Biographical Data Yuh-Jye Lee

图文 / 王治钧 审核 / 毕坤（中国农学会）细胞农业科学家伊莎·达塔（Isha Datar）提出“细胞农业”这一概念，并在TED 大会上展示了她在实验室“种植”的肉类。

吃肉，却不吃动物细胞农业指的是在实验室中用细胞培养物培育农产品。

2013年，一个成本为33万美元的牛肉汉堡，让细胞农业一词进入大众的视野。

这个汉堡中夹着的是世界上第一块在实验室中用细胞培养出来的牛肉饼。

细胞农业科学家伊莎·达塔（Isha Datar）未来舌尖上的幸福202252SEP.Copyright ©博看网. All Rights Reserved.53（责任编辑 / 高琳 张佳鑫 美术编辑 / 周游）俗话说“民以食为天”，到本世纪末，地球上的人口有可能达到110亿。

要保证这么多人吃饱饭并有足够的营养，种植业和畜牧业都需要更加高效的科技革新技术。

人造肉成了未来食物创新发展的大趋势。

人造肉按照原材料分为两种：植物肉和动物细胞培育肉。

植物肉是利用植物蛋白，通过技术生产加工成类似动物肉的口感、味道和外观的食品；动物细胞培育肉是在特定的培养条件下，利用动物肌肉细胞中的多能干细胞（一种具有自我更新和自我复制能力的干细胞）培养出来的具有传统肉类结构、风味口感的人造肉。

2020年，新加坡批准了动物细胞培育肉上市。

一些让食客大快朵颐的鸡肉，并不是通过宰杀鸡得来的，而是在实验室中用细胞培养物培育出来的。

未来饭碗里的高科技动物细胞培育肉的生产过程是怎样的呢？以牛肉为例，先从牛身上提取所需的细胞，然后放入实验室的生物反应器内培养，最终细胞发育成含有肌肉纤维、血管与胞外基质的肌肉组织。

动物细胞培育肉在满足人们口腹之欲的同时，减少了畜牧业给环境和气候带来的影响。

除了对环境友好外，动物细胞培育肉还可以减少动物性病毒的传播、降低对抗生素的使用，从而体现对健康食物的追求。

如今，动物细胞培育肉成为很多国家的发展重点。

中国在《“十四五”生物经济发展规划》中提到，有序发展全基因组选择、系统生物学、合成生物学、人工智能等生物育种技术，发展合成生物学技术，探索研发“人造蛋白”等新型食品。

西红花提取物调控免疫细胞，提高程序性死亡受体-1抑制剂治疗肺腺癌效果的实验研究作者：李诗颖李存雅张雪钟薏来源：《上海医药》2024年第01期摘要目的：多项研究提示，西红花提取物能影响肿瘤的发展进程。

本实验探究西红花提取物在肺腺癌小鼠模型中对肿瘤免疫微环境和免疫治疗的影响，为西红花提取物抗肿瘤研究提供更多基础性数据。

方法：构建Lewis肺癌细胞和萤光素酶稳定结合的小鼠皮下瘤模型，观察西红花提取物对小鼠皮下瘤和肿瘤免疫微环境的影响：运用活体成像技术跟踪肿瘤生长情况；运用流式细胞技术检测小鼠CD4+、CD8+ T细胞的数量及占比；运用反转录-聚合酶链式反应技术检测程序性死亡受体配体1、含有T细胞免疫球蛋白和黏蛋白结构域的蛋白3（T cell immunoglobulin and mucin domaincontaining protein 3， TIM3）、淋巴细胞活化基因-3（lymphocyte-activation gene-3， LAG3）、具有免疫球蛋白和ITIM结构域的T细胞免疫受体（T cell immunoreceptor with immunoglobulin and ITIM domain， TIGIT）、胸腺细胞选择相关的高迁移率族蛋白（thymocyte selection-associated high mobility group box， TOX）1、TOX2、TOX3基因的mRNA表达情况。

结果：与对照组相比，给予西红花提取物能一定程度地抑制小鼠皮下瘤的生长（P关键词西红花免疫微环境肺腺癌免疫治疗中图分类号：R965； R282.71 文献标志码：A 文章编号：1006-1533（2024）01-0003-09引用本文李诗颖，李存雅，张雪，等. 西红花提取物调控免疫细胞，提高程序性死亡受体-1抑制剂治疗肺腺癌效果的实验研究[J]. 上海医药， 2024， 45（1）： 3-11； 28.基金项目：上海市2022年度“科技创新行动计划”医学创新研究专项项目（22Y31920104）；上海市虹口区第二轮“国医强优”三年行动计划（2022—2024年）中西医结合重点专科、薄弱专科建设项目（HKGYQYXM-2022-10）；上海市2021年度“科技创新行动计划”扬帆计划项目（21YF444400）；上海市2022年度“科技创新行动计划”启明星培育（扬帆专项）项目（22YF1444900）；山东省乡村振兴基金会张秀兰慈善基金项目Experimental study of saffron extracts to modulate immune cells to improve the efficacy of a programmed death-1 inhibitor in the treatment of lung adenocarcinomaLI Shiying1， LI Cunya1， ZHANG Xue2， ZHONG Yi1（1. Department of Oncology， Shanghai TCM-Integrated Hospital， Shanghai University of Traditional Chinese Medicine， Shanghai 200082， China； 2. Shanghai Traditional Chinese Medicine Co.， Ltd.， Shanghai 200082， China）ABSTRACT Objective： A number of studies have shown that saffron extracts can affect the development of tumor. This study explored the effect of saffron extract on tumor immune microenvironment and immunotherapy in a mouse model of lung adenocarcinoma so as to provide more basic data for the anti-tumor research of saffron extracts. Methods： The transplanted tumor model of Lewis lung carcinoma-luciferase in mice was established to detect the effect of saffron extracts on the transplanted tumor in vivo. At the same time， the tumor growth was tracked by in vivo imaging technique. The number and proportion of CD4+ and CD8+ T cells were determined by flow cytometry. The mRNA levels of programmed death-ligand 1， T cell immunoglobulin and mucin domain-containing protein 3 （TIM3）， lymphocyte-activation gene-3 （LAG3）， T cell immunoreceptor with immunoglobulin and ITIM domain （TIGIT）， thymocyte selection-associated high mobility group box （TOX） 1， TOX2 and TOX3 were detected by reverse transcription-polymerase chain reaction （RT-PCR） and immunohistochemical techniques to verify the effect of saffron extracts on the regulation of tumor immune microenvironment. Results：Compared with the control group， the administration of saffron extracts could inhibit the growth of subcutaneous tumor in mice to a certain extent， and the number and proportion of CD4+ and CD8+ T cells were increased （PKEY WORDS saffron； immune microenvironment； lung adenocarcinoma； immunotherapy肿瘤是一类恶性疾病，2018年全球肿瘤死亡病例数达约960万人，较2008年增加26.3%，其中男性肿瘤死亡病例数增加最多的是肺癌，增加了23.4万人[1-2]。

马来西亚留学生物科技专业的最新介绍马来西亚留学生物科技专业是一门研究生物学知识，以达到某特定效果的科系。

此科系透过各种技术，如基因的操纵、筛选与转殖、蛋白质生物技术、细胞生物技术和微生物生物技术等，开发各种相关科学技术以提升人类的生活品质。

学生将学习各种生物学学问，并学习如何将这些学问结合其他领域，如医学上，以改善诊断方法、疫苗制造、基因治疗、药物开发;或农业上，以增强农作物抵抗力、改良品种、提升产量、生产生物农药和生物肥料等。

此外，学生也将学习如何将此科系使用于工业，以生产或改良各种食物与化学物品。

开办相关科系的国立学府：马大 UM生物工艺理学士学位 Ijazah Sarjana Muda Sains Bioteknologi生物健康理学士学位 Ijazah Sarjana Muda Sains Biokesihatan理大 USM工艺荣誉学士学位(生物工艺)Ijazah Sarjana Muda Sainsdengan Kepujian(Teknologi Bioproses)国大 UKM理学荣誉学士学位(植物生物工艺)Ijazah Sarjana Muda Sains dengan Kepujian(Bioteknologi Tumbuhan)理学荣誉学士学位(生物工艺与管理学)Ijazah Sarjana Muda Sains dengan Kepujian(Bioteknologi dengan Pengurusan)博大 UPM生物工艺学士学位 Bacelor Sains(Bioteknologi)工大UTM工业生物学士学位 Ijazah Sarjana Muda Sains(Biologi Industri)砂大 UNIMAS科学荣誉学士学位(生物工艺)Sarjana Muda Sains Dengan Kepujian(Bioteknologi Sumber)沙大UMS科学荣誉学士学位(生物工艺)Sarjana Muda Sains dengan Kepujian(Bioteknologi)达鲁依曼大学UDM理学荣誉学士学位(农业生物工艺)Ijazah Sarjana Muda Sains Bioteknologi Pertanian dengan Kepujian丹大UMK应用科学学士学位(生物工业工艺)Sarjana Muda Sains Gunaan(Teknologi Bioindustri)开办相关科系的私立学府：The University of Nottingham Malaysia Campus 诺丁汉大学马来西亚分校Universiti Tunku Abdul Rahman 拉曼大学AIMST UniversityINTI International Education Group 英迪国际教育集团。

迪肯大学有6所校园，3所在墨尔本，2所在基朗，1所在瓦南图尔，公共交通均可方便到达。

墨尔本校园位于墨尔本东郊，从市中乘电车约30分钟即可到达，校区内公共交通也十分便利，Toorak校园靠近墨尔本近郊的中心并有便利的公共交通系统。

迪肯大学生物技术专业基本信息：
注：
（1）IELTS单项不低于6.0分。

（2）迪肯大学学位项目无申请截止日期，考虑到申请材料审核和签证办理所需时间，建议申请者至少在入学前3个月递交申请材料。

申请说明
360教育集团介绍，迪肯大学生物技术（Biotechnology）专业研究生阶段设有理学硕士（研究）（Master of Science （Research））学位项目。

该项目属授课型项目，为期2年，要求申请者已拥有理学学士学位，且本科最后一年GPA65%以上。

研究领域
理学硕士（生物技术）项目有以下研究领域，如农业生物技术、细胞与分子生物技术实验室方法、纳米技术和工业与分析技术。

=============================================================- Kernel Statistics toolbox- Last Update:2006/10/29- For questions or comments, please emailYuh-Jye Lee, yuh-jye@.tw orYi-Ren Yeh, yeh@.tw orSu-Yun Huang, syhuang@.tw- Web site: .tw/downloads/=============================================================Table of Contents=================- Introduction- Key Features- Data FormatFor classificationFor regression- Code Usage with ExamplesKDRUseKDRKPCAKSIR- Dimension Reduction Using KPCA or KSIR with ExamplesKPCA procedureKSIR procedure for classificationKSIR procedure for regression- LicenseIntroduction============Kernel Statistics toolbox is still in development. Two algorithms are now available. One is kernel principal component analysis (KPCA). The otheris kernel sliced inverse regression (KSIR).Key Features============* Construct principal components in the input space and feature space.* Provide a preprocess for preventing ill-posed problem encountered in KSIR. * Support linear, polynomial and radial basis kernels.* Can handle large scale problems by using reduced kernel.Data Format===========Kernel Statistics toolbox is implemented in Matlab. Use a data format which can be loaded into Matlab. The instances are represented by a matrix (rows for instances and columns for variables) and the labels (1,2,...,k) or responses are represented by a column vector. Note that you also canrepresent the labels of binary classification by 1 or -1.For classification-------------------instances| 10 -5 0.8 | => inst 1| 15 -6 0.2 | => inst 2| . | .| . | .| . | .| 21 1 -0.1 | => inst nlabels| 1 | => label of inst 1 => class 1| 5 | => label of inst 2 => class 5| . | .| . | .| . | .| 10 | => label of inst n => class 10For regression---------------instances| 11 -5 0.2 | => inst 1| 14 -7 0.8 | => inst 2| . | .| . | .| . | .| 20 2 -0.9 | => inst nlabels| 3.2 | => response of inst 1| 1.7 | => response of inst 2| . | .| . | .| . | .| -1.1 | => response of inst nCode Usage with Examples========================Kernel Statistics toolbox contains two main functions: KDR for constructingPCs and UseKDR for using the results of KDR. Besides, users also can directlyuse the two core programs, KPCA and KSIR, for a specific kernel matrix.Description for codesKDR : the main program for constructing PCs via different methods.UseKDR : using the results of KDR to build up the projected data matrix.KPCA : finds dimension reduction directions by PCA of a kernel matrix.KSIR : finds dimension reduction directions by sliced inverse regression of a kernel matrix.Usage of KDR:>>[Info] = KDR(label, inst, 'options')---------------------------------------------------------------------*Inputs of KDR:label : training data class label or responseinst : training data inputsoptions:-s statistic method. 0-PCA, 1-SIR (default:0)-t kernel type. 0-linear, 1-polynomial, 2-radial basis (default:2)-r ratio of random subset size to the full data size (default:1)-z number of slices (default:20)If NumOfSlice >= 1, it represents NumOfSlice slices.If NumOfSlice = 0, it extracts slices according toclass labels.-p number of principal components (default:1)If NumOfPC= r >= 1, it extracts the first r leadingeigenvectors.If NumOfPC= r < 1, it extracts leading eigenvectorswhose sum of eigenvalues is greater than 100*r% ofthe total sum of eigenvalues.-g gamma in kernel function (default:0.1)-d degree of polynomial kernel (default:2)-b constant term of polynomial kernel (default:0)-m scalar factor of polynomial kernel (default:1)*Outputs of KDR:Info results of Kernel Statistics method (a structure in Matlab).PC principal components of data.EV eigenvalues respect to the principal components.Ratio.RS reduced set.Space the space of Kernel Statistics method.Params parameters specified by the user in the inputsExample: Construct ten PCs via KPCA by using Gaussian kernel>>[Info_one] = KDR([], inst, '-s 0 -t 2 -g 0.1 -p 10');Example: Construct five PCs via reduced KPCA (10%) by using Gaussian kernel>>[Info_two] = KDR([], inst, '-s 0 -t 2 -p 5 -g 0.2 -r 0.1');Example: Construct PCs of 90% eigenvalue via KPCA by using reduced polynomial kernel >>[Info_three] = KDR([], inst, '-s 0 -t 1 -p 0.9 -r 0.1 -d 2 -m 3 -b 2'); Example: Construct one PCs via KSIR by using Gaussian kernel for 2-class problems>>[Info_four] = KDR(label, inst, '-s 1 -t 2 -p 1 -z 0 -g 0.3');Example: Construct 5 PCs via KSIR (30 slices) by using Gaussian kernel for regression problems>>[Info_five] = KDR(label, inst, '-s 1 -t 2 -p 1 -z 30 -g 0.2');Usage of UseKDR:>>[ProjInst] = UseKDR(inst, Info)---------------------------------------------------------------------*Inputs of UseKDR:inst :testing data inputsInfo :results of Kernel Statistics method*Output of UseKDR:ProjInst: the projected instancesExample: Get the projected inst form Info_one>>[ProjInst_one] = UseKDR(inst, Info_one);Example: Get the projected inst form Info_four>>[ProjInst_four] = UseKDR(inst, Info_four);Usage of KPCA:>>[EigenVectors, EigenValues, ratio] = KPCA(K, NumOfPC);---------------------------------------------------------------------*Inputs of KPCAK : kernel matrix (reduced or full)NumOfPC: If NumOfPC= r >= 1, it extracts the first r leading eigenvectors.If NumOfPC= r < 1, it extracts leading eigenvectors whose sumof eigenvalues is greater than 100*r% of the total sum of eigenvalues.*Outputs of KPCAEigenValues : leading eigenvaluesEigenVectors: leading eigenvectorsratio : sum of leading eigenvalues over total sum of all eigenvalues. Example: Construct ten PCs via KPCA by using a specific kernel matrix.>>[EigenVectors, EigenValues, ratio] = KPCA(K, 10);Example: Construct PCs whose ratio to the total sum is 95%.>>[EigenVectors, EigenValues, ratio] = KPCA(K, 0.95);Usage of KSIR:>>[EigenVectors, EigenValues, ratio] = KSIR(K, y, NumOfSlice, NumOfPC)---------------------------------------------------------------------*Inputs of KSIRK : kernel matrix (reduced or full)y : class labels or responsesNumOfSlice: If numerical, it represents the number of slices.If a string 'Class', the number of slices is equal to number ofdistinct classes in y.NumOfPC : If NumOfPC >= 1, it extracts the leading NumOfPC eigenvectors.If NumOfPC < 1, it extracts leading eigenvectors whose sumof eigenvalues is greater than 100*r% of the total sum of eigenvalues. *Outputs of KISREigenValues : leading eigenvaluesEigenVectors: leading eigenvectorsratio : sum of leading eigenvalues over total sum of all eigenvalues. Example: Construct PCs via KSIR for classification by using a specific kernel matrix. >>[EigenVectors, EigenValues, ratio] = KSIR(K, y, 'CLASS');Example: Construct two PCs via KSIR for 3-class problem.>>[EigenVectors, EigenValues, ratio] = KSIR(K, y, 'CLASS', 2);Example: Construct PCs via KSIR for regression (20 slices).>>[EigenVectors, EigenValues, ratio] = KSIR(K, y, 20);Example: Construct five PCs via KSIR for regression .>>[EigenVectors, EigenValues, ratio] = KSIR(K, y, 20, 5);Example: Construct PCs whose ratio to the total sum is 95% via KSIR for regression. >>[EigenVectors, EigenValues, ratio] = KSIR(K, y, 20, 0.95);Dimension Reduction Using KPCA or KSIR with Examples===================================================KPCA procedure-------------------------*Change your current directory to Kernel Statistics toolbox folder*Load dataset Ionosphere_dataset.mat (can be found in Kernel Statistics toolbox)>>load Ionosphere_dataset.mat*Construct PCs via KPCA>>[Info] = KDR([], inst, '-s 0 -t 2 -g 0.1 -p 10');*Read the contents of Info (PCs, eigenvalues, parameters, ...etc)>>Info>>Info.PC*Get the projected inst form the PCs>>[ProjInst] = UseKDR(inst, Info);KSIR procedure for classification---------------------------------*Change your current directory to Kernel Statistics toolbox folder*Load dataset Ionosphere_dataset.mat (can be found in Kernel Statistics toolbox)>>load Ionosphere_dataset.mat*Construct PCs via KSIR (note that we extracts m-1 PCs for m-class problems)>>[Info] = KDR(label, inst, '-s 1 -t 2 -g 0.165 -z 0 -p 1');*Read the contents of Info (PCs, eigenvalues, parameters, ...etc)>>Info>>Info.PC*Get the projected inst form the PCs>>[ProjInst] = UseKDR(inst, Info);KSIR procedure for regression-----------------------------*Change your current directory to Kernel Statistics toolbox folder*Load dataset Housing_dataset.txt (can be found in Kernel Statistics toolbox)>>load Housing_dataset.txt*Split the Housing data into inst and label>>inst =Housing_dataset(:,1:13);>>label = Housing_dataset(:,14);*Construct PCs via KSIR(note that we usually use 10~30 slices and extracts 3~5 PCs for regression problems) >>[Info] = KDR(label, inst, '-s 1 -t 2 -g 0.0037 -z 20 -p 5');*Read the contents of Info (PCs, eigenvalues, parameters, ...etc)>>Info>>Info.PC*Get the projected inst form the PCs>>[ProjInst] = UseKDR(inst, Info);License=======This software is available for non-commercial use only. The authors are not responsible for implications from the use of this software.。

免疫组学的研究进展唐康侯永利王亚珍陈丽华（中国人民解放军空军军医大学基础医学院免疫学教研室，西安 710032）中图分类号R392.9 文献标志码 A 文章编号1000-484X（2024）01-0185-07［摘要］随着高通量测序技术、生物信息学等相关领域进展以及人类对免疫系统功能认识的逐步深入，免疫组学从最初解析B细胞受体（BCR）、T细胞受体（TCR）基因序列逐渐发展为解析和绘制宿主免疫系统和抗原的互作关系以及宿主免疫系统应答机制的全景图谱，主要包括抗原表位组学、免疫基因组学、免疫蛋白质组学、抗体组学和免疫信息学等方面的研究，并基于大量免疫学研究数据建立了ImmPort、VDJdb和IEDB等免疫学数据库，加速了新抗原表位的发现和免疫应答机制等研究。

免疫组学能够揭示免疫系统与疾病的关联，促进新型疫苗和免疫治疗策略开发，将有效推动个体化医疗和精准药物治疗。

近年免疫组与暴露组等的整合以及与人工智能的融合将对全面理解免疫系统对环境因素的响应和调节机制、解析疾病发生和发展的分子机制产生重大影响。

［关键词］免疫组；免疫组学；免疫信息学；人工智能Advances in immunomics researchTANG Kang， HOU Yongli， WANG Yazhen， CHEN Lihua. Department of Immunology， School of Basic Medicine，Air Force Medical University， Xi'an 710032， China［Abstract］With the progress of high-throughput sequencing technologies and bioinformatics， and deepening understanding of immune system，immunomics has evolved from initially deciphering gene sequences of B cell receptor （BCR）and T cell receptor （TCR） to unraveling and mapping interactions between host immune system and antigens， as well as panorama of host immune system response mechanisms， which now encompasses various research areas， such as antigen epitopeomics， immunogenomics， immunopro‐teomics， antibodyomics and immunoinformatics. Based on a large amount of immunological research data， immunological databases such as ImmPort， VDJdb and IEDB have been established to accelerate discovery of new antigen epitopes and study of immune response mechanisms. Immunomics has revealed the association between immune system and diseases， promoted the development of novel vac‐cines and immunotherapeutic strategies， and effectively drove the development of personalized medicine and precision medicine. In recent years， integration of immunome with exposome and fusion it with artificial intelligence will have a significant impact on compre‐hensively understanding immune system's response and regulatory mechanisms to environmental factors， as well as deciphering molecular mechanisms underlying disease occurrence and progression.［Key words］Immunome；Immunomics；Immunoinformatics；Artificial intelligence免疫组（immunome）是宿主免疫系统与抗原的互作关系以及宿主免疫系统应答机制的全景图谱，包括免疫系统的识别对象、识别受体以及参与免疫应答过程的其他分子［1-3］。

*基金项目：赣州市指导性科技计划项目（GZ2021ZSF379）①江西省赣州市立医院　江西　赣州　341000通信作者：王芳不同营养摄入方式在脑卒中后吞咽障碍中的临床应用*王芳①　易珲①　邱青青①　徐晓涓①　邹蒙蒙①【摘要】　目的：探讨间歇经口至食管管饲（IOE）和经鼻留置胃管管饲（NGT）两种营养摄入方式在脑卒中后吞咽障碍中的应用效果。

方法：选取2022年1—11月赣州市立医院收治的70例脑卒中后吞咽障碍患者作为研究对象，采用随机抽签分组的方式将研究对象分为研究组和对照组，各35例。

两组均给予常规干预，对照组行NGT，研究组行间歇IOE，比较两组的临床效果。

结果：治疗14 d 后，研究组治疗有效率为97.14%，高于对照组的77.14%；研究组鼻胃管综合征（NGTS）发生率为5.71%，低于对照组的25.71%，差异均有统计学意义（P <0.05）。

两组治疗前标准吞咽功能评定量表（SSA）评分差异无统计学意义（P >0.05），治疗30 d 后，研究组SSA 评分高于对照组（P <0.05）。

血清检测显示，两组治疗前前白蛋白（PA）、白蛋白（ALB）、转铁蛋白（TRF）指标差异均无统计学意义（P >0.05），研究组治疗30 d 后，三项指标均高于对照组（P <0.05）。

两组治疗前吞咽生命质量量表（SWAL-QOL）评分比较，差异无统计学意义（P >0.05），治疗30 d 后，研究组评分高于对照组（P <0.05）。

结论：在脑卒中后吞咽障碍患者的治疗中，IOE 较之NGT 能够更好地规避NGTS 的发生，在改善吞咽及气道功能方面效果更加理想。

【关键词】　脑卒中　吞咽障碍　经口至食管管饲　经鼻留置胃管管饲　鼻胃管综合征 Clinical Application of Different Nutritional Intake Patterns in Dysphagia after Stroke/WANG Fang, YI Hui, QIU Qingqing, XU Xiaojuan, ZOU Mengmeng. //Medical Innovation of China, 2023, 20(29): 131-134 [Abstract]　Objective: To investigate the clinical effect of intermittent oro-esophageal tube feeding (IOE) and nasal indwelling gastric tube feeding (NGT) in dysphagia after stroke. Method: A total of 70 patients with post-stroke dysphagia admitted to Ganzhou Municipal Hospital from January to November 2022 were selected as the research objects. The research objects were divided into the study group and the control group by random drawing method, with 35 cases each group. Both groups were given routine intervention, the control group was given NGT, the study group was given intermittent IOE, and the treatment effects of the two groups were compared. Result: 14 days after treatment, the effective rate of the study group was 97.14%, which was higher than 77.14% of the control group, the 71-73.[17]王健民，高启红，邱守芳，等.骨硬化蛋白和dkk1与2型糖尿病患者骨密度相关性研究[J].中国骨质疏松杂志，2019，25（7）：924-928.[18]陈柏龄，崔昊文，林焘，等.骨质疏松小鼠模型骨密度及骨代谢等相关指标的变化[J].热带医学杂志，2017，17（2）：177-180.[19] SILHA J V，MISHRA S，ROSEN C J，et al.Perturbationsin bone formation and resorption in insulin-like growth factor binding protein-3 transgenic mice[J].J Bone Miner Res，2003，18（10）：1834-1841.[20]陈小香，邓伟民，魏秋实，等.从GH/IGF-1轴与PI3K/Akt通路探讨老年骨质疏松症的发病机制[J].中国骨质疏松杂志，2015，21（11）：1412-1415.[21] HUANG Z，REN P G，MA T，et al.Modulating osteogenesis ofmesenchymal stem cells by modifying growth factor availability[J].Cytokine，2010，51（3）：305-310.[22] WEN Y，LI H，ZHANG X，et al.Correlation of osteoporosisin patients with newly diagnosed type 2 diabetes: a retrospective study in Chinese population[J].Front Endocrinol (Lausanne)，2021，12（27）：531904.（收稿日期：2022-11-29）　（本文编辑：白雅茹） 脑卒中是临床上常见的一种潜在致残致死风险高的脑血管疾病，吞咽障碍则是脑卒中高发的合并症及后遗症，会大幅度增加误吸和吸入性肺炎的发生率[1]，加上经口进食量降低，会造成不同程度的营养问题，这也是脑卒中患者预后不良及致残率和死亡率高的主要原因之一[2-3]。

㊃综述㊃铁死亡调控机制及其在心血管疾病中的研究进展马赛㊀左庆娟㊀和丽丽㊀张国瑞㊀郭艺芳050051石家庄,河北省人民医院疼痛科(马赛),老年心血管内一科(左庆娟㊁和丽丽㊁郭艺芳);050011石家庄市第三医院心血管内科(张国瑞)通信作者:郭艺芳,电子信箱:yifangguo@DOI:10.3969/j.issn.1007-5410.2023.06.013㊀㊀ʌ摘要ɔ㊀铁死亡是一种近期发现的可调节的程序性细胞坏死方式,涉及铁代谢㊁脂质代谢㊁氨基酸代谢等多种代谢过程,其主要特征为脂质过氧化物生成㊁活性氧超载和谷胱甘肽消耗等㊂研究证实铁死亡参与了多种心血管疾病病理生理过程㊂本文总结了铁死亡调控机制及国内外关于铁死亡在心血管疾病中的研究进展,旨在为心血管疾病的预防和治疗提供新思路㊂ʌ关键词ɔ㊀铁死亡;㊀心力衰竭;㊀心肌病;㊀心血管疾病基金项目:河北省创新能力提升计划项目(199776249D);河北省重点研发计划项目(19277787D)Ferroptosis homeostasis regulation and its research progress in cardiovascular diseases㊀Ma Sai,ZuoQingjuan,He Lili,Zhang Guorui,Guo YifangDepartment of Pain,Hebei General Hospital,Shijiazhuang050051,China(Ma S);Ward1,Department ofGeriatric Cardiology,Hebei General Hospital,Shijiazhuang050051,China(Zuo QJ,He LL,Guo YF); Department of Cardiology,The Third Hospital of Shijiazhuang,Shijiazhuang050011,China(Zhang GR)Corresponding author:Guo Yifang,Email:yifangguo@ʌAbstractɔ㊀Ferroptosis is a recently discovered regulated form of programmed cell death,involvingvarious metabolic processes such as iron metabolism,lipid metabolism,and amino acid metabolism.Its mainfeatures include lipid peroxide generation,oxidative stress,and glutathione depletion.Studies have confirmed the involvement of ferroptosis in various pathophysiological processes of cardiovascular diseases.This article summarizes the regulatory mechanisms of ferroptosis and the research progress on ferroptosis in cardiovascular diseases both domestically and internationally,aiming to provide new insights for the prevention and treatment of cardiovascular diseases.ʌKey wordsɔ㊀Ferroptosis;㊀Heart failure;㊀Cardiomyopathy;㊀Cardiovascular diseaseFund program:Hebei Province Innovation Capability Enhancement Plan Project(199776249D);Hebei Province Key Research and Development Plan Project(19277787D)㊀㊀铁是机体维持正常氧气运输㊁脂质代谢㊁氧化磷酸化等线粒体功能,DNA㊁蛋白质生物合成功能以及其他细胞生物学进程必不可少的微量元素㊂机体内铁稳态维持受多方因素调控,过量的游离铁可与过氧化氢发生芬顿反应,形成羟基自由基及活性氧(reactive oxygen species,ROS),从而造成核酸㊁蛋白质及细胞膜等损伤,水解酶转移,进而引发细胞死亡[1]㊂1981年Sullivan等[2]提出了 铁源性心脏病 假说,阐明铁超载在心血管疾病进程中发挥着重要作用㊂2012年Dixon等提出了 铁死亡 的概念,铁死亡是一种铁依赖的程序性细胞坏死方式,以脂质过氧化物生成㊁ROS超载和谷胱甘肽(glutathione,GSH)消耗等为标志性改变,参与了多种心血管疾病病理生理过程㊂但与其他细胞死亡方式不同,铁死亡既可由实验性小分子物质(如埃拉斯汀㊁原癌基因致死性小分子3和磺胺嘧啶等)以及某些药物(如柳氮磺吡啶㊁索拉非尼和青蒿琥酯等)所诱导,亦可被铁抑素1(ferrostatin-1,Fer-1)和脂血抑素1等物质所抑制[3]㊂即铁死亡机制本身具有可调控性,其中正向调节生物分子包括电压依赖性阴离子通道(voltage-dependent anion channel,VDAC)2/3㊁原癌基因㊁还原型烟酰胺腺嘌呤二核苷酸磷酸(reduced nicotinamide adenine dinucleotide phosphate,NADPH)氧化酶㊁p53等,负向调节生物分子包括谷胱甘肽过氧化物酶4 (glutathione peroxidase4,GPX4)㊁溶质载体家族7成员11 (solute carrier family7-member11,SLC7A11/xCT)㊁热休克蛋白B1㊁核因子E2相关因子2(nuclear factor E2related factor 2,Nrf2)等[4]㊂现将铁死亡调控机制及其在心血管疾病中的研究进展做一综述㊂1㊀铁死亡调控机制1.1㊀铁代谢转铁蛋白(transferrin,Tf)与细胞膜上转铁蛋白受体1 (transferrin receptor1,TFR1)结合后可将Fe3+由细胞外内吞至内含体中,经前列腺六跨膜表皮抗原3还原为Fe2+后,在二价金属离子转运体或锌铁转运蛋白的介导下释放到胞质内不稳定铁池中发挥其生理作用,多余的铁以Fe3+形式存储在铁蛋白中或经膜铁转运蛋白(ferroportin-1,FPN1)转至细胞外[5]㊂铁蛋白能够螯合4500个铁原子,从而保护细胞免受游离铁的干扰,维持铁稳态,铁反应元件结合蛋白2作为调控铁代谢的主要转录因子,可抑制其表达[6]㊂FPN1是目前唯一已知的铁输出蛋白[7]㊂在此过程中,TFR1是关键蛋白㊂Manz等[8]在对铁死亡敏感的原癌基因突变细胞的研究中发现,铁死亡细胞的TFR1表达升高,伴铁蛋白重链(ferritin heavy chain,FTH1)㊁铁蛋白轻链(ferritin light chain, FTL)表达降低,铁超载进而诱导铁死亡发生㊂铁调节蛋白与缺氧诱导因子1等均可增强TFR1的表达[9],热休克蛋白B1等可抑制TFR1的表达[10](图1)㊂1.2㊀脂质代谢细胞膜及细胞器膜上磷脂中多不饱和脂肪酸在酰基辅酶A合成酶长链家族成员4(acyl-CoA synthetase long-chain family member4,ACSL4)的作用下酰基化,在溶血磷脂酰胆碱酰基转移酶3的作用下酯化,生成含多不饱和脂肪酸磷脂㊂该物质结构不稳定,极易被脂氧合酶氧化为4-羟基壬烯醛(4-hydroxy-trans-2-nonenal,4-HNE)和丙二醛(malondialdehyde, MDA)[11]㊂其中ACSL4及脂氧合酶是限速酶,抑制其表达和生物活性可提高细胞对铁死亡的耐受性(图1)㊂1.3㊀氨基酸代谢在GPX4催化作用下GSH可将脂质过氧化产物转化为无毒的脂肪醇㊂GSH是由谷氨酸㊁半胱氨酸和甘氨酸组成的三肽,半胱氨酸由胱氨酸转化生成,胱氨酸经由xCT从胞外运输至胞内,此转运过程决定了GSH的合成效率㊂研究表明,铁死亡诱导剂埃拉斯汀靶向作用[12],p53抑制xCT转录[13],FTH1缺陷心肌细胞xCT表达下调[14]等,均可促进铁死亡发生㊂GSH还可通过NADPH氧化生成,NADPH作为清除脂质过氧化物所必需的还原剂,是铁死亡敏感性的生物标志物㊂GPX4是众所周知的铁死亡关键调节剂,各种内源性分子(如硒㊁多巴胺㊁维生素E㊁辅酶Q10等)和化学药品(如Fer-1㊁右雷佐生等)通过直接抑制或间接失活GPX4来激发铁死亡[15](图1)㊂1.4㊀甲羟戊酸途径甲羟戊酸途径是脂代谢中重要的生物合成途径,广泛存在于真核生物中,以乙酰辅酶A为原料,以类固醇等为主要产物㊂辅酶Q10作为该途径代谢产物之一,是铁死亡的内源性抑制剂,若消耗增多则可增加细胞对铁死亡的敏感性(图1)㊂1.5㊀铁自噬铁自噬的概念在2014年由Mancias等[16]提出,是指由核受体辅激活因子4介导的将铁蛋白靶向转运至自噬体中降解并释放游离铁的一种选择性自噬方式,是一种保守的细胞分解代谢过程㊂适当的铁自噬可以维持细胞内铁含量稳定,但是过度的铁自噬由于释放出大量游离铁而诱发铁死亡(图1)㊂1.6㊀电压依赖性阴离子通道VDAC是位于线粒体外膜的转运离子和代谢产物的跨膜通道蛋白,具有VDAC1㊁VDAC2和VDAC3三种亚型,可调AA/AdA:花生四烯酸/二十二碳四烯酸;CoA:辅酶A;GSS:谷胱甘肽合成酶;γ-GS:γ-谷胺酰半胱氨酸;GPX4:谷胱甘肽过氧化物酶4;ROS:活性氧;CoQ10:辅酶Q10;MVA:甲羟戊酸:HMG-CoA:3-羟基-3-甲基戊二酸单酰辅酶A;Acetyl-CoA:乙酰辅酶a羧化酶;ACSL4:酰基辅酶A 合成酶长链家族成员4;LPCAT3:溶血卵磷脂酰基转移酶3;ALOX15:15-脂氧合酶;VDAC:电压依赖性阴离子通道;NOX:NADPH氧化酶; DMT1:二价金属转运体1;ZIP8/14:锌-铁调节蛋白家族8/14;PCBP1/2:分子伴侣多聚结合蛋白1/2;LC3:微管结合蛋白1轻链3;NCOA4:核受体辅激活剂4图1㊀铁死亡调控机制模式图节线粒体代谢产能过程,参与细胞生存和死亡信号调控㊂有研究证明,铁死亡抑制剂脂血抑素1可通过下调VDAC1表达水平来抑制铁死亡[17]㊂铁死亡诱导剂埃拉斯汀可作用于VDAC2及VDAC3,使得线粒体通透性增加,进而诱导线粒体功能障碍和细胞铁死亡的发生[18]㊂敲低VDAC2和VDAC3基因可抑制铁死亡发生,但过表达VDAC2和VDAC3并没有显著诱发铁死亡,具体机制有待进一步研究[19](图1)㊂2　铁死亡在心血管疾病中的研究进展2.1㊀铁死亡与心力衰竭越来越多的证据表明,铁死亡是心力衰竭(heart failure, HF)病理生理机制中不可或缺的重要环节㊂Lapenna等[20]研究发现,老龄兔心脏组织中铁含量㊁氧化应激标记物等均明显高于正常成年兔,表明铁代谢可能与衰老㊁功能障碍等病理生理相关㊂Liu等[21]在体内通过主动脉缩窄术建立压力超负荷诱导大鼠HF模型,在体外培养经埃拉斯汀或异丙肾上腺素处理的H9c2心肌细胞,结果发现两者均可观察到以铁超载及脂质过氧化物生成增多为特征的铁死亡过程㊂在通过高盐饲料喂养盐敏感大鼠建立的射血分数保留HF模型中,铁死亡相关指标TFR1㊁ACSL4㊁4-HNE表达明显升高, FTH1㊁xCT表达明显降低,提示铁死亡是射血分数保留HF 发病机制之一[22]㊂为了探索铁死亡在HF中的调控机制, Zheng等[23]对GEO公共数据库进行了分析并发现,M2型巨噬细胞外泌体传递CircSnx12是参与铁代谢相关铁死亡的关键调节因子,可通过与miR-224-5p相互作用实现靶向调节与铁死亡相关的FTH1基因,进而调控诱导HF发生的铁死亡机制,所以环状RNA可能成为治疗HF的前瞻性靶标和新型药物研发的突破口㊂另外,阿尔茨海默病小鼠模型具有心脏结构和功能异常等特点,伴ACSL4㊁核受体辅激活因子4表达上调,xCT㊁GPX4表达下调,即存在脂质过氧化㊁氧化应激水平升高,GSH代谢异常以及铁死亡的激活㊂线粒体醛脱氢酶缺陷与阿尔茨海默病患者心功能不全相关,阿尔茨海默病小鼠铁死亡相关指标变化可通过线粒体醛脱氢酶转基因得以逆转,心脏结构和功能亦能得以改善[24]㊂这些发现表明铁死亡与HF㊁心功能不全密切相关㊂2.2㊀铁死亡与心肌缺血/再灌注损伤迄今为止,血运重建仍然是缺血性心肌病最有效的治疗方法,但心肌缺血/再灌注(ischemia/reperfusion,I/R)损伤不可避免,并会造成多种类型细胞死亡,包括铁死亡㊂我们知道,多元醇途径参与了I/R损伤诱导的心肌梗死,Tang等[25]研究发现抑制多元醇途径关键酶可减弱I/R损伤介导的缺氧诱导因子1α㊁Tf㊁TFR1和细胞内铁含量的增加,减少心脏梗死区域面积,过表达多元醇途径关键酶可激活缺氧诱导因子1α,诱导TFR1的表达和铁的积累,加剧脂质过氧化和氧化损伤,故多元醇途径参与调节了I/R损伤介导的铁死亡㊂还有研究发现,泛素特异性蛋白酶7在心脏I/R损伤期间通过激活p53/TFR1通路参与调节铁死亡[26]㊂铁死亡可通过TLR4/Trif/Type1IFN信号通路促进中性粒细胞向冠状动脉内皮细胞黏附以及向受损心肌募集,造成坏死性炎症,加重心脏移植后心肌损伤[27]㊂铁死亡抑制剂可减轻心肌I/R损伤[28]㊂Gao等[29]研究证明细胞内谷氨酰胺分解代谢在铁死亡机制中发挥了关键作用,抑制谷氨酰胺代谢可抑制铁死亡,改善离体心脏模型I/R损伤,为I/R损伤治疗提供了新策略㊂亦有研究表明铁死亡不是发生在心肌缺血阶段,而是发生在心肌再灌注阶段[30],可能与这一阶段氧化的磷脂酰胆碱生成相关[31],为I/R损伤甚至心肌梗死患者建立精准医疗奠定了基础㊂同时,雷帕霉素可通过其靶标哺乳动物雷帕霉素靶蛋白基因过表达抑制铁死亡[32],进而改善心肌缺血,减少I/R损伤㊂在糖尿病心肌I/R损伤模型中,抑制铁死亡可减少内质网应激相关性心肌损伤[33]㊂故抑制铁死亡是I/R心肌损伤治疗的有效策略㊂2.3㊀铁死亡与蒽环类药物心脏毒性多柔比星(doxorubicin,DOX)是临床上常用的蒽环类化疗药物,具有心脏毒性,可造成DOX相关性心肌病,限制其临床应用并产生不良预后[34]㊂有研究表明,高铁基因可通过调节心肌细胞铁沉积来增加DOX诱导的心脏毒性的易感性[35]㊂Fang等[28]研究表明铁死亡机制在DOX诱导小鼠心肌病模型中发挥了关键作用,经Fer-1干预后可显著改善小鼠心肌病变及死亡率㊂通过全转录组测序发现DOX可通过Nrf2上调血红素加氧酶1表达,降解血红素铁,进而诱发铁死亡,且证实铁超载和脂质过氧化主要定位于心肌细胞线粒体,更加明确了线粒体损伤在DOX心肌损伤中的因果关系㊂Tadokoro等[36]同样证实线粒体依赖性铁死亡在DOX心肌损伤进展中的关键作用㊂Liu等[37]应用RNA测序方法发现,在DOX干预后小鼠心脏中,脂代谢途径中的Acot1基因明显下调,经Fer-1处理后部分逆转,且Acot1过表达可抑制铁死亡,进而实现心脏获益㊂因此,Acot1可能是通过抑制铁死亡来预防和治疗DIC的潜在靶点㊂2.4㊀铁死亡与糖尿病性心肌病糖尿病是心血管疾病常见的合并症,可增加心脏对I/R 损伤的易感性和糖尿病性心肌病(diabetic cardiomyopathy, DCM)的发生风险㊂氧化应激已被证实为DCM心脏结构和功能改变的重要因素㊂2022年发表的一项研究首次报道了铁死亡在DCM发病机制中起着至关重要的作用,萝卜硫素可通过AMPK激活NrF2,上调铁蛋白和xCT水平进而抑制铁死亡过程,改善DCM小鼠心脏病变[38]㊂2.5㊀铁死亡与败血症相关心脏损伤败血症致心脏损伤的发生率和死亡率均较高㊂盲肠结扎和穿刺是研究败血症最常用的造模方法,该模型可增加心脏铁含量和脂质过氧化水平,并降低GSH含量和GPX4表达水平,提示败血症引起的心脏损伤可能涉及铁死亡机制,而右美地托咪定可通过抑制铁死亡改善败血症引起的心脏损伤[39]㊂此外,铁死亡已被证明在脂多糖诱导的败血性心肌病模型中起重要作用[40]㊂2.6㊀铁死亡与心律失常目前,全球正面临新型冠状病毒(COVID-19)大流行,而COVID-19感染会导致小鼠心脏起搏细胞功能障碍并诱导铁死亡,酪氨酸激酶抑制剂伊马替尼和铁螯合剂去铁胺可阻断病毒感染和铁死亡相关过程,可能是改善病毒感染后心律失常的潜在机制[41]㊂另一项关于小鼠的研究亦表明铁死亡与心律失常相关,频繁过量饮酒会诱发铁死亡,并增加心房颤动发生率,而铁死亡抑制剂可部分逆转过量饮酒引起的不良反应[42]㊂2.7㊀铁死亡与心肌纤维化Wang等[43]发现在主动脉缩窄致压力超负荷HF模型中,HF晚期心肌纤维化主要由MLK3调节的JNK/p53信号通路介导的铁死亡引起,miR-351基因表达上调可抑制MLK3表达,进而改善心肌纤维化及心功能㊂xCT基因缺失亦可加剧血管紧张素Ⅱ介导的心肌纤维化和功能障碍,为铁死亡参与心肌纤维化提供了证据[44]㊂2.8㊀铁死亡与内皮功能障碍㊁动脉粥样硬化内皮功能障碍是糖尿病标志性病变,是糖尿病心血管并发症的起始和关键因素㊂有研究表明,在糖尿病db/db小鼠的主动脉内皮中观察到xCT表达下降㊁铁积累和脂质过氧化物生成增多以及去内皮化改变,且高糖和白细胞介素-1β可通过p53-xCT-GSH途径诱导静脉内皮细胞发生铁死亡[45]㊂高脂饮食可诱导ApoE-/-小鼠形成动脉粥样硬化,Bai等[46]发现在动脉粥样硬化血管中铁死亡相关蛋白明显上调,Fer-1干预后可部分抑制铁超载和脂质过氧化,并显著降低了xCT 和GPX4的表达水平,同时抑制铁死亡可改善主动脉内皮细胞的活力㊂另一项关于不同严重程度动脉粥样硬化的尸检报告数据表明,重度动脉粥样硬化患者的冠状动脉标本中前列腺素内过氧化物合成酶2㊁ACSL4表达上调,GPX4表达下调[47]㊂故铁死亡与内皮功能障碍和动脉粥样硬化病理学相关,并参与其发生及发展㊂2.9㊀铁死亡与其他镰状细胞病是一种以溶血㊁器官缺血和心血管并发症等为特征的遗传性疾病,该疾病小鼠血红素水平升高,导致心脏铁超载㊁脂质过氧化和铁死亡,抑制铁死亡减轻了与镰状细胞病相关的心肌病[48]㊂有研究发现,吸烟与腹主动脉瘤的发生㊁发展和破裂显著相关[49]㊂Sampilvanjil等[50]首次证实香烟提取物可引起血管平滑肌细胞发生铁死亡,并可能通过铁死亡机制诱导主动脉瘤或夹层㊂此外,Ma等[51]首次证实铁死亡是血管钙化发生的新机制㊂3㊀铁死亡抑制剂在心血管疾病中的应用由于铁死亡机制是治疗和预防心血管疾病的潜在靶点,铁死亡抑制剂在心血管疾病中的应用逐渐增多㊂UAMC-3203作为比Fer-1更稳定和有效的铁螯合剂,能更好地预防动物模型中铁死亡驱动的多器官功能障碍,可能更适合临床试验推广[52]㊂针对脂血抑素1的研究相对较少,但具有与Fer-1相似的保护作用,可显著减少棕榈酸诱导的心脏损伤[53]㊂抗氧化剂N-乙酰半胱氨酸可提高半胱氨酸的生物利用度,其抗铁死亡作用已得到证实[54],并可减少糖尿病大鼠心肌I/R损伤,为临床应用提供了理论基础[55]㊂去铁酮㊁化合物968在心脏I/R损伤中亦发挥了心脏保护作用[56,29]㊂右雷佐生是乙二胺四乙酸环状衍生物,是唯一一个被美国食品药品监督管理局批准的用来预防DOX相关性心肌病的铁螯合剂,可以直接进入心肌细胞线粒体并减少铁积累[57]㊂人脐带血中间充质干细胞的外泌体可通过抑制急性心肌梗死小鼠模型中的铁死亡来实现心脏保护作用[58]㊂卡格列净㊁葛根素㊁阿托伐他汀可抑制铁死亡改善心功能[21-22,59],为HF提供了潜在的治疗策略,而氧化锌纳米粒子可诱导铁死亡,促进内皮损伤发生㊂此外,常用的心脏药物可能具有未发现的抗铁死亡活性,如卡维地洛已被证明可以抑制铁死亡,而与其对β-肾上腺素能受体的作用无关㊂尽管抑制铁死亡已在多种动物模型中显示出心脏获益,但迄今为止尚未进行使用铁死亡抑制剂治疗心血管疾病的临床试验㊂4㊀小结铁死亡作为最近发现的程序性细胞死亡类型,是心血管疾病发生发展的关键机制之一㊂近年来日益引起人们的重视,相关研究不断增加㊂本文总结了铁死亡相关调控机制及其在心血管疾病中的研究进展和应用㊂但铁死亡研究领域的一些关键机制尚待研究和验证,需要我们进一步探索去揭示铁死亡的精细分子机制,从而为靶向铁死亡以减少主要不良心血管事件以及防治心血管疾病提供更加充分的理论依据,为预防和治疗心血管疾病提供新的生物标志物和前瞻性靶标㊂利益冲突:无参㊀考㊀文㊀献[1]Ward 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非等位基因概述非等位基因是指同一基因座上的不同等位基因。

等位基因是指在某个给定的基因座上，可以存在多种不同的变体。

每个个体继承了一对等位基因，一对等位基因可能会导致不同的表型表达。

非等位基因的存在使得遗传学研究更加复杂，因为不同的等位基因会对个体的表型产生不同的影响。

背景在生物学中，基因座是指染色体上一个特定的位置，该位置上的基因决定了某个特征的表达方式。

每个基因座上可以有多种不同的等位基因。

等位基因是指在某个特定基因座上的不同基因变体。

每个个体都会继承一对等位基因，通过这对等位基因的不同组合，决定了个体的表型。

然而，并非所有基因座上的等位基因都具有相同的表现型。

非等位基因的影响非等位基因的存在导致不同等位基因会对个体表型产生不同的影响。

有些非等位基因会表现出显性效应，也就是说，当个体继承了一个突变的等位基因时，即使同时继承了一个正常的等位基因，但显性效应会使得突变的等位基因的表型表达得到体现。

相反，有些非等位基因会表现出隐性效应，当个体继承了两个突变的等位基因时，才会表现出突变的表型。

除了显性和隐性效应之外，非等位基因还可能发生两种其他类型的表型效应。

一种是共显效应，当个体继承了两个不同的突变等位基因时，在表型表达上会表现出一种新的特征，这个特征并不是单个突变等位基因所能导致的。

另一种是部分显性效应，当个体继承了两个不同的突变等位基因时，表型表达将介于两个单独突变等位基因的表型之间。

重组和非等位基因重组是指两个不同的染色体交换部分基因序列的过程。

在重组的过程中，非等位基因可能会发生改变，导致新的等位基因组合形成。

这一过程使得非等位基因的表型效应更加复杂，因为新的等位基因可能将不同基因座的效应组合起来。

非等位基因的重要性非等位基因对生物的适应性和多样性起着重要作用。

通过对等位基因的各种组合的研究，人们可以更好地理解基因与表型之间的关系，并揭示遗传变异对物种适应环境的重要性。

总结非等位基因是指同一基因座上的不同等位基因。

一种本周氏蛋白测定的简易对照
谢晏如
【期刊名称】《现代检验医学杂志》
【年(卷),期】2002(017)004
【摘要】@@ 本周氏蛋白是一种免疫球蛋白的轻链或及聚合体,可大量出现在多发性骨髓瘤或轻链病患者的尿液中.
【总页数】1页(P2-2)
【作者】谢晏如
【作者单位】浙江省浦江县人民医院,浙江,浦江,322200
【正文语种】中文
【中图分类】R446.12+9
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Package‘chromoMap’October12,2022Type PackageTitle Interactive Genomic Visualization of Biological DataVersion4.1.1Maintainer Lakshay Anand<************************>Description Provides interactive,configurable and elegant graphics visualization of the chromo-somes or chromosome regionsof any living organism allowing users to map chromosome ele-ments(like genes,SNPs etc.)on the chromosome plot.It introducesa special plot viz.the``chromosome heatmap''that,in addition to mapping elements,can visual-ize the dataassociated with chromosome elements(like gene expression)in the form of heat col-ors which can be highlyadvantageous in the scientific interpretations and research work.Be-cause of the large size of the chromosomes,it is impractical to visualize each element on the same plot.However,the plot provides a magni-fied view for eachof chromosome locus to render additional information and visualization specific for that loca-tion.You can mapthousands of genes and can view all mappings ers can investigate the detailed informa-tion about the mappings(like gene names or total genes mapped on a location)or can view the magnified single or dou-ble stranded view of thechromosome at a location showing each mapped element in sequential order.The package pro-vide multiple featureslike visualizing multiple sets,chromosome heat-maps,group annotations,adding hyperlinks,and labelling.The plots can be saved as HTML documents that can be customized and shared easily.In addi-tion,you can include them in R Markdown or in R'Shiny'applications.Depends R(>=4.0)License GPL-3|file LICENSEEncoding UTF-8Imports htmltools(>=0.3.6),htmlwidgets(>=1.0)Suggests knitr,rmarkdown1VignetteBuilder knitrRoxygenNote7.1.2NeedsCompilation noAuthor Lakshay Anand[aut,cre]Repository CRANDate/Publication2022-03-1608:40:02UTCR topics documented:chromoMap (2)chromoMap-shiny (7)Index9chromoMap render interactive chromosome plots of any living organism and an-notate elementsDescriptionrender an interactive graphics visualization of entire chromosomes or chromosomal regions of any living organism.Chromosomal elements such as genes can be annotated easily using this tool.required for creating widgetsUsagechromoMap(ch.files,data.files,title=c(),ch_gap=5,ploidy=1,top_margin=25,left_margin=50,chr_width=15,chr_length=4,chr_color=c("black"),data_based_color_map=FALSE,segment_annotation=FALSE,lg_x=0,lg_y=0,data_type=c("numeric","categorical"),labels=FALSE,canvas_width=NULL,canvas_height=NULL,data_colors=list(),anno_col=c("#10B85F"),chr_text=c(TRUE),discrete.domain=NULL,legend=c(FALSE),hlinks=FALSE,aggregate_func=c("avg"),plots=c("none"),tag_filter=list(c("none",0)), plot_height=c(30),plot_ticks=c(4),plot_color=c("blue"),plot_y_domain=list(c(0,0)), ch2D.colors=NULL,ch2D.cat.order=NULL,ch2D.lg_x=0,ch2D.lg_y=0,ref_line=c(FALSE),refl_pos=c(0),refl_color=c("grey"),refl_stroke_w=c(2),tagColor=c("red"),heat_map=c(TRUE),text_font_size=c(10),chr_curve=5,title_font_size=12,label_font=9,label_angle=-90,vertical_grid=FALSE,grid_array=c(0,5000,10000), grid_color="grey",grid_text=NULL,grid_text_size=12,grid_text_y=20,plot_filter=list(c("none",0)), id=c("chromap"),region=NULL,show.links=FALSE,loci_links="none", directed.edges=F,y_chr_scale=0,links.colors=NULL,links.lg_x=0,links.lg_y=0,n_win.factor=1,chr.scale.ticks=5,export.options=F,fixed.window=F,window.size=NULL,win.summary.display=F,st.window=T,guides=F,guides_color="lightgrey",ann.h=1,chr.2D.plot=F,display.chr=T,plot.shift=c(1),bels=c(""),bel="",bels=c(""),b.x=10,b.y=0,b.size=15,scale.suffix="bp",numeric.domain=NULL,interactivity=T)Argumentsch.filesfilename(s)as character vector OR list of data.frames containing co-ordinates of the chromosomes to renderdata.filesfilename(s)as character vector OR list of data.frames containing data to annotate on the chromosomes.title a character string to be used as a title in plotch_gap provide spacing between chromosomes.ploidy specify the number of sets of chromsomes being passed.top_margin specify the margin from top of the plotleft_margin specify the margin from the left of the plotchr_width specify the width of each chromsomechr_length specify the length of each chromsome.chr_color a vector specifying the color of each chromsome in a set.A color can be assigned to each set by passing a different color values as vectordata_based_color_mapa boolean to tell the plot to use the data provided infile for visualizing annotationsegment_annotationa boolean to use segment-annotation algorithmlg_x specify the x or horizontal distance of the legend from origin(bottom right cor-ner)lg_y specify the y or vertical distnce of the legend from the origindata_type specifying the data type of the data used.takes value either’categorical’or ’numeric’labels a boolean to include labels in plotcanvas_width width of the plotcanvas_height height of the plotdata_colors specify annotation colors for the dataanno_col a vector to specify annotation color for each set.chr_text a boolean vector to enable or disable chromsome texts for each ploidy.set discrete.domainmanually specify the order of categories.legend a boolean vector to enable or disable legend for each set/ploidyhlinks a boolean to use hyperlinks supplied in dataaggregate_func takes either’sum’or’avg’to specift aggregate function for each lociplots specify the type of plot to visualize.takes either’scatter’,’bar’or’tags’.(default:’none’)tag_filter a list to specify thefilter operation and operands for each ploidy.plot_height specify plot height for each ploidy.default:c(30)plot_ticks specify number of ticks for plot axis.default:c(4)plot_color specify the plot color for each ploidy.default:c("blue")plot_y_domain specify plot y-axis domain.default:list(c(0,0))ch2D.colors specify the group colors for visualizing categories on2D chromosome plots ch2D.cat.order manually setting the order of categories for2D-Chromsome plotch2D.lg_x specify the x or horizontal distance of2D plot legend from the origin(bottom right corner)ch2D.lg_y specify the y or vertical distance of2D plot legendref_line a boolean to use horizontal reference line in plot.default:c(FALSE)refl_pos specify the position of reference line.default:c(0)refl_color specify the color of the reference line.default:c("grey")refl_stroke_w specify the stroke width of the reference line.default:c(2)tagColor specify the color of tags.default:c("red")heat_map a boolean to use if chromosome heatmaps are shown.default:c(TRUE),text_font_size specify chromosome text font-size.default:c(10)chr_curve specify the chromosome curves at the telomeres or centromere loci.default:5 title_font_sizespecify the font-size of the title.default:12label_font specify the font-size of the labels.default:9label_angle specify the angle of rotation of labels.default:-90vertical_grid a boolean to use vertical grid lines.default:FALSEgrid_array specify the position(s)of grid line(s)in bp to highlight locations across genome.default:c(0,5000,10000)grid_color specify the color of the grid lines.default:"grey"grid_text specify the text to be attached at the top end of gridlinesgrid_text_size specify the font-size of the textgrid_text_y specify the y-distance(from top)for the textplot_filter a list specify the plotfilter operation,operands,andfilter-color for each ploidy.id specify a unique id doe chromoMap plot.default:c("chromap")region specify the region of interest for chromosome(s)for zoom-in.Format:"chrName:Ploidy:Start:Stop" show.links a boolean to specify whether links are visualized.default:FALSEloci_links a character vector specifyingfile name or a data.frame for links input datadirected.edges a boolean to visualize directed edgesy_chr_scale adjust the chromosome scale along y-axislinks.colors specify the links colorslinks.lg_x specify x or horizontal distance of links legend from the originlinks.lg_y specify y or vertical distance of linksn_win.factor specify the factor by which the chr will be scaled;increases number of windows(default:1)chr.scale.ticksspecify the number of ticks for chr scale(default:5)export.options boolean to include export buttons in the plotfixed.window Boolean to specify wether to usefixed window visualizationwindow.size specify the window size,iffixed.window is TRUEwin.summary.displayboolean to display window summary to consolest.windowForfixed window analysis,boolean to specify whether to include last windowof chromosomesguides boolean to display guidesguides_color set guides color.ann.h set annotation bar height in2D-Chromosome plotchr.2D.plot boolean to specify visualize2d Chromosome plotdisplay.chr boolean to show.hide chromosomeplot.shift shifting the plots in y direction in case hiding chromosomesbelsspecify plot legend labelsbelspecify categorical-data legends labelbels specify plots y-axis labelsb.x adjust plot y labels in x-directionb.y adjust plot y labels in y-directionb.sizeset size of plot y labelsscale.suffix set the suffix for chromosome scale(default:’bp’)numeric.domain manually set data domain(min,max)for heat colors for numeric datainteractivity boolean to enable/disable interactivity on chromosomesExamples##Not run:library(chromoMap)#simple annotationschromoMap("chromosome_file.txt","annotation_file.txt")#polyploidy examplechromoMap(c("chromosome_set1.txt","chromosome_set2.txt"),c("annotation_set1.txt","annotation_set2.txt"),ploidy=2)#plotting group annotationchromoMap("chromosome_file.txt","annotation_file.txt",data_base_color_map=T,data_type="categorical")#plotting chromsome heatmapschromoMap("chromosome_file.txt","annotation_file.txt",data_based_color_map=T,data_type="numeric")#enabling hyperlinkschromoMap("chromosome_file.txt","annotation_file.txt",hlinks=T)#enabling labelschromoMap("chromosome_file.txt","annotation_file.txt",labels=T)#change chromosome colorchromoMap("chromosome_file.txt","annotation_file.txt",chr_color="red")##End(Not run)chromoMap-shiny Shiny bindings for chromoMapDescriptionOutput and render functions for using chromoMap within Shiny applications and interactive Rmd documents.UsagechromoMapOutput(outputId,width="100%",height="400px")renderChromoMap(expr,env=parent.frame(),quoted=FALSE)ArgumentsoutputId output variable to read fromwidth,height Must be a valid CSS unit(like 100% , 400px , auto )or a number,which will be coerced to a string and have px appended.expr An expression that generates a chromoMapenv The environment in which to evaluate expr.quoted Is expr a quoted expression(with quote())?This is useful if you want to save an expression in a variable.IndexchromoMap,2chromoMap-shiny,7chromoMapOutput(chromoMap-shiny),7 renderChromoMap(chromoMap-shiny),79。

·综述·摘 要：了解H5亚型高致病性禽流感发展现状，为优化相关政策提供信息支持，本文简述了全球高致病性禽流感（HPAI ）的流行现状。

2020—2022年，全球累计91个国家/地区报告发生HPAI 疫情，累计报告疫情16 146起，累计发病禽为4 449.5万余只，累计扑杀销毁禽2.51亿只。

其中，H5N1亚型是目前全球流行的主要血清型，欧洲、美洲、亚洲等国家累计报告的疫情数占比超过90%。

鉴于H5亚型禽流感病毒是当前全球危害养禽业的主要流行亚型，本文进一步分析了H5N1、H5N6、H5N8等亚型的流行特点，概述了我国H5亚型HPAI 流行现状，并强调了我国实施的强制免疫与扑杀相结合的禽流感综合防控策略已取得显著成效。

此外，全球H5亚型HPAI 疫情启示，我国应持续加大HPAI 防控力度，推进养禽业产业结构优化升级，提高生物安全水平，加强疫情信息分析，强化野生动物和病原变异情况监测力度，坚定实施扑杀感染禽和疫苗免疫相结合等综合防控策略。

关键词：高致病性禽流感；流行状况；流行特点；全球中图分类号：S851.3文献标志码： A文章编号：1674-6422（2023）04-0229-08Epidemiological Analysis of H5 Subtype Highly Pathogenic Avian Infl uenza in 2020-2022SUN Rongzhao 1,2, GONG Fengju, GE Dong 2, GAO Xiangxiang 3, JIANG Wenming 3, TANG Lijie 1(1. Northeast Agricultural University, Harbin 150030, China; 2. Qingdao Lijian Bio-Tech Co., Ltd., Qingdao 266032, China; 3. ChineseCenter for Animal Health and Epidemiology, Qingdao 266032, China)收稿日期：2023-07-07基金项目：山东省重点研发计划项目（2022CXGC010606）；“十四五”国家重点年研发计划项目（2021YFD1800201）作者简介：孙荣钊，男，博士研究生，预防兽医专业通信作者：唐丽杰，E-mail：*****************；蒋文明，E-mail：**************2020—2022年H5亚型高致病性禽流感流行情况分析孙荣钊1,2，宫枫举2，葛 栋2，高向向3，蒋文明3，唐丽杰1（1.东北农业大学，哈尔滨150030；2.青岛立见生物科技有限公司，青岛266032；3.中国动物卫生与流行病学中心，青岛266032）Abstract: To understand the development status of H5 subtype highly pathogenic avian influenza (HPAI) and provide information support for optimizing relevant policies, this paper briefl y describes the global epidemic status of HPAI. From 2020 to 2022, a total of 91 countries/regions have reported HPAI outbreaks, and a total of 16 146 cases have been reported. The total number of infected birds was 44.95 million, and 251 million birds were killed and destroyed. Among them, the H5N1 subtype is the main serotype in the current global epidemic, and the cumulative number of reported cases in Europe, the Americas, Asia and other countries accounts for more than 90%. In view of the fact that the H5 subtype of Avian infl uenza virus is the main epidemic subtype in the world, this paper further analyzes the epidemic characteristics of H5N1, H5N6, H5N8 and other subtypes, summarizes the epidemic status of H5 subtype HPAI in China, and emphasizes that the comprehensive prevention and control strategy of compulsory immunization and culling has achieved remarkable results. In addition, the global H5 subtype of HPAI epidemic has revealed that China should continue to increase HPAI prevention and control efforts, promote the optimization and upgrading of the poultry industry structure, improve the level of biosafety, strengthen the analysis of epidemic information, strengthen the monitoring of wildlife and pathogen variation, and fi rmly implement a comprehensive prevention and control strategy such as the combination of culling infected birds and vaccine immunization.Key words: Highly pathogenic avian influenza; epidemic status; popular characteristics; global Chinese Journal of Animal Infectious Diseases中国动物传染病学报2023,31(4):229-236· 230 ·中国动物传染病学报2023年8月高致病性禽流感（Highly pathogenic avian influenza, HPAI）是影响养禽业健康发展与国际贸易的重要疫病，其有效防控对公共卫生学也具有重要意义。

绿色金融标准化研究的文献计量学分析何 源1，2 李鹏程1 杨 洁1（1.中国标准化研究院；2.清华大学环境学院）摘　要：绿色金融标准是界定项目、资产和活动是否“绿色”、避免“洗绿”的关键技术依据，绿色金融标准体系是绿色金融发展的重要基础设施。

本文以2015-2023年中国知网和Web of Science数据库主题为“绿色金融标准”的中英论文作为研究对象，进行绩效分析和科学图谱分析，总结中外绿色金融标准化研究趋势，提出未来我国绿色金融标准化工作的重点方向。

结果表明：中文论文绿色金融标准化的研究热点从“一带一路”、可持续发展等相关政策，绿色债券等绿色金融体系研究向环境信息披露、ESG、高质量发展转变，自2022年，研究前沿为“双碳”目标背景下的转型金融。

英文论文的研究热点从绿色债券等绿色金融体系向公司社会责任、ESG转变，自2019年，研究前沿为欧盟可持续金融分类法。

未来我国绿色金融标准化应进一步提升绿色金融标准体系的国内外一致性程度，推动我国话语体系与国际趋同；加快补齐绿色金融标准短板，出台转型金融统一指导标准；优化绿色金融标准体系的供给结构，建立国家颁布标准和市场自主制定标准共同构成的二元绿色金融标准体系。

关键词：绿色金融，标准化，文献计量学，科学图谱分析DOI编码：10.3969/j.issn.1674-5698.2024.01.001Bibliometrics Analysis of Standardization Research on Green FinanceHE Yuan1,2 LI Peng-cheng1 YANG Jie1(1.China National Institute of Standardization; 2.School of Environment, Tsinghua University）Abstract: The green finance standards serve as the crucial technical foundation to define whether projects, assets, and activities are green and prevent greenwashing, which are also the vital infrastructure for the development of green finance. This paper focuses on Chinese and English academic papers from the China National Knowledge Infrastructure and Web of Science databases, with the theme of “green finance standards”, covering the years 2015 to 2023. The study involves performance analysis and scientific mapping, summarizes the trends in domestic and international research on green finance standardization. It also suggests key directions for future green finance standardization efforts in China. The findings reveal that the research hotspots in Chinese papers have shifted from topics like the Belt and Road Initiative, sustainable development policies such as green bonds to areas like environmental information disclosure, ESG, and the transition of high-quality development. Since 2022, the forefront of research has been the transformation of finance in the context of the “dual-carbon” goals. In English papers, the research focal points have transitioned from green financial system like green bonds towards corporate social responsibility and ESG. Since 2019, the leading edge has been the European Union’s Sustainable Finance Taxonomy. Future endeavors in green finance standardization in China should aim to enhance the domestic and international 基金项目： 本文受中央基本科研业务费项目“基于双重重要性的典型绿色金融支持项目评估方法研究”（项目编号：542023Y- 10362）资助。

bio_data函数的用法`bio_data`函数通常用于处理生物数据，其具体用法可能因编程语言或库的不同而有所差异。

以下是一个示例，展示了如何在 Python 中使用`bio_data`函数来处理生物数据：```pythonfrom Bio import SeqIOdef bio_data(fp):# 读取 FASTA 文件records = SeqIO.parse(fp, "fasta")# 遍历记录for record in records:# 获取记录的名称record_id = record.id# 获取记录的序列sequence = record.seq# 打印名称和序列print(f"名称: {record_id}")print(f"序列: {sequence}")print("-" * 50)# 示例用法fasta_file = "example.fasta"bio_data(fasta_file)```在上述示例中，我们定义了一个名为`bio_data`的函数，它接受一个文件路径`fp`作为参数。

在函数内部，我们使用`Bio`库中的`SeqIO`模块读取指定路径下的 FASTA 文件，并解析文件中的记录。

然后，我们使用一个循环遍历记录，并获取每个记录的名称和序列。

最后，我们打印出名称、序列以及一个分隔线。

你可以根据自己的需求和使用的编程语言来调整和扩展`bio_data`函数的功能。

请确保在使用任何生物数据处理函数时，你已经安装并导入了相关的库或模块，并按照其文档进行正确的用法和参数配置。

生物制药专业英文简历范文Basic CVName: yjbys nationality: ChinaCurrent location: Guangzhou National: HanExit and Entry: Guangxi tall: 175 cm 60 kgMarital Status: Single Age: 22 years oldTraining Certification: integrity badge:Job search intention and work experiencePersonnel types: ordinary jobPosition: Biological Chemical / Pharmaceutical Engineering: .. bio-engineering chemical engineeringWork Experience: 1 Title: nullJob type: full-time can be reported for duty - at any timeMonthly requirements: 1500 - 2000 hope that the working area: Guangzhou, Foshan, ZhongshanPersonal experience: in July 2005 -2005 people in September in major pharmacy chains Co., Ltd. salesOctober 2006 -2007 in Wuhan Health Bureau in July Biological Technology Co., Ltd. sales representativeIn the region responsible for the collection of client resources, regularly call on customers, the company is expected to reach sales targets.August 2007 -2007 in a letter dated 11月盈Southern Biological Technology Co., Ltd. Account ManagerIn charge of the region visited by the client, its demand for the type and quantity to build customer relations and promote sales.Achievements: the establishment of a stable customer relationship to enhance the visibility of the company's products, product sales increase steadily.Educational backgroundGraduate institutions: Wuhan Institute of Biological EngineeringHighest level of education: college graduates - 2007-07-01Studies by one: the bio-pharmaceutical Science II:Experienced by the education and training: 2004-9 ---- 2007-6 Wuhan Biological Engineering College of Pharmaceutical Engineering2005-9 ---- 2006-7 Wuhan Biological Engineering College of Traditional Chinese Medicine Quality2 computers can Proficiency in word, excle as well as the basic operation of the Internet.Language abilityForeign Languages: English wellMandarin level: the level of proficiency in Cantonese: ExcellentThe ability to work and other expertiseI am honest and prudent but not a lack of passion. On the work of a conscientious and responsible, hard-working hard, to hard. Others integrity, hi friends, with good communication skills and sense of teamwork. Learning ability, and have a good capacity for implementation. Want to accept challenges. Challenge goals, challenge themselves and achieve self-worth.Detailed personal autobiographyPersonality: a mild, modest, self-discipline, self-confidencePersonal credo: I can accept failure, but I can not accept to give up. The minds of their own wisdom, strong perseverance, strength and team goals.Personal Hobbies: football, computers, music and so on.Personal Contact。