PT),thrombin time (TI),fibrinogen (Fbg),and D-dimer weredetected in an automated coagulation analyzer (Sysmex CA-7000,Japan).Blood lipidswere detected using an automatic biochemical analyzer (Hitachi 7170A,Tokyo,Japan) at Department of Laboratory Medicine.
Results Plasma levels of thrombomodulin,fibrinogen,and activity of tissue factorwere significantly higher in cases than in control subjects (P＜0.001,P＜0.01,and P＜0.05,respectively);Concentration of high-density lipoprotein cholesterol (HDL-C) wassignificantly lower in cases than in controls (P＜0.01).Multivariate logistic regressionanalysis showed that hypertension and high plasma levels of thrombomodulin,fibrinogen,and aTF were significantly associated with presence of ACI (odds ratio [OR],143.74,P＜0.001;OR,2.05,P＜0.05;OR,2.09,P＜0.05;OR,1.02,P＜0.05,respectively).
Conclusion Our findings indicate that hypertension and elevation of plasmathrombomodulin,fibrinogen,and aTF are independent risk factors for ACI.
PartⅡ
Assessment of Risk Factors for Acute Deep Venous Thrombosis
Objective To observe the changes of coagulation function in patients with acutelower-limb deep venous thrombosis (DVT) and evaluate the risk factors for DVT.
Methods Plasma APTT,PT,TT,fibrinogen (Fbg),and D-dimer were detected by anautomated coagulation analyzer in 62 acute lower-limb DVT patients and 70 controlsubjects;And retrospective studies on the clinical data of all patients were done by binarylogistic regression analysis.
Results (1) In DVT group,plasma APTT,PT,TT,D-dimer,and fibrinogen andD-dimer / fibrinogen ratio (D/F ratio) were higher when compared with control group;allof the differences were significant (all P＜0.01);(2) There were positive correlationsbetween D-dimer and fibrinogen both in DVT and control groups (r= 0.475,P＜0.01;r=0.564,P＜0.01,respectively);(3) Logistic analysis indicated that acute lower-limb DVTwas associated with the presence of hypertension and increased plasma level of fibrinogen(odds ratio [OR],24.99,P＜0.01;OR,4.346,P＜0.01,respectively).
Conclusions Hypertension and elevated plasma level of fibrinogen are independentrisk factors for acute lower-limb DVT.
PartⅢ
Study on Plasma Protein Fingerprints for Acute Thrombotic Diseases
Experiment 1 Proteomic study on plasma potentialbiomarkers for acute cerebral infarction
Objective Previous researches indicated that no single biologic marker was used inthe routine diagnosis of acute cerebral infarction (ACI).In this experiment,surfaceenhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS)technology was used to screen potential specific biomarkers in plasma samples frompatients with ACI.
Methods Plasma samples were drawn from 32 cases with ACI and from 60 healthycontrol subjects.Plasma proteomic profiling was detected by the SELDI-TOF-MStechnology coupled with weak cation exchange arrays (CM10 ProteinChip,CiphergenBiosystems).A diagnostic pattern was introduced with the help of a bioinformatics tool(nonlinear support vector machine) and the method of leave-one-out cross validation wasused to estimate the discriminating power of this pattern.
Results A differential pattern consisting of 13 biomarkers was selected based ontheir collective contribution to the optimal separation between patients with ACI andcontrol subjects with a sensitivity of 84.4% and specificity of 95.0%,respectively.
Conclusion Plasma proteomic profiling with SELDI-TOF-MS and ProteinChiptechnologies shows potential in discriminating patients with acute cerebral infarction andcontrol subjects.Diagnosis of acute cerebral infarction should probably depend on the useof a panel of biomarkers.
Experiment 2 Proteomic study on plasma potential biomarkers foracute myocardial infarction and deep vein thrombosis
Objective The present study is concerned about searching for specific biomarkersfor thromboembolic (arterial and venous) diseases by the use of Surface-Enhanced LaserDesorption / Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS).
Methods We screened for potential biomarkers in 69 plasma samples,includingsamples from 20 patients with idiopathetic deep vein thrombosis (DVT),20 patients withacute myocardial infarction (AMI),and 29 healthy controls without a history ofthromboembolism.Pretreated plasma samples were analyzed on the Protein BiologySystem IIc plus SELDI-TOF-MS (Ciphergen Biosystems,Fremont,CA).Proteomic spectraof mass to charge ratio (m/z) were generated by the application of plasma to immobilizedmetal affinity capture (IMAC-3) ProteinChip arrays activated with copper.
Results A pattern of three biomarkers (m/z:2 667,5 914,and 6 890 Da,respectively)with a total accuracy of 100% was selected based on their collective contribution to theoptimal separation between patients with AMI and healthy controls.Another patternconsisting of only one biomarker (m/z:5 914 Da) could totally discriminate patients withDVT and control subjects.For further analysis between patients with AMI and those withDVT,a pattern of four biomarkers (m/z:3 418,5 271,33 378,and 68 125 Da,respectively)was selected with a total accuracy of 82.5%.
Conclusions Plasma proteomic profiling with SELDI-TOF-MS and ProteinChiptechnologies provides high sensitivity and specificity in discriminating patients withthrombosis and healthy subjects.The discovered biomarkers might show great potential forearly diagnosis of thromboembolic diseases.
引文
1. The sixth report of the Joint National Committee on prevention, detection, evaluation,and treatment of high blood pressure. Arch Intern Med 1997; 157:2413-2446.
2. Kario K, Eguchi K, Hoshide S, et al. U-curve relationship between orthostatic blood pressure change and silent cerebrovascular disease in elderly hypertensives: orthostatic hypertension as a new cardiovascular risk factor. J Am Coll Cardiol 2002; 40:133-141.
3. Rothwell PM, Howard SC, Power DA, et al. Fibrinogen Concentration and Risk of Ischemic Stroke and Acute Coronary Events in 5113 Patients With Transient Ischemic Attack and Minor Ischemic Stroke. Stroke 2004; 35:2300-2305.
4. Iwashita M, Matsushita Y, Sasaki J, et al. Relation of Serum Total Cholesterol and Other Factors to Risk of Cerebral Infarction in Japanese Men with Hypercholesterolemia. Circ J 2005; 69:1-6.
5. Kubo M, Kiyohara Y, Ninomiya T, et al. Decreasing incidence of lacular vs other types of cerebral infarction in a Japanese population. Neurology 2006; 66:1539-1544.
6. Olivot JM, Labreuche J, Aiach M, et al. Soluble thrombomodulin and brain infarction:case-control and prospective study. Stroke 2004; 35:1946-1951.
7. Elkind MS, Sciacca RR, Boden-Albala B, et al. Relative elevation in baseline leukocyte count predicts first cerebral infarction. Neurology 2005; 64:2121-2125.
8. Yang DT, Flanders MM, Kim H, et al. Elevated factor XI activity levels are associated with an increased odds ratio for cerebrovascular events. Am J Clin Pathol 2006;126:411-415.
9. Wang XD. Study on cerebrovascular disease of the elderly in China. J Hypertens Suppl 1996; 14:S139-145.
10. Basu AK, Pal SK, Saha S, et al. Risk factor analysis in ischaemic stroke: a hospital-based study. J Indian Med Assoc 2005; 103:586-588.
11. Dickinson CJ. Strokes and their relationship to hypertension. Curr Opin Nephrol Hypertens 2003; 12:91-96.
12. Van de Wouwer M, Collen D, Conway EM. Thrombomodulin-protein C-EPCR system:integrated to regulate coagulation and inflammation. Arterioscler Thromb Vase Biol 2004; 24:1374-1383.
13. Esmon NL, Owen WG, Esmon CT. Isolation of a membrane-bound cofactor for thrombin-catalyzed activation of protein C. J Biol Chem 1982; 257:859-864.
14. Bajzar L, Manuel R, Nesheim M. Purification and characterization of TAFI, a thrombin-activatable fibrinolysis inhibitor. J Biol Chem 1995; 270:14477-14484.
15. Blann AD, Amiral J, McCollum CN. Prognostic value of increased soluble thrombomodulin and increased soluble E-selectin in ischaemic heart disease. Eur J Haematol 1997; 59:115-120.
16. Nomura E, Kohriyama T, Kozuka K, et al. Significance of serum soluble thrombomodulin level in acute cerebral infarction. Eur J Neurol 2004; 11:329-334.
17. Salomaa V, Matei C, Aleksic N, et al. Soluble thrombomodulin as a predictor of incident coronary heart disease and symptomless carotid artery atherosclerosis in the Atherosclerosis Risk in Communities (ARIC) Study: a case-cohort study. Lancet 1999;353:1729-1734.
18. Dohi Y, Ohashi M, Sugiyama M, et al. Circulating thrombomodulin levels are related to latent progression of atherosclerosis in hypertensive patients. Hypertens Res 2003;26:479-483.
19. Drouet L. Fibrinogen: a treatable risk factor? Cerebrovasc Dis 1996; 6:2-6.
20. Kannel WB. Influence of fibrinogen on cardiovascular disease. Drugs 1997; 54(Suppl3):32-40.
21. Harley SL, Powell JT. Fibrinogen upregulates the expression of monocyte chemoattractant protein-1 in human saphenous vein endothelial cells. Biochem J 1999;341:739-744.
22. Baker IA, Pickering J, Elwood PC, et al. Fibrinogen, viscosity and white blood cell count predict myocardial, but not cerebral infarction: evidence from the Caerphilly and Speedwell cohort. Thromb Haemost 2002; 87:421-425.
23.Rnuiman MW, Folsom AR, Chambless LE, et al. Association of hemostatic variables with MRI-detected cerebral abnormalities: the atherosclerosis risk in communities study.Neuroepidemiology 2001; 20:96-104.
24. Di Lecce VN, Loffredo L, Fimognari FL, et al. Fibrinogen as predictor of ischemic stroke in patients with non-valvular atrial fibrillation. J Thromb Haemost 2003;1:2453-2455.
25. Turaj W, Slowik A, Dziedzic T, et al. Increased plasma fibrinogen predicts one-year mortality in patients with acute ischemic stroke. J Neurol Sci 2006; 246:13-19.
26. Khan TM, Marwat MA, Rehman H. Comparison of plasma viscosity and fibrinogen concentration in hypertensive and normotensive diabetics. J Ayub Med Coll Abbottabad 2005; 17:45-47.
27. Nemerson Y. Tissue factor and hemostasis. Blood 1988; 71:1-8.
28. Misumi K, Ogawa H, Yasue H, et al. Comparison of plasma tissue factor levels in unstable and stable angina pectoris. Am J Cardiol 1998; 81:22-26.
29. Seljeflot I, Hurlen M, Hole T, et al. Soluble tissue factor as predictor of future events in patients with acute myocardial infarction. Thromb Res 2003;lll:369-372.
30. He M, Wen Z, He X, et al. Observation on tissue factor pathway and some other coagulation parameters during the onset of acute cerebrocardiac thrombotic diseases. Thromb Res 2002; 107:223-228.
31. Wannamethee SG, Shaper AG, Ebrahim S. HDL-cholesterol, total cholesterol, and the risk of stroke in middle-aged British men. Stroke 2000; 31:1882-1888.
32. Soyama Y,Miura K, Morikawa Y, et al. High-density lipoprotein cholesterol and risk of stroke in Japanese men and women: The Oyabe Study. Stroke 2003; 34:863-868.
33. Sakata T, Kario K, Katayama Y, et al. Clinical significance of activated protein C resistance as a potential marker for hypercoagulable state. Thromb Res 1996;82:235-244.
34. van der Bom JG, Bots ML, Haverkate F, et al. Reduced response to activated protein C is associated with increased risk of cerebrovascular disease. Ann Intern Med 1996;125:265-269.
1.Fowkes FJI,Price JF,Fowkes FGR.Incidence of diagnosed deep vein thrombosis in the general population:systematic review.Eur J Vasc Endovasc Surg,2003,25:1-5.
2.Kniffin WD,Baron JA,Barrett J,et al.The epidemiology of diagnosed pulmonary embolism and deep venous thrombosis in the elderly.Arch Intern Med,1994,154:861-866.
3.White RH,Zhou H,Romano PS.Incidence of symptomatic venous thromboembolism after different elective or urgent surgical procedures.Thromb Haemost,2003,90:446-455.
4.Baron JA,Gridley G,Weiderpass E,et al.Venous thromboembolism and cancer.Lancet,1998,351:1077-1080.
5.Geerts WH,Code KI,Jay RM,et al.A prospective study of venous thromboembolism after major trauma.N Engl J Med,1994,331:1601-1606.
6.Hughes R,Heuser T,Hill S,et al.Recent air travel and venous thromboembolism resulting in hospital admission.Respirology,2006,11:75-79.
7.James AH,Tapson VF,Goldhaber SZ.Thrombosis during pregnancy and the postpartum period.Am J Obstet Gynecol,2005,193:216-219.
8.Heuser P,Tonga K,Hopkins R,et al.Specific oral contraceptive use and venous thromboembolism resulting in hospital admission.N Z Med J,2004,117:U1176.
9.Douketis JD,Julian JA,Kearon C,et al.Does the type of hormone replacement therapy influence the risk of deep vein thrombosis? A prospective case-control study.J Thromb Haemost,2005,3:943-948.
10.Tirado I,Mateo J,Soria JM,et al.The ABO blood group genotype and factor Ⅷ levels as independent risk factors for venous thromboembolism.Thromb Haemost,2005,93:468-474.
11.Moser KM,LeMoine JR.Is embolic risk conditioned by location of deep venous thrombosis.Ann Intern Med,1981,94:439-444.
12．王振义，李家增，阮长耿，等．血栓与止血基础理论与临床．上海：上海科学技术出版社，2004：875-878．
13．徐凌，毕红霞，蔡柏蔷，等．深静脉血栓形成103例临床分析．中华内科杂志，2000，39：513-516．
14．孙葵葵，王辰，古力夏提，等．深静脉血栓形成的危险因素及临床分析．中华结核和呼吸杂志，2004，27：727-730．
15.Khan TM,Marwat MA,Rehman H.Comparison of plasma viscosity and fibrinogen concentration in hypertensive and normotensive diabetics.J Ayub Med Coll Abbottabad,2005,17:45-47.
16.Drouet L.Fibrinogen:a treatable risk factor.Cerebrovasc Dis,1996,6:2-6.
17.Kannel WB.Influence of fibrinogen on cardiovascular disease.Drugs,1997,54 (Suppl 3):32-40.
18.Harley SL,Powell JT.Fibrinogen upregulates the expression of monocyte chemoattractant protein-1 in human saphenous vein endothelial cells.Biochem J,1999,341:739-744.
19.van Hylckama Vlieg A,Rosendaal FR.High levels of fibrinogen are associated with the risk of deep venous thrombosis mainly in the elderly.J Thromb Haemost,2003,1:2677-2678.
20. Wuillemin WA, Korte W, Waser G, et al. Usefulness of the D-dimer/fibrinogen ratio to predict deep venous thrombosis. J Thromb Haemost, 2005, 3: 385-387.
21. Keeling DM, Mackie IJ, Moody A, et al. The diagnosis of deep vein thrombosis in symptomatic outpatients and the potential for clinical assessment and D-dimer assays to reduce the need for diagnostic imaging. Br J Haematol, 2004,124: 15-25.
22. Heim SW, Schectman JM, Siadaty MS, et al. D-dimer testing for deep venous thrombosis: a meta-analysis. Clin Chem, 2004, 50:1136-1147.
23. Kelly J, Rudd A, Lewis RR, et al. Plasma D-dimers in the diagnosis of venous thromboembolism. Arch Intern Med, 2002,162: 747-756.
24. Kucher N, Kohler HP, Dornhofer T, et al. Accuracy of D-dimer/fibrinogen ratio to predict pulmonary embolism: a prospective diagnostic study. J Thromb Haemost, 2003,1: 708-713.
25. Anderson DR, Kovacs MJ, Kovacs G, et al. Combined use of clinical assessment and D-dimer to improve the management of patients presenting to the emergency department with suspected deep vein thrombosis (the EDITED Study). J Thromb Haemost, 2003,1: 645-651.
26. Kyrle PA, Eichinger S. Deep vein thrombosis. Lancet, 2005, 365:1163-1174.
1.Ministry of Health.Chinese Health Statistical Digest 2006.Ministry ofHealth,People's Republic of China,2006:45.1989-2005.
2.Liu M,Wu B,Wang WZ,et al.Stroke in China:epidemiology,prevention,and management strategies.Lancet Neurol.2007;6:456-464.
3.Kelly J,Rudd A,Lewis R,et al.Venous thromboembolism after acute stroke.Stroke.2001;32:262-267.
4.Herrmann M,Vos P,Wunderlich MT,et al.Release of glial tissue-specific proteins after acute stroke:A comparative analysis of serum concentrations of protein S-100B and glial fibrillary acidic protein.Stroke.2000;31:2670-2677.
5.Castillo J,Rodriguez I.Biochemical Changes and Inflammatory Response as Markers for Brain Ischaemia:Molecular Markers of Diagnostic Utility and Prognosis in Human Clinical Practice.Cerebrovasc Dis.2004;17(Suppl.1):7-18.
6.Silvestri A,Vitale C,Ferretti F,et al.Plasma levels of inflammatory C-reactive protein and interleukin-6 predict outcome in elderly patients with stroke.J Am Geriatr Soc.2004;52:1586-1587.
7.Smith CJ,Emsley HC,Gavin CM,et al.Peak plasma interleukin-6 and other peripheral markers of inflammation in the first week of ischaemic stroke correlate with brain infarct volume,stroke severity and long-term outcome.BMC Neurol.2004;4:2.
8.Tomitori H,Usui T,Saeki N,et al.Polyamine oxidase and acrolein as novel biochemical markers for diagnosis of cerebral stroke. Stroke. 2005; 36:2609-2613.
9. Geiger S, Holdenrieder S, Stieber P, et al. Nucleosomes as a new prognostic marker in early cerebral stroke. J Neurol. 2007; 254:617-623.
10.Alevizaki M, Synetou M, Xynos K, et al. Low triiodothyronine: a strong predictor of outcome in acute stroke patients. Eur J Clin Invest. 2007; 37:651-657.
11.Reynolds MA, Kirchick HJ, Dahlen JR, et al. Early biomarkers of stroke. Clin Chem.2003; 49:1733-1739.
12.Lynch JR, Blessing R, White WD, et al. Novel diagnostic test for acute stroke. Stroke.2004; 35:57-63.
13.Yao XH, Yu HM, Koide SS, et al. Identification of a key protein associated with cerebral ischemia. Brain Res. 2003; 967:11-18.
14.Allard L, Lescuyer P, Burgess J, et al. ApoC-I and ApoC-? as potential plasmatic markers to distinguish between ischemic and hemorrhagic stroke. Proteomics. 2004;4:2242-2251.
15.Chen A, Liao WP, Lu Q, et al. Upregulation of dihydropyrimidinase-related protein 2, spectrin alpha II chain, heat shock cognate protein 70 pseudogene 1 and tropomodulin 2 after focal cerebral ischemia in rats-a proteomics approach. Neurochem Int. 2007;50:1078-1086.
16.Sanchez JC, Lescuyer P, Hochstrasser D, et al. Detection of biomarkers of stroke using SELDI-TOF. Methods Mol Biol. 2007; 357:343-350.
17.Zhang X, Hu Y, Hong M, et al. Plasma thrombomodulin, fibrinogen, and activity of tissue factor as risk factors for acute cerebral infarction. Am J Clin Pathol. 2007;128:287-292.
18.Liu Y. Active learning with support vector machine applied to gene expression data for cancer classification. J Chem Inf Comput Sci. 2004; 44:1936-1941.
19.Missler U, Wiesmann M, Friedrich C, et al. S-100 protein and neuron-specific enolase concentrations in blood as indicators of infarction volume and prognosis in acute ischemic stroke. Stroke. 1997; 28:1956-1960.
20. Zimmermann-Ivol CG, Burkhard PR, Le Floch-Rohr J, et al. Fatty acid binding protein as a serum marker for the early diagnosis of stroke: a pilot study. Mol Cell Proteomics.2004; 3:66-72.
21. Dambinova SA, Khounteev GA, Skoromets AA. Multiple panel of biomarkers for TIA/stroke evaluation. Stroke. 2002; 33:1181-1182.
22. Kalafut MA, Schriger DL, Saver JL, et al. Detection of early CT signs of >l/3 middle cerebral artery infarctions : interrater reliability and sensitivity of CT interpretation by physicians involved in acute stroke care. Stroke. 2000; 31:1667-1671.
23. Patel SC, Levine SR, Tilley BC, et al. Lack of clinical significance of early ischemic changes on computed tomography in acute stroke. JAMA. 2001; 286:2830-2838.
24. Wardlaw JM, Lewis SC, Dennis MS, et al. Is visible infarction on computed tomography associated with an adverse prognosis in acute ischemic stroke? Stroke.1998; 29:1315-1319.
25. Lee LJ, Kidwell CS, Alger J, et al. Impact on stroke subtype diagnosis of early diffusion-weighted magnetic resonance imaging and magnetic resonance angiography.Stroke. 2000; 31:1081-1089.
26. Floyd JS, Laskowitz DT. A new approach to the diagnosis of acute ischemic stroke:Blood-borne biochemical markers. Stroke Clinical Updates. 2004; 14:1-5.
27. Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Comm Mass Spectrum. 1993; 7:576-580.
28. Smith RD, Anderson GA, Lipton MS, et al. An accurate mass tag strategy for quantitative and high-throughput proteme measurements. Proteomics. 2002; 2:513-523.
29. Foret F, Preisler J. Liquid phase interfacing and miniaturization in matrix-assisted laser desorption/ionixation mass spectrometry. Proteomics. 2002; 2:360-370.
30. Wright GL, Cazares LH, Leung SM. ProteinChip surface enhanced laser desorption/ionization (SELDI) mass spectromatry: a novel protein biochip technology for detection of biomarker in complex protein mixtures. Prostate Cancer Prostatic Dis. 2000; 2:264-276.
31. Dirnagl U,Iadecola C, Moskowitz MA. Pathobiology of ischaemic stroke: an integrated view. Trends Neurosci. 1999; 22:391-397.
32. Ross R. Atherosclerosis: an inflammatory disease. N Engl J Med. 1999; 340:115-126.
33. Lindsberg PJ, Grau AJ. Inflammation and infections as risk factors for ischemic stroke.Stroke. 2003; 34:2518-2532.
34. Schroeter M, Zickler P, Denhardt DT, et al. Increased thalamic neurodegeneration following ischaemic cortical stroke in osteopontin-deficient mice. Brain. 2006;129:1426-1437.
35. Culmsee C, Krieglstein J. Ischaemic brain damage after stroke: new insights into efficient therapeutic strategies. International Symposium on Neurodegeneration and Neuroprotection. EMBO Rep. 2007; 8:129-133.
36. Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics. 2004;20:777-785.
37. Seibert V, Ebert MP, Buschmann T. Advances in clinical cancer proteomics: SELDI-TOF-mass spectrometry and biomarker discovery. Brief Funct Genomic Proteomic. 2005; 4:16-26.
38. Liotta LA, Lowenthal M, Mehta A, et al. Importance of communication between producers and consumers of publicly available experimental data. J Natl Cancer Inst.2005; 97:310-314.
39. Banks RE, Stanley AJ, Cairns DA, et al. Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem. 2005; 51:1637-1649.
40. Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics. 2003; 2:67-77.
41. Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model. 2005; 45:549-561.
1 Mowatt G,Vale L,Brazzelli M,et al.Systematic review of the effectiveness and cost-effectiveness,and economic evaluation,of myocardial perfusion scintigraphy for the diagnosis and management of angina and myocardial infarction.Health Technol Assess 2004;8:1-207.
2 Fowkes FJI,Price JF,Fowkes FGR.Incidence of diagnosed deep vein thrombosis in the general population:systematic review.Eur J Vasc Endovasc Surg 2003;25:1-5.
3 White RH.The epidemiology of venous thromboembolism.Circulation 2003;107(Suppl 1):14-8.
4 Hasdai D,Califf RM,Thompson TD,et al.Predictors of cardiogenic shock after thrombolytic therapy for acute myocardial infarction. J Am Coll Cardiol 2000; 35:136-43.
5 Hirsh J, Bates SM. Prognosis in acute pulmonary embolism. Lancet 1999; 353:1375-6.
6 Cushman M, Tsai AW, White RH, et al. Deep vein thrombosis and pulmonary embolism in two cohorts: the longitudinal investigation of thromboembolism etiology. Am J Med2004; 117: 19-25.
7 Goodacre S, Sampson F, Thomas S, et al. Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis. BMC Med Imaging 2005; 5: 6.
8 Keeling DM, Mackie IJ, Moody A, et al. The diagnosis of deep vein thrombosis in symptomatic outpatients and the potential for clinical assessment and D-dimer assays to reduce the need for diagnostic imaging. Br J Haematol 2004; 124: 15-25.
9 Wuillemin WA, Korte W, Waser G, et al. Usefulness of the D-dimer/fibrinogen ratio to predict deep venous thrombosis. J Thromb Haemost 2005; 3: 385-7.
10 Atalar E, Haznedaroglu IC, Kilic H, et al. Increased soluble glycoprotein V concentration during the acute onset of unstable angina pectoris in association with chronic cigarette smoking. Platelets 2005; 16: 329-33.
11 Aleil B, Mossard JM, Wiesel ML, et al. Increased plasma levels of soluble platelet glycoprotein V in patients with acute myocardial infarction. J Thromb Haemost 2003; 1:1846-7.
12 Kyrle PA, Eichinger S. Deep vein thrombosis. Lancet 2005; 365: 1163-74.
13 Alpert JS, Thygesen K, Antman E, et al. Myocardial infarction redefined-a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000; 36: 959-69.
14 Yip TT, Lomas L. SELDI ProteinChip array in oncoproteomic research. Technol Cancer Res Treat 2002; 1:273-80.
15 Poon TC, Yip TT, Chan AT, et al. Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin Chem 2003; 49: 752-60.
16 Gravett MG, Novy MJ, Rosenfeld RG, et al. Diagnosis of intra-amniotic infection by proteomic profiling and identification of novel biomarkers. JAMA 2004; 292: 462-9.
17 Svensson AM, Whiteley GR, Callas PW, et al. SELDI-TOF plasma profiles distinguish individuals in a protein C-deficient family with thrombotic episodes occuring before age 40. Thromb Haemost 2006; 96: 725-30.
18 Peronnet E, Becquart L, Poirier F, et al. SELDI-TOF MS analysis of the Cardiac Troponin I forms present in plasma from patients with myocardial infarction. Proteomics 2006; 6: 6288-99.
19 Wells PS, Anderson DR, Bormanis J, et al. Application of a diagnostic clinical model for the management of hospitalized patients with suspected deep-vein thrombosis.Thromb Haemost 1999; 81: 493-97.
20 Liu Y. Active learning with support vector machine applied to gene expression data for cancer classification. J ChemInf Comput Sci 2004; 44: 1936-41.
21 Nissen S. Coronary angiography and intravascular ultrasound. Am J Cardiol 2001; 87:15A-20A.
22 Celis JE, Kruh0ffer M, Gromova I, et al. Gene expression profiling: monitoring transcription and translation products using DNA micro array sand proteomics. FEBS Lett 2000; 480: 2-16.
23 Wilkins MR, Pasquali C, Appel RD, et al. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y) 1996; 14: 61-5.
24 Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Comm Mass Spectrum 1993; 7: 576-80.
25 Pusch W, Flocco MT, Leung SM, et al. Mass spectrometry-based clinical proteomics. Pharmacogenomics 2003; 4: 463-76.
26 Tang N, Tornatore P, Weinberger SR. Current developments in SELDI affinity technology. Mass Spectrom Rev 2004; 23: 34-44.
27 Wright GL, Cazares LH, Leung SM. ProteinChip surface enhanced laser desorption/ionization (SELDI) mass spectromatry: a novel protein biochip technology for detection of biomarker in complex protein mixtures. Prostate Cancer Prostatic Dis 2000; 2: 264-76.
28 Rosendaal FR. Venous thrombosis: the role of genes, environment, and behavior. Hematology Am Soc Hematol Educ Program 2005:1-12.
29 Ajjan R, Grant PJ. Coagulation and atherothrombotic disease. Atherosclerosis 2006;186: 240-59.
30 Amiral J, Fareed J. Thromboembolic diseases: biochemical mechanisms and new possibilities of biological diagnosis. SeminThromb Hemost 1996; 22(Suppl 1): 41-8.
31 Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics 2004; 20:777-85.
32 Seibert V, Ebert MP, Buschmann T. Advances in clinical cancer proteomics: SELD I-TOF-mass spectrometry and biomarker discovery. Brief Funct Genomic Proteomic 2005; 4: 16-26.
33 Liotta LA, Lowenthal M, Mehta A, et al. Importance of communication between producers and consumers of publicly available experimental data. J Natl Cancer Inst2005; 97: 310-4.
34 Banks RE, Stanley AJ, Cairns DA, et al. Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem 2005; 51: 1637-49.
35 Byvatov E, Schneider G Support vector machine applications in bioinformatics. Appl Bioinformatics 2003; 2: 67-77.
36 Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model 2005; 45: 549-61.
1 Mowatt G,Vale L,Brazzelli M,et al.Systematic review of the effectiveness and cost-effectiveness,and economic evaluation,of myocardial perfusion scintigraphy for the diagnosis and management of angina and myocardial infarction.Health Technol Assess 2004;8:1-207.
2 Ministry of Health.Chinese Health Statistical Digest 2006.Ministry of Health,People's Republic of China, 2006: 45.1989-2005.
3 Fowkes FJI, Price JF, Fowkes FGR. Incidence of diagnosed deep vein thrombosis in the general population: systematic review. Eur J Vase Endovasc Surg 2003; 25:1-5.
4 White RH. The epidemiology of venous thromboembolism. Circulation 2003;107(Suppl 1): 14-8.
5 Moser KM, LeMoine JR. Is embolic risk conditioned by location of deep venous thrombosis. Ann Intern Med 1981; 94: 439-44.
6 Alpert JS, Thygesen K, Antman E, et al. Myocardial infarction redefined-a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000; 36: 959-69. 37 Nissen S. Coronary angiography and intravascular ultrasound. Am J Cardiol 2001; 87:15A-20A.
8 Kyrle PA, Eichinger S. Deep vein thrombosis. Lancet 2005; 365: 1163-74.
9 Cushman M, Tsai AW, White RH, et al. Deep vein thrombosis and pulmonary embolism in two cohorts: the longitudinal investigation of thromboembolism etiology. Am J Med 2004;117:19-25.
10 Goodacre S, Sampson F, Thomas S, et al. Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis. BMC Med Imaging 2005; 5: 6.
11 Zhou M, Conrads TP, Veenstra TD. Proteomics approaches to biomarker detection.Brief Funct Genomic Proteomic 2005; 4: 69-75.
12 Vitzthum F, Behrens F, Anderson NL, et al. Proteomics: from basic research to diagnostic application. A review of requirements & needs. J Proteome Res 2005; 4:1086-97.
13 Wilkins MR, Pasquali C, Appel RD, et al. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y) 1996; 14: 61-5.
14 International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860-921.
15 Pusztai L, Gregory BW, Baggerly KA, et al. Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma. Cancer 2004; 100: 1814-22.
16 Malik G, Ward MD, Gupta SK, et al. Serum levels of an isoform of apolipoprotein A-?as a potential marker for prostate cancer. Clin Cancer Res 2005; 11:1073-85.
17 Woong-Shick A, Sung-Pil P, Su-Mi B, et al. Identification of hemoglobin-alpha and-beta subunits as potential serum biomarkers for the diagnosis and prognosis of ovarian cancer. Cancer Sci 2005; 96:197-201.
18 Bernhard OK, Diefenbach RJ, Cunningham AL. New insights into viral structure and virus-cell interactions through proteomics. Expert Rev Proteomics 2005; 2: 577-88.
19 Wouters BG. Proteomics: methodologies and applications in oncology. Semin Radiat Oncol 2008; 18:115-25.
20 Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Comm Mass Spectrum 1993; 7: 576-80.
21 Merchant M, Wenberger SR. Recent advancements in surface-enhanced laser desorption-ionization-time of flight-mass spectrometry. Electrophoresis 2000; 21:1164-77.
22 Langbein S. Identification of disease biomarkers by profiling of serum proteins using SELDI-TOF mass spectrometry. Methods Mol Biol 2008; 439: 191-7.
23 Whelan LC, Power KA, McDowell DT, et al. Applications of SELDI-MS technology in oncology. J Cell Mol Med 2008; 12(5A): 1535-47.
24 Kanmura S, Uto H, Kusumoto K, et al. Early diagnostic potential for hepatocellular carcinoma using the SELDI ProteinChip system. Hepatology 2007; 45: 948-56.
25 Fung ET, Thulasiraman V, Weinberger SR, et al. Protein biochips for differential profiling. Curr Opin Biotechnol 2001; 12: 65-9.
26 Vanhoutte KJ, Laarakkers C, Marchiori E, et al. Biomarker discovery with SELDI-TOF MS in human urine associated with early renal injury: evaluation with computational analytical tools. Nephrol Dial Transplant 2007; 22: 2932-43.
27 Badri AM, Coenen K, Vaillancourt LP, et al. On-chip detection of low-molecular-weight recombinant proteins in plant crude extracts by SELDI-TOF MS.Methods Mol Biol 2009; 483: 313-24.
28 Engwegen JY, Gast MC, Schellens JH, et al. Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry. Trends Pharmacol Sci 2006; 27:251-9.
29 Florian-Kujawski M, Hussain W, Chyna B, et al. Biomarker profiling of plasma from acute coronary syndrome patients. Application of ProteinChip Array analysis.Int Angiol 2004; 23: 246-54.
30 Peronnet E, Becquart L, Poirier F, et al. SELDI-TOF MS analysis of the Cardiac Troponin I forms present in plasma from patients with myocardial infarction. Proteomics 2006; 6: 6288-99.
31 Pinet F, Beseme O, Cieniewski-Bernard C, et al. Predicting left ventricular remodeling after a first myocardial infarction by plasma proteome analysis. Proteomics 2008; 8:1798-808.
32 Zhang G, Zhou B, Zheng Y, et al. Time course proteomic profile of rat acute myocardial infarction by SELDI-TOF MS analysis. Int J Cardiol 2009; 131: 225-33.
33 Hong M, Zhang X, Hu Y, et al. The potential biomarkers for thromboembolism detected by SELDI-TOF-MS. Thromb Res 2009; 123: 556-64.
34 Allard L, Lescuyer P, Burgess J, et al. ApoC-I and ApoC-? as potential plasmatic markers to distinguish between ischemic and hemorrhagic stroke. Proteomics 2004; 4:2242-51.
35 Prados J, Kalousis A, Sanchez JC, et al. Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents. Proteomics 2004; 4: 2320-32.
36 Suzuyama K, Shiraishi T, Oishi T, et al. Combined proteomic approach with SELDI-TOF-MS and peptide mass fingerprinting identified the rapid increase of monomeric transthyretin in rat cerebrospinal fluid after transient focal cerebral ischemia. Brain Res Mol Brain Res 2004; 129: 44-53.
37 Zhang X, Guo T, Wang H, et al. Potential biomarkers of acute cerebral infarction detected by SELDI-TOF-MS. Am J Clin Pathol 2008; 130: 299-304.
38 Sanchez JC, Lescuyer P, Hochstrasser D, et al. Detection of biomarkers of stroke using SELDI-TOF. Methods Mol Biol 2007; 357: 343-50.
39 Vitzthum F, Behrens F, Anderson NL, et al. Proteomics: from basic research to diagnostic application. A review of requirements & needs. J Proteome Res 2005; 4:1086-97.
40 Svensson AM, Whiteley GR, Callas PW, et al. SELDI-TOF plasma profiles distinguish individuals in a protein C-deficient family with thrombotic episodes occuring before age 40. Thromb Haemost 2006; 96: 725-30.
41 Master SR. Diagnostic proteomics: back to basics? Clin Chem 2005; 51: 1333-4.
42 Beretta L. Proteomics from the clinical perspective: many hopes and much debate. Nat Methods 2007; 4: 785-6.
43 Gast MC, Engwegen JY, Schellens JH, et al. Comparing the old and new generation SELDI-TOF MS: implications for serum protein profiling. BMC Med Genomics 2008;1:4.
2. Kario K, Eguchi K, Hoshide S, et al. U-curve relationship between orthostatic blood pressure change and silent cerebrovascular disease in elderly hypertensives: orthostatic hypertension as a new cardiovascular risk factor. J Am Coll Cardiol 2002; 40:133-141.
3. Rothwell PM, Howard SC, Power DA, et al. Fibrinogen Concentration and Risk of Ischemic Stroke and Acute Coronary Events in 5113 Patients With Transient Ischemic Attack and Minor Ischemic Stroke. Stroke 2004; 35:2300-2305.
4. Iwashita M, Matsushita Y, Sasaki J, et al. Relation of Serum Total Cholesterol and Other Factors to Risk of Cerebral Infarction in Japanese Men with Hypercholesterolemia. Circ J 2005; 69:1-6.
5. Kubo M, Kiyohara Y, Ninomiya T, et al. Decreasing incidence of lacular vs other types of cerebral infarction in a Japanese population. Neurology 2006; 66:1539-1544.
6. Olivot JM, Labreuche J, Aiach M, et al. Soluble thrombomodulin and brain infarction:case-control and prospective study. Stroke 2004; 35:1946-1951.
7. Elkind MS, Sciacca RR, Boden-Albala B, et al. Relative elevation in baseline leukocyte count predicts first cerebral infarction. Neurology 2005; 64:2121-2125.
8. Yang DT, Flanders MM, Kim H, et al. Elevated factor XI activity levels are associated with an increased odds ratio for cerebrovascular events. Am J Clin Pathol 2006;126:411-415.
9. Wang XD. Study on cerebrovascular disease of the elderly in China. J Hypertens Suppl 1996; 14:S139-145.
10. Basu AK, Pal SK, Saha S, et al. Risk factor analysis in ischaemic stroke: a hospital-based study. J Indian Med Assoc 2005; 103:586-588.
11. Dickinson CJ. Strokes and their relationship to hypertension. Curr Opin Nephrol Hypertens 2003; 12:91-96.
12. Van de Wouwer M, Collen D, Conway EM. Thrombomodulin-protein C-EPCR system:integrated to regulate coagulation and inflammation. Arterioscler Thromb Vase Biol 2004; 24:1374-1383.
13. Esmon NL, Owen WG, Esmon CT. Isolation of a membrane-bound cofactor for thrombin-catalyzed activation of protein C. J Biol Chem 1982; 257:859-864.
14. Bajzar L, Manuel R, Nesheim M. Purification and characterization of TAFI, a thrombin-activatable fibrinolysis inhibitor. J Biol Chem 1995; 270:14477-14484.
15. Blann AD, Amiral J, McCollum CN. Prognostic value of increased soluble thrombomodulin and increased soluble E-selectin in ischaemic heart disease. Eur J Haematol 1997; 59:115-120.
16. Nomura E, Kohriyama T, Kozuka K, et al. Significance of serum soluble thrombomodulin level in acute cerebral infarction. Eur J Neurol 2004; 11:329-334.
17. Salomaa V, Matei C, Aleksic N, et al. Soluble thrombomodulin as a predictor of incident coronary heart disease and symptomless carotid artery atherosclerosis in the Atherosclerosis Risk in Communities (ARIC) Study: a case-cohort study. Lancet 1999;353:1729-1734.
18. Dohi Y, Ohashi M, Sugiyama M, et al. Circulating thrombomodulin levels are related to latent progression of atherosclerosis in hypertensive patients. Hypertens Res 2003;26:479-483.
19. Drouet L. Fibrinogen: a treatable risk factor? Cerebrovasc Dis 1996; 6:2-6.
20. Kannel WB. Influence of fibrinogen on cardiovascular disease. Drugs 1997; 54(Suppl3):32-40.
21. Harley SL, Powell JT. Fibrinogen upregulates the expression of monocyte chemoattractant protein-1 in human saphenous vein endothelial cells. Biochem J 1999;341:739-744.
22. Baker IA, Pickering J, Elwood PC, et al. Fibrinogen, viscosity and white blood cell count predict myocardial, but not cerebral infarction: evidence from the Caerphilly and Speedwell cohort. Thromb Haemost 2002; 87:421-425.
23.Rnuiman MW, Folsom AR, Chambless LE, et al. Association of hemostatic variables with MRI-detected cerebral abnormalities: the atherosclerosis risk in communities study.Neuroepidemiology 2001; 20:96-104.
24. Di Lecce VN, Loffredo L, Fimognari FL, et al. Fibrinogen as predictor of ischemic stroke in patients with non-valvular atrial fibrillation. J Thromb Haemost 2003;1:2453-2455.
25. Turaj W, Slowik A, Dziedzic T, et al. Increased plasma fibrinogen predicts one-year mortality in patients with acute ischemic stroke. J Neurol Sci 2006; 246:13-19.
26. Khan TM, Marwat MA, Rehman H. Comparison of plasma viscosity and fibrinogen concentration in hypertensive and normotensive diabetics. J Ayub Med Coll Abbottabad 2005; 17:45-47.
27. Nemerson Y. Tissue factor and hemostasis. Blood 1988; 71:1-8.
28. Misumi K, Ogawa H, Yasue H, et al. Comparison of plasma tissue factor levels in unstable and stable angina pectoris. Am J Cardiol 1998; 81:22-26.
29. Seljeflot I, Hurlen M, Hole T, et al. Soluble tissue factor as predictor of future events in patients with acute myocardial infarction. Thromb Res 2003;lll:369-372.
30. He M, Wen Z, He X, et al. Observation on tissue factor pathway and some other coagulation parameters during the onset of acute cerebrocardiac thrombotic diseases. Thromb Res 2002; 107:223-228.
31. Wannamethee SG, Shaper AG, Ebrahim S. HDL-cholesterol, total cholesterol, and the risk of stroke in middle-aged British men. Stroke 2000; 31:1882-1888.
32. Soyama Y,Miura K, Morikawa Y, et al. High-density lipoprotein cholesterol and risk of stroke in Japanese men and women: The Oyabe Study. Stroke 2003; 34:863-868.
33. Sakata T, Kario K, Katayama Y, et al. Clinical significance of activated protein C resistance as a potential marker for hypercoagulable state. Thromb Res 1996;82:235-244.
34. van der Bom JG, Bots ML, Haverkate F, et al. Reduced response to activated protein C is associated with increased risk of cerebrovascular disease. Ann Intern Med 1996;125:265-269.
1.Fowkes FJI,Price JF,Fowkes FGR.Incidence of diagnosed deep vein thrombosis in the general population:systematic review.Eur J Vasc Endovasc Surg,2003,25:1-5.
2.Kniffin WD,Baron JA,Barrett J,et al.The epidemiology of diagnosed pulmonary embolism and deep venous thrombosis in the elderly.Arch Intern Med,1994,154:861-866.
3.White RH,Zhou H,Romano PS.Incidence of symptomatic venous thromboembolism after different elective or urgent surgical procedures.Thromb Haemost,2003,90:446-455.
4.Baron JA,Gridley G,Weiderpass E,et al.Venous thromboembolism and cancer.Lancet,1998,351:1077-1080.
5.Geerts WH,Code KI,Jay RM,et al.A prospective study of venous thromboembolism after major trauma.N Engl J Med,1994,331:1601-1606.
6.Hughes R,Heuser T,Hill S,et al.Recent air travel and venous thromboembolism resulting in hospital admission.Respirology,2006,11:75-79.
7.James AH,Tapson VF,Goldhaber SZ.Thrombosis during pregnancy and the postpartum period.Am J Obstet Gynecol,2005,193:216-219.
8.Heuser P,Tonga K,Hopkins R,et al.Specific oral contraceptive use and venous thromboembolism resulting in hospital admission.N Z Med J,2004,117:U1176.
9.Douketis JD,Julian JA,Kearon C,et al.Does the type of hormone replacement therapy influence the risk of deep vein thrombosis? A prospective case-control study.J Thromb Haemost,2005,3:943-948.
10.Tirado I,Mateo J,Soria JM,et al.The ABO blood group genotype and factor Ⅷ levels as independent risk factors for venous thromboembolism.Thromb Haemost,2005,93:468-474.
11.Moser KM,LeMoine JR.Is embolic risk conditioned by location of deep venous thrombosis.Ann Intern Med,1981,94:439-444.
12．王振义，李家增，阮长耿，等．血栓与止血基础理论与临床．上海：上海科学技术出版社，2004：875-878．
13．徐凌，毕红霞，蔡柏蔷，等．深静脉血栓形成103例临床分析．中华内科杂志，2000，39：513-516．
14．孙葵葵，王辰，古力夏提，等．深静脉血栓形成的危险因素及临床分析．中华结核和呼吸杂志，2004，27：727-730．
15.Khan TM,Marwat MA,Rehman H.Comparison of plasma viscosity and fibrinogen concentration in hypertensive and normotensive diabetics.J Ayub Med Coll Abbottabad,2005,17:45-47.
16.Drouet L.Fibrinogen:a treatable risk factor.Cerebrovasc Dis,1996,6:2-6.
17.Kannel WB.Influence of fibrinogen on cardiovascular disease.Drugs,1997,54 (Suppl 3):32-40.
18.Harley SL,Powell JT.Fibrinogen upregulates the expression of monocyte chemoattractant protein-1 in human saphenous vein endothelial cells.Biochem J,1999,341:739-744.
19.van Hylckama Vlieg A,Rosendaal FR.High levels of fibrinogen are associated with the risk of deep venous thrombosis mainly in the elderly.J Thromb Haemost,2003,1:2677-2678.
20. Wuillemin WA, Korte W, Waser G, et al. Usefulness of the D-dimer/fibrinogen ratio to predict deep venous thrombosis. J Thromb Haemost, 2005, 3: 385-387.
21. Keeling DM, Mackie IJ, Moody A, et al. The diagnosis of deep vein thrombosis in symptomatic outpatients and the potential for clinical assessment and D-dimer assays to reduce the need for diagnostic imaging. Br J Haematol, 2004,124: 15-25.
22. Heim SW, Schectman JM, Siadaty MS, et al. D-dimer testing for deep venous thrombosis: a meta-analysis. Clin Chem, 2004, 50:1136-1147.
23. Kelly J, Rudd A, Lewis RR, et al. Plasma D-dimers in the diagnosis of venous thromboembolism. Arch Intern Med, 2002,162: 747-756.
24. Kucher N, Kohler HP, Dornhofer T, et al. Accuracy of D-dimer/fibrinogen ratio to predict pulmonary embolism: a prospective diagnostic study. J Thromb Haemost, 2003,1: 708-713.
25. Anderson DR, Kovacs MJ, Kovacs G, et al. Combined use of clinical assessment and D-dimer to improve the management of patients presenting to the emergency department with suspected deep vein thrombosis (the EDITED Study). J Thromb Haemost, 2003,1: 645-651.
26. Kyrle PA, Eichinger S. Deep vein thrombosis. Lancet, 2005, 365:1163-1174.
1.Ministry of Health.Chinese Health Statistical Digest 2006.Ministry ofHealth,People's Republic of China,2006:45.1989-2005.
2.Liu M,Wu B,Wang WZ,et al.Stroke in China:epidemiology,prevention,and management strategies.Lancet Neurol.2007;6:456-464.
3.Kelly J,Rudd A,Lewis R,et al.Venous thromboembolism after acute stroke.Stroke.2001;32:262-267.
4.Herrmann M,Vos P,Wunderlich MT,et al.Release of glial tissue-specific proteins after acute stroke:A comparative analysis of serum concentrations of protein S-100B and glial fibrillary acidic protein.Stroke.2000;31:2670-2677.
5.Castillo J,Rodriguez I.Biochemical Changes and Inflammatory Response as Markers for Brain Ischaemia:Molecular Markers of Diagnostic Utility and Prognosis in Human Clinical Practice.Cerebrovasc Dis.2004;17(Suppl.1):7-18.
6.Silvestri A,Vitale C,Ferretti F,et al.Plasma levels of inflammatory C-reactive protein and interleukin-6 predict outcome in elderly patients with stroke.J Am Geriatr Soc.2004;52:1586-1587.
7.Smith CJ,Emsley HC,Gavin CM,et al.Peak plasma interleukin-6 and other peripheral markers of inflammation in the first week of ischaemic stroke correlate with brain infarct volume,stroke severity and long-term outcome.BMC Neurol.2004;4:2.
8.Tomitori H,Usui T,Saeki N,et al.Polyamine oxidase and acrolein as novel biochemical markers for diagnosis of cerebral stroke. Stroke. 2005; 36:2609-2613.
9. Geiger S, Holdenrieder S, Stieber P, et al. Nucleosomes as a new prognostic marker in early cerebral stroke. J Neurol. 2007; 254:617-623.
10.Alevizaki M, Synetou M, Xynos K, et al. Low triiodothyronine: a strong predictor of outcome in acute stroke patients. Eur J Clin Invest. 2007; 37:651-657.
11.Reynolds MA, Kirchick HJ, Dahlen JR, et al. Early biomarkers of stroke. Clin Chem.2003; 49:1733-1739.
12.Lynch JR, Blessing R, White WD, et al. Novel diagnostic test for acute stroke. Stroke.2004; 35:57-63.
13.Yao XH, Yu HM, Koide SS, et al. Identification of a key protein associated with cerebral ischemia. Brain Res. 2003; 967:11-18.
14.Allard L, Lescuyer P, Burgess J, et al. ApoC-I and ApoC-? as potential plasmatic markers to distinguish between ischemic and hemorrhagic stroke. Proteomics. 2004;4:2242-2251.
15.Chen A, Liao WP, Lu Q, et al. Upregulation of dihydropyrimidinase-related protein 2, spectrin alpha II chain, heat shock cognate protein 70 pseudogene 1 and tropomodulin 2 after focal cerebral ischemia in rats-a proteomics approach. Neurochem Int. 2007;50:1078-1086.
16.Sanchez JC, Lescuyer P, Hochstrasser D, et al. Detection of biomarkers of stroke using SELDI-TOF. Methods Mol Biol. 2007; 357:343-350.
17.Zhang X, Hu Y, Hong M, et al. Plasma thrombomodulin, fibrinogen, and activity of tissue factor as risk factors for acute cerebral infarction. Am J Clin Pathol. 2007;128:287-292.
18.Liu Y. Active learning with support vector machine applied to gene expression data for cancer classification. J Chem Inf Comput Sci. 2004; 44:1936-1941.
19.Missler U, Wiesmann M, Friedrich C, et al. S-100 protein and neuron-specific enolase concentrations in blood as indicators of infarction volume and prognosis in acute ischemic stroke. Stroke. 1997; 28:1956-1960.
20. Zimmermann-Ivol CG, Burkhard PR, Le Floch-Rohr J, et al. Fatty acid binding protein as a serum marker for the early diagnosis of stroke: a pilot study. Mol Cell Proteomics.2004; 3:66-72.
21. Dambinova SA, Khounteev GA, Skoromets AA. Multiple panel of biomarkers for TIA/stroke evaluation. Stroke. 2002; 33:1181-1182.
22. Kalafut MA, Schriger DL, Saver JL, et al. Detection of early CT signs of >l/3 middle cerebral artery infarctions : interrater reliability and sensitivity of CT interpretation by physicians involved in acute stroke care. Stroke. 2000; 31:1667-1671.
23. Patel SC, Levine SR, Tilley BC, et al. Lack of clinical significance of early ischemic changes on computed tomography in acute stroke. JAMA. 2001; 286:2830-2838.
24. Wardlaw JM, Lewis SC, Dennis MS, et al. Is visible infarction on computed tomography associated with an adverse prognosis in acute ischemic stroke? Stroke.1998; 29:1315-1319.
25. Lee LJ, Kidwell CS, Alger J, et al. Impact on stroke subtype diagnosis of early diffusion-weighted magnetic resonance imaging and magnetic resonance angiography.Stroke. 2000; 31:1081-1089.
26. Floyd JS, Laskowitz DT. A new approach to the diagnosis of acute ischemic stroke:Blood-borne biochemical markers. Stroke Clinical Updates. 2004; 14:1-5.
27. Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Comm Mass Spectrum. 1993; 7:576-580.
28. Smith RD, Anderson GA, Lipton MS, et al. An accurate mass tag strategy for quantitative and high-throughput proteme measurements. Proteomics. 2002; 2:513-523.
29. Foret F, Preisler J. Liquid phase interfacing and miniaturization in matrix-assisted laser desorption/ionixation mass spectrometry. Proteomics. 2002; 2:360-370.
30. Wright GL, Cazares LH, Leung SM. ProteinChip surface enhanced laser desorption/ionization (SELDI) mass spectromatry: a novel protein biochip technology for detection of biomarker in complex protein mixtures. Prostate Cancer Prostatic Dis. 2000; 2:264-276.
31. Dirnagl U,Iadecola C, Moskowitz MA. Pathobiology of ischaemic stroke: an integrated view. Trends Neurosci. 1999; 22:391-397.
32. Ross R. Atherosclerosis: an inflammatory disease. N Engl J Med. 1999; 340:115-126.
33. Lindsberg PJ, Grau AJ. Inflammation and infections as risk factors for ischemic stroke.Stroke. 2003; 34:2518-2532.
34. Schroeter M, Zickler P, Denhardt DT, et al. Increased thalamic neurodegeneration following ischaemic cortical stroke in osteopontin-deficient mice. Brain. 2006;129:1426-1437.
35. Culmsee C, Krieglstein J. Ischaemic brain damage after stroke: new insights into efficient therapeutic strategies. International Symposium on Neurodegeneration and Neuroprotection. EMBO Rep. 2007; 8:129-133.
36. Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics. 2004;20:777-785.
37. Seibert V, Ebert MP, Buschmann T. Advances in clinical cancer proteomics: SELDI-TOF-mass spectrometry and biomarker discovery. Brief Funct Genomic Proteomic. 2005; 4:16-26.
38. Liotta LA, Lowenthal M, Mehta A, et al. Importance of communication between producers and consumers of publicly available experimental data. J Natl Cancer Inst.2005; 97:310-314.
39. Banks RE, Stanley AJ, Cairns DA, et al. Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem. 2005; 51:1637-1649.
40. Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics. 2003; 2:67-77.
41. Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model. 2005; 45:549-561.
1 Mowatt G,Vale L,Brazzelli M,et al.Systematic review of the effectiveness and cost-effectiveness,and economic evaluation,of myocardial perfusion scintigraphy for the diagnosis and management of angina and myocardial infarction.Health Technol Assess 2004;8:1-207.
2 Fowkes FJI,Price JF,Fowkes FGR.Incidence of diagnosed deep vein thrombosis in the general population:systematic review.Eur J Vasc Endovasc Surg 2003;25:1-5.
3 White RH.The epidemiology of venous thromboembolism.Circulation 2003;107(Suppl 1):14-8.
4 Hasdai D,Califf RM,Thompson TD,et al.Predictors of cardiogenic shock after thrombolytic therapy for acute myocardial infarction. J Am Coll Cardiol 2000; 35:136-43.
5 Hirsh J, Bates SM. Prognosis in acute pulmonary embolism. Lancet 1999; 353:1375-6.
6 Cushman M, Tsai AW, White RH, et al. Deep vein thrombosis and pulmonary embolism in two cohorts: the longitudinal investigation of thromboembolism etiology. Am J Med2004; 117: 19-25.
7 Goodacre S, Sampson F, Thomas S, et al. Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis. BMC Med Imaging 2005; 5: 6.
8 Keeling DM, Mackie IJ, Moody A, et al. The diagnosis of deep vein thrombosis in symptomatic outpatients and the potential for clinical assessment and D-dimer assays to reduce the need for diagnostic imaging. Br J Haematol 2004; 124: 15-25.
9 Wuillemin WA, Korte W, Waser G, et al. Usefulness of the D-dimer/fibrinogen ratio to predict deep venous thrombosis. J Thromb Haemost 2005; 3: 385-7.
10 Atalar E, Haznedaroglu IC, Kilic H, et al. Increased soluble glycoprotein V concentration during the acute onset of unstable angina pectoris in association with chronic cigarette smoking. Platelets 2005; 16: 329-33.
11 Aleil B, Mossard JM, Wiesel ML, et al. Increased plasma levels of soluble platelet glycoprotein V in patients with acute myocardial infarction. J Thromb Haemost 2003; 1:1846-7.
12 Kyrle PA, Eichinger S. Deep vein thrombosis. Lancet 2005; 365: 1163-74.
13 Alpert JS, Thygesen K, Antman E, et al. Myocardial infarction redefined-a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000; 36: 959-69.
14 Yip TT, Lomas L. SELDI ProteinChip array in oncoproteomic research. Technol Cancer Res Treat 2002; 1:273-80.
15 Poon TC, Yip TT, Chan AT, et al. Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin Chem 2003; 49: 752-60.
16 Gravett MG, Novy MJ, Rosenfeld RG, et al. Diagnosis of intra-amniotic infection by proteomic profiling and identification of novel biomarkers. JAMA 2004; 292: 462-9.
17 Svensson AM, Whiteley GR, Callas PW, et al. SELDI-TOF plasma profiles distinguish individuals in a protein C-deficient family with thrombotic episodes occuring before age 40. Thromb Haemost 2006; 96: 725-30.
18 Peronnet E, Becquart L, Poirier F, et al. SELDI-TOF MS analysis of the Cardiac Troponin I forms present in plasma from patients with myocardial infarction. Proteomics 2006; 6: 6288-99.
19 Wells PS, Anderson DR, Bormanis J, et al. Application of a diagnostic clinical model for the management of hospitalized patients with suspected deep-vein thrombosis.Thromb Haemost 1999; 81: 493-97.
20 Liu Y. Active learning with support vector machine applied to gene expression data for cancer classification. J ChemInf Comput Sci 2004; 44: 1936-41.
21 Nissen S. Coronary angiography and intravascular ultrasound. Am J Cardiol 2001; 87:15A-20A.
22 Celis JE, Kruh0ffer M, Gromova I, et al. Gene expression profiling: monitoring transcription and translation products using DNA micro array sand proteomics. FEBS Lett 2000; 480: 2-16.
23 Wilkins MR, Pasquali C, Appel RD, et al. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y) 1996; 14: 61-5.
24 Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Comm Mass Spectrum 1993; 7: 576-80.
25 Pusch W, Flocco MT, Leung SM, et al. Mass spectrometry-based clinical proteomics. Pharmacogenomics 2003; 4: 463-76.
26 Tang N, Tornatore P, Weinberger SR. Current developments in SELDI affinity technology. Mass Spectrom Rev 2004; 23: 34-44.
27 Wright GL, Cazares LH, Leung SM. ProteinChip surface enhanced laser desorption/ionization (SELDI) mass spectromatry: a novel protein biochip technology for detection of biomarker in complex protein mixtures. Prostate Cancer Prostatic Dis 2000; 2: 264-76.
28 Rosendaal FR. Venous thrombosis: the role of genes, environment, and behavior. Hematology Am Soc Hematol Educ Program 2005:1-12.
29 Ajjan R, Grant PJ. Coagulation and atherothrombotic disease. Atherosclerosis 2006;186: 240-59.
30 Amiral J, Fareed J. Thromboembolic diseases: biochemical mechanisms and new possibilities of biological diagnosis. SeminThromb Hemost 1996; 22(Suppl 1): 41-8.
31 Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics 2004; 20:777-85.
32 Seibert V, Ebert MP, Buschmann T. Advances in clinical cancer proteomics: SELD I-TOF-mass spectrometry and biomarker discovery. Brief Funct Genomic Proteomic 2005; 4: 16-26.
33 Liotta LA, Lowenthal M, Mehta A, et al. Importance of communication between producers and consumers of publicly available experimental data. J Natl Cancer Inst2005; 97: 310-4.
34 Banks RE, Stanley AJ, Cairns DA, et al. Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem 2005; 51: 1637-49.
35 Byvatov E, Schneider G Support vector machine applications in bioinformatics. Appl Bioinformatics 2003; 2: 67-77.
36 Jorissen RN, Gilson MK. Virtual screening of molecular databases using a support vector machine. J Chem Inf Model 2005; 45: 549-61.
1 Mowatt G,Vale L,Brazzelli M,et al.Systematic review of the effectiveness and cost-effectiveness,and economic evaluation,of myocardial perfusion scintigraphy for the diagnosis and management of angina and myocardial infarction.Health Technol Assess 2004;8:1-207.
2 Ministry of Health.Chinese Health Statistical Digest 2006.Ministry of Health,People's Republic of China, 2006: 45.1989-2005.
3 Fowkes FJI, Price JF, Fowkes FGR. Incidence of diagnosed deep vein thrombosis in the general population: systematic review. Eur J Vase Endovasc Surg 2003; 25:1-5.
4 White RH. The epidemiology of venous thromboembolism. Circulation 2003;107(Suppl 1): 14-8.
5 Moser KM, LeMoine JR. Is embolic risk conditioned by location of deep venous thrombosis. Ann Intern Med 1981; 94: 439-44.
6 Alpert JS, Thygesen K, Antman E, et al. Myocardial infarction redefined-a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000; 36: 959-69. 37 Nissen S. Coronary angiography and intravascular ultrasound. Am J Cardiol 2001; 87:15A-20A.
8 Kyrle PA, Eichinger S. Deep vein thrombosis. Lancet 2005; 365: 1163-74.
9 Cushman M, Tsai AW, White RH, et al. Deep vein thrombosis and pulmonary embolism in two cohorts: the longitudinal investigation of thromboembolism etiology. Am J Med 2004;117:19-25.
10 Goodacre S, Sampson F, Thomas S, et al. Systematic review and meta-analysis of the diagnostic accuracy of ultrasonography for deep vein thrombosis. BMC Med Imaging 2005; 5: 6.
11 Zhou M, Conrads TP, Veenstra TD. Proteomics approaches to biomarker detection.Brief Funct Genomic Proteomic 2005; 4: 69-75.
12 Vitzthum F, Behrens F, Anderson NL, et al. Proteomics: from basic research to diagnostic application. A review of requirements & needs. J Proteome Res 2005; 4:1086-97.
13 Wilkins MR, Pasquali C, Appel RD, et al. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology (N Y) 1996; 14: 61-5.
14 International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860-921.
15 Pusztai L, Gregory BW, Baggerly KA, et al. Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma. Cancer 2004; 100: 1814-22.
16 Malik G, Ward MD, Gupta SK, et al. Serum levels of an isoform of apolipoprotein A-?as a potential marker for prostate cancer. Clin Cancer Res 2005; 11:1073-85.
17 Woong-Shick A, Sung-Pil P, Su-Mi B, et al. Identification of hemoglobin-alpha and-beta subunits as potential serum biomarkers for the diagnosis and prognosis of ovarian cancer. Cancer Sci 2005; 96:197-201.
18 Bernhard OK, Diefenbach RJ, Cunningham AL. New insights into viral structure and virus-cell interactions through proteomics. Expert Rev Proteomics 2005; 2: 577-88.
19 Wouters BG. Proteomics: methodologies and applications in oncology. Semin Radiat Oncol 2008; 18:115-25.
20 Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Comm Mass Spectrum 1993; 7: 576-80.
21 Merchant M, Wenberger SR. Recent advancements in surface-enhanced laser desorption-ionization-time of flight-mass spectrometry. Electrophoresis 2000; 21:1164-77.
22 Langbein S. Identification of disease biomarkers by profiling of serum proteins using SELDI-TOF mass spectrometry. Methods Mol Biol 2008; 439: 191-7.
23 Whelan LC, Power KA, McDowell DT, et al. Applications of SELDI-MS technology in oncology. J Cell Mol Med 2008; 12(5A): 1535-47.
24 Kanmura S, Uto H, Kusumoto K, et al. Early diagnostic potential for hepatocellular carcinoma using the SELDI ProteinChip system. Hepatology 2007; 45: 948-56.
25 Fung ET, Thulasiraman V, Weinberger SR, et al. Protein biochips for differential profiling. Curr Opin Biotechnol 2001; 12: 65-9.
26 Vanhoutte KJ, Laarakkers C, Marchiori E, et al. Biomarker discovery with SELDI-TOF MS in human urine associated with early renal injury: evaluation with computational analytical tools. Nephrol Dial Transplant 2007; 22: 2932-43.
27 Badri AM, Coenen K, Vaillancourt LP, et al. On-chip detection of low-molecular-weight recombinant proteins in plant crude extracts by SELDI-TOF MS.Methods Mol Biol 2009; 483: 313-24.
28 Engwegen JY, Gast MC, Schellens JH, et al. Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry. Trends Pharmacol Sci 2006; 27:251-9.
29 Florian-Kujawski M, Hussain W, Chyna B, et al. Biomarker profiling of plasma from acute coronary syndrome patients. Application of ProteinChip Array analysis.Int Angiol 2004; 23: 246-54.
30 Peronnet E, Becquart L, Poirier F, et al. SELDI-TOF MS analysis of the Cardiac Troponin I forms present in plasma from patients with myocardial infarction. Proteomics 2006; 6: 6288-99.
31 Pinet F, Beseme O, Cieniewski-Bernard C, et al. Predicting left ventricular remodeling after a first myocardial infarction by plasma proteome analysis. Proteomics 2008; 8:1798-808.
32 Zhang G, Zhou B, Zheng Y, et al. Time course proteomic profile of rat acute myocardial infarction by SELDI-TOF MS analysis. Int J Cardiol 2009; 131: 225-33.
33 Hong M, Zhang X, Hu Y, et al. The potential biomarkers for thromboembolism detected by SELDI-TOF-MS. Thromb Res 2009; 123: 556-64.
34 Allard L, Lescuyer P, Burgess J, et al. ApoC-I and ApoC-? as potential plasmatic markers to distinguish between ischemic and hemorrhagic stroke. Proteomics 2004; 4:2242-51.
35 Prados J, Kalousis A, Sanchez JC, et al. Mining mass spectra for diagnosis and biomarker discovery of cerebral accidents. Proteomics 2004; 4: 2320-32.
36 Suzuyama K, Shiraishi T, Oishi T, et al. Combined proteomic approach with SELDI-TOF-MS and peptide mass fingerprinting identified the rapid increase of monomeric transthyretin in rat cerebrospinal fluid after transient focal cerebral ischemia. Brain Res Mol Brain Res 2004; 129: 44-53.
37 Zhang X, Guo T, Wang H, et al. Potential biomarkers of acute cerebral infarction detected by SELDI-TOF-MS. Am J Clin Pathol 2008; 130: 299-304.
38 Sanchez JC, Lescuyer P, Hochstrasser D, et al. Detection of biomarkers of stroke using SELDI-TOF. Methods Mol Biol 2007; 357: 343-50.
39 Vitzthum F, Behrens F, Anderson NL, et al. Proteomics: from basic research to diagnostic application. A review of requirements & needs. J Proteome Res 2005; 4:1086-97.
40 Svensson AM, Whiteley GR, Callas PW, et al. SELDI-TOF plasma profiles distinguish individuals in a protein C-deficient family with thrombotic episodes occuring before age 40. Thromb Haemost 2006; 96: 725-30.
41 Master SR. Diagnostic proteomics: back to basics? Clin Chem 2005; 51: 1333-4.
42 Beretta L. Proteomics from the clinical perspective: many hopes and much debate. Nat Methods 2007; 4: 785-6.
43 Gast MC, Engwegen JY, Schellens JH, et al. Comparing the old and new generation SELDI-TOF MS: implications for serum protein profiling. BMC Med Genomics 2008;1:4.