血清标志物发现研究的一般流程
血清学鉴定的实验步骤及判定方法
血清学鉴定的实验步骤及判定方法
血清学鉴定的实验步骤及判定方法
血清学鉴定是对血清样本的微量元素、蛋白质和抗原/抗体的检测，用于诊断和预防疾病的有效和可靠的方法。
它主要是用于诊断疾病的诊断，临床治疗和疾病控制的影响。
该技术是在明确病人的病史，病理检查，影像学检查，免疫学和临床化学检查结果的基础上，对血清进行各种各样的检查，用以诊断疾病。
血清学鉴定的实验步骤主要包括：
1、血液采样：采样前应空腹或者清晨空腹采血，并将血液用采血管或收集液瓶装入，一般收集24毫升血液，血浆在室温下或在摇床或离心机上离心30分钟，分离血浆和红细胞，冷却离心时离心速度不要太快，以免造成血浆沉淀，影响细胞膜和细胞结构的破坏。
2、实验条件的准备：将血清加入实验容器，将参考液分为几部分，每部分的数量等于血清的体积，用以反映血清中各物质的浓度；液体实验材料和悬浮的颗粒；酶标等工具。
3、实验过程：按照特定的实验步骤，实验者进行实验，通过不同的方法实现血清样本中物质的检测。
4、数据分析与判定：实验者收集的数据将被记录在实验报告中，比较实验结果和总结报告中的标准值，以便判断样本是否满足诊断要求。
血清学鉴定判定有多种，具体方法可根据检测物质的不同而有所不同。
如，血清学检测可以结合血清蛋白电泳，血清抗原抗体的检测，
血清抗体酶标板，酶联免疫吸附试验（ELISA），血清微量元素检测，微量数据检测等，综合应用；血清学检测实验报告中的比较结果和报告标准的对比，来判定血清检测结果是否符合诊断要求；也可以根据血清检测数据计算出几种指数，和指定的阈值进行比较，以判断血清检测结果是否正常。
研究血清肿瘤标志物对早期肺癌诊断的临床意义
研究血清肿瘤标志物对早期肺癌诊断的临床意义发表时间：2018-11-27T09:56:09.237Z 来源：《中国医学人文》(学术版)2018年6月下第12期作者：张丽红[导读] 还可将肿瘤标志物诊断法与其他检测方法相结合，并根据检测结果对患者做出相关的处理措施，提高患者的生活质量，延长期生存时间。
黑龙江省密山市人民医院黑龙江密山 158300【摘要】目的：探讨血清肿瘤标志物对肺癌早期诊断的临床意义。
方法：本次调查对象共包括肺癌患者100例，良性疾病患者100例，健康对照者100例。
调查对象均于次日空腹条件下静脉取血2ml，做离心处理后提取血清，按照规定要求进行检测。
结果：三组患者中，血清中肿瘤标志物含量差异显著，其中肺癌组患者肿瘤标志物含量明显高于良性疾病组与健康对照组，具体情况如表1、2所示。
结论：血清肿瘤标志物在肺癌早期诊断中具有较高的临床意义。
【关键词】肺癌；血清；肿瘤标志物；诊断肺癌发病率逐年升高，且具有较高的致死率，可对患者的生活质量产生不良影响。
引发肺癌的病因较多，如烟龄较长、环境污染严重等，由于现实生活中大部分人群缺少相应的疾病预防、护理知识，且对疾病的发展无基本概念，因此常常错失疾病的最佳治疗时期。
早期对肺癌进行诊断，并进行相应的控制，可有效改善患者的生活质量，延长患者的存活时间【1】。
临床上用作肿瘤疾病的诊断方法较多，如影像学检测、病理学检测等，但其对早期诊断敏感度较低，易出现漏诊、误诊等情况，为了提高诊断的准确率，临床上常以肿瘤标志物对患者进行诊断。
在此次调查中，主要涉及血清中癌胚抗原（CEA）、可溶性细胞角蛋白-19片段（CYFRA 21-1）、神经元特异性烯醇化酶（NSE）、鳞状细胞癌相关抗原（SCC）四项。
具体情况如下：1.资料与方法1.1临床资料本次调查对象共包括肺癌患者100例，良性疾病患者100例，健康对照者100例。
肺癌组中男患72例、女患28例，年龄为38~72岁，平均年龄为（48.9±4.2）岁。
血清学试验流程
血清学试验流程一、准备工作。
血清学试验嘛，咱得先把要用的东西都准备好。
就像做饭得先备菜一样。
那需要啥呢？首先得有血清样本呀，这个样本可得采集好，要是采集的时候出了岔子，后面的试验可就不好搞喽。
然后呢，各种试剂也不能少，像是抗原、抗体之类的，这些就像是做菜的调料，缺了哪样都不行。
还有那些个仪器设备，像试管、移液枪之类的，这就是咱的锅碗瓢盆啦，得保证它们干净、好用。
二、样本处理。
拿到血清样本之后呢，可不能就直接用。
有时候得处理一下，让它达到适合试验的状态。
比如说可能要稀释一下，这就像冲咖啡，太浓了不好喝，太淡了也没味，血清稀释也是这个道理，得找到那个刚刚好的比例。
而且在处理样本的时候呀，动作得轻一点，温柔一点，可别把血清里的那些个成分给破坏掉了，那可就麻烦大了。
三、加样。
接下来就是加样啦。
这就像是给蛋糕分层，一层一层得放好。
先把处理好的血清样本按照规定的量加到试管或者孔板里，然后再加入对应的抗原或者抗体试剂。
这个加样可不能马虎，量一定要准确。
就像做菜放盐，放多了咸得没法吃，放少了没味道，加样的量不准确，试验结果可能就不对喽。
四、反应阶段。
加样之后就等着它们反应啦。
这个时候呀，就像是种子在土里发芽，需要一点时间。
血清里的成分和试剂之间就开始发生奇妙的反应啦。
这个反应的时间可很关键，不能太短也不能太长。
太短了反应不完全，太长了可能又会有其他的变化。
在这个过程中，就像等待花开一样，得有点耐心。
五、检测结果。
反应完成之后呢，就要检测结果啦。
检测的方法有好多呢，比如说有的可以通过观察颜色变化，就像看树叶变色一样，不同的颜色代表着不同的结果。
还有的可能是用仪器来检测一些数值之类的。
这个时候可得仔细看，仔细分析结果，就像寻宝一样，从那些数据或者颜色变化里找到我们想要的答案。
血清学试验虽然看起来有点复杂，但是只要一步一步认真做，就像走在一条有趣的小路上，总能到达目的地的。
每一个步骤都像是一个小挑战，但是当你看到最后的结果，就会觉得所有的努力都是值得的啦。
血清学生物标志物-概述说明以及解释
血清学生物标志物-概述说明以及解释1.引言1.1 概述概述：血清学生物标志物是指可以在血清中检测到的具有特定生物学功能和临床意义的分子指标。
随着生物技术和检测技术的不断发展，血清学生物标志物在疾病的早期诊断、治疗效果监测以及预后评估方面发挥着越来越重要的作用。
本文将系统地探讨血清学生物标志物的定义、分类、特点，以及其在疾病诊断和治疗中的应用，旨在为读者提供对该领域的深入了解和认识。
的内容文章结构:本文共分为引言、正文和结论三部分。
引言部分将概括介绍血清学生物标志物的概念、意义和目的。
正文部分将详细探讨血清学生物标志物的定义、作用、分类和特点，以及在疾病诊断和治疗中的应用。
结论部分将对全文进行总结，并展望血清学生物标志物未来的发展方向，最后以结语结束全文，强调血清学生物标志物在医学领域的重要性和潜力。
章1.2 文章结构部分的内容1.3 目的本文旨在系统性地介绍血清学生物标志物在医学领域中的重要性和应用。
通过对血清学生物标志物的定义、分类、特点以及在疾病诊断和治疗中的应用进行详细阐述，旨在帮助读者更好地理解血清学生物标志物在医学实践中的作用和意义，促进相关领域的研究和发展。
同时，本文也将对血清学生物标志物的未来发展方向进行探讨，为相关领域的研究和临床实践提供参考和借鉴。
通过本文的阐述，希望能够为读者提供全面、系统的了解和认识，促进血清学生物标志物在医学领域中的应用与发展。
2.正文2.1 血清学生物标志物的定义和作用血清学生物标志物是指在血清中能够反映机体生理和病理状态的分子，如蛋白质、核酸、糖类等。
这些生物标志物可以通过血清样本的检测和分析来提供信息，帮助医生进行疾病的诊断、分型、预后评估和疗效监测。
血清学生物标志物具有以下几个主要作用：1. 诊断疾病：血清中的某些特定标志物在某些疾病中会有显著性的变化，通过检测这些标志物可以帮助医生进行疾病的早期诊断和分析。
2. 预后评估：有些血清学生物标志物与疾病的预后密切相关，可以帮助医生判断疾病的发展趋势和患者的预后情况。
心功能不全诊断金标准
心功能不全诊断金标准全文共四篇示例，供读者参考第一篇示例：心功能不全是指心脏无法满足身体对氧气和养分的需求，是一种常见的心血管疾病，严重者可导致心力衰竭和心源性休克，甚至危及生命。
及早诊断和治疗心功能不全尤为重要。
金标准是指在临床上被广泛认可和公认的诊断标准，具有高准确性和可靠性。
本文将探讨心功能不全的诊断金标准，并详细介绍其诊断流程和方法。
一、病史询问和临床评估心功能不全的诊断首先需要进行详细的病史询问和临床评估。
患者一般会出现呼吸困难、胸闷、乏力、水肿等症状。
医生需要询问患者病史、家族史、心血管病史等信息，全面了解患者的病情。
医生还需要通过体格检查来评估患者的心肺功能，包括听诊心脏杂音、观察心脏搏动、测量血压、观察水肿等。
这些信息对确定诊断和评估病情的严重程度非常重要。
二、实验室检查1. 血液学检查：心功能不全患者一般会出现贫血、白细胞计数升高、血红蛋白浓度降低等情况。
血液学检查可以帮助医生确定患者是否存在贫血等异常情况。
2. 生化检查：心功能不全患者一般会伴随有肝功能异常和肾功能异常。
生化检查可以帮助医生评估患者的肝肾功能，及时发现并处理相关并发症。
三、影像学检查1. 胸部X线检查：胸部X线片是诊断心功能不全的常用检查方法，可以帮助医生评估心脏的大小、形态、轮廓以及肺部情况。
典型的心功能不全X线表现包括心脏增大、肺充血、肺水肿等。
2. 超声心动图检查：超声心动图是诊断心功能不全的关键检查方法，可以直观地观察心脏结构和功能，评估心脏收缩和舒张功能，检测心脏瓣膜功能。
超声心动图可以帮助医生确定心功能不全的程度和类型，指导后续治疗方案的制定。
四、心导管检查心功能不全的诊断金标准还包括心导管检查。
心导管检查是通过在冠状动脉、心脏血管中插入导管，测量心脏内各腔压力、氧合情况、血流速度等参数，从而评估心脏功能和血管病变程度。
心导管检查是诊断冠心病、心力衰竭等心血管疾病的重要手段，对于心功能不全的诊断和评估具有重要意义。
血清肿瘤标志物检测在肺癌诊断与临床分期中的应用
血清肿瘤标志物检测在肺癌诊断与临床分期中的应用【摘要】目的：探讨三种血清肿瘤标志物：血清癌胚抗原（CEA）、细胞角蛋白19片段21-1（CYFRA21-1）、神经元特异性烯醇化酶（NSE）对肺癌检测的敏感性以及联合检测对肺癌的应用价值。
方法：研究对象采用我院2013年1月至2014年6月经我院健康体检受检者60例，另选取呼吸科疗区其他肺部疾病患者60例以及同期肺癌患者60例，其中腺癌患者28例，鳞癌患者14例，小细胞癌患者18例，对全部受检患者均行电化学发光免疫分析测定以及CEA、CY21-1、NSE试剂盒测定。
结果：三种血清肿瘤标志物CEA、CYFRA21-1、NSE 的检测结果均明显高于健康组和良性病变组；三种血清肿瘤标志物联合检测对各种类型肺癌均有较高的阳性检测率，在肺癌分期诊断的研究结果中可见，IIIa期至IV期的三种血清肿瘤标志物水平明显高于I期和II期。
结论：肿瘤标志物CEA、CYFRA21-1、NSE对肺癌的诊断具有较高的敏感性和特异性，三种肿瘤标志物联合检对肺癌的诊断有较好的特异性，且对IIIa期至IV期患者具有更高的敏感性，可作为临床诊断重要的参考依据。
【关键词】肺癌；癌胚抗原；细胞角蛋白19片段21-1；神经元特异性烯醇化酶；化学发光R2【文献标号】A1671-8725（2014）11-0021-01肺癌在临床上主要分为小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC），后者包括鳞癌、腺癌、未分化的大细胞癌三种组织学分型。
由于肺癌与其他呼吸系统疾病的鉴别较为困难，且肺癌具有恶化度高、生长快速、病因复杂等特点，早期诊断对于患者的生存率和有效治疗干预至关重要，所以临床上针对肺癌需要更加灵敏、更具特异性的诊断手段[1]。
本研究就血清肿瘤标志物三项癌胚抗原（CEA）、血清细胞角蛋19片段21-1（CYFRA21-1）、神经元特异性烯醇化酶（NSE）对肺癌诊断的敏感性和特异性进行研究，探讨其临床诊断价值以及在肺癌不同临床分期的应用价值。
血清生物标记者发现的途径
Hindawi Publishing CorporationJournal of Biomedicine and BiotechnologyVolume2010,Article ID927917,8pagesdoi:10.1155/2010/927917Methodology ReportA Proteomic Approach for Plasma Biomarker Discovery with iTRAQ Labelling and OFFGEL FractionationEmilie Ernoult,Anthony Bourreau,Erick Gamelin,and Catherine GuetteCentre INSERM R´e gional de Recherche sur le Cancer U892,Centre R´e gional de Lutte Contre le Cancer Paul Papin,2rue Moll,49933Angers Cedex9,FranceCorrespondence should be addressed to Catherine Guette,c.guette@unimedia.frReceived4June2009;Revised17July2009;Accepted3August2009Academic Editor:Pieter C.DorresteinCopyright©2010Emilie Ernoult et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use,distribution,and reproduction in any medium,provided the original work is properly cited.Human blood plasma contains a plethora of proteins,encompassing not only proteins that have plasma-based functionalities,but also possibly every other form of low concentrated human proteins.As it circulates through the tissues,the plasma picks up proteins that are released from their origin due to physiological events such as tissue remodeling and cell death.Specific disease processes or tumors are often characterized by plasma“signatures,”which may become obvious via changes in the plasma proteome profile, for example,through over expression of proteins.However,the wide dynamic range of proteins present in plasma makes their analysis very challenging,because high-abundance proteins tend to mask those of lower abundance.In the present study,we used a strategy combining iTRAQ as a reagent which improved the peptide ionization and peptide OFFGEL fractionation that has already been shown,in our previous research,to improve the proteome coverage of cellular extracts.Two prefractioning methods were compared:immunodepletion and a bead-based library of combinatorial hexapeptide technology.Our data suggested that both methods were complementary,with regard to the number of identified proteins.iTRAQ labelling,in association with OFFGEL fractionation,allowed more than300different proteins to be characterized from400μg of plasma proteins.1.IntroductionBlood circulates throughout every part of the body,and no other biofluid has the same degree of intimacy with the body. Therefore,it is not surprising that it possesses such a wealth of information concerning the overall pathophysiology of a patient.As an example,alterations in protein abundance can serve to indicate pathological abnormalities:diseases,toxic effects of clinical treatments and so forth.The choice between plasma and serum has been abun-dantly documented in the literature[1,2].When blood is collected,many changes occur in the proteins it contains,due to the presence of proteolytic enzymes(proteases)and other enzymes,which remain active in the blood sample during handling and processing.The HUPO Committee and its research collaborators concluded with the recommendation that plasma is the preferred specimen taken from the blood. The reasons for this are(i)less ex vivo degradation,and (ii)much less variability than in the case of the protease-rich process of clotting.Misek et al.[3]showed with Cy5-, Cy3-,Cy2-labeled serum and plasma on DIGE-2D-PAGE, after extensive fractionation of intact proteins before tryptic digestion,that isoforms of abundant proteins were more often shifted to lower-than-expected MW in serum than in plasma.Tammen et al.[4]reported that40%of the low-MW peptides detected were serum specific.Biomarker discovery in plasma is often limited by the availability of sufficient volumes.It is also complicated by the wide dynamic range of the human plasma proteome, which comprises proteins spanning concentrations of more than11orders of magnitude,with the top10most abundant plasma proteins accounting for approximatively90%of the total plasma proteins.Potential disease biomarkers are often present in low concentrations,and the dynamic range of the plasma proteins poses a significant analytical challenge to proteomic approaches.A prefractionation method isnecessary in the biomarker process.The most common technique is immunodepletion,which has been extensively used for the specific removal of high abundance proteins, based on the action of specific antibodies[5,6].More recently,saturation protein binding to a random pep-tide library has been proposed as an alternative method [7,8].One of the methods used to discover biomarkers is the identification and quantification of proteins,based on an iTRAQ quantitative proteomic approach.iTRAQ is ideally suited for biomarker applications,as it provides both quantification and multiplexing in a single reagent,and has been applied to the analysis of clinical samples such as human cerebrospinalfluid,and disease tissues,and has been used for the in vitro profiling of cells to identify differentially expressed proteins.To the best of our knowledge,there are currently only two published papers where iTRAQ has been used to study human serum and plasma.Hergenroeder et al.[9]employed iTRAQ and electrospray ionization tandem mass spectrometry(ESI-MS/MS)to serum depleted of12high abundance proteins,leading to the identification of160proteins.Song et al.[10]used iTRAQ protocol and MALDI-MS/MS to identify105proteins in human plasma.We recently demonstrated that iTRAQ labelling and peptide OFFGEL fractionation in afirst dimension improved the identification of weakly concentrated proteins from a cellular extract[11].The aim of the present study wasfirstly, to use iTRAQ as reagent to improve the MALDI ionization of peptides and secondly to evaluate the performance of our previous strategy for the study of the human plasma proteome,in terms of the number of identified proteins,the presence of high abundance proteins,and the identification of medium and weakly concentrated proteins.2.Materials and Methods2.1.Human Blood Plasma Samples.A citrated plasma pool, composed of a collection of10methylene blue virus-inactivated plasma samples,obtained from healthy donors using apheresis,was provided by the French National Public Blood Institution(Etablissement Franc¸ais du Sang Bourgogne Franche Comt´e,CHU Le Bocage,Dijon,France). The plasma pool(Internal Quality Control)was loaded into 0.5ml bar-coded straws,stored at4◦C for2hours,and then transferred into liquid nitrogen.The plasma straws were packed in a dry ice container during transportation to our laboratory and were immediately stored in a−80◦C freezer until they were needed.2.2.Immunoaffinity Depletion of High-Abundance Proteins. In a four independent experiments,the14most highly abundant proteins were removed from the plasma,using antibody-based depletion with a Human14Multiple Affinity Removal System,MARS-Hu14(Agilent Technologies,Santa Clara,CA,USA).Two different spin cartridges were used to deplete10times10μL of0.22μm-filtered plasma.This process required2buffers,A and B(Agilent Technologies).The pH7.4phosphate salt-containing buffer A was used for the equilibration,loading and washing steps.Flow-through fractions containing low-abundance proteins were collected and stored at−80◦C until they were ready for analysis.A pH 2.5urea buffer B was used for elution of the bound,highly abundant proteins from the cartridge.The experiment was conducted at room temperature according to the protocol supplied by the manufacturer.2.3.Hexapeptide Ligand Library Treatment.Plasma proteins were“equalized”using the ProteoMiner Protein Enrichment Kit(Bio-rad laboratories,Hercules,CA,USA).Four different spin columns were run in parallel,according to the manufac-turer’s instructions.Each was loaded with900μL of0.22μm-filtered plasma,for2hours at room temperature,and with 100μL of1M sodium citrate,and20mM of HEPES,at pH7.4.No bead agglomeration was observed.The proteins were desorbed using a two-step elution.Thefirst beads were incubated twice with100μL of the kit elution reagent(4M urea,1%(w/v)CHAPS,5%(v/v)acetic acid)for15minutes. Then,100μL of6M guanidine-HCl,at pH6.0,were added twice for15minutes.For each column,the four elution fractions were pooled and stored at−80◦C,until they were needed for analysis.2.4.Buffer Exchange and Protein Content ing 2000MWCO Hydrosart Vivaspin2spin concentrators (Sartorius Stedim Biotech,G¨o ttingen,Germany),the pre-fractionated plasma samples were concentrated and buffer-exchanged,by subjecting them to repeated(four times) centrifugation,to an appropriate0.5M triethylammonium bicarbonate(TEAB)pH8.5buffer(Sigma-Aldrich Corpora-tion,Saint Louis,MO,USA),for downstream analysis.The protein concentrations of whole and prefractionated plasma samples were determined using a FluoroProfile Protein Quantification Kit(Sigma-Aldrich Corporation),with BSA as the standard.2.5.1-Dimensional Gel Electrophoresis(1DGE).Equal amounts of proteins from crude and prefractionated plasma, obtained by immunoaffinity depletion or hexapeptide ligand library treatment,were diluted in an SDS loading buffer at 2mg/mL,and heated to100◦C for10minutes.The elution beads from one ProteoMiner column were then washed and directly boiled at100◦C for10minutes in a100μL SDS loading buffer.The proteins were then separated onto a home-made12.5%SDS-PAGE gel(16cm long and1.5mm thick),using a standard Laemmli buffer system in an SE 600Ruby electrophoresis unit(GE Healthcare,Chalfont Saint Giles,UK).Precision Plus Protein Standards(Bio-rad laboratories)were loaded in the molecular weight marker lane.The gel image was acquired on an Ettan DIGE Imager (GE Healthcare),after total proteinfluorescent poststaining with Deep Purple(excitation,532nm;emission610nm), according to the standard protocol(GE Healthcare).2.6.Protein Digestion and Peptide iTRAQ Labelling.100μg of protein solutions,at5mg/mL in0.5M TEAB,at pH8.5,were4human blood plasma aliquotsFigure1:Illustration of the workflow.reduced for1hour at60◦C with5mM tris-(2-carboxyethyl) phosphine(TCEP)and were cysteine-blocked with10mM methyl methanethiosulfonate(MMTS)at room temperature for10minutes.The proteins were then digested for40 hours at37◦C,by10μg of TPCK-treated trypsin,with CaCl2 (Applied Biosystems,Foster City,CA,USA).Each peptide solution was labelled for3hours at room temperature, using an iTRAQ reagent previously reconstituted in70μL of ethanol,according to the iTRAQ Reagents Multiplex Kit protocol(Applied Biosystems).The reaction was stopped by adding milliQ water,and the samples labelled,respectively, with114,115,116,and117mass-tagged iTRAQ reagents were combined according to the experimental protocol shown in Figure1.2.7.Peptide OFFGEL-IEF Fractionation.For pI-based pep-tide separation,the3100OFFGEL Fractionator(Agilent Technologies)was used with a24-well setup.Prior to electrofocusing,the peptide samples were desalted onto a Sep-Pak C18cartridge(Waters Corporation,Milford,MA, USA)and were resolubilized in3.6mL of5%(v/v)glycerol and1%(v/v)IPG buffer,at pH3–10(GE Healthcare).The 24cm-long IPG gel strips(GE Healthcare),with a3–10linear pH range,were rehydrated for15minutes according to the manufacturer’s manual.Then,150μL of sample was loaded in each of the24wells.Electrofocusing of the peptides was performed at20◦C until a level of50kVh was reached.After focusing,the24 peptide fractions were withdrawn and the wells were rinsed with200μL of a solution of milliQ water/methanol/formic acid(49/50/1).After15minutes,each of the rinsing solutions was pooled with its corresponding peptide fraction.Allof the fractions were evaporated by centrifugation undervacuum and were maintained at−20◦C.Just prior to nano-LC separation,the fractions were resuspended in20μL ofmilliQ water with0.1%(v/v)TFA.2.8.Nano-LC Separation.The peptide fractions were sep-arated on an Ultimate3000nano-LC system(DionexCorporation,Sunnyvalle,CA,USA),using a C18column(PepMap100,3μm,100A,75μm id×15cm,Dionex Corpo-ration)at aflow rate of300nL/minute.Buffer A comprised2%ACN in milliQ water,with0.05%TFA,and buffer Bcomprised80%ACN in milliQ water,with0.04%TFA.The peptide solutions werefirst desalted for3minutesusing buffer A only on the precolumn,and the separationoccurred over a period of70minutes,with the followinggradient:0to20%B in10minutes,20%to55%B in55minutes,and55%to100%B in5minutes.Chromatogramswere recorded at a wavelength of214nm.Following a12-minute run,the peptide fractions were collected for10seconds using a Probot microfraction collector(DionexCorporation),and spotted directly onto a MALDI sampleplate(1664spots per plate,Applied Biosystems).The CHCAmatrix(LaserBioLabs,Sophia-Antipolis,France),with aconcentration of2mg/mL in70%ACN,in milliQ water with0.1%TFA,was continuously added to the column effluent viaa micro“T”mixing piece at aflow rate of1.2μL/min.2.9.MALDI-MS/MS.MS and MS/MS analyses of offlinespotted peptide samples were performed using the4800MALDI-TOF/TOF Analyser(Applied Biosystems).Afterscreening of all LC-MALDI sample positions in MS-positivereflector mode,using1500laser shots,the fragmentationof automatically selected precursors was performed at acollision energy of1kV using air as the collision gas(pressure ∼2×10−6Torr).MS spectra were acquired between m/z 800and4000.The parent ion of the Glu-1fibrinopeptideat m/z1570.677,diluted in the matrix(3femtomoles perspot),was used for internal calibration.Up to12of themost intense ion signals per spot position,characterised byan S/N>12,were selected as precursors for MS/MS acqui-sition.Peptide and protein identifications were performedusing ProteinPilot Software v2.0(Applied Biosystems)andthe Paragon algorithm[12].Each MS/MS spectrum wassearched against the Uniprot/Swissprot database(release96,September2008)for Homo Sapiens species,with thefixed modification of methyl methanethiosulfonate-labelledcysteine parameter enabled.Other parameters such as thetryptic cleavage specificity,the precursor ion mass accuracyand the fragment ion mass accuracy,are MALDI4800built-in functions of the ProteinPilot software.The ProteinPilotsoftware calculated a confidence percentage(the unusedscore),which reflects the probability of a hit being a“falsepositive,”meaning that at the95%confidence level,thereis a false positive identification probability of about5%.While this software automatically accepts all peptides withan identification confidence level>1%,only proteins havingat least one peptide above the95%confidence level wereinitially recorded.Low confidence peptides cannot give a positive protein identification by themselves but may support the presence of a protein identified using other peptides with higher confidence.Searches against a concatenated database containing both forward and reversed sequences enabled the false discovery rate to be kept below1%.2.10.Data Analysis.In order to analyse the quality of pI fractionation after OFFGEL-IEF and MALDI-MS/MS identi-fication,the experimental pI was calculated for each peptide, using the pI/MW tool of the ExPASy Proteomic Server[13] checking all the deamidation modifications which could influence its value.Then,the average experimental pI of peptides(afterfiltering for false positive responses)was compared,for each of the24fractions,with the theoretical pH values provided by Agilent Technologies for24cm-long IPG gel strips with a3–10linear pH range.To study the relative abundance of proteins in the plasma, the MS/MS spectra,which enabled protein identification with at least2peptides,were counted for each protein [14].3.Results and DiscussionOur strategy for the study of the human plasma proteome was based on three-step fractionation.In thefirst step,the plasma samples were prefractionated using either an immun-odepletion method,or a peptide ligand library strategy.The proteins were then digested by trypsin,resulted peptides were iTRAQ-labelled and OFFGEL-fractionated in24fractions. Each fraction was then analysed by nano-LC on a C18 column(Figure1).3.1.Identification of Proteins.The experimental design for the iTRAQ labeling of proteins from the immunodepleted and bead-treated plasma was the same.The prefractionated plasma samples were concentrated and dissolved in the appropriate iTRAQ buffer using spin concentrators before the steps of reduction,MMTS blocking,digestion and iTRAQ labelling(Figure1).After OFFGEL separation of 400μg of iTRAQ-labelled peptides in24fractions,Protein Pilot software leads to the identification of332proteins in immunodepleted plasma and320proteins from the hexapeptide ligand library treated plasma(Figure2).The average experimental pH value of each OFFGEL fraction is indicated by a bar in Figure3.The theoretical pH values provided by the manufacturer are also shown by a dashed line.The pI value for each identified peptide was calculated using Bjellqvist’s algorithm[15],without taking the iTRAQ groups in the N-term position,and/or the lateral lysine chain,into ing these data,average pI values with standard deviations were calculated for all of the peptides identified in each fraction(Figure3).The average experimental pI value deviated from the theoretical pI value by an average error of±0.90,for both prefractionation strategies.From the immunodepleted plasma,243proteins were characterized by at least2peptides,and from the plasma treated by the hexapeptide bead library,228wereassociated Figure2:Venn diagram presenting the number of total plasma proteins identified with two or more peptides(in brackets,with one peptide)after immunoaffinity depletion of high-abundant proteins(IM),hexapeptide ligand library treatment(PM)or both prefractionationmethods.34567891011IsoelectricpointFraction numberIMPMFigure3:Analysis of the quality of the OFFGEL fractionation of iTRAQ-labelled peptides from immunoaffinity depleted(IM) or hexapeptide ligand library treated(PM)plasma.The average experimental pI of all peptides in each of the24OFFGEL fractions afterfiltering for false positive is presented as bars.Error bars indicate the SD of each fraction’s experimental pI.The broken line is based on the theoretical pI values for an IPG strip of24cm,pH 3–10provided by Agilent Technologies.with at least2peptides,suggesting that both prefractioning methods produced virtually the same number of identified proteins.Among these,158were common to both methods (Figure2).Nevertheless,in addition to these mutual pro-teins,85proteins(with at least2peptides)were identified by immunodepletion technology only,and70proteins(with at least2peptides)were identified by the equalizer strategy only,suggesting that these strategies are complementary.The merging of both sets of data allowed a total of313proteins with at least2peptides to be identified.Table1:Protein recovery after plasma prefractionation using immunoaffinity depletion of high-abundant proteins or hexapeptide ligand library treatment.Protein content was estimated using FluoroProfile Protein Quantification Kit.MARS-Hu14Proteo Miner Plasma volume loaded(μL)10×10depletion cycles900Protein quantity loaded(mg)(whole plasma:63mg/mL proteins)6,356,7Protein quantity recovered after prefractionation(μg)3404014152991420115811951572 Average protein quantity recovered after prefractionation(μg)364(or36.4μg per depletion cycle)1336%of total protein mass removal by prefractionation94.297.6A previous study conducted by us with400μg ofimmunodepleted plasma and treated in the same con-ditions,except iTRAQ labelling,showed115identi-fied proteins(Supplementary Material available online at doi:10.1155/2010/927917)in agreement with Song et al. results[10].This result demonstrated the efficiency of iTRAQ labelling for the peptide ionization and the protein identification according our previous study[11].3.2.Evaluation of the First Prefractioning Step.The human plasma was prefractionated using two different methods:an immunodepletion strategy on a human MARS-14,which depleted the14most abundant plasma proteins,and a peptide ligand library technology with a ProteoMiner column,which should“equalize”the plasma proteins. The reproducibility of these approaches was evaluated by four independent experiments(Figure1).The total protein content was used to investigate reproducibility and pro-tein recovery(Table1).The total protein concentration of untreated plasma was63mg/mL.The mean recovery rate of the eluted bead-treated plasma was2.4%(in agreement with previous results[8]),compared with5.8%following the depletion process using the MARS-14column.Both methodologies showed a reproducibility of around15%,in the determination of total protein content.For each approach,reproducibility of biological experi-ment and iTRAQ labelling were evaluated with the coefficient variation calculation from ProteinPilot results.An average of±18%variation across the4experiments with the human MARS-14strategy and an average of±11%with the ProteoMiner approach were exhibited.Separation of native plasma,immunodepleted,and bead-treated plasma samples by SDS-PAGE revealed a significant reduction in the dynamic range of protein concentration in the treated fractions,when compared with native plasma(Figure4)but did not identify one prefractionation method as being superior to the other. Comparison of the20most abundant proteins evaluated by the MS/MS spectra counting technique indicated that both prefractioning methods were equivalent,in terms of the estimated protein concentrations(Figure5).With ProteoMiner treatment,fibrinogen alpha and beta chains were the most commonly found proteins,thus suggesting that this technique could be more suitable for serum samples.3.3.Gene Ontology Annotations.Gene ontology analysis for cellular localization revealed that a largeproportion152025375075100150250MW(kDa)1234MW(kDa)Figure4:1DGE analysis of human blood plasma before and after prefractionation using immunoaffinity depletion of high-abundant proteins or hexapeptide ligand library treatment.An equal amount of proteins from whole plasma(lane1),plasma fraction from MARS-Hu14(lane2),plasma fraction from ProteoMiner(lane 3),and potentially remaining proteins from boiled in SDS loading buffer ProteoMiner beads after elution(lane4)were separated on 12.5%SDS-PAGE gel and were stained with Deep Purple.of proteins,with predicted extracellular locations(33%) (Figure6(b))was present in our plasma proteome map. Functional classification also revealed that most of the proteins are involved in“binding”and enzyme activities (Figure6(a)).Among these proteins,we identified medium con-centrated proteins with concentrations ranging around 30ng/mL,such as P-selectin,cadherin5[16,17](Table2). We successfully detected the low-concentrated proteins Hep-atocyte growth factor activator,insulin like growth factor binding-protein2and Sex hormone-binding globulin in the both experiments with ProteinPilot unused score>2 (proteins identified with at least2peptides;95%confidence). The literature data showed that the concentration of these proteins was in the range of8–18ng/mL[16,18,19]. Compared with the concentration of the most abundantF i b r i n o g e n b e t a F i b r i n o g e n a l p h a B e t a -1B -g l y c o p r o t e i n F i b r o n e c t i n F e r r o x i d a s e A p o l i p o p r o t e i n A -I A p o l i p o p r o t e i n B -100 C o m p l e m e n t C 4-B p r e c u r s o r P r o t h r o m b i n A p o l i p o p r o t e i n A -I p r e c u r s o r F i b r o n e c t i n p r e c u r s o r V i t r o n e c t i n F i b r o c y s t i n -L C o m p l e m e n t C 3 S e r o t r a n s f e r r i n p r e c u r s o r V i t a m i n D -b i n d i n g p r o t e i n p r e c u r s o r A p o l i p o p r o t e i n A -I V A l p h a -1B -g l y c o p r o t e i n P r o t h r o m b i n p r e c u r s o r C o m p l e m e n t f a c t o r H p r e c u r s o r C l u s t e r i n A l p h a -2-H S -g l y c o p r o t e i n p r e c u r s o r A n t i t h r o m b i n -I I I p r e c u r s o r (A T I I I )C o m p l e m e n t f a c t o r B p r e c u r s o r P l a s m i n o g e n p r e c u r s o r C 4b -b i n d i n g p r o t e i n a l p h a c h a i n A p o l i p o p r o t e i n A -I V p r e c u r s o r I n t e r -a l p h a -t r y p s i n i n h i b i t o r h e a v y c h a i n A p o l i p o p r o t e i n B -100 K i n i n o g e n -1 p r e c u r s o r C o m p l e m e n t f a c t o r H P l a s m i n o g e n S e r u m a l b u m i n H i s t i d i n e -r i c h g l y c o p r o t e i n p r e c u r s o r A l p h a -1-a n t i c h y m o t r y p s i n p r e c u r s o r A l p h a -2-H S -g l y c o p r o t e i n A p o l i p o p r o t e i n E H i s t i d i n e -r i c h g l y c o p r o t e i n C e r u l o p l a s m i n I g k a p p a c h a i n V -I r e g i o n A50010001500200025003000M S /M S s p e c t r a l c o u n t sIM PMFigure 5:Relative abundance of the twenty most abundant proteins determined by counting the number of MS/MS spectra for each protein identified after immunoa ffinity depletion (IM)or Hexapeptide ligand library treatment (PM)ofplasma.Structural moleculeTransferase Molecular activity 5%Hydrolase Enzyme regulator activity 7%Signal transducer activity 5%Binding 45%(a)Membrane 12%33%24%19%(b)Figure 6:Pie charts showing the gene ontology classification of identified plasma proteins according to cellular function (a)and to cellular component category (b).plasma protein (HSA)which is around 50mg ·mL,we can conclude that the dynamic range to detect low-abundant plasma proteins could be extended to 106-107.4.ConclusionsThe number of proteins which could be identified in 400μg of plasma proteins was markedly increased in this study,when compared to similar samples studied without iTRAQ labelling,or with iTRAQ labelling,but without OFFGEL fractionation [10],suggesting that our strategy improved the proteome coverage of human plasma.The limited number of individual proteins identified in this study,despite prefractionation,highlights the challenge of plasma-based biomarker discovery.From our experience,similar iTRAQ analyses of cellular extracts are able to identifyTable2:Some of the weakly abundant plasma proteins identified with two or more peptides after immunoaffinity depletion of high-abundant proteins(IM)or hexapeptide ligand library treatment(PM).Associated concentrations were found in the literature in the mid-range from5to50000ng/mL serum or plasma.Protein name Prefractionation Concentration Bibliographic referencePlasma retinol-binding protein PM44.4μg/mL(plasma)[20] IMKallistatin IM22.1μg/mL(plasma)[21] Ficolin-3IM21.6μg/mL(plasma)[22] Tetranectin IM13.75μg/mL(serum)[23] Selenoprotein P IM 5.3μg/mL(plasma)[24]Von Willebrand factor PM1.3μg/mL(plasma)[16] IMIntelectin-1PM0.1to1.0μg/mL(serum)[25]Extracellular SOD PM150ng/mL(plasma)[26] IMMannose-binding lectin(Protein C)IM97ng/mL(plasma)[16]Insulin-like growth factor binding protein3PM59ng/mL(plasma)[16] IMP-selectin IM30ng/mL(plasma)[17]Cadherin5PM30ng/mL(plasma)[16] IMMacrophage colony-stimulating factor1receptor(M-CSF R)IM26ng/mL(plasma)[16]Hepatocyte growth factor activator PM17.6ng/mL(serum)[18] IMInsulin-like growth factor binding protein2PM15ng/mL(plasma)[16] IMSex hormone-binding globulin PMto8.1ng/mL(plasma)[19] IMmore than1000different proteins[11].Clearly,the presence of many highly abundant proteins in human plasma and therefore,after trypsin digestion,the presence of many highly concentrated peptides prevent a good MALDI ionization of weak-concentrated peptides and therefore limit the depth of analysis.Theses results argue in favour of the search for new strategies for the removal of abundant plasma proteins[27], or for the enrichment of less abundant proteins,in order to facilitate the efficient discovery of biomarkers. AcknowledgmentThis research was supported by a grant from“La Ligue Nationale Contre le Cancer”(Equipe labellis´e e). References[1]A.J.Rai,C.A.Gelfand,B.C.Haywood,et al.,“HUPO plasmaproteome project specimen collection and handling:towards the standardization of parameters for plasma proteome sam-ples,”Proteomics,vol.5,no.13,pp.3262–3277,2005.[2]G.S.Omenn,D.J.States,M.Adamski,et al.,“Overviewof the HUPO plasma proteome project:results from the pilot phase with35collaborating laboratories and multiple analytical groups,generating a core dataset of3020proteinsand a publicly-available database,”Proteomics,vol.5,no.13, pp.3226–3245,2005.[3]D.E.Misek,R.Kuick,H.Wang,et al.,“A wide range of proteinisoforms in serum and plasma uncovered by a quantitative intact protein analysis system,”Proteomics,vol.5,no.13,pp.3343–3352,2005.[4]H.Tammen,I.Schulte,R.Hess,et al.,“Peptidomic analysis ofhuman blood specimens:comparison between plasma spec-imens and serum by differential peptide display,”Proteomics, vol.5,no.13,pp.3414–3422,2005.[5]N.Zolotarjova,J.Martosella,G.Nicol,J.Bailey,B.E.Boyes,and W.C.Barrett,“Differences among techniques for high-abundant protein depletion,”Proteomics,vol.5,no.13,pp.3304–3313,2005.[6]Y.Y.Wang,P.Cheng,and D.W.Chan,“A simple affinityspin tubefilter method for removing high-abundant common proteins or enriching low-abundant biomarkers for serum proteomic analysis,”Proteomics,vol.3,no.3,pp.243–248, 2003.[7]P.G.Righetti,E.Boschetti,L.Lomas,and A.Citterio,“Proteinequalizer technology:the quest for a“democratic proteome”,”Proteomics,vol.6,no.14,pp.3980–3992,2006.[8]C.Sihlbom,I.Kanmert,H.von Bahr,and P.Davidsson,“Evaluation of the combination of bead technology with SELDI-TOF-MS and2-D DIGE for detection of plasma proteins,”Journal of Proteome Research,vol.7,no.9,pp.4191–4198,2008.。
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Ha
Hb
Hc
Hd
A-F群多价 A-F群多价
Hi
Vi
O
O
O抗原、H抗原和Vi抗原
❖ O抗原:是一种沙门氏菌细胞壁表面的 耐热多糖抗原 ，100 ℃，2.5 h不被破 坏，它的特异性依赖于细胞壁脂多糖 侧链多糖的组成。
9,12,Vi 3,10 11
原 H
a:b:1,2 f,g:i:1,2
c:1,5
d:e,h:1,6
i:1,2
位相变异的解释
❖ 将一个双相沙门氏菌的菌株在琼脂平板上划线分离，所得的 菌落中，有的有第1相H抗原，有的则有第2相H抗原。若任 意挑取一个菌落在培养基上多次传代，其后代又可出现部分 是第1相而另一部分是第2相的菌落。这种两个相的H抗原可 以交相产生的现象称为位相变异。
小鼠血清IL-10测定实验步骤
小鼠血清IL-10测定实验步骤小鼠血清IL-10测定实验步骤一、试剂的准备：浓缩的缓冲液应该在实验前拿到室温下并且在实验开始前进行稀释。
如果缓冲液中有沉淀，可以缓慢的加温直到沉淀完全溶解。
1、Wash buffer：20倍的wash buffer concentrate 用蒸馏水稀释成1倍的，溶质：溶剂=1:19轻柔混匀，避免产生泡沫。
PH值7.4,储存在2~25℃，有效期30天。
2、Assay buffer：20倍的assay buffer concentrate 用蒸馏水稀释成1倍的，溶质：溶剂=1:19轻柔混匀，避免产生泡沫。
储存在2~8℃，有效期30天3、Biotin-conjugate:（注意Biotin-conjugate应该在稀释后的30分钟内使用完）用已经配好的1倍的assay buffer 把Biotin-conjugate 稀释100倍，溶质：溶剂=1:99Biotin-conjugate.颜色标记为绿色：1ml assay buffer（1×）加10μl Green-Dye4、Streptavidin-HRP:（注意Streptavidin-HRP应该在稀释后的30分钟内使用完）用已经配好的1倍的assay buffer 把Streptavidin-HRP 稀释200倍，溶质：溶剂=1:199Streptavidin-HRP.颜色标记为红色：1ml assay buffer（1×）加4μl Red-Dye5、Mouse IL-10 standard标准品的稀释可以直接在微孔板上进行，共7个孔标记为S1—S7,S1---S7放标准品，最后一孔做空白对照，具体操作见后面步骤4。
Mouse IL-10 standard.颜色标记为蓝色：1ml Sample Diluent 加4μl Blue-Dye二、实验操作过程1、计算使用微孔的数量，包括要检测的样品、标准品、对照所用的微孔，每一个样品、标准品、空白对照应该做两个孔，一式两份。
脑出血患者血清生物标志物的检测及应用
脑出血患者血清生物标志物的检测及应用作者：郭宗培马大程任胜辉郑玲娟王娜杜威来源：《中国现代医生》2019年第02期[摘要] 血清生物標志物（serum biological markers）是存在于血清中的生物物质，作为一种标识反映正常的生物学机能、病理过程及治疗干预后的效果，具有可以被客观测量和评估的特性。
在脑出血患者的病情演变、急救处理、疗效评估和康复预后等方面起着重要的作用，它们在不同时期特异性的表达或分泌，而在正常患者中不表达或不分泌。
本文就近年来开展的血清生物标志物在脑出血中的检测及应用作一综述。
[关键词] 脑出血;血清生物标志物;检测;应用[中图分类号] R743.34; ; ; ; ; [文献标识码] A; ; ; ; ; [文章编号] 1673-9701（2019）02-0164-05脑出血作为一种高致残率、高致死率的疾病，已然成为中国居民健康的一大杀手，及时准确的诊断并积极开展后续抢救治疗至关重要，但因该疾病发展迅速，不确定性因素众多，许多患者仍遗留神经功能障碍及意识障碍[1]，生活质量严重下降。
如若病情进展期不能及时发现并进行药物处理及手术干预，其疗效和后期康复就尤为被动，多数患者可出现记忆力下降、反应能力差、情感障碍、逻辑分析及计算能力降低等[2，3]。
虽然现代医学发展日新月异，各种手段层出不穷，但临床医师的丰富经验对患者病情演变的判断至关重要，在病情急剧恶化时，准确有效的判断及处理仍是不可替代的，这样观点上的分歧就自然而然的产生。
所以，除去医师在人为主观上的判定，研究并开发一种具有简单、易行、外界影响小且客观等优点的血清生物标志物势在必行。
患者脑出血后，受责任血管支配的脑细胞急性受损，占位效应使颅内压增高，周围组织压迫水肿、血管痉挛，血液循环受阻，血脑屏障遭到破坏，不可预知的生化代谢反应释放多种血管活性产物，引发一系列的炎症和生化的级联反应，细胞毒性反应明显;早期负反馈调节血压增高，后期颅内压增高，颅内代偿空间减少，颅内缺血、缺氧明显，血压下降、电解质紊乱，脑细胞受损不可逆。
唐氏综合征产前筛查
采用酶联免疫技术（ELISA），加样孔包被抗体，样品 初次孵育捕获抗原，然后与偶合有辣根过氧化物酶的 第二种抗体再次孵育，通过检测显色深度，得出该标 记物的浓度。
操作简单、可作定 量、较难质控
化学发 光法
将灵敏的化学发光或生物发光体系与特异的抗原抗体免 疫测定结合在一起，由于化学反应产生的电子能级处 于激发态，通过跃迁释放能量产生光子，导致发光现 象，直接通过发光反应测定标记物。
周血，测定血清学指标，并计算出风险，解释筛查报告； 5、对高风险人群进展遗传咨询，对同意介入性产前诊断者行羊膜腔穿刺、细胞培养与染色体核
型分析； 6、随访妊娠结局； 7、产前筛查诊断工作流程图〔见以下图〕
产前筛查知情同意原那么
知情 孕妇对所患疾病的发病率理解 孕妇对所患疾病的诊疗方法理解 孕妇对所患疾病的预防措施理解
根据中国优生科学协会推算，平均20分钟就 有1例“唐氏综合征〞患儿出生，每年约有 26600个唐氏患儿出生。目前我国大约有60 万以上的唐氏综合征患者。
目前唯一有效可行的措施是进展DS的 产前筛查(简称唐氏筛查)
早期发现,早期诊断,及时终止妊娠合征的遗传学和发病机理
产前诊断
产前诊断又称宫内诊断，是基于近代生物化 学、分子生物学、细胞遗传学和临床医学理 论开展而新兴起来的一门学科。采用B超声波 诊断、绒毛细胞培养、羊水细胞培养、胎血 细胞培养、生化分析、基因分析等技术，对 子宫内的胎儿状况作出诊断，以便对异常胎 儿进展选择性流产，防止畸形儿的出生。
我国是出生缺陷高发国家之一，每年出生缺 陷新生儿约占出生人口总数的4-6％，一年 新增的残疾儿童总数高达80-120万。
血清标志物产前筛查相关内容
唐氏产前筛查开展史 唐氏产前筛查血清筛查检测方法
血清肿瘤标志物联合检测在恶性肿瘤诊断中的临床应用
血清肿瘤标志物联合检测在恶性肿瘤诊断中的临床应用【摘要】恶性肿瘤是一种严重的疾病，其发病率与死亡率持续上升，给人们的健康带来严重威胁。
血清肿瘤标志物在恶性肿瘤诊断中扮演着重要角色，其联合检测能够提高诊断准确性和早期发现率。
本文将重点探讨多个血清肿瘤标志物联合检测的优势，结合临床应用实例进行详细阐述。
文章也会指出该方法的局限性和面临的挑战，以及展望血清肿瘤标志物联合检测在恶性肿瘤诊断中的前景。
本文还将探讨未来进一步研究的方向，为临床实践提供更多有益参考。
【关键词】关键词：血清肿瘤标志物、恶性肿瘤、联合检测、临床应用、诊断、发病率、死亡率、优势、实例、局限性、挑战、前景、研究方向1. 引言1.1 背景介绍恶性肿瘤是一种严重威胁人类健康的疾病，其发病率与死亡率不断上升成为全球性的公共卫生问题。
据统计数据显示，恶性肿瘤已成为引发死亡的主要原因之一，造成了许多家庭的不幸和痛苦。
随着社会的发展和生活水平的提高，人们对健康问题的关注也越来越高，恶性肿瘤的防治工作备受关注。
在恶性肿瘤的诊断过程中，血清肿瘤标志物的检测起着至关重要的作用。
血清肿瘤标志物是指被恶性肿瘤细胞合成、分泌或脱落到血液中的具有特异性的分子，通过检测这些标志物的水平可以帮助医生判断肿瘤的性质、位置和分期情况，从而为患者提供更准确的诊断和治疗方案。
血清肿瘤标志物在恶性肿瘤诊断中具有重要的临床意义。
随着科技的进步和研究的不断深入，目前已有多个血清肿瘤标志物可以用于联合检测，从而提高诊断的准确性和灵敏度。
多个标志物的联合检测可以综合考虑各个标志物的特异性和敏感性，减少单一标志物检测可能存在的误诊和漏诊情况，为临床医生提供更全面的信息，有助于提高恶性肿瘤的早期诊断率和治疗效果。
1.2 研究目的血清肿瘤标志物联合检测在恶性肿瘤诊断中的临床应用，是为了提高恶性肿瘤的早期诊断率和准确性，在治疗和监测疗效过程中发挥重要作用。
通过本文的研究，旨在探讨血清肿瘤标志物联合检测的优势和局限性，探讨其在恶性肿瘤诊断中的实际应用情况及未来发展趋势。
生物标志物开发流程2023
生物标志物开发流程2023生物标志物的开发流程通常包括以下几个阶段：1. 研究设计阶段，在这个阶段，研究人员会根据疾病或健康状况的需求，确定研究的目标和假设。
他们会进行文献综述，收集相关信息，并制定研究方案。
2. 样本采集和处理阶段，在这个阶段，研究人员会收集样本，例如血液、尿液、组织等，以获取生物标志物。
采集的样本需要经过处理和净化，以去除干扰物质并保持生物标志物的稳定性。
3. 生物标志物筛选和验证阶段，在这个阶段，研究人员会使用各种技术和方法对样本中的生物标志物进行筛选和验证。
常用的方法包括基因表达分析、蛋白质组学、代谢组学等。
筛选出的候选生物标志物需要经过进一步的验证，确保其与特定疾病或健康状况之间的关联性。
4. 生物标志物开发和验证阶段，在这个阶段，研究人员会使用更大规模的样本集合来验证和评估生物标志物的准确性和可靠性。
他们会进行统计分析，建立模型，并评估生物标志物的敏感性、特异性、阳性预测值等指标。
5. 临床应用和验证阶段，在这个阶段，研究人员会将开发出的生物标志物应用于临床实践中，并进行临床验证。
他们会与医生和患者合作，评估生物标志物在疾病诊断、预后评估、治疗反应监测等方面的应用效果。
6. 上市和监管阶段，如果生物标志物被证明在临床实践中具有一定的应用价值，研究人员可以将其提交给监管机构，如药品监管机构或医疗器械监管机构，进行上市申请或注册。
监管机构会对生物标志物的安全性和有效性进行评估，并根据评估结果做出决定。
总之，生物标志物的开发流程是一个复杂而系统的过程，需要经过多个阶段的研究和验证。
这个过程需要多学科的合作，包括生物学、医学、统计学等领域的专家。
通过不断的研究和验证，我们可以开发出更准确、可靠的生物标志物，为疾病的早期诊断和治疗提供更好的支持。
生物标志物采集流程及注意事项
生物标志物采集流程及注意事项下载温馨提示:该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。
文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by theeditor. I hope that after you download them,they can help yousolve practical problems. The document can be customized andmodified after downloading,please adjust and use it according toactual needs, thank you!In addition, our shop provides you with various types ofpractical materials,such as educational essays, diaryappreciation,sentence excerpts,ancient poems,classic articles,topic composition,work summary,word parsing,copy excerpts,other materials and so on,want to know different data formats andwriting methods,please pay attention!生物标志物是生物体内可以用来指示生理、病理状态或生物过程的特异性分子，它们在临床医学、疾病诊断、健康监测等领域具有重要应用价值。
疾病标志物的挖掘与鉴定方法
疾病标志物的挖掘与鉴定方法疾病是人类健康的大敌，因此，对于许多疾病的早期诊断与及时干预尤为重要。
这就需要找到疾病的标志物，通过疾病标志物的检测方法，实现对疾病的早期诊断与监测，从而提高诊疗效果和治疗效率。
那么，什么是疾病标志物呢？简单地说，疾病标志物是指在特定的生理或病理条件下，能够产生显著变化或与疾病发生相关的生物分子。
例如，存在于人体血液、尿液和唾液中的一些特定蛋白质、核酸或代谢产物等，都可以成为疾病标志物的候选者。
疾病标志物的挖掘与鉴定方法可以分为以下几种：1. 基于代谢组学的疾病标志物挖掘代谢组学是指研究生物体代谢物质组成及其在生理和病理状态下的变化规律的学科。
通过代谢组学技术，可以鉴定出与特定疾病发生相关的代谢产物，并作为潜在的疾病标志物进行进一步的研究。
代谢组学技术主要包括质谱和核磁共振等，这些技术可以使我们在不影响生物体正常代谢的同时，检测到微量代谢物质的变化，从而提高了疾病标志物的发现率和准确性。
2. 基于蛋白组学的疾病标志物挖掘蛋白组学是指对生物体内的所有蛋白质进行全面和系统性的研究。
通过蛋白组学技术，可以发现众多与疾病发生相关的蛋白质，并作为潜在的疾病标志物进行研究。
蛋白组学技术主要包括二维凝胶电泳、液质联用和蛋白质芯片等。
这些技术可以使我们快速、高通量地鉴定蛋白质样本中的成分，从而提高了疾病标志物的筛选效率和鉴定准确性。
3. 基于基因组学的疾病标志物挖掘基因组学是指研究生物体基因组组成及其在生理和病理状态下的变化规律的学科。
通过基因组学技术，可以鉴定出与疾病发生相关的基因表达水平变化，并作为潜在的疾病标志物进行进一步的研究。
基因组学技术主要包括芯片技术和高通量测序技术等。
这些技术可以帮助我们快速、高通量地检测生物体中的基因组组成，从而发现与疾病相关的基因变化，并为疾病标志物研究提供有力的支持。
总之，疾病标志物的挖掘与鉴定方法是一个多学科交叉的领域，需要结合代谢组学、蛋白组学、基因组学等多个方面的技术手段，并结合临床实践，进行系统性的研究和验证。
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HuProt™人类全蛋白质组芯片用于全局性的
血清自身抗体标志物的发现研究
技术背景：
血清标志物的发现对于早期、及时、准确的疾病诊断至关重要。
利用蛋白质组芯片进行全局性、系统性地筛查对于特定疾病的生物标志物是一项有着巨大价值的研究工作。
HuProt TM人类蛋白质组芯片包含~20,000个人源重组蛋白质，是目前世界上通量最高的蛋白质芯片。
该芯片适用于全局性地进行蛋白-蛋白互作、蛋白-核酸互作、蛋白-小分子互作以及翻译后修饰研究，同时适用于系统性地发现肿瘤相关抗原的研究，以发现可用于诊断或其它表征的标志物。
实验原理：
在血清标志物研究中，样本包括实验组和对照组，分别独立地与芯片反应，以检测实验组中针对某一种或几种抗原蛋白，具有普遍性的特异性的抗体（IgG或IgM），这些抗原蛋白可以作为诊断标志物来表征疾病状态。
特异性的抗体（包括IgG、IgM或其它类型抗体）与固定于芯片上的蛋白进行结合，清洗去除未结合的抗体和其它蛋白质，再用抗人IgM荧光标记二抗（cy5标记，呈现红色）和抗人IgG荧光二抗（cy3标记，呈现绿色）检测，通过荧光扫描仪读取信号，信号的强弱与抗体的亲和力和数量呈正相关。
实施方案举例：
第一步，初筛：低成本探索性实验
——初步结果可以用来申请课题；
使用涵盖~20,000个人类重组蛋白的HuProt TM Proteome Microarray（组芯片）进行初步筛选，以发现具有一定差异的潜在的自身抗原。
具体方案：
疾病组：10－30例；要求：典型的病例样本；明确的临床诊断结论；样本保存完好（－80度保存）；完善度临床信息（如用药或治疗情况等）；
健康对照组：各10-30例；要求：整体的性别、年龄同疾病组相当；样本保存完好。
数据分析：
因样本量较少，因此以较低的标准对潜在标志物进行选择：P值<0.05; 或阳性率在疾病组一致性或显著高于健康对照组。
潜在标志物个数控制在200个蛋白以内。
第二步，验证：高效率，大样本，低单价验证
——基本数据可以很快发篇文章，申请若干专利；
基于第一部分的数据，依照潜在标志物列表，定制芯片（focused Array）。
然后用更大规模的样本进行统计学验证。
具体：
疾病组：>100例；
健康组：>100 例；
疾病对照组：>100例；
以上各组整体的性别、年龄同疾病组相当；样本保存完好。
每12例样本对应1张芯片（12个重复阵列），并获得所有响应数据。
数据分析：
对上述大量样本的响应值进行分析，依据P value和阳性率评价潜在标志物的区分能力，并绘制boxplot，Heatmap图等统计可视化分析；另外，构建区分函数，选定一组（或一个）联合诊断标志物组，通过ROC曲线评价其特异性和灵敏度。
第三步，临床测试：严格的诊断指标评价
——进一步诊断试剂的开发，巨大商业价值
基于前两部分，制定基于联合诊断标志物的诊断芯片或基于ELISA等经典方法，进行进一步验证，开发商业试剂盒。
同时，基于发现的自身抗原，进一步发掘其致病机理，贡献于基础研究，甚至于药物开发。