Relationship Extraction from Biomedical Documents using Conditional Random Fields
Relationship Extraction from Biomedical Documents usingConditional Random FieldsSaurav Sahay, Jinhan Lee, Niyant KrishnamurthiCollege of Computing, Georgia Tech.AbstractExtracting complex relationships automatically from unstructured information resources is a challenging problem. It is an important problem in this present age of abundant machine processable information as there is a need to build intelligent knowledge-aware applications for tasks such search, extraction and reasoning. We have used Conditional Random Fields (CRFs) to identify various relationships from biomedical abstracts.1. IntroductionWe look at the problem of relationship extraction as a sequence labelling and segmentation problem of observation instances. By relationship, we mean the type of association between two neighbouring entities in the domain. More explicitly, a relationship in our context is a {Concept1, Relationship, Concept2} triple. Figure 1 shows a part of the map extracted automatically from Medline abstracts related to Nuclear Cardiology.Figure 1. Map of Relationships extracted from Medline abstractsGraphical models such as Hidden Markov models (HMMs) have been most commonly applied in several bioinformatics, linguistics, modelling and recognition problem. HMMs are directed generative models that involve computation of P(y,x) whereas CRFs are undirected discriminative models that involve computation of P(y|x) (comparable to naïve bayes and logistic regression).2. Related WorkThis problem is similar to the task of named entity recognition from text and there is a lot of work in this area of language processing such as Part of Speech Tagging, Noun Phrase Chunking and Semantic Role Labelling. McCallum, Lafferty and Sutton [1-5] who invented the CRF formalism have several work using CRF to sequence and label data. Bunescu et al [6] have compared linearchain CRFs with Relational Markov Network (RMN)[7] for information extraction, the problem of identifying phrases in natural language text that refer to specific types of entities. Craven et al[8] have used relational learning methods to extract facts from text in order to construct molecular biology knowledgebases. Zhao et al [9] have described collective classification of actors, events and relationships in affiliation networks using RMNs.3. Conditional Random FieldConditional Random Fields (CRFs) are conditional probability distribution models that factorize based on an undirected model. It models the conditional distribution P(Y|X) where X is a set of input variable we observe and Y is a set of hidden variables we predict. By modeling the conditional distribution directly, it does not require to model the distribution P(X), which can includes complex dependencies. In other words, dependencies among the X do not need to be explicitly represented so that CRF can afford the use of rich global features of the input X.3.1 Linear-chain CRFA linear-chain CRF assumes a first-order Markov assumption on the dependencies among hiddenvariable y.Figure 2. Linear Chain CRFA linear-chain conditional random field takes form, where a set of real-valued feature functions isK k t k x y y f 1)},',({= and an instance-specific normalization function is )(x Z . A feature function ),,(1t t t k x y y f − could be arbitrary.∑=−=K k t t t k k x y y f x Z x y p 11)},,(exp{)(1)|(λ, ∑∑=−=y Kk t t t k k x y y f x Z 11)},,(exp{)(λ3.2 Dynamic CRFA dynamic CRF is a generalization of linear-chain CRFs that factorizes based on an undirected model whose structure and parameters are repeated. Model can have a set of hidden variables and complex interaction between hidden variables.Figure 3. Dynamic CRFA dynamic conditional random field takes form ∏∏∑∈=−=t C c K k t t t k k x y y f x Z x y p 11)},,(exp{)(1)|(λ ∑∏∏∑∈==y t C c Kk t c t k k x y f x Z 1,)},(exp{)(λInference in these models can be done using any of the inference algorithms for undirected models. Viterbi decoding is generally used for sequence labelling and marginal computation is used for parameter estimation. Exact inference is generally expensive in Dynamic models hence approximate inference techniques such as loopy Belief Propagation is applied to compute probabilities.4. MethodsWe have setup our relationship extraction task as a text classification problem where we have used rich linguistic and semantic features apart from local contextual features as feature vectors for the sentence phrases. More specifically, we have assigned features to each syntactic phrase of the sentences in biomedical abstracts. Relationships span across several phrases in a sentence. In order to capture this complex structure, we have used features from the neighbouring phrases and added them to each phrase’s set of feature.We have extracted relationships from biomedical abstracts in order to create our benchmark dataset. Our relationship extraction process is described in Figure 4. We have used NLM'sUnified Medical Language System (UMLS), a very large ontology of biomedical and health data as a dictionary for mapping biomedical terms to their Concept types and Semantic Types. We have used WordNet Lexical database for finding synonyms of verb phrases in abstracts in order to categorize them into a set of predefined relationships. The relationships are categorized as ‘affects ’, ‘causes ’, ‘exhibits ’, ‘analyzes ’, etc between the pair of concepts surrounding the verb phrases.The algorithm for relationship extraction is summarized in Figure 4[10].For each abstract AFor each sentence S in AFind occurrences of domain concept pairs in S Æ PairsFor each concept pair <C1, C2> in PairsApply verbGroup matching classify <C1,C2> into relations Æ RAdd R to Relationship Mapreturn MapFigure 4: Relation extraction algorithmExamples of some extracted and categorized concepts and relationships are as follows (Figure 5):left ventricle DIVIDED <PART_OF> regional myocardial uptakesstudy EVALUATED <MEASURES> impactprognosis population COMPRISED <CONTAINS> 16,020 consecutive patientscardioinhibitory response SHOWED <EXHIBITS> vasodepressor responseeighteen patients UNDERWENT <BRINGS_ABOUT> i-123 mibgconstant supply SUSTAIN <PREVENTS> contractile functionFigure 5: Categorized relationshipsWe have used this set of categorized relationships as labels for the phrases surrounding the concepts. For a majority of phrases, where there are no semantic relationships between surrounding concepts, we categorize them as having no relationships. Thus, we have a sparse dataset of few relationships among phrases in our training as well as test dataset.Our feature extraction algorithm is described in the following pseudo-code (Figure 6).For each phrase P in sentence SFor phrase neighborhood of {-n,+n} phrasesExtract contextual, linguistic and semantic featuresIf phrase matches in relationship R from MapLabel PhraseFeatureVector with verbGroup of RElse Label PhraseFeatureVector with type ‘None’Return PhraseFeatureVectorFigure 6: Feature extraction algorithm4.1 Choice of FeaturesThe choice of features to describe the relationship labels is extremely important to get good results for this problem. In most text classification problems, a simple ‘bag of words’ approach is taken to populate the vector space of features. These features are statistically extracted using techniques like ‘term frequency – inverse document frequency’ (TFIDF) or z-score method. These statistical features make the space of possible feature set extremely large thus requiring huge training data to come up with good decision boundaries for classification of data into the right categories.In contrast, we have used rich syntactic and semantic features for our data exploiting the rich and freely available ontologies like UMLS[11] and WordNet. We have extracted Parts of Speech information for the phrases using a Biomedical Parts of Speech tagger MedPostSKRTagger[12].The feature categories that we have used are as follows:1.Phrase2.Concept Types of phrase3.Semantic Type of phrase4. Part of Speech of phraseWe have used these features for the current as well as neighbouring phrases for capturing the rich semantics of the text to be categorized.Examples of these features are:Phrase – myocardial perfusion, Concept Type – Myocardial Perfusion, Semantic Type – Organism Attribute, Part of Speech – Noun PhrasePhrase – examined, Concept Type – Examine, Semantic Type – Finding, Part of Speech – Verb Phrase5. Experiments5.1 Recognizing a relation phraseThe aim of this experiment was to label a phrase as a relation if it defines any relationship. This binary classification experiment labelled the phrases as a ‘relation’ phrase or ‘none’. This is still a difficult classification because relationship phrases constitute less than 5% of the entire text corpus. The CRF must learn this differentiation using features which contain the POS tags, concepts, types of concepts and the actual phrases. The concepts of phrases that come in a window are specified along with their relative position. The concepts of phrases that come before the current relation phrase are coded differently than those that come after to capture the structure of concepts in a relationship.We used the Mallet CRF implementation for our tasks. The CRF performed quite well in this task, labelling most of the relation phrases correctly. Even though it had many false positives there are no false negatives and all the phrases categorized as ‘None’ had a 100% precision as seen in the table below.Label N Correct Returned P R F1None 980 966 966 1 0. 985 0.993Relation 40 40 54 0.740 1.0 0.851Testing Accuracy: 0.9861006 out of 1020 instances correctly labelledTable 1: Binary classification of relationships5.2 Segmentation of Relation PhrasesWe designed this experiment to segment each relationship triple from the entire corpus. The relationship triple consisted of simple phrases like ‘P53 gene affects cancer’. In this example‘P53 gene’ and ‘cancer’ are concepts and the experiment labels the entire phrase as a relationship.We label the relation phrases in the training data using a ‘B’ tag to indicate the start of a relationship, ‘I’ tag to indicate following phrases in the relationship and a ‘O’ tag to mark all other non relationship phrases. The model used to learn this segmentation is a factorial CRF with one of the linear CRF chains learning to label the relations and the other linear CRF to learn the segmentation.The advantage of this model which simultaneously discovers both the labels and the segmentation in comparison with an algorithm which uses the label to segment is that error does not propagate from one step to the next. The errors in the labelling step do not lead to errors in the segmentation.RPF1ReturnedLabel NCorrect(Start) 21 8 11 0.727 0.381 0.5B(Intermediate) 52 15 24 0.652 0.288 0.399I(Other) 591 582 630 0.953 0.984 0.953OTable 2: Segmentation of relationship triplesThe results of the experiment show that the system does learn to an extent to segment the relation phrases with a high precision. The low recall is mostly because of the sparse data and should improve if a larger amount of data is used to train the CRF.5.3 Relation ExtractionThe experiment uses a linear chain CRF to label each individual interesting relationship in the test corpus. The features were extracted from 100 abstracts and used for training. The features consisted of the POS tags, concepts, semantic types and the actual phrase. The noun phrase for the concepts can occur a few phrases before or after the relation verb.Label NReturnedP R F1Correct1.00.976884None 863863brings_about 2 1 1 1.0 0.5 0.6660.2750.190OTHER 21 4 8 0.5Performs 0 0 0 1.0 1.0 1.0Analyzes 2 0 0 1.0 0.0 0.0Causes 1 0 0 1.0 0.0 0.0Disrupts 0 0 0 1.0 1.0 1.0part_of 1 0 0 1.0 0.0 0.0Exhibits 2 2 2 1.0 1.0 1.0Traverses 0 0 0 1.0 1.0 1.0Uses 1 0 0 1.0 0.0 0.0location_of 0 0 0 1.0 1.0 1.0Accuracy: 0.972 Correct 870 out of 895 Joint accuracy: 0.972Table 3: Relationship labellingThe experiment extracted a few relations well from the test set. The sparseness and skew of the training data might account for it not being able to learn the low frequency relations but the relations with a larger number of examples in the training seem to work well. Another way that the efficiency of this algorithm might be hampered is if the concepts discovered in the mining stage are not general enough.6. Future WorkMany interesting experiments can be done to improve the overall efficiency of the relation discovery task of our system. Instead of giving the actual index of the relative position we can specify only if the concept came before or after a verb phrase. The NLP based relation extractor can be further improved to capture more complex relationship for training the CRF and we can use models such as skip CRF and relational markov networks to learn more complex relationships.7 ConclusionLinear chain CRF was used for labelling relations in a corpus. The experiment discovered a few relations and should perform much better with a larger training corpus. The task of binary classification of a relation and a non-relation phrase performed well with high precision and recall values. The suggestions listed in the future work can be used to further improve the experiment results.Referencesfferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models forsegmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning,Morgan Kaufmann, San Francisco, CA (2001) 282–2892.Dynamic Conditional Random Fields for Jointly Labeling Multiple Sequences. AndrewMcCallum, Khashayar Rohanimanesh, and Charles Sutton. In NIPS Workshop on Syntax,Semantics, and Statistics. December 2003.3.Collective Segmentation and Labeling of Distant Entities in Information Extraction. CharlesSutton and Andrew McCallum. In ICML Workshop on Statistical Relational Learning and ItsConnections to Other Fields. 2004.4.Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling andSegmenting Sequence Data. Charles Sutton, Khashayar Rohanimanesh, and Andrew McCallum.In International Conference on Machine Learning (ICML). 2004.5.Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling andSegmenting Sequence Data. Charles Sutton, Andrew McCallum, and Khashayar Rohanimanesh.Journal of Machine Learning Research. vol. 8. March 2007. pp. 693--723.6.Statistical Relational Learning for Natural Language Information Extraction.Razvan Bunescu and Raymond J. MooneyStatistical Relational Learning, Lise Getoor and Ben Taskar (Eds.) book, 2005.7.Benjamin Taskar, Pieter Abbeel, and D. Koller. Discriminative probabilistic models for relationaldata. In Proceedings of 18th Conference on Uncertainty in Artificial Intelligence (UAI-2002),pages 485–492, Edmonton, Canada, 2002.8.Craven, M. and Kumlien, J. 1999. Constructing Biological Knowledge Bases by ExtractingInformation from Text Sources. In Proceedings of the Seventh international Conference onintelligent Systems For Molecular Biology (August 06 - 10, 1999). T. Lengauer, R. Schneider, P.Bork, D. L. Brutlag, J. I. Glasgow, H. Mewes, and R. Zimmer, Eds. AAAI Press, 77-86.9.Bin Zhao, Prithviraj Sen, Lise Getoor, 2006. Entity and Relationship Labeling in AffiliationNetworks. ICML Workshop on Statistical Network Analysis, 200610.Domain Ontology Construction from Biomedical Text, S Sahay, B Li, EV Garcia, E Agichtein, ARam. International Conference on Artificial Intelligence (ICAI-07).11./12.L. Smith, T. Rindflesch, and W. J. Wilbur. Medpost: a part-of-speech tagger for biomedical text.Bioinformatics, 20(14), 2004.。
取样分离法的英文缩写
取样分离法的英文缩写The Abbreviation of Sampling Separation MethodIntroduction:Sampling separation is a crucial process in various scientific fields, aiming to isolate and extract specific components from a mixture for further analysis or application. In order to simplify its usage, an abbreviation, also known as an acronym or initialism, is often assigned to represent the sampling separation method. This article explores the significance of abbreviations for sampling separation methods in the English language.Abbreviations in Sampling Separation Methods:Abbreviations play a vital role in simplifying complex terminologies, especially in scientific research. By condensing a long and technical name into a shorter form, abbreviations make it easier for researchers, professionals, and even general readers to refer to a particular sampling separation method efficiently. These abbreviations are widely accepted and recognized across the scientific community and help in creating a standardized language for communication.Importance of Abbreviations:1. Enhanced Communication:Abbreviations act as effective communication tools, as they facilitate easy and precise exchange of information between researchers and experts. The use of abbreviations enables efficient and concise communication in scientific journals, research papers, and conferences. Additionally,abbreviations assist in the dissemination of knowledge, allowing researchers to present their work without the cumbersome repetition of lengthy methodologies.2. Time-Saving:The utilization of abbreviations saves valuable time for both the writer and the reader. Instead of repeatedly writing the complete name of a sampling separation method, researchers can use the respective abbreviation, reducing the overall length of the text. This time-saving benefit enables authors to focus on explaining the nuances of the method rather than wasting words on repetitive instances of its name.3. Standardization:Standardization plays a pivotal role in establishing a common platform for sharing and understanding scientific concepts. Abbreviations ensure uniformity and consistency in scientific literature and research, as the same abbreviation is used for a specific sampling separation method by different scientists and researchers. This standardization eliminates confusion and ambiguity that may arise due to variations in the nomenclature of different techniques.Examples of Abbreviations:1. Liquid-Liquid Extraction (LLE): LLE is a widely used method for extracting a compound of interest from a liquid mixture by partitioning it between two immiscible liquids.2. Solid Phase Extraction (SPE): SPE is a technique that involves the use of a solid adsorbent to extract and isolate specific analytes from a sample matrix.3. Gas Chromatography (GC): GC is an analytical method used to separate and analyze volatile compounds in a gaseous mixture.4. High-Performance Liquid Chromatography (HPLC): HPLC is a technique that utilizes high-pressure pumps to separate and identify components in a liquid mixture.Conclusion:Sampling separation methods are pivotal in various scientific fields for accurate analysis and understanding of mixtures. Abbreviations associated with these methods have become essential tools for efficient communication, time-saving writing practices, and standardization of terminologies in scientific literature. It is crucial for scientists and researchers to utilize these abbreviations accurately and consistently, ensuring effective knowledge dissemination across the scientific community.。
马齿苋中抗炎活性物质的提取、分离及结构鉴定
马齿苋中抗炎活性物质的提取、分离及结构鉴定张会敏1，邢岩2，仇润慷1，张丽梅2，倪贺3，赵雷1*（1.华南农业大学食品学院，广东广州 510642）（2.国珍健康科技（北京）有限公司，北京 100000)（3.华南师范大学生命科学学院，广东广州 510640）摘要：以活性物质示踪为导向，建立脂多糖诱导的RAW264.7巨噬细胞炎症模型对马齿苋中的抗炎物质进行跟踪，采用柱层析提取法、硅胶柱色谱分离法、制备液相色谱法及气相色谱-质谱联用技术对抗炎物质进行提取分离和结构鉴定。
结果表明，石油醚-乙醇、无水乙醇和纯水溶剂依次对马齿苋样品进行提取，三种粗提物将细胞中一氧化氮（Nitric Oxide，NO）的分泌量分别减少至33.13、25.83和20.53 μmol/L，其中石油醚相粗提物的抑制效果最强（P<0.05）。
对石油醚相进一步分离得到四个组分，Fr.1、Fr.2和Fr.3组分具有较强的抗炎效果，但Fr.1和Fr.2组分含有潜在的毒性成分，选择Fr.3组分继续分离。
Fr.3组分经硅胶柱分离得到三个组分，Fr.3.1组分表现出最强的抑制NO的分泌量效果（11.80 μmol/L）。
经制备液相色谱进一步纯化及气质分析，确定Fr.3.1组分的主要成分为硬脂酸（47.09%）、邻苯二甲酸二(2-乙基己)酯（13.21%）和其他成分。
该研究建立了一种从马齿苋中分离纯化出抗炎物质方法，为马齿苋的开发利用提供理论参考。
关键词：马齿苋；抗炎活性；提取分离；鉴定文章编号：1673-9078(2024)03-191-199 DOI: 10.13982/j.mfst.1673-9078.2024.3.0324Extraction, Separation and Structural Identification of Anti-inflammatory Active Substances from Purslane (Portulaca oleracea L.)ZHANG Huimin1, XING Y an2, QIU Runkang1, ZHAGN Limei2, NI He3, ZHAO Lei1*(1.College of Food Science, South China Agricultural University, Guangzhou 510642, China)(2.Guozhen Health Technology (Beijing) Co. Ltd., Beijing 100000, China)(3.College of Life Sciences, South China Normal University, Guangzhou 510640, China)Abstract: To track the anti-inflammatory substances in purslane, the lipopolysaccharide-induced RAW264.7 macrophage inflammation model was established, which was guided by the tracer of active substances. The extraction, separation and structural identification of anti-inflammatory substances in purslane were performed by column chromatography (for extraction), silica gel column chromatography (for separation), and preparative high performance liquid chromatography and gas chromatography-mass spectrometry (for analyses). The results showed that the three crude extracts obtained from purslane through sequential extractions with petroleum ether-ethanol, anhydrous ethanol and pure引文格式：张会敏,邢岩,仇润慷,等.马齿苋中抗炎活性物质的提取、分离及结构鉴定[J] .现代食品科技,2024,40(3):191-199.ZHANG Huimin, XING Yan, QIU Runkang, et al. Extraction, separation and structural identification of anti-inflammatory active substances from purslane (Portulaca oleracea L.) [J] . Modern Food Science and Technology, 2024, 40(3): 191-199.收稿日期：2023-03-16基金项目：国家自然科学基金资助项目（31771980）；广东省自然科学基金（2023A1515012599）作者简介：张会敏（1996-），女，硕士研究生，研究方向：活性物质分离提取，E-mail：；共同第一作者：邢岩（1981-），女，博士，助理研究员，研究方向：抗氧化与抗衰老，E-mail：通讯作者：赵雷（1982-），男，博士，教授，研究方向：天然产物绿色修饰及热带水果加工，E-mail：191water solvents reduced the secretion of nitric oxide (NO) in the cells to 33.13, 25.83 and 20.53 μmol/L, respectively, with the crude petroleum ether extract exhibiting the strongest inhibitory effect (P<0.05). The petroleum ether phase was further separated into four fractions, with the Fr.1, Fr.2 and Fr.3 fractions had stronger anti-inflammatory effects, though the Fr.1 and Fr.2 fractions contained potential toxic components. Therefore, the Fr.3 fraction was selected for further separation. The Fr.3 fraction was separated through a silica gel column to obtain three fractions. The Fr.3.1 subfraction exhibited the strongest inhibitory effect against the NO secretion (11.80 μmol/L). The Fr.3.1 subfraction was further purified by the preparative liquid chromatography and GC-MS analysis, and the main components of the Fr.3.1 subfraction were identified as stearic acid (47.09%), di(2-ethylhexyl)phthalate (13.21%) and other components. This study established a method for separating and purifying anti-inflammatory substances from purslane, and provides a theoretical reference for the development and utilization of purslane.Key words: Portulaca oleracea L.; anti-inflammatory activity; extraction and isolation; identification炎症是机体受到外部刺激时做出的一种保护性生理反应，能够及时清除体内受损或死亡的细胞，帮助机体恢复内部平衡[1] 。
生物分离工程的英语
生物分离工程的英语Biological Separation Engineering is a specialized field that focuses on the isolation and purification of biological products. It plays a crucial role in the pharmaceutical, food, and biotechnology industries, where the extraction ofbioactive compounds from natural sources is essential.The process typically begins with the selection of an appropriate feedstock, which could be anything from plant material to microorganisms. Once the feedstock is identified, it undergoes a series of steps to separate the desired components. These steps may include:1. Pre-treatment: This involves breaking down the complex structure of the feedstock to release the target molecules. Techniques such as mechanical disruption, enzymatic digestion, or chemical treatment may be used.2. Extraction: The target molecules are then extractedfrom the pre-treated material. This can be done using solvent extraction, where a solvent is used to dissolve the desired compounds, or by using methods like supercritical fluid extraction, which employs high-pressure gases to extract the compounds.3. Concentration: After extraction, the solution is often diluted and needs to be concentrated to increase the concentration of the target molecules. This can be achievedthrough evaporation, membrane filtration, or centrifugation.4. Purification: The concentrated solution may still contain impurities, so further purification is necessary. Chromatography is a common technique used at this stage, which separates molecules based on their affinity to the stationary phase.5. Polishing: The final step is to polish the purified product to ensure it meets the required specifications. This may involve additional rounds of purification or the use of specific techniques to remove any remaining impurities.Biological separation engineering is a complex process that requires a deep understanding of both the properties of the target molecules and the various separation techniques available. Advances in this field are continually improving the efficiency and selectivity of these processes, making it possible to produce high-quality biological products for a wide range of applications.。
Senserelations语义关系
Semantic relationships are the foundation of language understanding. By analyzing semantic relationships, one can understand the meaning of words and sentences, and thus comprehend the meaning of the entire text.
要点一
要点二
Detailed description
Semantic conflict refers to the situation where two concepts or entities are contradictory or mutually exclusive in meaning and nature. For example, "peace" and "war" are conflicting because they represent opposite meanings and states.
Semantic relevance
Refers to the existence or attribute of one concept or entity containing the existence or attribute of another concept or entity.
Summary word
Statistical methods
Deep learning based methods
Summary: Based on deep learning methods, neural network models are used to recognize and calculate semantic relationships by learning semantic patterns from corpora.
不同方法保存的患病异育银鲫中鲤疱疹病毒2型DNA提取和PCR扩增效果分析
第45卷第6期Vol.45 No.6淡水渔业Freshwater Fisheries2015年11月Nov.2015不同方法保存的患病异育银鲫中鲤疱疹病毒2型D N A提取和P C R扩增效果分析卢俊，陆宏达，岳蒙蒙，操艮萍(上海海洋大学水产与生命学院，上海201306)摘要：以患鲤疱瘆病毒2型（C y A n i h#AeOTAu 2, CyHV-2)疾病的异育银卿（C a ra s u auratos —纟！/)肾脏为实验材料，采用-20丈冷冻、100%乙醇、Dafan o P液、10%福尔马林、Zenker P液、Muller S液、 2.5%戊二醛、Hel-l y p液、Mossmanp液、Bouinp液和Cam oyp液不同方法进行保存，通过微量分光光度计检测和琼脂糖凝胶电泳探讨不同保存方法对病毒DNA提取效果、常规PC R扩增效果和巢式PC R扩增效果的影响。
结果表明：从病鱼肾脏中提取D NA时，除100%乙醇保存方法与-20丈冷冻保存组一样可以提取高质量的DNA外，其它保存方法对DNA提取有不同程度的影响，100%乙醇保存方法和保存的材料可以用作于鲤疱疹病毒2型D N A的提取；常规PCR扩增时，100%乙醇、ZenkemS液、 2.5%戊二醛、Helly P液、BO u in P液和CamO y P液保存方法与-20m冷冻保存组具有相同的效果，在362 b p处出现明亮一致的清晰单一目的条带，这6种保存方法及其保存的材料可以用作鲤疱疹病毒2型的常规PCR扩增；巢式PCR扩增时，100%乙醇、DafanoP液、10%福尔马林、Zenkers液、Muller’s液、2. 5%戊二醛、H ellyp液、Mossman’s液、Bouin’s液和Carnoy’s液所有保存方法与-20 m冷冻保存组具有相同的效果，在339 b p处出现明亮一致的清晰单一目的条带，这些保存方法及其保存的材料均可以用作于鲤疱疹病毒2型的巢式P CR扩增，在采用巢式PCR进行检测和诊断患鲤疱疹病毒2型疾病上具有应用价。
Microbial DNA Extract
Microbial DNA ExtractMicrobial DNA extraction is a crucial process in microbiology and molecular biology, playing a fundamental role in various scientific and medical applications. The extraction of microbial DNA involves isolating and purifying the genetic material from microorganisms such as bacteria, fungi, and viruses. This process is essential for studying the genetic makeup of microorganisms, understanding their roles in various ecosystems, and diagnosing infectious diseases. In this discussion, we will explore the significance of microbial DNA extraction, the methods and techniques involved, as well as the applications and implications of this process. First and foremost, it is important to recognize the significanceof microbial DNA extraction in scientific research and medical diagnostics. Microorganisms play a pivotal role in various ecological processes, and understanding their genetic makeup is crucial for comprehending their functionsand interactions within different environments. Additionally, microbial DNA extraction is essential for the diagnosis of infectious diseases caused by bacteria, viruses, and fungi. By isolating and analyzing the DNA of pathogenic microorganisms, healthcare professionals can accurately identify and treat infectious diseases, thereby contributing to public health and disease management. In terms of methods and techniques, there are several approaches to microbial DNA extraction, each with its own advantages and limitations. Common methods include the use of chemical and mechanical lysis to break open microbial cells, followedby purification of the DNA using techniques such as phenol-chloroform extraction, silica membrane-based purification, or magnetic bead-based purification. Thechoice of extraction method depends on various factors such as the type of microorganism, the sample source, and the downstream applications of the extracted DNA. It is essential to carefully select the most suitable method to ensure the purity and integrity of the extracted DNA for accurate and reliable analysis. Furthermore, the applications of microbial DNA extraction are diverse and far-reaching. In research settings, extracted microbial DNA is used for various molecular biology techniques, including polymerase chain reaction (PCR), DNA sequencing, and metagenomic analysis. These techniques allow scientists to study the genetic diversity of microorganisms, investigate their evolutionaryrelationships, and explore their potential biotechnological and industrial applications. Moreover, in clinical diagnostics, microbial DNA extraction is integral to the detection and identification of pathogenic microorganisms, enabling rapid and accurate diagnosis of infectious diseases, as well as the monitoring of antimicrobial resistance. From an ethical standpoint, the implications of microbial DNA extraction raise important considerations regarding biosecurity, biosafety, and the responsible use of genetic information. The handling and storage of microbial DNA must adhere to strict biosafety protocols to prevent accidental release or misuse of potentially hazardous genetic material. Additionally, the ethical implications of accessing and analyzing microbial genetic information should be carefully considered to ensure that privacy and confidentiality are maintained, particularly in the context of human-associated microorganisms and infectious disease diagnostics. In conclusion, microbial DNA extraction is a fundamental process with significant implications for scientific research, medical diagnostics, and ethical considerations. The extraction of microbial DNA enables the study of microorganisms' genetic makeup, their roles in ecosystems, and the diagnosis of infectious diseases. The diverse methods and techniques involved in microbial DNA extraction, along with its wide-ranging applications, underscore its importance in various fields of microbiology and molecular biology. It is imperative to approach microbial DNA extraction with a thorough understanding of its significance, methods, applications, and ethical implications to ensure its responsible and beneficial use in scientific and medical endeavors.。
孟德尔随机化方法在胃癌危险因素研究中的应用
doi:10.3971/j.issn.1000-8578.2023.22.1411孟德尔随机化方法在胃癌危险因素研究中的应用王梦圆1，许恒敏1，汪靖暄2，潘凯枫1，李文庆1Mendelian Randomization Analysis of Research on Risk Factors for Gastric Cancer WANG Mengyuan 1, XU Hengmin 1, WANG Jingxuan 2, PAN Kaifeng 1, LI Wenqing 11. Department of Cancer Epidemiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing 100142, China;2. School of Basic Medical Science, Peking University, Beijing 100191, China CorrespondingAuthor:LIWenqing,E-mail:*******************.cn李文庆 北京大学肿瘤医院研究员、博士生导师、临床流行病学研究中心副主任、北京大学国际癌症研究院PI ，入选国家中组部海外优青项目、北京市海外高层次人才计划、北京市特聘专家、北京市优秀人才青年拔尖个人。
北京大学博士，美国哈佛医学院博士后，美国国立癌症研究所（NCI ）访问研究员，归国前任美国常青藤名校布朗大学助理教授和博士生导师。
研究方向为肿瘤流行病学和分子流行病学。
在包括BMJ 、JCO 、JAMA Intern Med 、Hepatology 、JNCI 等高水平杂志在内的SCI 收录期刊上发表论文132篇。
生物材料中提取的案例
生物材料中提取的案例英文回答：Extraction of biomaterials: A review of methods and applications.Biomaterials are materials that are used to interact with living biological systems for a medical purpose. They can be used to replace or repair damaged tissues, todeliver drugs or other therapeutic agents, or to provide structural support.The extraction of biomaterials from natural sources is a complex process that requires careful attention to the properties of the material and the desired application. The most common methods of extraction include:Mechanical extraction: This method involves the physical removal of the biomaterial from its source. This can be done using a variety of techniques, such as cutting,grinding, or milling.Chemical extraction: This method involves the use of chemicals to dissolve or extract the biomaterial from its source. This can be done using a variety of chemicals, such as acids, bases, or solvents.Biological extraction: This method involves the use of biological agents, such as enzymes or bacteria, to extract the biomaterial from its source.The choice of extraction method depends on a number of factors, including the nature of the biomaterial, the desired application, and the desired properties of the extracted material.Once the biomaterial has been extracted, it can be further processed to improve its properties or to make it more suitable for a specific application. This processing may include:Purification: This process removes impurities from thebiomaterial.Sterilization: This process kills bacteria and other microorganisms that may be present in the biomaterial.Modification: This process alters the properties of the biomaterial to make it more suitable for a specific application.Biomaterials have a wide range of applications in medicine, including:Tissue engineering: Biomaterials can be used to create scaffolds for the growth of new tissues.Drug delivery: Biomaterials can be used to deliver drugs or other therapeutic agents to specific parts of the body.Structural support: Biomaterials can be used to provide structural support to damaged tissues or organs.The extraction of biomaterials from natural sources is a critical step in the development of new medical technologies. By understanding the different methods of extraction and processing, researchers can develop biomaterials that are more effective and more suitable for a wider range of applications.中文回答：生物材料提取，方法和应用综述。
高度耐热的地衣聚糖酶
Journal of Molecular Catalysis B:Enzymatic 115(2015)8–12Contents lists available at ScienceDirectJournal of Molecular Catalysis B:Enzymaticj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /m o l c a tbA highly thermostable lichenase from Bacillus sp.UEB-S:Biochemical and molecular characterizationSameh Maktouf a ,b ,c ,d ,∗,Claire Moulis b ,c ,d ,Nabil Miled e ,Semia Ellouz Chaabouni a ,Magali Remaud-Simeon b ,c ,daUniversitéde Sfax,ENIS,UnitéEnzymes et Bioconversion,Route de Soukra km 4,3038Sfax,Tunisia bUniversitéde Toulouse,INSA,UPS,INP,LISBP,135Avenue de Rangueil,F-31077Toulouse,France cINRA,UMR792,Ingénierie des Systèmes Biologiques et des Procédés,F-31400Toulouse,France dCNRS,UMR5504,F-31400Toulouse,France eLaboratoire de Biochimie et de Génie Enzymatique des Lipases,ENIS,BP1173,3038Sfax,Tunisiaa r t i c l ei n f oArticle history:Received 12November 2014Received in revised form 21January 2015Accepted 27January 2015Available online 4February 2015Keywords:Lichenase Thermostable PurificationMolecular characterization 3D structurea b s t r a c tA highly thermostable and alkaline lichenase was isolated from the newly isolated strain Bacillus UEB-S.Single step purification was achieved by heating the enzyme extract for 30min at 90◦C.The enzyme was a monomeric protein with a molecular weight of 28kDa.The optimal temperature and pH for UEB-S lichenases activity were 60◦C and 6.0,respectively.More remarkably,the purified lichenase was stable over a broad range of temperature and pH.It retained more than 60%of its activity after incubation at 90◦C for 30min.Substrate specificity studies revealed that the enzyme is a true lichenase.A genomic library was screened.It allows the identification of a gene that encodes a putative lichenase showing 98%identity with the lichenase from Bacillus subtilis 168.Sequence comparison revealed that the two enzymes differed by two mutations at positions 69and 83,where Val69and Ser83are replaced by Met and Ala amino acids,respectively.Therefore,a theoretical structural model was built using the lichenase from B.subtilis 168Pdb code (3o5sA)structure as parison of the two 3D structures suggested that Val69stabilizes a calcium-binding site and could be involved in the higher stability of the enzyme.©2015Elsevier B.V.All rights reserved.1.IntroductionBiofuels as renewable energy suppliers currently are of increas-ing interest.Their potential for sustainability as well as reduction of greenhouse gas emissions makes them stand as attractive alternatives to the petroleum-based fuels.In particular,advanced biofuels (second and third generation)that could be produced from lignocellulosic material appear to be more competitive in terms of life-cycle emissions without threatening food stocks [1].The enzymatic cocktail for plant biomass saccharification and biofuel production must include cellulolytic hydrolases,such as cellobiohydrolases,endo--glucosidases,and endoglucanases [2,3].∗Corresponding author at:Universitéde Sfax,ENIS,UnitéEnzymes et Bioconver-sion,Route de Soukra km 4,3038Sfax,Tunisia.Tel.:+21674675331;fax:+21674275595.E-mail address:maktoufsameh@yahoo.fr (S.Maktouf).Endo-1,3-1,4--d -glucan 4-glucanohydrolase (lichenase)(E.C.3.2.1.73)hydrolyses the 1:4linkage of the 3-O-substituted glucose units of water soluble barley (1:3),(1:4)--d -glucan (-glucan).Most of the known lichenases are of bacterial origin and belong to glycoside hydrolase family 16.The most studied lichenases are produced by Bacillus sp.[4–7].These enzymes have been reported to constitute important biotechnological aids because of their potential applications in various industrial processes includ-ing improvement of digestibility of animal feedstock,clarification of fruit juices,and bioconversion of lignocellulosic materials into fermentative products [8].Due to their limited properties,the currently marketed commercial -1,3:1,4-glucanases may not be ideally suitable for brewing,poultry industries,or for biomass degradation.Therefore,isolation of thermostable of -1,3-1,4-glucanases and with interesting properties is very challenging.We have recently reported the solid-state fermentation of Bacil-lus subtilis UEB-S strain isolated in a Tunisian area and optimized the production a lichenase activity [9].Herein,we describe the bio-chemical and molecular characterization of the enzyme responsible for this activity./10.1016/j.molcatb.2015.01.0161381-1177/©2015Elsevier B.V.All rights reserved.S.Maktouf et al./Journal of Molecular Catalysis B:Enzymatic115(2015)8–1292.Experimental2.1.StrainsBacillus sp.UEB-S(accession number HM10077)was isolated from the Ben Gardene area,in Southern Tunisia.The identification of this strain was based on both catabolic and molecular methods. Morphological,biochemical,and physiological analyses indicated that the UEB-S strain possessed the typical features of Bacillus sp.[9].2.2.Enzyme assaysLichenase activity was assayed by measuring the release of reducing sugar resulting from lichenan substrate degradation(MP Biomedical,Solon,USA)using the3,5-dinitrosalicylic acid(DNS) method[10].The assay mixture(0.5%(w/v)lichenan,50mM acetate buffer,pH6.0)was incubated with the enzyme for10min and the reaction was stopped by addition of DNS reagent.Reduc-ing sugar release was quantified from a standard calibration curve established with glucose.All the assays were performed in trip-licate.One activity unit(U)is defined as the amount of enzyme catalyzing the formation of1mol of reducing sugar(equivalent to one1mol of glucose)per minute.2.3.Enzyme purification and biochemical characterizationLichenase production by Bacillus UEB-S was carried out by solid-state fermentation using millet the optimal fermentation conditions described in Maktouf et al.[9].The crude enzyme extract was then heated at90◦C for30min.The denatured proteins were removed by centrifugation at10,000rpm and4◦C.The supernatant was harvested and used as enzyme source.2.4.Electrophoretic analysisProteins were separated by electrophoresis using3–8%Tris-acetate gels(Invitrogen)in native or denaturing conditions.After migration in denaturing conditions,the gel was stained with Col-loidal Blue(Invitrogen).A zymogram was carried out from the electrophoresis carried out in native conditions.After migration, the gel was washed three times with50mM sodium acetate buffer pH6and incubated for30min in the same buffer supplemented with0.5%(w/v)lichenan.The active band was then detected by Congo red staining following the protocol described by Garciacar-reno et al.[11].2.5.Substrate specificityThe enzymatic activity of the purified protein was assayed using a variety of substrates including-1,3-1,4-glucan,avicel,carboxyl-methylcellulose(CMC),laminarin,lichenan,oat spelt xylan,and starch.The activity measurements were all determined in20mM acetate buffer(pH6.0)at60◦C using the dinitrosalicylic acid assay method.2.6.Hydrolytic properties of the purified lichenaseTen unit of purified lichenase was incubated with10mg of lichenan in1ml of acetate buffer pH6.0at60◦C.Aliquots were peri-odically withdrawn and the reaction was stopped by placing the samples in boiling water for10min.Products of enzymatic hydrol-ysis(10l)were analyzed by HPAEC-PAD using a4×250mm DionexCarbo-pack PA100column.A gradient of sodium acetate from6to300mM in28min in150mM NaOH was applied at 1ml/minflow rate.Detection was performed using a Dionex ED40 module with a gold working electrode and anAg/AgCl pH reference.2.7.Effect of temperature and pH on lichenase activityThe optimal temperature was determined by incubating the reaction mixture for30min at pH6.0and temperatures ranging from30to100◦C.For determination of thermal stability,the puri-fied enzyme fractions were dialyzed against acetate buffer pH6.0. The dialyzed enzyme samples were incubated at temperatures ran-ging from30to90◦C and aliquots were withdrawn at regular time intervals and immediately cooled on ice.Residual activities were determined using the DNS assay.The effect of pH on activity was determined at60◦C in50mM buffers over the pH range of2–12 (acetate buffer pH2–7;Tris–HCl pH8–9;glycine NaOH pH10–12).2.8.Construction of the fosmidic libraryBacillus sp.UEB-S genomic DNA isolation was carried out using the Blood and Cell Culture DNA Maxi kit(Qiagen).It was further fragmentized by pipetting,loaded on0.8%low-melting-temperature gel(Bio-Rad),and separated for18h by pulsed-field gel electrophoresis at 4.5V/cm with5-to40-sec pulse times with a CHEFDRIII apparatus(Bio-Rad).DNA fragments with sizes ranging from30to40kb were recovered from the gel with GELase(Epicentre Technologies)and cloned into fosmids using the pCC1FOS fosmid library production kit(Epicentre Technolo-gies)as recommended by the manufacturer.Recombinant colonies were transferred to384-well microliter plates containing freez-ing medium(Luria–Bertani,8%(v/v)glycerol complemented with 12.5g/ml chloramphenicol),using an automated colony picker (QpixII;Genetix).After22h of growth at37◦C without any agi-tation,the plates were stored at80◦C.2.9.High-throughput functional screensRecombinant clones were screened for polysaccharide diges-tion activities by spotting them on22cm×22cm bioassay trays containing LB-agar medium supplemented with12.5mg/l chlor-amphenicol and the chromogenic polysaccharide AZCL-Barley -1,3-1,4-glucan using a QPixII colony picker(Genetix).The pos-itive clones were visually detected by the presence of a blue halo resulting from the production of colored oligosaccharides that diff-used around the bacterial colonies.2.10.Pyrosequencing,read assembly,and gene predictionPyrosequencing of the fosmid insert was performed on a454 Life Sciences GS FLX system(Roche)by the GeT of Toulouse (Toulouse,France).Read assembly was done using CAP3[12]a DNA Sequence Assembly Program.PCC1FOS sequences were iden-tified using cross match(/ phredphrapconsed.html),discarded,and replaced by NNN.The Metagene program(http://metagene.cb.k.u-tokyo.ac.jp/metagene) was used to predict open reading frames from the resulting sequence.For the selected clone,the large contig sequence has been deposited in DDBJ/EMBL/GenBank under accession number JX649943.2.11.Lichenase sequence analysisThe sequenced lichenase gene was compared with avail-able sequences from GenBank using the BLASTX programs (/Blast/).The deduced amino acid sequence was analyzed with EXPASY tools(/). The signal peptide sequence was predicted by SignalP 3.010S.Maktouf et al./Journal of Molecular Catalysis B:Enzymatic 115(2015)8–12Fig.1.SDS–PAGE analysis (A)and activity staining (B)of the purified ne 1:molecular mass markers,lane 2:crude extract,lane 3:purified lichenase.Server (http://www.cbs.dtu.dk/services/SignalP/).The nucleotide sequence of the UEB-S lichenase was deposited into the GenBank database under accession number AEJ89794.1.2.12.3D model of lichenase UEB-SThe 3-D coordinates of the B.subtilis 168lichenase structure (PDB code:3o5sA)were extracted from the Protein Data Bank (/pdb )and used as template to build a model of UEB-S lichenase 3D-structure by using the structure-modelling program Deep View/Swiss-Pdb Viewer v 3.7[13–15],using the alignment mode.The model was then minimized using the Gromos software implemented to View/Swiss-Pdb.Three steps contain-ing each 20cycles of minimization using a conjugated gradient were applied.The quality of the model structure was checked using the Procheck program [16].The visualization and picture generation were carried out using PyMol version 0.99beta06( ).3.Results and discussion3.1.Enzyme purification and biochemical characterizationLichanase was purified to homogeneity in one step by simply heating the enzyme extract at 90◦C for 30min.The purification factor and recovery yield values were respectively 67fold and 62%.The purified enzyme had an apparent molecular weight of approximately 28kDa (Fig.1A).This molecular weight is close to that of Bacillus licheniformis UEB CF and Bacillus brevis lichenases,which are of 30and 29kDa,respectively [17,18].As shown in Fig.1B,activity staining of the purified lichenase revealed the pres-ence of a single clear band confirming the lichenan hydrolysisability.Fig.2.Hydrolysis properties of the purified lichenase:product pattern obtained by HPAEC-PAD after hydrolysis for 1,2,and 24h.Incubation time (h)was indicated.The 1,3:1,4--glucotriose and the 1,3:1,4--glucotetraose were used as standards.3.2.Substrate specificityUEB-lichenase was active on barley -glucan and lichenan but exhibited no activity onto ␣-1,4,␣-1,6-glucan;-1,4,-1,6-glucan;laminarin;and CMC.Therefore it was considered a true lichenase [19].Hydrolysis products formed during the action of the purified UEB-S lichenase on lichenan are shown in Fig.2.After 24h,the percentage of lichenan hydrolysis by UEB-S lichenase reached 68%,a yield higher than that reported by Chaari et al.[20],which was only 37%using a lichenase produced by B.licheniformis UEB-CF.The major products formed were 1,3:1,4--glucotriose (60%)and 1,3:1,4--glucotetraose (30%).Similar results were reported by McCleary [21]who obtained mainly trisaccharide (53%)and tetrasaccharide (25%)fractions,with lesser amounts (17%)of oligomers of higher DP (5–15)and an insoluble precipitate (3%)using a purified lichenase from B.subtilis [21].3.3.Effect of pH on enzyme activityThe enzyme was highly active between pH 2.0and 11.0(Fig.3).The enzyme retained 89%and 80%of activity at pH 9.0and 11.The relative activity decreased rapidly above pH 11.0(12%at pH 12).This result is comparable with other Bacillus lichenases,which presented a pH optimum around (6–7.5)except for the enzymes of B.brevis (pH opt 9)and the alkophilic Bacillus sp.N137(which maintained more than 80%of its activity in the pH range of 7–12[5].The pH stability was determined after 72h-incubation in differ-ent buffers of pH ranging from 7.0to 12.0at 4◦C.The determination of residual activity was carried out at pH 6.0and 60◦C.The enzyme retained its activity at pH ranging from 7.0to 11for 48h but decreased rapidly above pH 12as it retained only 15%of its activity.After 72h,lichenase UEB-S maintained more than 60%of its activity at pH range 7–11.The high activity lichenases UEB-S at alkaline pHS.Maktouf et al./Journal of Molecular Catalysis B:Enzymatic 115(2015)8–1211Fig.3.Effect of pH on the activity of the purifiedlichenase.Fig.4.Effect of temperature on the activity of the purified lichenase.solutions is a very important characteristic for its eventual use as laundry detergent additives.3.4.Effect of temperature on enzyme activityAs shown in Fig.4,the purified UEB-S lichenase was active at temperatures ranging from 30to 100◦C with an optimum at 60◦C.The relative activity values at 30and 95◦C were 96%and 44%,respectively.At 100◦C,the enzyme remained active and showed 20%of residual activity.Many Bacillus sp.secrete -1,3-1,4-glucanases but a majority of them exhibit optimal temperatureslower than 65◦C.For example,the optimal temperature for B.sub-tilis MA139enzyme was 40◦C (Qiao et al.[22])and for B.subtilis 168lichenase it was 50◦C (Furtado et al.[23]).B.macerans -1,3-1,4-glucanase is among the most thermostable -1,3-1,4-glucanases,which shows an optimal temperature at 65◦C and has a half-life of 40min at 65◦C [24].Lichenase UEB-S retained its activity after incubation at temper-atures ranging from 30to 60◦C for 60min.The residual activity is higher than 60%and 30%after incubation at 90◦C for 30and 60min,respectively.The thermostability of the purified enzyme was higher than that reported for the two lichenases pJ1and pJ2produced by B.subtilis GN156,which were not stable at 60◦C and for which the residual activity decreased to 20–50%after 2h [25].It was also higher than that reported for lichenases EG1from B.licheniformis UEB-CF,which lost 80%of its activity after 1h incubation at 70◦C [17].Therefore,Bacillus sp.UEB-S could be considered as a promising enzyme for commercial use in the future.3.5.Enzyme molecular characterizationA genomic library consisted of 100,000Escherichia coli fosmid clones was first constructed.Three thousand clones were screened for their ability to hydrolyze -glucan,previously treated with dyes to allow visual detection of the positive clones.In total,6posi-tive clones were obtained.One positive clone showing an ability to degrade lichenan was selected for further analyses.3.6.Pyrosequencing,gene prediction,and homology modelingThe insert showing lichenase activity was sequenced.Read assembly resulted in one large contig obtained with a coverage sequencing depth of 115.The high sequencing depth allowed accu-rate gene predictions.The total number of predicted genes sizing at least 93nt was 39.The analysis of the sequence matching with those of known lichenases showed that the insert contained an open reading frame of 727bp,which encodes a putative lichenase of 242amino acids a with an estimated molecular weight of 27.26kDa and a predicted p I of 6.41.A signal peptide of 28amino acids was predicted by the Sig-nalP program [26].The deduced amino acid sequence shared 98.1%similarity with that of B.subtilis 168lichenase.Fig.5.(A)Ribbon representation of the structure of Bacillus subtilis 168lichenase and the model of UEB-S lichenase showing catalytic residues Glu103and Glu105as sticks.Residues Ser83/Ala and Val79/Met of the model structure as compared to the template are also shown as sticks.Residues Pro37,Gly73,and Asp235involved in calcium binding site are also shown as sticks.(B)Zoom from (A)showing as sticks the residues Pro37,Gly73,and Asp235involved in calcium site as well as residue Val69/Met69making hydrophobic interactions with residues Phe38and Met75.12S.Maktouf et al./Journal of Molecular Catalysis B:Enzymatic115(2015)8–123.7.Homology modelingA model was built for the UEB-S lichenase structure using as template the3D structure of B.subtilis168lichenase.The UEB-S lichenase sequence displayed two differences compared to the template’s one:Val69and Ser83instead of Met69and Ala83for B. subtilis168lichenase sequence.Both Val69and Ser83are located far from the catalytic pocket(Fig.5A).Val69is at the opposite side of the catalytic cavity.Interestingly this residue is located nearby a calcium site involving amino acids Pro37,Gly73,and Asp235[27]. The presence of Met69seems to have changed the configuration of the calcium site due to its large hydrophobic radical(Fig.5B).Met69 made strong hydrophobic interactions with residues Phe38and Met75,which could destabilize calcium-binding residues Pro37 and Gly73(Fig.5B).The presence of Val69seems to be compatible with a higher stability of the calcium site.This fact might explain the higher stability of the UEB-S lichenase compared to lichenases to B.subtilis168lichenase,which retained less than40%of its activity after10min incubation at60◦C[23].Similar results were observed by Welfle et al.[27]who suggested that the geometry of the Ca2+ binding site might explain the different extent of stabilization of three hybrid glucanases due to Ca2+binding.4.ConclusionWe report here the biochemical characterization of a highly thermostable lichenase namely UEB-S lichenase.The purification of this protein was achieved to homogeneity in one step.It showed remarkable stability at high temperatures,which promote its use in commercial applications.In addition,the gene encoding this enzyme was identified from a genomic library of Bacillus UEB-S,which was screened for lichenan hydrolyzing activity.The3D model analysis of UEB-S lichenase suggested that a modification in the geometry of the calcium-binding site could explain the higher stability.The structure–function relationships of this recombinant protein will be further characterized through sub-cloning of the putative lichenase encoding gene and site-directed mutagenesis experiments to confirm the suggested stabilization possibly due to improved binding of calcium.AcknowledgmentsThe high-throughput screening work was performed at the Lab-oratory for BioSystems&Process Engineering(Toulouse,France) with the ICEO automated facility.ICEO is supported by grants from the Region Midi-Pyrenees,France,the European Regional Develop-ment Fund,and the Institut National de la Recherche Agronomique, France(the French National Institute for Agricultural Research).We thank Sophie Bozonnet and Sandrine Laguerre for their assistance. The authors are thankful to the French Institute of Cooperation (Campus France)for providingfinancial support to carry out this work.References[1]M.E.Himmel,S.-Y.Ding,D.K.Johnson,W.S.Adney,M.R.Nimlos,J.W.Brady,T.D.Foust,Science315(2007)804–807.[2]A.Boyce,G.Walsh,J.Biotechnol.132(2007)82–87.[3]S.Yang,Y.Qiaojuan,Z.Jiang,G.Fan,L.Wang,J.Agric.Food Chem.56(2008)5345–5351.[4]M.S.Ekinci,S.I.McCrae,H.J.Flint,Appl.Environ.Microbiol.63(1997)3752–3756.[5]A.Planas,Biochim.Biophys.Acta Protein Struct.Mol.Enzymol.1543(2000)361–382.[6]X.-J.Tang,G.-Q.He,Q.-H.Chen,X.-Y.Zhang,M.A.M.Ali,Bioresour.Technol.93(2004)175–181.[7]H.Huang,P.Yang,H.Luo,H.Tang,N.Shao,T.Yuan,Y.Wang,Y.Bai,B.Yao,Appl.Microbiol.Biotechnol.78(2008)95–103.[8]L.Beckmann,O.Simon,W.Vahjen,J.Basic Microbiol.46(2006)175–185.[9]S.Maktouf,C.Moulis,A.Kamoun,F.Chaari,S.E.Chaabouni,M.Remaud-Simeon,Ind.Crops Prod.43(2013)349–354.[10]B.Sumner,S.F.Howell,J.Biol.Chem.108(1935)51–54.[11]F.L.Garciacarreno,L.E.Dimes,N.F.Haard,Anal.Biochem.214(1993)65–69.[12]X.Huang,A.Madan,Genome Res.9(1999)868–877.[13]K.Arnold,L.Bordoli,J.Kopp,T.Schwede,Bioinformatics22(2006)195–201.[14]T.Schwede,J.Kopp,N.Guex,M.C.Peitsch,Nucleic Acids Res.31(2003)3381–3385.[15]N.Guex,M.C.Peitsch,Electrophoresis18(1997)2714–2723.[16]skowski,M.W.MacArthur,D.S.Moss,J.M.Thornton,J.Appl.Crystallogr.26(1993)283–291.[17]F.Chaari,F.Bhiri,M.Blibech,S.Maktouf,S.Ellouz-Chaabouni,R.Ellouz-Ghorbel,Process Biochem.47(2012)509–516.[18]M.Louw,S.Reid,T.G.Watson,Appl.Microbiol.Biotechnol.38(1993)507–513.[19]S.G.Grishutin,A.V.Gusakov,E.I.Dzedzyulya,A.P.Sinitsyn,Carbohydr.Res.341(2006)218–229.[20]F.Chaari,M.Blibech,F.Bhiri,S.Maktouf,R.Ellouz-Ghorbel,S.Ellouz-Chaabouni,Appl.Biochem.Biotechnol.168(2012)616–628.[21]B.V.McCleary,Lichenase from Bacillus subtilis,in:S.T.K.Willis,A.Wood(Eds.),Methods in Enzymology,Academic Press,1988,pp.572–575.[22]J.Qiao,B.Dong,Y.Li,B.Zhang,Y.Cao,Appl.Biochem.Biotechnol.152(2009)334–342.[23]G.P.Furtado,L.F.Ribeiro,C.R.Santos,C.C.Tonoli,A.R.de Souza,R.R.Oliveira,M.T.Murakami,R.J.Ward,Process Biochem.46(2011)1202–1206.[24]R.Borriss,R.Manteuffel,J.Hofemeister,J.Basic Microbiol.28(1988)3.[25]J.Apiraksakorn,S.Nitisinprasert,R.Levin,Appl.Biochem.Biotechnol.149(2008)53–66.[26]O.Emanuelsson,S.Brunak,G.von Heijne,H.Nielsen,Nat.Protoc.2(2007)953–971.[27]K.Welfle,R.Misselwitz,H.Welfle,O.Politz,R.Borriss,Eur.J.Biochem.229(1995)726–735.。
（仅供参考）药学英语整理
（仅供参考）药学英语整理药学英语一、Physiology and Pathology（P9）生理学与病理学（一）概念1、Physiology is the scientific study of function in living systems.（ppt）the study of how living organisms work.(书里)2、Pathology is a significant component of the causal study of disease and a major field in modern medical practice and diagnosis.(ppt)the science of disease,which deal with the studies of etiology, pathogenesis, morphologic structures, changes in functions and metabolism in the living organisms by means of natural science.(书里) 3、Pathophysiology is the study of functional changes in the body which occur in response to disease or injury.a convergence of Pathology with Physiology4、etiology is the study of causation,or the origination of diseases.5、the pathogenesis of a disease is the mechanism that causes the disease.6、inflammation is a response of body tissues to injury or irritation; (刺激）characterized by pain and swelling and redness and heat. （红热胀痛）(二)思考题1.How do you understand pathology and pathophysiology?Pathology is the science of diseasePathophysiology is the study of functional changes in the body which occur in response to disease or injury.2. what is the difference between etiology and pathogenesis?Etiology is the study of causation, or the origination ofdiseases.the pathogenesis of a disease is the mechanism that causes the disease 3. Could you explain the symptoms and signs of a disease?symptoms of a disease(“症”): as certain biologic proce sses are encroached on(侵害) ,the patient begins to feel subjectively that something is wrong., These subjective feelings are called symptoms of disease.Symptoms are subjective and can be reported only by the patient to an observer.signs of a disease（“征”）：when manifestations of the disease can be objectively identified by an observer, these are terms signs of the disease.4、what is the pathogenesis of tuberculosis?the pathogenesis of tuberculosis would include the mechanisms whereby the invasion of the body by tubercle bacillus ultimately leads to the observed abnormalities Biochemistry is the study of chemical processes in living organisms.二、Medicinal Chemistry药物化学(一)概念1、Medicinal chemistry(药物化学)is the science that deal with the discovery or design of new therapeutic chemicals and the development of these chemicals into useful medicine.2、Medicine（药品drug, pharmaceutics) is a compound that interacts with a biological system, and produces a biological response (ideally desired and positive)3、Therapeutic index（治疗指数）sure of the ratio of undesirable to desirable drug effects. Therapeutic index=LD50/ED50The larger the Therapeutic index, the greater the margin of safety of drug!4、LD50（半数致死量）:the lethal dose for 50% of the test animals5、ED50（半数有效量）:the effective dose that produces the maximum therapeutic effect in 50% of the test animals.6、Log P（脂水分配系数）is the base-ten logarithm of the partition coefficients (分配系数)7、Chirality（手征性）: a molecule is considered chiral if there exists another molecule that is of identical composition ,but which is arranged in a non-superposable mirror image.8、Bioisosteres(生物电子等排体): are substituents or groups that havechemical or physical similarities ,and which produce broadly similar biological properties.9、Bioisosterism(生物电子等排性) is a lead modification approach that has been shown to be useful to attenuate toxicity （降低毒性）,modify the activity of a lead （修饰活性）and may have a significant role in the alteration of metabolism of the lead.10、Prodrug（前药）is drug which is given (taken) in an inactive form. Once administered ,the prodrug is metabolized by the body into the biologically active compound.（二）知识点1、The difference of “good” and “bad” drugs:Depend on dosage and chronic exposure. and therapeutic index can be the measure of safety of drugs2、classification of drugsFour main groups:（1）B y biological effect---varied assortment of drugsanalgesics（止痛剂）,anti-asthmatics（平喘药）,antipsychotics（抗精神病药）etc.（2）B y chemical structure---common skeletonPenicillin （青霉素类）,opiates(阿片类药物) etc.（3）B y target system（靶向系统）----affect a target system(synthesis, release, receptor)antihistamine (抗组胺药)etc.（4）B y target site of action(作用靶点)—target enzyme or receptor Anti-cholinesterase (抗胆碱酯酶)3、Medicinal chemistry involves:(1)synthesis (2)structure-activity relationships(SAR)(3)receptor interactions(4)absorption, distribution, metabolism and excretion (ADME)4、Medicinal chemistry cover 3 critical steps:● A discovery step●An optimization step● A development step5、important functional groups on drugs:(1)Alkanes&alkenes(烷烃和烯烃) (2)alcohol 醇（3）phenols 酚（4）ethers 醚（5）aromatic hydrocarbons 芳香化合物6、a drug usually has 3 names:（1）chemical (化学名)Mostly following rules by chemical abstracts service(CAS)One compound can only have one name（2）international non-proprietary names(INN,通用名)Convenient to remember ,needed when apply for registration, cannot be trade marked（商标）or patented(取得专利权)One compound can only have one name（3）commercial (商品名)Named by manufactures ,can be trade marked to protect thebrand. One compound can have many different names7、prodrug strategies are used to overcome a variety of problems by:(1)Altering solubility 改变溶解度(2)Improving membrane permeability 提高细胞膜通透性(3)Slow release of the active agent 缓慢释放活性(4)Masking drug toxicity or side effects 掩蔽药物毒副作用三、Phytochemistry and Natural Products 天然药物化学和天然产物(一)概念1、Phytochemistry（天然药物化学）is in the strict sense of the study of phytochemicals, which are derived from plants. In a narrower sense the terms are often used to describe the large number of secondary metabolic compounds found in plants.2、Primary metabolites(初级代谢产物): compounds that are common to many types of organisms, that fulfill basic biological functions(e.g.respiration 呼吸,photosynthesis光合作用,DNA replication ＤＮＡ复制)3、Secondary metabolites（次级代谢产物）：compounds that are not essential to daily ,common metabolism of cells and individual organisms. Instead, these compounds are unique to certain taxa and fulfill secondary functions ,often involved in signaling between organisms (e.g.mate recognition配偶识别, chemical defense化学防御, chemotaxis 趋化作用)4、natural products: a natural product is a chemical compound or substance produced by a living organism found in nature.（Primary metabolites & Secondary metabolites）（二）知识点1、summary:●natural products are compounds synthesized by living organisms,usually organic molecules with 5-100 carbons●natural products can be primary or secondary metabolites●secondary metabolites are produced via enzymatic pathways fromprimary metabolic building-blocks2、how to get natural products?/the process to purify natural products?(1)Plant collection植物采集(2)Extraction萃取(3)Fractionation分馏(4)Isolation分离(5)Structural determination 结构测定（UV，IR，MS,NMR）3、Journals in Phytochemistry and Natural Products Chemistry:●Journal of Natural Products 天然产物杂志●Phytochemistry●Journal of Ethnopharmacology 民族药物学杂志●Planta Medica 天然药物学会志（欧洲）●Phytochemical Analysis 植物化学分析●Chemical &Pharmaceutical Bulletin 化学与药学通报（日本）Review journals 综述期刊●Natural P roduct Reports 天然产物报告●Phytochemistry Review四、biochemistry 生物化学1、What is the goal of biochemistry？The basic goal of the science of biochemistry is to determine how the collections of inanimate molecules （无生命的分子）that constitute living organisms interact with each other to maintainand perpetuate life.（延长生命）2、Macromolecules（生物大分子）that constitute living organisms include proteins, nucleic acid (DNA and RNA), and polysaccharides.3、20种氨基酸：1、丙氨酸Alanine/Ala/A2、半胱氨酸Cysteine/Cys/C3、天冬氨酸Asparticacid/ Asp /D4、谷氨酸Glutamic acid/Glu/E5、苯基丙氨酸Phenylalanine/Phe/F6、甘氨酸glycine/Gly/G7、组氨酸Histidine/His/H 8、异亮氨酸Isoleucine/Ile/I9、赖氨酸Lysine/Lys/K 10、亮氨酸Leucine/leu/L11、蛋氨酸Methionine/Met/M 12、天冬酰胺Asparagine/Asn/N 13、脯氨酸Procine/pro/P 14、谷氨酰胺Glutamine/Gln/Q 15、精氨酸Arginine/Arg/R 16、丝氨酸Serine/Ser/S17、苏氨酸Threonine/Thr/T 18、缬氨酸Valine/Val/V19、色氨酸Tryptophan/Try/W 20、酪氨酸Tyrosine/Tyr/Y五、Microbiology 微生物学1、Microbiology is the study of microscopic organisms such as bacteria, fungi, protozoa,virus and some types of algae, which encompass various sub-disciplines including virology, mycology, parasitology, bacteriology, and other branches.2、几位科学家的成就Bacteriology was found in the 19th century by Ferdinand CohnCohn was also the first to formulate the scheme for the taxonomic classification of bacterial and discover spores 费迪南德?科恩, Ferdinand Cohn德国博物学家和植物学家，以研究藻类、细菌和蕈类著称，被视为细菌学的创始者之一。