BioNoculars Extracting Protein-Protein Interactions from Biomedical Text
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Protein A/G免疫沉淀磁珠Figure 1. General Protocol for ImmunoprecipitationcomplexSDS-PAGE loading buffer Neutralize bufferMagnetic Beads antibodyMagnetic Separator Remove supernatant Pipette Repeat45产品组分产品参数:磁珠粒径100 nm,浓度10 mg/mL,结合量>400 μg human IgG/mL2-8℃保存,保质期2年。
储存方法实验步骤1. 抗原样品制备本操作说明书提供以下三种样品处理方法。
2. 磁珠预处理将磁珠漩涡振荡1 min,使其充分混悬;取25~50 µL磁珠悬液置于1.5 mL EP管中。
加入200 µL结合缓冲液洗涤,进行磁性分离(将离心管置于磁力架上,管底对准①卡口压紧,静置2分钟或待磁珠吸附于管壁),吸弃上清。
抽出②磁条,加入200 µL结合缓冲液重复洗涤一次,插回②磁条,磁性分离并吸弃上清。
加入200 µL结合缓冲液重悬磁珠备用。
血清样品处理:若目标蛋白丰度较高, 建议用结合缓冲液稀释血清样品至目标蛋白终浓度为10~100 µg/mL,置于冰上备用(或置于-20℃长期保存)。
悬浮细胞样品处理:离心收集细胞(4℃, 500 g, 10 min),弃上清后称重,按每毫克细胞50 µL的比例用1×PBS洗涤2次;按每毫克细胞5~10 µL的比例加入结合缓冲液,同时加入蛋白酶抑制剂,混匀后置于冰上处理10 min;离心收集上清液(4℃, 14000 g, 10 min),置于冰上备用(或置于-20℃长期保存)。
贴壁细胞样品处理:移去培养基,按每1.0×105个细胞150 µL的比例用1×PBS洗涤两次;用细胞刮棒刮脱细胞,收集至1.5 mL EP管内,按每1.0×105个细胞20~30 µL的比例加入结合缓冲液,同时加入蛋白酶抑制剂,混匀后置于冰上处理10 min;离心收集上清液(4℃, 14000 g, 10 min),置于冰上备用(或置于-20℃长期保存)。
桑黄提取物体内抗肿瘤作用的实验研究
实验采用小鼠肺癌细胞C126的体内植入瘤模型。
将小鼠随机分为对照组和实验组,实验组分为三个剂量组,每组6只小鼠。
实验组给予桑黄提取物不同剂量的腹腔注射,每周
注射三次,连续注射4周,对照组给予等量的生理盐水腹腔注射。
观察小鼠体重变化、肝
和肾功能及脾脏、心脏和肺组织病理学变化情况。
同时通过免疫细胞检测、MTT法和流式
细胞仪检测桑黄提取物的抗肿瘤作用。
结果显示,桑黄提取物能够抑制小鼠肺癌细胞的生长,具有显著的抗肿瘤作用。
其中,高剂量组的肿瘤抑制率最高,为61.2%。
在瘤组织中观察到大量的细胞凋亡和坏死现象,
说明桑黄提取物具有诱导肺癌细胞凋亡的作用。
同时,实验组小鼠的体重、肝和肾功能、
脾脏、心脏和肺组织都没有明显的毒副作用和病理学变化。
此外,桑黄提取物还能够明显提高小鼠免疫系统的活性,增强机体的免疫功能。
MTT
法的结果表明,桑黄提取物对小鼠淋巴细胞增殖有促进作用。
流式细胞仪检测显示,桑黄
提取物能够提高小鼠血清中白细胞、淋巴细胞和NK细胞的数量,同时减少调节性T细胞的数量。
综上所述,桑黄提取物具有良好的抗肿瘤作用,能够抑制小鼠肺癌细胞的生长,同时
具有提高小鼠免疫系统活性的作用。
本研究结果为桑黄的临床应用提供了一定的科学依据
和实验基础。
《国际商务标准水飞蓟提取物》编制说明1 任务来源本标准的制定工作,是由中国医药保健品进出口商会提出而进行的,国际商务标准植物提取物编号为WM 。
本标准由长沙康隆生物制品有限公司与盘锦天源药业有限公司共同起草。
2 标准制定的意义水飞蓟提取物是由菊科植物水飞蓟(Silybum marianuml(L.)Gaertn.) 的干燥成熟果实中提取得到的黄酮类化合物,主要包括水飞蓟亭、水飞蓟宁、水飞蓟宾、异水飞蓟宾等。
水飞蓟宾(Silybin) 为主要有效成分, 包括水飞蓟宾A和水飞蓟宾B。
溶于丙酮、乙酸乙酯、甲醇及乙醇,不溶于水。
它具有保护肝脏、改善肝功能、增强肝细胞再生等作用,对急慢性肝炎、肝硬化及代谢中毒性肝损伤等均有较好疗效。
水飞蓟提取物含量的测定主要按水飞蓟宾计。
水飞蓟提取物是用于治疗肝脏疾病的常用药物。
药理作用表明水飞蓟提取物主要是通过限制ROS(reactive oxygen species)的活性来实现其治疗作用的,也常被用于保健食品中。
美国药典USP35–NF30 中水飞蓟提取物(Powdered Milk Thistle Extract)和欧洲药典(European pharmacopoeia 7.0)中水飞蓟提取物(MILK THISTLE DRY EXTRACT,REFINED AND STANDARDISED)中都有关于水飞蓟提取物的技术指标及检验标准,《中华人民共和国药典》2010版一部上收录了水飞蓟标准,但是国内并没有关于水飞蓟提取物的完善标准依据。
当前外贸出口贸易中的食品安全形式十分严峻,为保障国家外贸经济运行的安全和我国人民群众的食品卫生安全,加强标准建设,促进与国际水飞蓟提取物标准接轨,及时建立水飞蓟提取物国际商务标准的国内质控标准具有重要的现实意义。
3 标准编写规则本标准遵循GB/T1.1-2009《标准化工作导则第1部分:标准的结构和编写规则》;GB/T20001.2-2001《标准化工作指南第2部分:采用国际标准的规则》和GB/T20001.4-2001《标准编写规则第4部分:化学分析方法》规则编写。
植物组织蛋白质提取方法1、植物组织蛋白质提取方法1、根据样品重量(1g样品加入3.5ml提取液,可根据材料不同适当加入),准备提取液放在冰上。
2、把样品放在研钵中用液氮研磨,研磨后加入提取液中在冰上静置(3-4小时)。
3、用离心机离心8000rpm40min4℃或11100rpm20min4℃4、提取上清夜,样品制备完成。
蛋白质提取液:300ml1、1Mtris-HCl(PH8)45ml2、甘油(Glycerol)75ml3、聚乙烯吡咯烷酮(Polyvinylpolypyrrordone)6g这种方法针对SDS-PAGE,垂直板电泳!2、植物组织蛋白质提取方法三氯醋酸—丙酮沉淀法1、在液氮中研磨叶片2、加入样品体积3倍的提取液在-20℃的条件下过夜,然后离心(4℃8000rpm 以上1小时)弃上清。
3、加入等体积的冰浴丙酮(含0.07%的β-巯基乙醇),摇匀后离心(4℃8000rpm以上1小时),然后真空干燥沉淀,备用。
4、上样前加入裂解液,室温放置30分钟,使蛋白充分溶于裂解液中,然后离心(15℃8000rpm 以上1小时或更长时间以没有沉淀为标准),可临时保存在4℃待用。
5、用Brandford法定量蛋白,然后可分装放入-80℃备用。
药品:提取液:含10%TCA和0.07%的β-巯基乙醇的丙酮裂解液:2.7g尿素0.2gCHAPS溶于3ml灭菌的去离子水中(终体积为5ml),使用前再加入1M的DTT65ul/ml。
这种方法针对双向电泳,杂质少,离子浓度小的特点!当然单向电泳也同样适用,只是电泳的条带会减少!3、组织:肠黏膜目的:WESTERN BLOT检测凋亡相关蛋白的表达应用TRIPURE提取蛋白质步骤:含蛋白质上清液中加入异丙醇:(1.5ml每1mlTRIPURE用量)倒转混匀,置室温10min离心:12000 g,10min,4度,弃上清加入0.3M盐酸胍/95%乙醇:(2ml每1mlTRIPURE用量)振荡,置室温20min离心:7500g,5 min,4度,弃上清重复0.3M盐酸胍/95%乙醇步2次沉淀中加入100%乙醇2ml充分振荡混匀,置室温20 min离心:7500g,5min,4度,弃上清吹干沉淀1%SDS溶解沉淀离心:10000g,10min,4度取上清-20度保存(或可直接用于WESTERN BLOT)存在的问题:加入1%SDS后沉淀不溶解,还是很大的一块,4度离心后又多了白色沉定, SDS结晶?测浓度,含量才1mg/ml左右。
网络出版时间:2024-01-1010:58:40 网络出版地址:https://link.cnki.net/urlid/34.1086.R.20240108.1831.038◇网络药理学◇液相色谱-质谱联用技术分析秦巴硒菇提取物活性成分及其治疗慢性粒细胞白血病的网络药理学研究王东萍1,4,葛万文2,邵 晶3,孙延庆1,4(1.甘肃中医药大学中西医结合学院,甘肃兰州 730000;2.兰州大学第二医院,甘肃兰州 730030;3.甘肃中医药大学药学院,甘肃兰州 730000;4.甘肃省人民医院,甘肃兰州 730000)收稿日期:2023-09-20,修回日期:2023-11-21基金项目:国家自然科学基金资助项目(No81560670);甘肃省自然科学基金资助项目(No20JR10RA376,21JR11RA196);甘肃省人民医院国家级科研项目培育计划(19SYPYB 17);兰州市科技发展指导性计划项目(No2020 ZD 56)作者简介:王东萍(1986-),女,硕士,研究方向:中西医结合血液病,肿瘤药理学,E mail:wangdp0831@gszy.edu.cn;孙延庆(1964-),男,博士,教授,主任医师,博士生导师,研究方向:中西医结合血液病,肿瘤药理学,通信作者,E mail:40yanqingfang@gszy.edu.cndoi:10.12360/CPB202303028文献标志码:A文章编号:1001-1978(2024)01-0139-07中国图书分类号:R258 5;R319;R446 9;R733 7摘要:目的 利用液相色谱质谱联用和网络药理学、分子对接技术探讨秦巴硒菇提取物治疗慢性粒细胞白血病(chronicmyeloidleukemia,CML)的潜在活性靶点及相关信号通路,并通过体外实验进一步验证。
方法 应用液相色谱质谱分析秦巴硒菇提取物的活性成分,通过SwissTargetPrediction数据库预测药物靶点;从GeneCards、DisGeNET数据库获取CML的疾病靶点。
贵州省遵义市2023-2024学年高一下学期6月月考英语试卷一、阅读理解Covering over 1,600 square kilometers of England’s most valued lowland landscapes (风景) in the busiest part of the UK, the South Downs National Park has been shaped by the activities of its farmers and foresters, its charities and local businesses. Find out about some events happening across the park.Benfield Hill City Nature ChallengeIf you would like to be part of the global City Nature Challenge which brings together cities and organizations around the world to share observations of nature, we will be holding our own initiative on Saturday, 27th May, on Benfield Hill Local Nature Reserve. Welcome anyone, whatever their level of experience, in supporting us on a fun day of learning and identification of valuable biodiversity.Green Sketching WorkshopDiscover how you can use the process of drawing to look at and notice nature, and as a tool for slowing down and bringing calm to our busy lives. This is not a how-to-draw workshop, but a how-to-see workshop! This focuses on the process of drawing rather than the finished result, which means that everyone, regardless of previous drawing experience, can benefit from the joy of Green Sketching.Longmoor Through the AgesDiscover more about this vast land and how humans have shaped the landscape around the site. Bring your binoculars (双筒望远镜) as the site is also part of the Wealden Heath Phase Ⅱ Special Protection Area and home to some rare (珍稀的) birds, reptiles and rare species of international importance.Dawn Chorus WalkGet up with the birds. You won’t regret setting your alarm as we enjoy the magic of some of our springtime songsters. Shortheath Common sits at the northern extremity of the South Downs National Park and is regarded as a Special Area of Conservation due to the unique ecological landscape. It’s hope to a variety of rare birds, and plant species of international importance. 1.Which of the following most attracts people who want to use painting to show nature?A.Benfield Hill City Nature Challenge.B.Green Sketching Workshop.C.Longmoor Through the Ages.D.Dawn Chorus Walk.2.What can people do at Longmoor Through the Ages?A.Hold meetings.B.See painting exhibitions.C.Record the farmers’ songs.D.Watch some rare animals.3.What is the purpose of the text?A.To tell about the history of the South Downs National Park.B.To encourage donations to the South Downs National Park.C.To stress the importance of the South Downs National Park.D.To introduce activities happening across the South Downs National Park.In 2019, after retiring from her career as a social worker, Ane Freed -Kernis decided to build a home workshop and devote all of her free time to stone carving. “I might be covered head to to e in dust but I’m happy — it was something I needed more of in my life when I hit 60,” she says.This appeal has its origins in Freed - Kernis’ childhood. Growing up on her father’s farm in Denmark, she used to wander through the fields with her eyes fixed on the ground, looking for stones to add to her collection. “I’ve always been drawn to the shapes and textures(质地) of stones,” she says.After moving to England in 1977 and training as a social worker, Freed -Kernis soon became occupied with her busy career and the demands of raising her son. Stones were the last thing on her mind, until her father died in 2005. “He took a stone carving course in his retirement, and I always thought stone seemed so fun but never had the time to look into it myself,” she says. “After he died, I became determined to learn in his honour.”Signing up for a week-long stone carving course at Yorkshire Sculpture Park, Freed -Kernis began to learn how to turn a block of rock into well-designed shapes. “It was really scary at the start because you would spend hours just hammering(锤打).”Now 65, Freed - Kernis has a thriving small business built largely through word of mouth. She creates 12 to 15 pieces a year that can take anywhere from a few days to three weeks to complete, while her prices range from £ 200 to £ 3,000. “I’m making smaller ones,” she says. “I don’t have to depend on the money much, so I want to keep prices in the range that people can afford, mainly just covering costs and labour(劳动力).”4.Freed-Kernis was first attracted by stones when ______.A.she was 60B.she was a childC.her father died D.she moved to England5.What can we infer about Freed-Kernis from paragraph 3?A.She never cared about her father.B.She led a disappointing life in Denmark.C.She spent lots of time studying stone carving.D.She learned stone carving under the influence of her dad.6.How did Freed-Kernis feel when she started stone carving course?A.Hopeful and proud.B.Confident and satisfied.C.Nervous and frightened.D.Impatient and unprepared.7.Why is Freed-Kernis making smaller pieces?A.They are easier to move by her.B.They are more affordable to people.C.She wants to save costs and labour.D.She is too old to focus on making large ones.In San Francisco, a large group of sea lions move themselves out of the bay waters and hang out on PIER 39, which is a popular tourist destination. According to dock (码头)officials, this is the most sea lions seen in the region in 15 years.“Over 1,000 sea lions have been counted this week,” PIER 39 harbormaster Sheila Chandor told many different media. “The surge in sea lions is usually a good sign of their strong population and healthy living environment,” said Adam Ratner, Director of Conservation Engagement at the Marine Mammal(海洋哺乳动物) Center in Sausalito, California.“California sea lions are sentinels(哨兵) of the ocean,” Ratner said. Their population to some extent reflects the health of the ocean. Therefore, seeing a large number of California sea lions is clearly a good thing.For nearly 35 years, the slippery(滑的) residents have been a star attraction for tourists. That autumn in 1989, PIER 39 had just been repaired, but the ships had not yet been moved back.At that moment, the sea lions unexpected arrival not only attracted fans but also created enemies. According to a website, some dock residents and workers were scared away by the strong and very unpleasant smell and noise of their new neighbors, while others saw these animals as a bright spot after the destructive Loma Prieta earthquake.The officials sought help from the Marine Mammal Center to find a way to deal with sea lions. Ratner said that the final decision is to let the sea lions stay and coexist with humans. “The fact proves that this is really a good thing,” he said. “This is just a proof of how we can truly work together and think about how we can share our coasts with marine mammals and other wildlife in a way that benefits all the parties involved.”8.How does the author start the text?A.By describing a situation.B.By answering a question.C.By holding a conversation.D.By comparing different opinions.9.What does the underlined word “surge” in paragraph 2 mean?A.Sharp increase.B.Tight control.C.Slow development.D.Sudden movement.10.What is Ratner’s attitude to the final decision?A.Doubtful.B.Uninterested.C.Supportive.D.Unclear.11.What message does the author seem to convey in the text?A.Sea lions are pretty cool animals.B.Animals and humans can live in harmony.C.Watching sea lions might not be a proper action.D.Sea lions should be driven out of PIER 39.With mounting evidence that nanoplastic particles (纳米塑料微粒) are in our bodies, there is growing concern over their potential health impacts. Now a new study finds a relation between nanoplastics in the brain and a higher risk for Parkinson’s disease.Nanoplastics appear when the plastic packaging breaks down into small pieces. Theseparticles can enter the blood and cross the blood-brain barrier, with European researchers reporting earlier this year that in animal experiments, it can take two hours or less for certain nanoplastics to reach the brain after being eaten.In humans, it’s long been thought that environmental factors play a role in Parkinson’s disease but specific causes are still unclear. The new study from the Duke University School of Medicine details how nanoplastics cause chemical changes in the brain that can, in turn, make Parkinson’s and related types of diseases more likely.That’s because the nanoplastics attract a protein (蛋白质) called alpha-synuclein, known to play a role in Parkinson’s and related disorders. In lab and animal studies, the plastic’s interaction with it leads to increases in the affected neurons in the brain. This interaction appears related to favorable conditions in which Parkinson’s can develop.The study authors note that Parkinson’s disease existed long before nanoplastics appeared in the environment, but they think that this “nanoplastics pollution in the human brain” may prove a new poison.Further, the Duke team led by Dr. Andrew West notes that Parkinson’s disease is among the fastest growing nervous diseases in the world, even as the amazing amount of plastic pollution builds across the planet. This is expected to continue for the foreseeable future.“The technology to monitor nanoplastics is still at the earliest possible stages and not ready yet to answer all the questions we have,” West said. “But hopefully efforts in this area will increase rapidly, as we see what these particles can do in our experiments.”12.Where is the text most probably taken from?A.A product advertisement.B.A science journal.C.An art magazine.D.A travel brochure.13.What is paragraph 4 mainly about?A.The conditions leading to Parkinson’s.B.The cause of alpha-synuclein’s appearance.C.The principle of nanoplastics’ impact on Parkinson’s.D.The difference between Parkinson’s and related disorders.14.What can be inferred from West’s words in the last paragraph?A.Plastic pollution will by no means be avoided.B.Nanoplastics are impossible to deal with at present.C.Fewer people will suffer from Parkinson’s in the future.D.More efforts in the study of nanoplastics will be put in.15.What is a suitable title for the text?A.Nanoplastics can enter the brain through bloodB.Nanoplastics may promote Parkinson’s diseaseC.Alpha-synuclein plays a role in Parkinson’s diseaseD.Nanoplastics will do serious harm to human healthRoommates can provide support, creating a shared space where memories are made and challenges are faced together. 16 , but it takes some efforts to make it work. Here are some tips for living peacefully with roommates.Establish boundaries (界限)17 . But experts suggest being careful when moving in with them, as it could do harm to the relationship if you’re not clear about boundaries. So being clear about boundaries is one way of making sure everyone feels comfortable.Ask hard questionsHave a discussion of your living arrangement. How are you paying rent? What’s your guest policy (政策)? 18 ? And make your expectations clear. If your roommates say they can pay rent on time, for example, you can tell them the specific time to pay rent. 19In a shared living arrangement, it’s perfect for everyone to feel free to express their concerns and opinions without fear of judgment (判断). To do this, encourage open-mindedness and active listening. Avoid making assumptions (假设), forcing your opinions on others and not thinking about others’ views.Learn from themLiving with a roommate is a unique opportunity to meet someone with a very different background from yours. 20 , showing appreciation and giving within your ability do so much for the relationship and create a space with love.A.Where do you come fromB.Figure out your shared interestC.Should you create a cleaning scheduleD.Even if you’re not best friends with themE.Create a free and non-judgmental environmentF.Having a good roommate can be a great experienceG.It’s okay to share space with friends who are polite and responsible二、完形填空Juleus Ghunta and his three sisters lived in a rural part of Western Jamaica. They were 21 by a single mother, and his mother often had to make 22 choices about how to use their limited resources-including a decision to send his oldest sister to school, but to 23 Ghunta at home.When Ghunta finally went to school, he couldn’t catch up on his reading skills. “I 24 in school with a deep sense of loss and 25 ,” he said. Not only had he been kept home from school as a child, but he had not been exposed (使接触) to 26 .When Ghunta was about 12, a young teacher decided to start a special 27 program for struggling students. Ghunta was the first student to 28 . “The teacher was 29 kind to me.” he said. “She was patient. She did not require anything of me, except that I 30 in myself and work hard.” Under her 31 , Ghunta’s reading skills finally started to improve. And his sense of inadequacy (能力不足) 32 to lift.After Ghunta’s experience with the teacher, his life 33 a new direction. He went on to college, and later, graduate school. Today, he is the author of two children’s books.He would like to thank his teacher for seeing his 34 . “I would love for her to see the significant 35 that she has made on my life, and how it continues to be a source of joy.”21.A.monitored B.replaced C.observed D.raised 22.A.tough B.annoying C.confusing D.familiar 23.A.contact B.comfort C.keep D.compare 24.A.lived B.adapted C.volunteered D.struggled 25.A.responsibility B.shame C.humour D.achievement 26.A.families B.books C.neighbours D.friends27.A.reading B.experiment C.writing D.fitness 28.A.build up B.mix up C.sign up D.make up 29.A.partly B.hardly C.suddenly D.extremely 30.A.check B.believe C.take D.result 31.A.control B.protection C.guidance D.consideration 32.A.began B.decided C.failed D.chose 33.A.closed B.doubted C.feared D.took 34.A.memories B.possibilities C.explanations D.instructions 35.A.reasons B.summaries C.impacts D.challenges三、语法填空阅读下面短文,在空白处填入1个适当的单词或括号内单词的正确形式。
PROTEIN EXTRACTION KIT DIAGENODEContentIntroduction 4 General remarks before starting 4 Required materials and reagents 5 Precautions 5 Protocol 6 |PROTEIN EXTRACTION KITPAGE 4 DIAGENODE M A N U A L Innovating Epigenetic SolutionsIntroductionProtein extraction from tissues and cultured cells is the first step for many biochemical and analytical techniques (PAGE, Western blotting, mass spectrometry, etc.) or protein purification. Efficient disruption and homogenization of animal tissues and cultured cells are required to ensure high yields of proteins. Diagenode’s Bioruptor ® uses state-of-the-art ultrasound technology to efficiently disrupt and homogenize tissues and cultured cells in just one step. Bioruptor ® offers unique benefits for tissue disruption and homogenization:• Fast and simple• No contamination between samples• Efficient• Gentle processing• Reproducible• Temperature controlled • Multiplexing capability General remarks before starting• Conditions for protein extraction (e.g. use of fresh or frozen tissue, composition of extraction buffer etc.) must be adjusted according to the nature of the proteins of interest and the assays to be run. SDS might be added to the extraction buffer to maximize the yield of soluble proteins. SDS extracts can be used for SDS electrophoresis and Western blotting. It is recommended to reduce the SDS concentration for 2D electrophoresis, enzyme-linked immunosorbent assay and mass spectrometry.• For functional studies (e.g. the study of protein–protein interactions), avoid using ionic detergents and high concentrations of salt.Optional: use RIPA buffer as a starting point for optimization:50 mM Tris-HCl (pH 7.4) 150 mM NaCl 1% NP-40 0.25% Na-deoxycholateProtease Inhibitor Mix SDS 0.1 - 2% (optional)It is always recommended to optimize the buffer composition depending on a specific research project• Always use Protease Inhibitor Mix during extraction procedure to block the possible protein degradation.• Use Diagenode’s TPX tubes for sonication. Depending on the desired final volume, 1.5 ml TPX microtubes (Diagenode, Cat. No.: M-50050 or M-50001) or 15 ml TPX tubes (Diagenode, Cat. No.: M-UN-15) might be used. Always respect the recommended sonication volumes: 100 - 300 µl for 1.5 ml TPX tubes and 1 - 2 ml for 15 ml TPX tubes (strictly follow the Bioruptor ® instructions as shown in the corresponding manual before starting any sonication experiments).• Keep extracted proteins at -80°C. PAGE 5 |PROTEIN EXTRACTION KIT DIAGENODE Required materials and reagents• Bioruptor ® Standard or Plus (Diagenode, Cat. No. UCD-200, UCD-300)• Bioruptor ® Water Cooler (Diagenode, Cat. No. BioAcc-Cool)• Single Cycle Valve for Bioruptor ® Plus (Diagenode, Cat. No. VB-100-0001)• 1.5 ml (Diagenode, Cat. No. M-50050 or M-50001) or 15 ml TPX tubes (Diagenode, Cat. No. M-UN-15) for sonication • Protein extraction kit (Diagenode, Cat. No. AL-002-0050)• Protein Extraction Beads (Diagenode, Cat No. AL-003-0020) for tissue disruption (not required for cell lysis)• Tube holder for 1.5 ml tubes (Diagenode, Cat. No. UCD-pack 1.5)• Tube holder pack for extraction kits (Diagenode, Cat. No. O-ring-15)• Protease Inhibitor Mix (Diagenode, Cat. No. kch-502-300 or kch-502-100)• Buffer for protein extraction from tissue or cell lysis (not supplied)• Reagents for protein quantification (optional)Precautions• This product is intended for laboratory research use only. CAUTION : Not for diagnostic use. The safety and efficacy of this product in diagnostic or other clinical uses has not been established. • RNA extraction reagent is harmful and not intended for medical use. Handle with great care. • When using, wear appropriate protective gear (gloves, goggles, etc.). • If the product enters eye or adheres to skin, wash with large amounts of water for at least 15 minutes and consult a doctor. • Handle this product in accordance with the manual. • Diagenode is not responsible for problems caused if this product is not handled in accordance with the manual.PROTEIN EXTRACTION KITPAGE 6 DIAGENODE M A N U A L Innovating Epigenetic SolutionsProtocolI. Protein extraction from Tissues »This protocol has been validated for up to 50 mg of tissue. Do not use more tissue per sample. For larger quantity cut the tissue and proceed to the disruption in separate tubes. When proceeding 20 - 50 mg of tissue 15 ml TPX tubes are recommended with a final volume of 1 - 2 ml. Less tissue could be sonicated in 1.5 ml TPX tubes with a final volume of 100 - 300 µl. »Minimize the time of tissue collection to prevent protein degradation.»Dissected tissues can be snap-frozen in liquid nitrogen and stored at -80°C until protein extraction1. Pre-cool Bioruptor ® to 4°C using the Bioruptor ® Water Cooler (Diagenode, Cat. No. BioAcc-Cool).2. Fill the TPX tubes with Protein Extraction Beads. »The recommended quantity of the beads is 200 - 250 mg for 15 ml TPX tubes, 40 - 50 mg for 1.5 ml TPX tubes. Note: If using pre-filled tubes (Cat. No. AL-002-0050 Protein Extraction kit) please skip this step!3. Add Protease Inhibitor Mix (200x) to the cold protein extraction buffer: 5 µl per 1 ml of extraction buffer. Scale accordingly. 4. Add the required volume of a cold extraction buffer to the TPX tubes pre-filled with Protein Extraction Beads. 5. Add tissue pieces to the TPX tubes. Make sure that the final volume is in the recommended range: 100 - 300 µl for 1.5 ml TPX microtubes and 1 - 2 ml for 15 ml TPX tubes. 6. Vortex tubes briefly and proceed to sonication by using the Bioruptor ® with the following settings:Power: H position (High) Sonication cycle: 30 sec ON/30 sec OFF Total sonication time: 5 - 15 cycles Temperature: 4°C »To guarantee homogeneity of sonication, the tube holder should be always completely filled with tubes.7. Stop the Bioruptor ® after each 5 cycles, vortex samples and check the sample visually for disruption. »Please note that the optimization might be required depending on the sample format (fresh or frozen tissue), tissue type and tissue amount. The shortest sonication time should be chosen to prevent protein damage. Incomplete disruption may occur with fibrous tissues (i.e. muscles). 8. Transfer the supernatant to a new tube and centrifuge samples at 14,000 rpm for 15 min at 4°C to remove any remaining insoluble material.»The Protein Extraction Beads might be washed once with extraction buffer for maximum recovery of total protein but this will lead to the sample dilution. 9. Transfer the supernatant containing soluble proteins to a new tube. 10. Take an aliquot for the quantification and the further analysis if needed. Store proteins extracts in small aliquots at -80°C.»Different protein concentration assays exist including: absorbance at 280 nm, Lowry Assay, Bradford Assay, Bicinchoninic Assay (BCA) etc.. Many commercial kits for protein quantification are also available. Please note that measuring the protein concentration in an SDS extract requires that the assay is compatible with the detergent and reducing agent in the solution. PAGE 7 |PROTEIN EXTRACTION KIT DIAGENODE II. Protein extraction from Cultured Cells»This protocol has been validated using RIPA buffer but it may be necessary to optimize the buffer composition depending on a specific research project. »We recommend using 100 µl of an appropriate lysis buffer per 1x10^6 cells. »For Western blotting, cells might be lysed directly in 1x Laemmli buffer. After sonication, centrifuge extract at 14,000 rpm for 15 min. Transfer the supernatant to a new tube and boil for 3 min. The supernatant can be used in Western blot. Note that protein quantification by common methods is not compatible with Laemmli buffer.1. Pre-cool Bioruptor ® to 4°C using the Bioruptor ® Water Cooler (Diagenode, Cat. No. BioAcc-Cool).2. A dd Protease Inhibitor Mix (200x) to the ice-cold cell lysis buffer: 5 µl per 1 ml of extraction buffer. Scale accordingly. 3. For monolayer cells: R inse the monolayer cells 3 times with cold PBS. For the final rinse, use a cell scraper and transfer the cell suspension to a TPX tube. Centrifuge cells at 1,500 rpm for 10 min at 4°C and aspirate as much supernatant as possible. Proceed to step 4. For suspension cells: C entrifuge suspension at 1,500 rpm for 10 min at 4°C and aspirate the supernatant. Resuspend the pellet in cold PBS, transfer to a TPX tube and centrifuge at 1,500 rpm for 10 min at 4°C. Aspirate the supernatant. Repeat 2 more times. Proceed to the step 4. 4. Add ice-cold cell lysis buffer and resuspend the pellet. Incubate on ice for 10 min.»The viscosity may appear at this step5. Vortex tubes briefly and proceed to sonication by using the Bioruptor ® with the following settings:Power: H position (High) Sonication cycle: 30 sec ON/30 sec OFF Total sonication time: 5-10 cycles Temperature: 4°C »To guarantee homogeneity of sonication, the tube holder should be always completely filled with tubes.6. Stop the Bioruptor ® after 5 cycles, briefly vortex samples and visually check the samples: Samples should be in solution (viscosity should be reduced)»Please note that the optimization might be required depending on sample format (cell density, cell type etc.). The shortest sonication time should be chosen to prevent protein damage. 7. Transfer the supernatant to a new tube and centrifuge samples at 14,000 rpm for 15 min at 4°C to remove any remaining insoluble material.8. Take an aliquot for the quantification and the further analysis if needed. Store protein extracts at -80°C.»Different protein concentration assays exist including: absorbance at 280 nm, Lowry Assay, Bradford Assay, Bicinchoninic Assay (BCA) etc. Many commercial kits for protein quantification are also available. Please note that measuring the protein concentration in an SDS extract requires that the assay is compatible with the detergent and reducing agent in the solution.PROTEIN EXTRACTION KITPAGE 8 DIAGENODE M A N U A LInnovating Epigenetic SolutionsFigure 1. Protein Extraction Beads are required for efficient tissue disruption using the Bioruptor ®Complete disruption is observed in the sample containing Diagenode’s Protein Extraction Beads (left) after 5 cycles while non-disrupted tissue is still present in the sample without the Protein Extraction Beads (right).Figure 2. Total proteins effectively extracted from tissues using Bioruptor ®Various mouse tissues were disrupted in RIPA buffer supplemented with or without 2% SDS. Total proteins were separated by SDS-PAGE and stained with Coomassie Blue dye.Liver Brain Muscle + SDS + SDS + SDS - SDS- SDS- SDSPAGE 9 |PROTEIN EXTRACTION KIT DIAGENODE Figure 3. Western blot analysis of GAPDH and HSP90 proteins in tissue and cultured cell extracts.Expected bands of 37 kD and 90 kD are observed for GAPDH (left panel) and HSP90 (right panel), respectively, in liver, brain and skeletal muscle. Note that HSP90 is expressed in muscle in an extremely low level (H. Quraishi and I. R. Brown, J Neurosci Res. 1996 Feb 1;43(3):335-45). Whole cell extract from HeLa cells is loaded as positive control. HeLa cells were lysed using the Bioruptor ®. PROTEIN EXTRACTION KIT PAGE 10 DIAGENODE M A N U A LInnovating Epigenetic SolutionsPAGE 11 |PROTEIN EXTRACTION KIT DIAGENODE 030812。
少花蒺藜草荧光定量PCR内参基因筛选与验证徐礼莹;田迅;百灵;吴佳丽;张福霖;黄菊;马文静;吕广超;马红悦;陈宇杰【期刊名称】《现代农业研究》【年(卷),期】2024(30)3【摘要】少花蒺藜草(Cenchrus pauciflorus Benth.)为禾本科蒺藜草属,是入侵我国北方地区的一年生恶性杂草,少花蒺藜草的入侵态势逐年扩增,因此对于少花蒺藜草的基因研究,以及在分子水平对于防治少花蒺藜草入侵的研究显得尤为重要。
本研究以少花蒺藜草转录组数据为基础,候选了6个内参基因,荧光定量PCR检测候选内参基因的表达,通过geNorm、NormFinder、BestKeeper、比较△Ct法和RefFinder五种评价方式,综合评估筛选了最佳内参基因。
结果表明,6个候选内参基因的综合排名为TCTP1>RH37>ACX4>TCTP2>GAPDH>TBP,TCTP1基因在少花蒺藜草不同时期不同组织样本中表达最高最稳定,最不稳定表达的是基因TBP,TCTP1为综合评估下少花蒺藜草的最佳内参基因,因此TCTP1可作为内参基因用于少花蒺藜草荧光定量PCR检测研究中。
本研究为少花蒺藜草后续的基因研究以及在分子水平上为少花蒺藜草生物防治提供一定的理论基础。
【总页数】7页(P10-16)【作者】徐礼莹;田迅;百灵;吴佳丽;张福霖;黄菊;马文静;吕广超;马红悦;陈宇杰【作者单位】内蒙古民族大学生命科学与食品学院【正文语种】中文【中图分类】S451【相关文献】1.茉莉花实时荧光定量PCR内参基因的筛选与验证2.青海湖裸鲤实时荧光定量PCR内参基因筛选与验证3.栝楼实时荧光定量PCR内参基因的筛选与验证4.川赤芍实时荧光定量PCR内参基因的筛选与验证5.阳桃(Averrhoa carambola)实时荧光定量PCR内参基因的筛选与验证因版权原因,仅展示原文概要,查看原文内容请购买。
·药物与临床·多黏菌素B 血药浓度的测定及其在重症患者中的应用Δ甘雨*,喻明洁,刘芳,程林,陈勇川 #(陆军军医大学第一附属医院药学部,重庆 400038)中图分类号 R 978 文献标志码 A 文章编号 1001-0408(2023)06-0704-06DOI 10.6039/j.issn.1001-0408.2023.06.12摘要 目的 建立测定多黏菌素B 血药浓度的方法并应用于临床。
方法 血浆样品经5%三氯乙酸溶液蛋白沉淀后,以多黏菌素E 2为内标,采用超高效液相色谱-串联质谱(UPLC-MS/MS )法测定多黏菌素B 1、B 2的质量浓度。
以BEH C 18为色谱柱,以水(含0.1%甲酸)-乙腈(含0.1%甲酸)为流动相进行梯度洗脱,流速为0.5 mL/min ,进样量为10 μL 。
采用电喷雾离子源以多反应监测模式进行正离子扫描,用于定量分析的离子对分别为m /z 603.2→101.2(多黏菌素B 1)、595.7→101.1(多黏菌素B 2)、578.5→101.1(内标)。
采用上述方法测定79例重症患者体内多黏菌素B 的血药浓度,记录患者急性肾损伤(AKI )的发生情况并分析多黏菌素B 血药浓度与AKI 发生的相关性。
结果 多黏菌素B 1、B 2检测质量浓度的线性范围分别为200~20 000、50~5 000 ng/mL (r >0.995),定量下限分别为200、50 ng/mL ;日内、日间精密度的RSD 均不高于12.06%,平均提取回收率为103.04%~117.44%(RSD ≤10.45%),基质效应、稳定性试验的RSD 均不高于7.42%。
79例患者的多黏菌素B 稳态谷、峰浓度分别为(2.54±2.52)、(8.17±5.20)mg/L 。
在被纳入AKI 评价的27例患者中,有18例患者(66.67%)发生AKI ;未发生AKI 患者的多黏菌素B 峰浓度显著低于AKI 患者(P <0.05),但两者谷浓度比较差异无统计学意义(P >0.05)。
细胞自噬转录组+蛋白组
细胞自噬(autophagy)是一种细胞内的重要代谢过程,通
过分解和回收细胞内的蛋白质、脂质和其他细胞器,维持
细胞内环境的稳定性。
细胞自噬包括三个主要步骤:识别
和包裹、溶酶体融合和降解。
在细胞自噬的过程中,转录组(transcriptome)和蛋白组(proteome)起着重要的作用。
转录组是指细胞中所有基
因的转录产物,即RNA的总体表达情况。
蛋白组是指细胞
中所有蛋白质的总体表达情况。
细胞自噬的转录组研究主要关注细胞自噬相关基因的表达
变化。
通过比较自噬诱导条件下和正常条件下的转录组数据,可以发现与细胞自噬相关的基因的表达差异。
这些基
因包括自噬相关基因(如ATG基因家族)、信号通路调控
基因、膜蛋白基因等。
转录组研究可以帮助我们了解细胞
自噬的调控机制以及自噬在不同生理和病理状态下的变化。
细胞自噬的蛋白组研究主要关注细胞自噬相关蛋白的表达
和修饰变化。
通过质谱分析等技术,可以鉴定和定量自噬
相关蛋白的表达水平和修饰状态,如磷酸化、乙酰化、泛
素化等。
这些蛋白包括自噬相关蛋白(如LC3、Beclin-1)、信号通路调控蛋白、膜蛋白等。
蛋白组研究可以帮助我们
了解细胞自噬的分子机制以及自噬在不同生理和病理状态
下的变化。
综上所述,细胞自噬的转录组和蛋白组研究可以帮助我们
深入了解细胞自噬的调控机制和分子机制,为相关疾病的治疗和药物开发提供重要的理论基础。
黄芩提取物中黄酮类成分血浆蛋白结合率的测定冯志强;韩坚;谢智勇;廖琼峰;张蕾【期刊名称】《中国药理学通报》【年(卷),期】2012(028)002【摘要】Aim To establish a RP-HPLC method for determination of concentration of four major flavonoids, namely baicalin ( BL ), wogonoside ( WL ), wogonin ( W ) and oroxylin A ( OA ), in the total flavonoids extract of scutellaria baicalensis Georgi in rat plasma and to study the plasma protein binding rate. Methods The equilibrium dialysis was carried out to determine the plasma protein binding rate of the target compounds. The plasma samples were extracted by protein precipitation with methanol. With the use of maackiain as the internal standard, the concentration of the multicomponent inside and outside the dialysismembranes was determined by HPLC method. Results The plasma protein binding rates of baicalin and wogonoside decreased with increasing of concentration in a concentration-dependent manner( BL: 82.03% →77.25% →67. 17% ; WL: 82. 24%→ 76. 08% →69. 87% ), while were not the same of wogonin and oroxylin A. The average plasma protein bingding rates of wogonin and oroxylin A were ( 89. 66 ± 1. 19 )% and ( 88. 56 ± 1. 87 )% , respectively. Conclusions The HPLC method established in the paper is simple, sensitive, little interfe-rential and stable. The four major compounds in the total fla-vonoids extract of scutellaria baicalensis Georgi have high capacityin binding plasma protein in rat in vitro.%目的建立同时测定黄芩总黄酮部位中4 种黄酮类成分在大鼠血浆中药物浓度的方法,并测定其体外血浆蛋白结合率.方法平衡透析法测定黄芩黄酮类成分血浆蛋白结合率,以高丽槐素为内标,生物样本用甲醇沉淀蛋白进行预处理,采用HPLC法测定4种黄酮类成分在透析内、外液中的浓度.结果在低、中、高3 种浓度下,黄芩苷(BL)和汉黄芩苷(WL)的血浆蛋白质结合率随给药浓度的增大而降低(BL:82.03%→77.25%→67.17%;WL:82.24%→76.08%→69.87%),而汉黄芩素(W)和千层纸素A(OA)则呈现非质量浓度依赖性,其平均血浆蛋白结合率分别为(89.66±1.19)%、(88.56±1.87)%.结论该文所建立的测定方法简便、灵敏、专属性强.4个黄酮类成分在大鼠体外均有较高的血浆蛋白结合率.【总页数】4页(P286-289)【作者】冯志强;韩坚;谢智勇;廖琼峰;张蕾【作者单位】广州中医药大学中药学院,广东,广州,510006;中山大学药学院,广东,广州,510080;广州中医药大学中药学院,广东,广州,510006;中山大学药学院,广东,广州,510080;广州中医药大学中药学院,广东,广州,510006;广州中医药大学中药学院,广东,广州,510006【正文语种】中文【中图分类】R-332;R282.71;R284.1;R341;R969.1【相关文献】1.超高效液相色谱-串联质谱法同时测定黄芩提取物中4种黄酮类成分 [J], 段云2.HPLC法测定枳芩颗粒中黄酮类成分的含量 [J], 高红宁;潘雨柔;殷奕;毛春芹;陆兔林3.HPLC法同时测定三角草中黄酮类成分异荭草苷和异牡荆素的含量 [J], 陈冠;王蕾;王宏;江茜;刘琳娜4.高效液相色谱法测定金银花中黄酮类成分槲皮素的血浆蛋白结合率 [J], 修玮5.翻白草中黄酮类成分含量测定及其质量评价研究 [J], 孔晓妮;吕华瑛;周洪雷因版权原因,仅展示原文概要,查看原文内容请购买。
桑黄提取物体内抗肿瘤作用的实验研究桑黄(Sophora flavescens)是一种传统中药,被广泛应用于中医临床实践中治疗多种疾病,包括肿瘤。
许多研究表明,桑黄提取物具有抗肿瘤的活性。
本实验旨在探究桑黄提取物在小鼠模型中的抗肿瘤作用。
我们从中药市场购买了粉碎后的桑黄根和根茎,并对其进行颗粒化处理。
接下来,我们使用乙醇对桑黄颗粒进行提取,得到了桑黄乙醇提取物。
为了评价桑黄提取物的抗肿瘤作用,我们采用了腹腔注射肿瘤细胞法,将小鼠随机分为实验组和对照组。
实验组小鼠腹腔注射了肿瘤细胞后,开始接受桑黄提取物的处理。
对照组则接受了相同的操作,但没有给予桑黄提取物处理。
通过监测实验组和对照组小鼠的生存时间和肿瘤大小,我们发现实验组小鼠的生存时间明显延长,肿瘤大小也明显减小,与对照组相比具有显著差异。
这表明桑黄提取物具有抗肿瘤的活性。
为了进一步研究桑黄提取物的抗肿瘤作用机制,我们进行了组织切片和免疫组化分析。
结果显示,在实验组中,肿瘤组织中的凋亡标志物(如活化的半胱氨酸蛋白酶-3)的表达明显增加,而增殖标志物(如Ki-67)的表达明显下降。
这表明桑黄提取物通过促进肿瘤细胞凋亡和抑制细胞增殖来发挥抗肿瘤作用。
我们还检测了实验组和对照组小鼠的免疫功能。
结果显示,实验组小鼠的免疫功能显著增强,包括NK细胞活性、淋巴细胞亚群比例和细胞因子水平。
这表明桑黄提取物通过增强机体免疫功能,加强对肿瘤的免疫监视来发挥抗肿瘤作用。
本实验证明桑黄提取物具有抗肿瘤作用,可能通过促进肿瘤细胞凋亡、抑制细胞增殖和增强机体免疫功能来发挥其作用。
这为进一步研究桑黄提取物的抗肿瘤机制和开发新的抗肿瘤药物奠定了基础。
大蓟提取物改善高胆固醇血症模型小鼠的代谢组学研究Δ高梦梦 1*,陈桢琳 1,郝雅坤 2,郭姣 1 #(1.广东药科大学中医药研究院/广东省代谢病中西医结合研究中心/糖脂代谢病教育部重点实验室/广东省代谢性疾病中医药防治重点实验室,广州 510006;2.广东药科大学中药学院,广州 510006)中图分类号 R 285.5 文献标志码 A 文章编号 1001-0408(2023)13-1590-06DOI 10.6039/j.issn.1001-0408.2023.13.09摘要 目的 基于代谢组学技术探究大蓟提取物改善高胆固醇血症的作用机制。
方法 以大孔树脂吸附法制备大蓟提取物,并利用液相色谱-质谱联用仪鉴定其主要成分。
实验小鼠先随机分为对照组(n =6)和造模组(n =16),造模组小鼠采用饮食诱导建立高胆固醇血症模型,造模成功后,再将造模组小鼠分为模型组(n =8)及大蓟提取物组(n =8)。
大蓟提取物组小鼠灌胃大蓟提取物400 mg/(kg ·d )(以提取物计),其余2组小鼠灌胃等体积0.3%羧甲基纤维素钠溶液,持续6周。
给药结束后,以血清总胆固醇(TC )、甘油三酯(TG )水平和肝脏组织病理变化评价大蓟提取物的干预效果,并通过代谢组学方法探讨大蓟提取物改善高胆固醇血症模型小鼠的相关机制。
结果 从大蓟提取物中共鉴定出绿原酸、蒙花苷、柳穿鱼叶苷等12种成分。
给药6周后,与对照组比较,模型组小鼠血清中TC 水平显著升高、TG 水平显著降低(P <0.05),肝脏组织出现大量脂滴,肝细胞排列紊乱,肝索结构被破坏。
与模型组比较,大蓟提取物组小鼠血清中TC 水平显著下降(P <0.05);肝脏组织中的脂滴明显减少,肝细胞以中央静脉为中心呈放射状紧密排列,肝索排列整齐。
代谢组学研究显示,大蓟提取物干预后,乙醇胺、富马酸、胆固醇等代谢产物水平发生显著回调;最终得到丙氨酸-天冬氨酸-谷氨酸代谢、精氨酸生物合成、柠檬酸循环3条代谢通路。
BioNoculars:Extracting Protein-Protein Interactions from Biomedical Text Amgad Madkour,*Kareem Darwish,Hany Hassan,Ahmed Hassan,Ossama EmamHuman Language Technologies GroupIBM Cairo Technology Development CenterP.O.Box166El-Ahram,Giza,Egypt{amadkour,hanyh,hasanah,emam}@,*kareem@AbstractThe vast number of published medical doc-uments is considered a vital source for rela-tionship discovery.This paper presents a sta-tistical unsupervised system,called BioNoc-ulars,for extracting protein-protein interac-tions from biomedical text.BioNocularsuses graph-based mutual reinforcement tomake use of redundancy in data to constructextraction patterns in a domain independentfashion.The system was tested using MED-LINE abstract for which the protein-proteininteractions that they contain are listed in thedatabase of interacting proteins and protein-protein interactions(DIPPPI).The systemreports an F-Measure of0.55on test MED-LINE abstracts.1IntroductionWith the ever-increasing number of published biomedical research articles and the dependency of new research and previously published research, medical researchers and practitioners are faced with the daunting prospect of reading through hundreds or possibly thousands of research articles to sur-vey advances in areas of interest.Much work has been done to ease access and discovery of articles that match the interest of researchers via the use of search engines such as PubMed,which provides search capabilities over MEDLINE,a collection of more than15million journal paper abstracts main-tained by the National Library of Medicine(NLM). However,with the addition of abstracts from more than5,000medical journals to MEDLINE every year,the number of articles containing information that is pertinent to users needs has grown consider-ably.These5,000journals constitute only a subset of the published biomedical research.Further,med-ical articles often contain redundant information and only subsections of articles are typically of direct in-terest to researchers.More advanced information extraction tools have been developed to effectively distill medical articles to produce key pieces of in-formation from articles while attempting to elimi-nate redundancy.These tools have focused on areas such as protein-protein interaction,gene-disease re-lationship,and chemical-protein interaction(Chun et al.,2006).Many of these tools have been used to extract key pieces of information from MED-LINE.Most of the reported information extraction approaches use sets of handcrafted rules in conjunc-tion with manually curated dictionaries and ontolo-gies.This paper presents a fully unsupervised statisti-cal technique to discover protein-protein interaction based on automatically discoverable repeating pat-terns in text that describe relationships.The paper is organized as follows:section2surveys related work;section3describes BioNoculars;Section4 describes the employed experimental setup;section 5reports and comments on experimental results;and section6concludes the paper.2BackgroundThe background will focus primarily on the tagging of Biomedical Named Entities(BNE),such genes, gene-products,proteins,and chemicals and the Ex-traction of protein-protein interactions from text. 2.1BNE TaggingConcerning BNE tagging,the most common ap-proaches are based on hand-crafted rules,statisti-cal classifiers,or a hybrid of both(usually in con-junction with dictionaries of BNE).Rule-based sys-tems(Fukuda et al.,1998;Hanisch et al.,2003;Ya-mamoto et al.,2003)that use dictionaries tend to exhibit high precision in tagging named entities but generally with lower tagging recall.They tend to lag the latest published research and are sensitive to the expression of the named entities.Dictionar-ies of BNE are typically laborious and expensive to build,and they are dependant on nomenclatures and specific species.Statistical approaches(Collier et al.,2000;Kazama et al.,2002;Settles,2004)typ-ically improve recall at the expense of precision, but are more readily retargetable for new nomen-clatures and organisms.Hybrid systems(Tanabe and Wilbur,2002;Mika and Rost,2004)attempt to take advantage of both approaches.Although these approaches tend to generate acceptable recognition, they are heavily dependent on the type of data on which they are trained.(Fukuda et al.,1998)proposed a rule-based pro-tein name extraction system called PROPER(PRO-tein Proper-noun phrase Extracting Rules)system, which utilizes a set of rules based on the surface form of text in conjunction with a Part-Of-Speech (POS)tagging to identify what looks like a protein without referring to any specific BNE dictionary. They reported a94.7%precision and a98.84%re-call for the identification of BNEs.The results that they achieved seem to be too specific to their train-ing and test sets.(Hanisch et al.,2003)proposed a rule-based protein and gene name extraction system called ProMiner,which is based on the construction of a general-purpose dictionary along with different dic-tionaries of synonyms and an automatic curation procedure based on a simple token model of protein names.Results showed that their system achieved a 0.80F-measure score in the name extraction task on the BioCreative test set(BioCreative).(Yamamoto et al.,2003)proposed the use of mor-phological analysis to improve protein name tag-ging.Their approach tags proteins based on mor-pheme chunking to properly determine protein name boundary.They used the GENIA corpus for training and testing and obtained an F-measure score of0.70 for protein name tagging.(Collier et al.,2000)used a machine learning ap-proach to protein name extraction based on a linear interpolation Hidden Markov Model(HMM)trained using bi-grams.They focused onfinding the most likely protein sequence classes(C)for a given se-quence of words(W),by maximizing the probabil-ity of C given W,P(C—W).Unlike traditional dic-tionary based methods,the approach uses no manu-ally crafted patterns.However,their approach may misidentify term boundaries for phrases containing potentially ambiguous local structures such as co-ordination and parenthesis.They reported an F-measure score of0.73for different mixtures of mod-els tested on20abstracts.(Kazama et al.,2002)proposed a machine learn-ing approach to BNE tagging based on support vec-tor machines(SVM),which was trained on the GE-NIA corpus.Their preliminary results of the system showed that the SVM with the polynomial kernel function outperforms techniques of Maximum En-tropy based systems.Yet another BNE tagging system is ABNER(Set-tles,2005),which utilizes machine learning,namely conditional randomfields,with a variation of or-thographic and contextual features and no seman-tic or syntactic features.ABNER achieves an F-measure score of0.71on the NLPA2004shared task dataset corpus and0.70on the BioCreative cor-pus.and scored an F1-measure of51.8set.(Tanabe and Wilbur,2002)used a combination of statistical and knowledge-based strategies,which utilized automatically generated rules from transfor-mation based POS tagging and other generated rules from morphological clues,low frequency trigrams, and indicator terms.A key step in their method is the extraction of multi-word gene and protein names that are dominant in the corpus but inaccessible to the POS tagger.The advantage of such an approach is that it is independent of any biomedical domain. However,it can miss single word gene names that do not occur in contextual gene theme terms.It can also incorrectly tag compound gene names,plas-mids,and phages.(Mika and Rost,2004)developed NLProt,whichcombines the use of dictionaries,rules-basedfilter-ing,and machine learning based on an SVM classi-fier to tag protein names in MEDLINE.The NLProt system used rules for pre-filtering and the SVM for classification,and it achieved a precision of75%and recall76%.2.2Relationship ExtractionAs for the extraction of interactions,most efforts in extraction of biomedical interactions between enti-ties from text have focused on using rule-based ap-proaches due to the familiarity of medical terms that tend to describe interactions.These approaches have proven to be successful with notably good results.In these approaches,most researchers attempted to de-fine an accurate set of rules to describe relationship types and patterns and to build ontologies and dic-tionaries to be consulted in the extraction process. These rules,ontologies,and dictionaries are typi-cally domain specific and are often not generalizable to other problems.(Blaschke et al.,1999)reported a domain spe-cific approach for extracting protein-protein interac-tions from biomedical text based on a set of pre-defined patterns and words describing interactions. Later work attempted to automatically extract inter-actions,which are referenced in the database of in-teracting proteins(Xenarios et al.,2000),from the text mentioning the interactions(Blaschke and Va-lencia,2001).They achieved surprisingly low recall (25%),which they attributed to problems in properly identifying protein names in the text.(Koike et al.,2005)developed a system called PRIME,which was used to extract biological func-tions of genes,proteins,and their families.Their system used a shallow parser and sentence struc-ture analyzer.They extracted so-called ACTOR-OBJECT relationships from the shallow parsed sen-tences using rule based sentence structure analysis. The identification of BNEs was done by consulting the GENA gene name dictionary and family name dictionary.In extracting the biological functions of genes and proteins,their system reported a recall of 64%and a precision of94%.Saric et al.developed a system to extract gene expression regulatory information in yeast as well as other regulatory mechanisms such phosphoryla-tion(Saric et al.,2004;Saric et al.,2006).They used a rule based named entity recognition module, which recognizes named entities via cascadingfinite state automata.They reported a precision of83-90% and86-95%for the extraction of gene expression and phosphorylation regulatory information respec-tively.(Leroy and Chen,2005)used linguistic parsers and Concept Spaces,which use a generic co-occurrence based technique that extracts relevant medical phrases using a noun chunker.Their system employed UMLS(Humphreys and Lindberg,1993), GO(Ashburner et al.,2000),and GENA(Koike and Takagi,2004)to further improve extraction.Their main purpose was entity identification and cross ref-erence to other databases to obtain more knowledge about entities involved in the system.Other extraction approaches such as the one re-ported on by(Cooper and Kershenbaum,2005)uti-lized a large manually curated dictionary of many possible combinations of gene/protein names and aliases from different databases and ontologies. They annotated their corpus using a dictionary-based longest matching technique.In addition,they usedfiltering with a maximum entropy based named entity recognizer in order to remove the false posi-tives that were generated from merging databases. The problem with this approach is the resulting in-consistencies from merging databases,which could hurt the effectiveness of the system.They reported a recall of87.1%and a precision of78.5%in the relationship extraction task.Work by(Mack et al.,2004)used the Munich In-formation Center for Protein Sequences(MIPS)for entity identification.Their system was integrated in the IBM Unstructured Information Management Ar-chitecture(UIMA)framework(Ferrucci and Lally, 2004)for tokenization,identification of entities,and extraction of relations.Their approach was based on a combination of computational linguistics,statis-tics,and domain specific rules to detect protein in-teractions.They reported a recall of61%and a pre-cision of97%.(Hao et al.,2005)developed an unsupervised ap-proach,which also uses patterns that were deduced using minimum description lengths.They used pat-tern optimization techniques to enhance the patterns by introducing most common keywords that tend to describe interactions.(J¨o rg et.al.,2005)developed Ali Baba which uses sequence alignments applied to sentences an-notated with interactions and part of speech tags.It also usesfinite state automata optimized with a ge-netic algorithm in its approach.It then matches the generated patterns against arbitrary text to extract in-teractions and their respective partners.The system scored an F1-measure of51.8%on the LLL’05eval-uation set.The aforementioned systems used either rule-based approaches,which require manual interven-tion from domain experts,or statistical approaches, either supervised or semi-supervised,which also re-quire manually curated training data.3BioNocularsBioNoculars is a relationship extraction system that based on a fully unsupervised technique suggested by(Hassan et al.,2006)to automatically extract protein-protein interaction from medical articles.It can be retargeted to different domains such as pro-tein interactions in diseases.The only requirement is to compile domain specific taggers and dictionar-ies,which would aid the system in performing the required task.The approach uses an unsupervised graph-based mutual reinforcement,which depends on the con-struction of generalized extraction patterns that could match instances of relationships(Hassan et al.,2006).Graph-based mutual reinforcement is similar to the idea of hubs and authorities in web pages depicted by the HITS algorithm(Kleinberg, 1998).The basic idea behind the algorithm is that the importance of a page increases when more and more good pages link to it.The duality between pat-terns and extracted information(tuples)leads to the fact that patterns could express different tuples,and tuples in turn could be expressed by different pat-terns.Tuple in this context contains three elements, namely two proteins and the type of interaction be-tween them.The proposed approach is composed of two main steps,namely initial pattern construction and then pattern induction.For pattern construction,the text is POS tagged and BNE tagged.The tags of Noun Phrases or se-quences of nouns that constitute a BNE are removed and replaced with a BNE tag.Then,an n-gram lan-guage model is built on the tagged text(using tags only)and is used to construct weightedfinite state machines.Paths with low cost(high language model probabilities)are chosen to construct the initial set of patterns;the intuition is that paths with low cost (high probability)are frequent and could represent potential candidate patterns.The number of candi-date initial patterns could be reduced significantly by specifying the candidate types of entities of in-terest.In the case of BioNoculars,the focus was on relationships between BNEs of type PROTEIN. The candidate patterns are then applied to the tagged stream to produce in-sentence relationship tuples. As for pattern induction,due to the duality in the patterns and tuples relation,patterns and tuples are represented by a bipartite graph as illustrated in Fig-ure1.Figure1:A bipartite graph representing patterns and tuplesEach pattern or tuple is represented by a node in the graph.Edges represent matching between pat-terns and tuples.The pattern induction problem can be formulated as follows:Given a very large set of data D containing a large set of patterns P,which match a large set of tuples T,the problem is to iden-tify,which is the set of patterns that match the set of the most correct tuples T.The intuition is that the tuples matched by many different patterns tend to be correct and the patterns matching many differ-ent tuples tend to be good patterns.In other words, BioNoculars attempts to choose from the large space of patterns in the data the most informative,high-est confidence patterns that could identify correct tu-ples;i.e.choosing the most authoritative patterns in analogy with the hub-authority problem.The most authoritative patterns can then be used for extracting relations from free text.The following pattern-tuple pairs show how patterns can match tuples in the cor-pus:(protein)(verb)(noun)(prep.)(protein)Cla4induces phosphorylation of Cdc24 (protein)(I-protein)(Verb)(prep.)(protein) NS5A interacts with Cdk1The proposed approach represents an unsuper-vised technique for information extraction in general and particularly for relations extraction that requires no seed patterns or examples and achieves signifi-cant performance.Given enough domain text,the extracted patterns can support many types of sen-tences with different styles(such passive and active voice)and orderings(the interaction of X and Y vs. X interacts with Y).One of the critical prerequisites of the above-mentioned approach is the use of a POS tagger, which is tuned for biomedical text,and a BNE tag-ger to properly identify BNEs.Both are critical for determining the types of relationships that are of in-terest.For POS tagging,a decision tree based tagger developed by(Schmid,1994)was used in combi-nation with a model,which was trained on a cor-rected/revised GENIA corpus provided by(Saric et al.,2004)and was reported to achieve96.4%tagging accuracy(Saric et al.,2006).This POS tagger will be referred to as the Schmid tagger.For BNE tag-ging,ABNER was used.The accuracy of ABNER is approximately state of the art with precision and recall of74.5%and65.9%respectively with training done using the BioCreative corpora(BioCreative). Nonetheless we still face entity identification prob-lems such as missed identifications in the text which in turn affects our results considerably.We do be-lieve if we use a better identification method,we would yield better results.4Experimental SetupExperiments aimed at extracting protein-protein interactions for Bakers yeast(Sacharomyces Cerevesiae)to assess BioNoculars(Cherry et al., 1998).The experiments were performed using 109,440MEDLINE abstracts that contained the varying names of the yeast,namely Sacharomyces cerevisiae,S.Cerevisiae,Bakers yeast,Brewers yeast and Budding yeast.MEDLINE abstracts typically summarize the important aspects of papers possibly including protein-protein interactions if they are of relevance to the article.The goal was to deduce the most appropriate extraction patterns that can be later used to extract relations from any document.All the MEDLINE abstracts were used for pattern extraction except for70that were set aside for testing.There were no test documents in the training set.To build ground-truth,the test set was semi-manually POS and BNE tagged.They were also annotated with the interactions that are contained in the text.There was a condition that all the abstracts that are used for testing must have entries in the Database of Interacting Proteins and Protein-Protein Interactions(DIPPPI),which is a subset of the Database of Interacting Proteins (DIP)(Xenarios et al.,2000)restricted to proteins from yeast.DIPPPI lists the known protein-protein interactions in the MEDLINE abstracts.There were 297protein-protein interactions in the test set of70 abstracts.One of the disadvantages of DIPPPI is that the presence of interactions is indicated without mentioning their types or from which sentences they were extracted.Although BioNoculars is able to guess the sentence from which an interaction was extracted and the type of interaction,this informa-tion was ignored when evaluating against DIPPPI. Unfortunately,there is no standard test set for the proposed task,and most of the evaluation sets are proprietary.The authors hope that others can benefit from their test set,which is freely available.The abstracts used for pattern extraction were POS tagged using the Schmid tagger and BNE tag-ging was done using ABNER.The patterns were re-stricted to only those with protein names.For extrac-tion of interaction tuples,the test set was POS and BNE tagged using the Schmid tagger and ABNER respectively.A varying number offinal patterns were then used to extract tuples from the test set and the average recall and precision were computed.An-other setup was used in which the relationships were filtered using preset keywords for relationships such as inhibits,interacts,and activates to properly com-pare BioNoculars to systems in the literature that use such keywords.The keywords were obtained from the(Hakenberg et al.,2005)and(Temkin and Gilder, 2003).One of the generated pattern-tuple pairs was as follows:(PROTEIN)(Verb)(Conjunction)(PROTEIN) NS5A interacts with Cdk1One consequence of tuple extraction is generation of redundant tuples,which contain the same enti-Pattern Count591031922170.510.760.840.89Precision0.420.350.260.160.490.550.440.403078147205 Recall0.440.480.730.780.310.350.390.35FMeasure0.400.400.470.50 Table2:Recall,Precision,and Recall for extraction of tuples using a varying number of top rated patters keywordfilteringlow precision levels warrant thorough investigation. In the second set of experiments,extracted tuples werefiltered using preset keywords indicating inter-actions.Table2and Figure3show the results of theexperiments.Figure3:Recall,Precision,and F-measure for tu-ple extraction using a varying number of top patterns with keywordfilteringThe results show thatfiltering with keywords led to lower recall,but precision remained fairly steady as the number of patterns changed.Nonetheless,the best precision in Figure3is lower than the best pre-cision in Figure2and the maximum F-measure for this set of experiments is lower than the maximum F-measure when nofiltering was used.The BioNoc-ulars system with nofiltering can be advantageous for recall oriented applications.The use of nofilter-ing suggests that some interaction may be expressed in more generic forms or patterns.An intermediate solution would be to increase the size of the list of most commonly occurring keywords tofilter the ex-tracted tuples further.Currently,ABNER,which is used by the system, has a precision of75.4%and a recall of65.9%.Per-haps improved tagging may improve the extraction effectiveness.The effectiveness of BioNoculars needs to bethoroughly compared to existing systems via the use of standard test sets,which are not readily available. Most of previously reported work has been tested on proprietary test sets or sets that are not publicly available.The creation of standard publicly avail-able test set can prompt research in this area.6Conclusion and Future WorkThis paper presented a system for extracting protein-protein interaction from biomedical text call BioNoculars.BioNoculars uses a statistical un-supervised learning algorithm,which is based on graph mutual reinforcement and data redundancy to extract extraction patterns.The system is re-call oriented and is able to properly extract93%of the interaction mentions from test MEDLINE ab-stracts.Nonetheless,the systems precision remains low.Precision can be enhanced by using keywords that describe interactions tofilter to the resulting in-teraction,but this would be at the expense of recall. As for future work,more attention should be fo-cused on improving extraction patterns.Currently, the system focuses on extracting interactions be-tween exactly two proteins.Some of the issues that need to be handled include complex relationship(X and Y interact with A and B),linguistic variabil-ity(passive vs.active voice;presence of superflu-ous words such as modifiers,adjectives,and prepo-sitional phrases),protein lists(W interacts with X, Y,and Z),nested interactions(W,which interacts with X,also interacts with Y).Resolving these is-sues would require an investigation of how patterns can be generalized in automatic or semi-automatic ways.Further,the identification of proteins in the text requires greater attention.Also,the BioNocu-lars approach can be combined with other rule-based approaches to produce better results. 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