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Article(id=1220364309419443015, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1220364301731287745, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2020-0136, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1581955200000, receivedDateStr=2020-02-18, revisedDate=1582300800000, revisedDateStr=2020-02-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1768887124638, onlineDateStr=2026-01-20, pubDate=1583942400000, pubDateStr=2020-03-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768887124638, onlineIssueDateStr=2026-01-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768887124638, creator=13701087609, updateTime=1768887124638, updator=13701087609, issue=Issue{id=1220364301731287745, tenantId=1146029695717560320, journalId=1189982191388893191, year='2020', volume='55', issue='3', pageStart='349', pageEnd='536', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1768887122805, creator=13701087609, updateTime=1768957149580, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1220658015389270676, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1220364301731287745, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1220658015389270677, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1220364301731287745, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=374, endPage=383, ext={EN=ArticleExt(id=1220364311491429274, articleId=1220364309419443015, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Preliminary exploration of the mechanism of Qingfei Paidu decoction against novel coronavirus pneumonia based on network pharmacology and molecular docking technology, columnId=1220364302905692867, journalTitle=Acta Pharmaceutica Sinica, columnName=Focus on Anti Novel Coronavirus Drugs Research, runingTitle=null, highlight=null, articleAbstract=

Traditional Chinese medicine (TCM) network pharmacology and molecular docking technology were applied to explore the mechanism of anti-coronavirus pneumonia (coronavirus disease 2019, COVID-19) of Qingfei Paidu decoction. The Chinese Pharmacopoeia (2015 edition) and Traditional Chinese Medicine Systems Pharmacology (TCMSP), OMIM (Online Mendelian Inheritance in Man), GeneCard, STRING, and others online databases are used for building a series of network, and selecting the core target and analyzing the signal pathway. Finally, we make molecular docking predictions for the important compounds. The results showed that the Qingfei Paidu decoction compound-pneumonia target network contained 292 compounds and 214 corresponding targets, and the core targets involved AKT1 (AKT serine/threonine kinase 1), IL6 (interleukin 6), MAPK8 (mitogen-activated protein kinase 8), MAPK1 (mitogen-activated protein kinase 1), and JUN (jun proto-oncogene). GO (Gene Ontology) function enrichment analysis yielded 858 GO entries, and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment screening yielded 122 related pathways, including hypoxia inducible factor-1 (HIF-1) and Toll-like receptor (TLRs) signaling pathways related to pneumonia, as well as T-cell receptor (TCR) signaling pathway related to lung injury protection. The molecular docking results showed that some core compounds of the Chinese herbal medicine of Qingfei Paidu decoction have a certain degree of affinity for 2019-novel coronavirus (2019-nCoV) main protease (3C-like protease, 3CLpro) and angiotensin-converting enzyme 2 (ACE2). In this paper, we preliminarily explored the potential therapeutic mechanism for Qingfei Paidu decoction to against COVID-19 and predicted the active ingredients. We hope that the results will help to the further study on the active ingredients and mechanism of Qingfei Paidu decoction to COVID-19.

, correspAuthors=Chen-ning ZHANG, Yi-kun SUN, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2020 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Hao WU, Jia-qi WANG, Yu-wei YANG, Tian-yi LI, Yi-jia CAO, Yu-xia QU, Yu-jie JIN, Chen-ning ZHANG, Yi-kun SUN), CN=ArticleExt(id=1220364313596969025, articleId=1220364309419443015, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于网络药理学和分子对接技术初步探索“清肺排毒汤”抗新型冠状病毒肺炎作用机制, columnId=1209792669673452292, journalTitle=药学学报, columnName=聚焦抗新冠病毒药物研究, runingTitle=null, highlight=null, articleAbstract=

利用中药网络药理学和分子对接技术初步探索"清肺排毒汤"抗新型冠状病毒肺炎（coronavirus disease 2019，COVID-19）作用机制。结合中国药典（2015版）以及中药系统药理学数据库及分析平台（Traditional Chinese Medicine Systems Pharmacology，TCMSP）、OMIM（Online Mendelian Inheritance in Man）、GeneCard、STRING等在线数据库，进行一系列网络构建、核心靶点筛选以及信号通路富集分析，最后对重要化合物进行分子对接预测。结果发现，清肺排毒汤化合物-肺炎靶点网络包含292个化合物和相应靶点214个，核心靶点涉及AKT1（AKT serine/threonine kinase 1）、IL6（interleukin 6）、MAPK8（mitogen-activated protein kinase 8）、MAPK1（mitogen-activated protein kinase 1）和JUN（jun proto-oncogene）等。应用GO（Gene Ontology）数据库功能富集分析得到858个GO条目，应用KEGG（Kyoto Encyclopedia of Genes and Genomes）数据库富集筛选得到122条有关通路，其中包含低氧诱导因子-1（hypoxia inducible factor-1，HIF-1）通路、Toll样受体（Toll-like receptors，TLRs）通路等已报道与肺炎相关的通路，也包括T细胞受体（T-cell receptor，TCR）通路等与肺损伤保护相关的通路。分子对接结果显示，清肺排毒汤中药材部分核心化合物对新型冠状病毒（2019-nCoV）的3C类似蛋白酶（3C-like protease，3CLpro）和血管紧张素转换酶2（angiotensin-converting enzyme 2，ACE2）蛋白具有一定的亲和力。本文初步探索了清肺排毒汤抗COVID-19的作用机制，且预测了其药效物质基础，期待本结果能为进一步确证清肺排毒汤抗COVID-19有效成分和作用机制提供帮助。

, correspAuthors=张晨宁, 孙毅坤, authorNote=null, correspAuthorsNote=
*张晨宁, Tel:15972596857, E-mail:;
孙毅坤, Tel:15010706091, E-mail:
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Chemical Engineer (化学工程师), 2019, 33: 24-26., articleTitle=Determination of atractylone and other four effective components in Atractylodes macrocephala and its processed products by HPLC, refAbstract=null), Reference(id=1220394396260942400, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, doi=10.1097/MAJ.0b013e3182798583, pmid=null, pmcid=null, year=2014, volume=347, issue=null, pageStart=34, pageEnd=41, url=null, language=null, rfNumber=[19], rfOrder=18, authorNames=Tang L, Li Q, Bai J, journalName=Am J Med Sci, refType=null, unstructuredReference= Tang L , Li Q , Bai J et al . Severe pneumonia mortality in elderly patients is associated with downregulation of Toll-like receptors 2 and 4 on monocytes[J]. Am J Med Sci, 2014, 347: 34-41., articleTitle=Severe pneumonia mortality in elderly patients is associated with downregulation of Toll-like receptors 2 and 4 on monocytes, refAbstract=null)], funds=[Fund(id=1220394394117652971, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, awardId=19Y30, language=CN, fundingSource=十堰市科技局指导项目(19Y30), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1220394385267671081, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, xref=null, ext=[AuthorCompanyExt(id=1220394385276059691, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, companyId=1220394385267671081, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China), AuthorCompanyExt(id=1220394385376723000, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, companyId=1220394385368334390, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.十堰市太和医院, 湖北医药学院附属医院, 湖北 十堰 442000)])], figs=[ArticleFig(id=1220394391525573022, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=wOKGsZyfXIpVD6O60A08Sg==, figureFileBig=b4HGBGNmckiPxj9yNUayzQ==, tableContent=null), ArticleFig(id=1220394391663985059, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Figure 1, caption= The compound-target network diagram of Qingfei Paidu decoction (QFPDD). The pink labels represent herbs, the blue labels represent targets, the green labels represent compounds. ATR: Atractylodis macrocephalae; BUP: Bupleuri radix; CIT: Citri reticulatae pericarpium; FAR: Farfarae flos; POR: Poria; CIN: Cinnamomi ramulus; SCU: Scutellariae radix; POG: Pogostemonis herba; PIN: Pinelliae rhizoma praeparatumcum zingibere et alumine; EPH: Ephedrae herba; BEL: Belamcandae rhizoma; ZIN: Zingiberis rhizoma recens; RHI: Rhizoma gypsum fibrosum; DIO: Dioscoreae rhizoma; ARM: Armeniacae semen amarum; ASA: Asari radix et rhizoma; GLY: Glycyrrhizae radix et rhizoma praeparata cum melle; POL: Polyporus; AUR: Aurantii fructus immaturus; AST: Asteris radix et rhizoma; ALI: Alismatis rhizoma , figureFileSmall=wOKGsZyfXIpVD6O60A08Sg==, figureFileBig=b4HGBGNmckiPxj9yNUayzQ==, tableContent=null), ArticleFig(id=1220394391827562924, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=XTkvRBhYvwuvsFAtj1OtPQ==, figureFileBig=QB51HwQ74fZArymM+NK9KA==, tableContent=null), ArticleFig(id=1220394391949197747, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Figure 2, caption= The protein-protein interaction (PPI) network of potential targets in Qingfei Paidu decoction. The size of each label represents its degree, the color from cold to warm represents the value of betweenness centrality, and the thickness and color of the lines represent Edge betweenness and combine-score , figureFileSmall=XTkvRBhYvwuvsFAtj1OtPQ==, figureFileBig=QB51HwQ74fZArymM+NK9KA==, tableContent=null), ArticleFig(id=1220394392070832568, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=yevcN8End3oS7Tq6UizKpQ==, figureFileBig=XeRl7rXzUFmWev5dMsbpwg==, tableContent=null), ArticleFig(id=1220394392163107263, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Figure 3, caption= Module analysis results of PPI network. The quadrilateral label is a seed node, and MCODE expands from this node to find nodes that meet the requirements in its neighbor nodes. Eligible nodes are represented by round labels. The shade of each node's color represents its MCODE-score. (1. Score17.8, Nodes26, Edges222; 2. Score8.4, Nodes21, Edges84; 3. Score6, Nodes6, Edges15; 4. Score5.4, Nodes18, Edges46; 5. Score5.2, Nodes6, Edges13) , figureFileSmall=yevcN8End3oS7Tq6UizKpQ==, figureFileBig=XeRl7rXzUFmWev5dMsbpwg==, tableContent=null), ArticleFig(id=1220394392259576259, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=BqK/2xApXN4R+m7gJx0lKA==, figureFileBig=Hma7wRTUwf+Ir0hR3R00Sg==, tableContent=null), ArticleFig(id=1220394393161351628, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Figure 4, caption= GO enrichment analysis and KEGG pathway analysis of potential targets in Qingfei Paidu decoction. The size of the bubbles in each bubble chart represents the gene counts of this entry, and the colors from cold to warm represent the <i>P</i> values from large to small. Each bubble chart is sorted by <i>P</i> value. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes , figureFileSmall=BqK/2xApXN4R+m7gJx0lKA==, figureFileBig=Hma7wRTUwf+Ir0hR3R00Sg==, tableContent=null), ArticleFig(id=1220394393341706704, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=TNthAbm8QEmhauZPf13+Fg==, figureFileBig=TmAcS6SX885EE3x4X1/gmw==, tableContent=null), ArticleFig(id=1220394393438175697, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Figure 5, caption= Four best docking results. a: Ergosterol and 3CLpro (3C-like protease); b: Shionone and 3CLpro; c: Shionone and ACE2 (angiotensin-converting enzyme 2); d: Tussilagone and ACE2. The PDB number of 3CLpro is 6LU7, and the PDB number of ACE2 is 1R42. The structure of the compound is represented by a stick, and different branches of the protein are represented by different colors. Because only the hydrogen bond is formed in figure a, the green dotted line is used to represent its hydrogen bond, and the position of the hydrogen bond with the compound in the protein is marked , figureFileSmall=TNthAbm8QEmhauZPf13+Fg==, figureFileBig=TmAcS6SX885EE3x4X1/gmw==, tableContent=null), ArticleFig(id=1220394393551421911, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Cluster KEGG signaling pathway Count P-value FDR/%
1 Chagas disease (American 25 5.24E-16 7.11E-13
trypanosomiasis)
Osteoclast differentiation 25 1.42E-13 1.82E-10
Leishmaniasis 17 7.66E-11 9.85E-08
T cell receptor signaling 19 2.55E-10 3.28E-07
pathway
B cell receptor signaling 13 4.11E-07 5.28E-04
pathway
2 Pancreatic cancer 21 3.71E-16 4.33E-13
Hepatitis C 25 2.01E-13 2.59E-10
FoxO signaling pathway 22 1.16E-10 1.49E-07
Ras signaling pathway 21 5.33E-06 6.86E-03
3 Bladder cancer 19 7.04E-18 9.06E-15
Pancreatic cancer 21 3.71E-16 4.33E-13
Prostate cancer 23 1.63E-15 2.14E-12
Non-small cell lung cancer 17 1.45E-12 1.86E-09
Chronic myeloid leukemia 16 1.00E-09 1.29E-06
Glioma 15 2.33E-09 2.99E-06
Thyroid hormone signaling pathway 17 1.20E-07 1.54E-04
Melanoma 13 5.67E-07 7.30E-04
Endometrial cancer 11 1.49E-06 1.92E-03
4 TNF signaling pathway 27 7.32E-18 9.41E-15
Chagas disease (American 25 5.24E-16 7.11E-13
trypanosomiasis)
Toxoplasmosis 25 2.27E-15 2.85E-12
Influenza A 30 4.45E-15 5.72E-12
Toll-like receptor signaling 23 1.07E-13 1.38E-10
pathway
Pertussis 19 1.48E-12 1.90E-09
Tuberculosis 26 1.84E-11 2.36E-08
Leishmaniasis 17 7.66E-11 9.85E-08
NOD-like receptor signaling 15 2.77E-10 3.56E-07
pathway
Salmonella infection 16 8.01E-09 1.03E-05
Epithelial cell signaling in 12 2.30E-06 2.96E-03
Helicobacter pylori infection
RIG-I-like receptor signaling 12 3.59E-06 4.62E-03
pathway
Herpes simplex infection 19 3.67E-06 4.72E-03
Shigellosis 10 7.02E-05 9.03E-02
Cytosolic DNA-sensing 9 4.10E-04 5.26E-01
pathway
5 Chronic myeloid leukemia 16 1.00E-09 1.29E-06
Acute myeloid leukemia 13 3.59E-08 4.62E-05
B cell receptor signaling 13 4.11E-07 5.28E-04
pathway
), ArticleFig(id=1220394393668862428, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Table 1, caption=

Cluster analysis of KEGG analysis results. FDR: False discovery rate

, figureFileSmall=null, figureFileBig=null, tableContent=
Cluster KEGG signaling pathway Count P-value FDR/%
1 Chagas disease (American 25 5.24E-16 7.11E-13
trypanosomiasis)
Osteoclast differentiation 25 1.42E-13 1.82E-10
Leishmaniasis 17 7.66E-11 9.85E-08
T cell receptor signaling 19 2.55E-10 3.28E-07
pathway
B cell receptor signaling 13 4.11E-07 5.28E-04
pathway
2 Pancreatic cancer 21 3.71E-16 4.33E-13
Hepatitis C 25 2.01E-13 2.59E-10
FoxO signaling pathway 22 1.16E-10 1.49E-07
Ras signaling pathway 21 5.33E-06 6.86E-03
3 Bladder cancer 19 7.04E-18 9.06E-15
Pancreatic cancer 21 3.71E-16 4.33E-13
Prostate cancer 23 1.63E-15 2.14E-12
Non-small cell lung cancer 17 1.45E-12 1.86E-09
Chronic myeloid leukemia 16 1.00E-09 1.29E-06
Glioma 15 2.33E-09 2.99E-06
Thyroid hormone signaling pathway 17 1.20E-07 1.54E-04
Melanoma 13 5.67E-07 7.30E-04
Endometrial cancer 11 1.49E-06 1.92E-03
4 TNF signaling pathway 27 7.32E-18 9.41E-15
Chagas disease (American 25 5.24E-16 7.11E-13
trypanosomiasis)
Toxoplasmosis 25 2.27E-15 2.85E-12
Influenza A 30 4.45E-15 5.72E-12
Toll-like receptor signaling 23 1.07E-13 1.38E-10
pathway
Pertussis 19 1.48E-12 1.90E-09
Tuberculosis 26 1.84E-11 2.36E-08
Leishmaniasis 17 7.66E-11 9.85E-08
NOD-like receptor signaling 15 2.77E-10 3.56E-07
pathway
Salmonella infection 16 8.01E-09 1.03E-05
Epithelial cell signaling in 12 2.30E-06 2.96E-03
Helicobacter pylori infection
RIG-I-like receptor signaling 12 3.59E-06 4.62E-03
pathway
Herpes simplex infection 19 3.67E-06 4.72E-03
Shigellosis 10 7.02E-05 9.03E-02
Cytosolic DNA-sensing 9 4.10E-04 5.26E-01
pathway
5 Chronic myeloid leukemia 16 1.00E-09 1.29E-06
Acute myeloid leukemia 13 3.59E-08 4.62E-05
B cell receptor signaling 13 4.11E-07 5.28E-04
pathway
), ArticleFig(id=1220394393769525727, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Qingfei Paidu decoction Compound Molecular formula CAS Binding energy values (3CLpro) Binding energy values (ACE2)
Pogostemonis herba Patchouli alcohol C15H26O 5986-55-0 -5.68 -5.09
Bupleuri radix Saikosaponin A C42H68O13 20736-09-8 -2.69 -1.62
Saikosaponin B C42H68O13 58558-08-0 -6.19 -4.57
Citri reticulatae pericarpium Hesperidin C28H34O15 520-26-3 -1.6 -0.55
Belamcandae rhizoma Irisflorentin C20H18O8 41743-73-1 -4.83 -4.09
Poria Pachymic acid C33H52O5 29070-92-6 -4.67 -3.5
Glycyrrhizae radix et rhizoma Praeparata cum melle Glycyrrhizic acid C42H62O16 1405-86-3 -4.82 -3.2
Cinnamomi ramulus trans-Cinnamaldehyde C9H8O 14371-10-9 -4.04 -3.71
Pinelliae rhizoma praeparatumcum zingibere et alumineSuccinic acid C4H6O4 110-15-6 -2.57 -1.26
Scutellariae radix Baicalin C21H18O11 21967-41-9 -2.97 -1.58
Zingiberis rhizoma recens 6-Gingerol C17H26O4 23513-14-6 -2.67 -2.38
8-Gingerol C43H32O20 30462-35-2 -1.07 -0.95
10-Gingerol C21H34O4 23513-15-7 -1.58 -1.04
Farfarae flos Tussilagone C23H34O5 104012-37-5 -3.29 -5.54
Armeniacae semen amarum Amygdalin C20H27NO11 29883-15-6 -2.13 -0.77
Polyporus Ergosterol C28H44O 57-87-4 -6.46 -6.1
Aurantii fructus immaturus Synephrine C9H13NO2 94-07-5 -4.14 -4.31
Asari radix et rhizoma Asarinin C20H18O6 133-05-1 -5 -5.46
Ephedrae herba Ephedrine hydrochloride C10H16ClNO 50-98-6 -3.57 -5.09
(1S, 2S)-(+)-Pseudoephedrine hydrochloride C10H16ClNO 345-78-8 -3.54 -3.93
Asteris radix et rhizoma Shionone C30H50O 10376-48-4 -6.85 -6.76
Alismatis rhizoma 23-O-Acetylalisol B C32H50O5 19865-76-0 -5.78 -4.57
Atractylodis macrocephalae Atractylon C15H20O 6989-21-5 - -
Atractylenolide-1 C15H18O2 73069-13-3 - -
Rhizoma gypsum fibrosum - - - - -
Dioscoreae rhizoma - - - - -
), ArticleFig(id=1220394393912132068, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364309419443015, language=CN, label=Table 2, caption=

Docking results of quality control compounds in Qingfei Paidu decoction

, figureFileSmall=null, figureFileBig=null, tableContent=
Qingfei Paidu decoction Compound Molecular formula CAS Binding energy values (3CLpro) Binding energy values (ACE2)
Pogostemonis herba Patchouli alcohol C15H26O 5986-55-0 -5.68 -5.09
Bupleuri radix Saikosaponin A C42H68O13 20736-09-8 -2.69 -1.62
Saikosaponin B C42H68O13 58558-08-0 -6.19 -4.57
Citri reticulatae pericarpium Hesperidin C28H34O15 520-26-3 -1.6 -0.55
Belamcandae rhizoma Irisflorentin C20H18O8 41743-73-1 -4.83 -4.09
Poria Pachymic acid C33H52O5 29070-92-6 -4.67 -3.5
Glycyrrhizae radix et rhizoma Praeparata cum melle Glycyrrhizic acid C42H62O16 1405-86-3 -4.82 -3.2
Cinnamomi ramulus trans-Cinnamaldehyde C9H8O 14371-10-9 -4.04 -3.71
Pinelliae rhizoma praeparatumcum zingibere et alumineSuccinic acid C4H6O4 110-15-6 -2.57 -1.26
Scutellariae radix Baicalin C21H18O11 21967-41-9 -2.97 -1.58
Zingiberis rhizoma recens 6-Gingerol C17H26O4 23513-14-6 -2.67 -2.38
8-Gingerol C43H32O20 30462-35-2 -1.07 -0.95
10-Gingerol C21H34O4 23513-15-7 -1.58 -1.04
Farfarae flos Tussilagone C23H34O5 104012-37-5 -3.29 -5.54
Armeniacae semen amarum Amygdalin C20H27NO11 29883-15-6 -2.13 -0.77
Polyporus Ergosterol C28H44O 57-87-4 -6.46 -6.1
Aurantii fructus immaturus Synephrine C9H13NO2 94-07-5 -4.14 -4.31
Asari radix et rhizoma Asarinin C20H18O6 133-05-1 -5 -5.46
Ephedrae herba Ephedrine hydrochloride C10H16ClNO 50-98-6 -3.57 -5.09
(1S, 2S)-(+)-Pseudoephedrine hydrochloride C10H16ClNO 345-78-8 -3.54 -3.93
Asteris radix et rhizoma Shionone C30H50O 10376-48-4 -6.85 -6.76
Alismatis rhizoma 23-O-Acetylalisol B C32H50O5 19865-76-0 -5.78 -4.57
Atractylodis macrocephalae Atractylon C15H20O 6989-21-5 - -
Atractylenolide-1 C15H18O2 73069-13-3 - -
Rhizoma gypsum fibrosum - - - - -
Dioscoreae rhizoma - - - - -
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基于网络药理学和分子对接技术初步探索“清肺排毒汤”抗新型冠状病毒肺炎作用机制
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吴昊 1 , 王佳琪 1 , 杨雨薇 1 , 李天怡 1 , 曹一佳 1 , 曲玉霞 1 , 靳玉洁 2 , 张晨宁 1, 2, * , 孙毅坤 1, *
药学学报 | 聚焦抗新冠病毒药物研究 2020,55(3): 374-383
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药学学报 | 聚焦抗新冠病毒药物研究 2020, 55(3): 374-383
基于网络药理学和分子对接技术初步探索“清肺排毒汤”抗新型冠状病毒肺炎作用机制
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吴昊1, 王佳琪1, 杨雨薇1, 李天怡1, 曹一佳1, 曲玉霞1, 靳玉洁2, 张晨宁1, 2, * , 孙毅坤1, *
作者信息
  • 1.北京中医药大学中药学院, 北京 102488
  • 2.十堰市太和医院, 湖北医药学院附属医院, 湖北 十堰 442000

通讯作者:

*张晨宁, Tel:15972596857, E-mail:;
孙毅坤, Tel:15010706091, E-mail:
Preliminary exploration of the mechanism of Qingfei Paidu decoction against novel coronavirus pneumonia based on network pharmacology and molecular docking technology
Hao WU1, Jia-qi WANG1, Yu-wei YANG1, Tian-yi LI1, Yi-jia CAO1, Yu-xia QU1, Yu-jie JIN2, Chen-ning ZHANG1, 2, * , Yi-kun SUN1, *
Affiliations
  • 1. School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China
  • 2. Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
出版时间: 2020-03-12 doi: 10.16438/j.0513-4870.2020-0136
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利用中药网络药理学和分子对接技术初步探索"清肺排毒汤"抗新型冠状病毒肺炎（coronavirus disease 2019，COVID-19）作用机制。结合中国药典（2015版）以及中药系统药理学数据库及分析平台（Traditional Chinese Medicine Systems Pharmacology，TCMSP）、OMIM（Online Mendelian Inheritance in Man）、GeneCard、STRING等在线数据库，进行一系列网络构建、核心靶点筛选以及信号通路富集分析，最后对重要化合物进行分子对接预测。结果发现，清肺排毒汤化合物-肺炎靶点网络包含292个化合物和相应靶点214个，核心靶点涉及AKT1（AKT serine/threonine kinase 1）、IL6（interleukin 6）、MAPK8（mitogen-activated protein kinase 8）、MAPK1（mitogen-activated protein kinase 1）和JUN（jun proto-oncogene）等。应用GO（Gene Ontology）数据库功能富集分析得到858个GO条目，应用KEGG（Kyoto Encyclopedia of Genes and Genomes）数据库富集筛选得到122条有关通路，其中包含低氧诱导因子-1（hypoxia inducible factor-1，HIF-1）通路、Toll样受体（Toll-like receptors，TLRs）通路等已报道与肺炎相关的通路，也包括T细胞受体（T-cell receptor，TCR）通路等与肺损伤保护相关的通路。分子对接结果显示，清肺排毒汤中药材部分核心化合物对新型冠状病毒（2019-nCoV）的3C类似蛋白酶（3C-like protease，3CLpro）和血管紧张素转换酶2（angiotensin-converting enzyme 2，ACE2）蛋白具有一定的亲和力。本文初步探索了清肺排毒汤抗COVID-19的作用机制，且预测了其药效物质基础，期待本结果能为进一步确证清肺排毒汤抗COVID-19有效成分和作用机制提供帮助。

新型冠状病毒肺炎  /  清肺排毒汤  /  网络药理学  /  分子对接技术  /  机制初探

Traditional Chinese medicine (TCM) network pharmacology and molecular docking technology were applied to explore the mechanism of anti-coronavirus pneumonia (coronavirus disease 2019, COVID-19) of Qingfei Paidu decoction. The Chinese Pharmacopoeia (2015 edition) and Traditional Chinese Medicine Systems Pharmacology (TCMSP), OMIM (Online Mendelian Inheritance in Man), GeneCard, STRING, and others online databases are used for building a series of network, and selecting the core target and analyzing the signal pathway. Finally, we make molecular docking predictions for the important compounds. The results showed that the Qingfei Paidu decoction compound-pneumonia target network contained 292 compounds and 214 corresponding targets, and the core targets involved AKT1 (AKT serine/threonine kinase 1), IL6 (interleukin 6), MAPK8 (mitogen-activated protein kinase 8), MAPK1 (mitogen-activated protein kinase 1), and JUN (jun proto-oncogene). GO (Gene Ontology) function enrichment analysis yielded 858 GO entries, and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment screening yielded 122 related pathways, including hypoxia inducible factor-1 (HIF-1) and Toll-like receptor (TLRs) signaling pathways related to pneumonia, as well as T-cell receptor (TCR) signaling pathway related to lung injury protection. The molecular docking results showed that some core compounds of the Chinese herbal medicine of Qingfei Paidu decoction have a certain degree of affinity for 2019-novel coronavirus (2019-nCoV) main protease (3C-like protease, 3CLpro) and angiotensin-converting enzyme 2 (ACE2). In this paper, we preliminarily explored the potential therapeutic mechanism for Qingfei Paidu decoction to against COVID-19 and predicted the active ingredients. We hope that the results will help to the further study on the active ingredients and mechanism of Qingfei Paidu decoction to COVID-19.

COVID-19  /  Qingfei Paidu decoction  /  network pharmacology  /  molecular docking technology  /  preliminary exploration
吴昊, 王佳琪, 杨雨薇, 李天怡, 曹一佳, 曲玉霞, 靳玉洁, 张晨宁, 孙毅坤. 基于网络药理学和分子对接技术初步探索“清肺排毒汤”抗新型冠状病毒肺炎作用机制. 药学学报, 2020 , 55 (3) : 374 -383 . DOI: 10.16438/j.0513-4870.2020-0136
Hao WU, Jia-qi WANG, Yu-wei YANG, Tian-yi LI, Yi-jia CAO, Yu-xia QU, Yu-jie JIN, Chen-ning ZHANG, Yi-kun SUN. Preliminary exploration of the mechanism of Qingfei Paidu decoction against novel coronavirus pneumonia based on network pharmacology and molecular docking technology[J]. Acta Pharmaceutica Sinica, 2020 , 55 (3) : 374 -383 . DOI: 10.16438/j.0513-4870.2020-0136
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截至目前为止, 新型冠状病毒(2019-novel coronavirus, 2019-nCoV)在我国已经肆虐了近两月, 波及全球20余个国家及地区, 累计确诊新型冠状病毒肺炎(coronavirus disease 2019, COVID-19)患者已7万多人, 然而至今仍缺乏有效遏制该病毒的临床药物。尽管基础研究实验室捷报频传[1-4], 但世人仍对新型冠状病毒疫苗、新药(remdesivir等)临床试验或研发进展觉得缓不济急[5]。与此同时, 祖国医学在抗疫中的表现也可圈可点, 国内多个地区发布了中医药干预方案[6, 7], 已有诸多中医药实验室开展相关基础研究、临床观察及分子对接技术等[8-10]实验手段进行中药单体或者复方的快速筛选。尽管中医药的疗效和安全性在华夏五千年的历史长河中已久经考验, 但随着现代疾病谱的快速改变, 中医药遣方用药也应推陈出新。在当前紧迫阶段, 基于实验室开展天然药物化合物或中药复方针对新型冠状病毒的直接抑制药理实验有可能应用于临床的速度依然缓慢。而针对临床反馈的治疗新型冠状病毒诱导的肺炎效果良好的复杂中药复方进行有效成分识别和作用机制初探, 从而进行快速的处方优化和二次开发, 可能是中医药精简增效应对临床急症的一条出路。
2020年2月7日, 国家中医药管理局发布关于《推荐在中西医结合救治新型冠状病毒感染的肺炎中使用“清肺排毒汤”的通知》 [国中医药办医政函(2020)22号], 据介绍, 清肺排毒汤来源于中医经典方剂组合, 包括麻杏石甘汤、射干麻黄汤、小柴胡汤和五苓散, 四方皆出自《伤寒杂病论》, 性味平和, 其处方组成主要由麻黄、炙甘草、杏仁、生石膏等21味中药组成。根据中医理论等[11]对清肺排毒汤抗新型冠状病毒肺炎进行方解分析:新型冠状病毒肺炎以湿毒为病, 首先犯肺, 继而碍脾, 甚则伤肾, 湿性黏滞, 阻碍气机, 郁久化热。因此, 治疗需要清肺排毒、宣通气机、建运化湿为本。病势剧猛, 合方而治。以麻杏石甘汤加紫菀冬花射干清肺排毒, 缓解肺部症状; 以五苓散加细辛温阳化饮, 防止水饮凌肺; 以小柴胡汤调理气机, 促进三焦通利; 以山药陈皮枳实霍香健脾行气, 斡旋中焦运化。多方相合, 宣畅三焦, 既能清除肺部热毒, 又给湿以出路, 标本同治。据统计, 目前运用清肺排毒汤救治确诊病例, 总有效率达90%以上, 其中60%以上患者症状和影像学表现改善明显, 30%患者症状平稳且无加重。在国家中医药管理局发布通知后, 陕西、山西、河北、黑龙江等省纷纷跟进, 广泛推荐使用。
清肺排毒汤由21味中药组成, 处方量较大, 根据常理, 其临床患者给药顺应性可能较差, 有必要对该方进行进一步的精简优化, 以提质增效。因此, 尽快开展并初步探索清肺排毒汤抗新型冠状病毒肺炎的活性成分及作用机制势在必行。结合生物信息学手段, 可快速初步研究中药复方的体内生物学特征, 为后续基础实验研究和临床研究奠定基础。中药网络药理学[12]整合了系统生物学、多向药理学、计算生物学和网络分析等学科, 从整体角度探索中药与疾病间的关联性, 提供了一种从系统水平寻找中药复方潜在活性成分和作用靶点的新策略, 契合于中药多成分-多靶点的作用关系, 与中药从整体水平调控机体并发挥治疗作用的观点相吻合。分子对接技术[13]是采用计算机辅助进行化药直接设计的一种成熟技术, 其采用计算机技术, 通过化学计量学方法模拟分子的几何结构和分子间作用力, 寻找小分子(或配体)与已知结构的大分子(或受体)活性位点的低能结合模式的过程。本文通过对当前临床报道的抗COVID-19有效中药大处方清肺排毒汤进行网络药理学分析, 推测其相应作用的生物学途径, 并通过分子对接技术对复方中关键化合物与新型冠状病毒肺炎密切相关的靶点[14, 15], 如2019-nCoV的3C类似蛋白酶(3C-like protease, 3CLpro)和COVID-19相关蛋白血管紧张素转换酶2 (angiotensin-converting enzyme 2, ACE2)蛋白的结合能进行评估, 进行逆向分析, 评估采用网络药理学进行该复方活性物质的筛选和机理初探的可靠性, 以及考察分子对接技术在中医药领域的适用性, 期待本文能为清肺排毒汤的快速二次开发提供一些帮助。
材料与方法
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清肺排毒汤有效成分筛选   应用中药系统药理学数据库及分析平台(Traditional Chinese Medicine Systems Pharmacology, TCMSP)数据库(http://tcmspw.com/index.php), 以清肺排毒汤中的20味药材(除生石膏)为关键词进行检索, 对检索到的每味药材以口服利用度(oral bioavailability, OB)与类药性(drug-like, DL)为指标进行筛选, 其中OB≥30%, DL≥0.18, 筛选后得到每味药材的有效成分。为避免个别认可度较高的有效成分被筛选掉, 故同时查阅中国药典(2015版)与CNKI中相关文献, 得到此类成分并合并至TCMSP数据库筛选结果中。从TCMSP数据库下载这些化合物的靶点信息。
构建药理网络  在OMIM数据库(https://omim.org/)与GeneCard数据库(https://www.genecards.org/)中分别以“pneumonia”作为关键词进行检索, 得到肺炎相关靶点, 与所得有效化合物靶点取交集, 以得到清肺排毒汤治疗肺炎的潜在作用靶点。使用R语言生成“清肺排毒汤-药材-化合物-靶点-肺炎”的药理网络文本, 将文本录入到Cytoscape软件中进行可视化处理, 从而构建清肺排毒汤的药理网络。对构建好的药理网络进行拓扑分析以得到各个节点的中心度值(betweenness centrality)、亲中心度值(closeness centrality)和等级值(degree)。
蛋白质相互作用(protein-protein interaction, PPI)网络的构建  将得到的清肺排毒汤治疗肺炎的潜在作用靶点录入STRING网站(https://string-db.org/cgi/input.pl), 得到潜在作用靶点的PPI网络。将其数据导入Cytoscape软件中进行拓扑分析, 并使用Cytoscape的MCODE功能对PPI网络进行模块分析, 设置K-core值需大于4。
GO (Gene Ontology)富集分析与KEGG (Kyoto Encyclopedia of Genes and Genomes) 通路分析  通过DAVID网站(https://david.ncifcrf.gov/)对得到的潜在作用靶点进行GO富集分析和KEGG信号通路富集分析, 输入潜在作用靶点并限制物种为人, 得到的分析结果以P值小于0.05为指标进行筛选。使用R语言对的结果进行可视化处理。
分子对接  从清肺排毒汤的有效成分中选取每味药材的标志性成分与2019-nCoV的3C类似蛋白酶(3CLpro)和天然人类血管紧张素转换酶相关的羧肽酶(ACE2)进行分子对接, 从RCSB数据库(https://www.rcsb.org/)下载相关蛋白的结构, 使用Pymol软件去除溶剂分子与配体, 使用AutoDock软件进行加氢、加电子等操作。从ChemicalBook数据库(https://www.chemicalbook.com/)中下载化合物结构, 并使用AutoDock软件进行加氢、加电子、加ROOT等操作。完成后进行分子对接其中蛋白结构的设置为刚性大分子, 使用算法为Local Search Parameters, 得到结果中每个蛋白结合能最低的两组使用Pymol对得到的最佳结果进行绘图。
结果
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1 活性化合物筛选结果
通过TCMSP数据库检索后, 符合条件OB≥30%, DL≥0.18的化合物共有310个, 另从药典与相关文献[16-18]中得到每味药物的指标性成分, 共计347个。其中藿香含有12个, 陈皮含有6个, 枳实含有23个, 山药含有16个, 细辛含有9个, 射干含有18个, 冬花含有22个, 紫菀含有19个, 生姜含有11个, 姜半夏含有14个, 黄芩含有36个, 柴胡含有38个, 茯苓含有15个, 白术含有7个, 猪苓含有12个, 泽泻含有10个, 桂枝含有8个, 杏仁含有20个, 甘草含有92个, 麻黄含有25个。考虑到甘草作为使药, 含有符合OB及DL条件成分过多, 故单独设计条件OB≥60%, DL≥0.36对甘草化合物进行筛选, 结果发现该条件与原条件筛选出来的靶点数量差异很小, 故维持原条件。
2 药理网络
建立了清肺排毒汤-药材-有效成分-靶点-肺炎的药理网络(图 1)。
其中粉色节点代表药材, 绿色节点代表有效化合物, 蓝色节点代表潜在靶点。在这张图中包含了292个化合物节点和214个潜在靶点节点。在网络图中删除药物、疾病和药材3种要素的节点后得到的化合物-潜在靶点网络图, 可发现平均每个化合物连接了6.32个靶点, 同时每个靶点连接了9.24个化合物, 这样多个化合物同时作用于同一个靶点且单个化合物又作用于多个靶点的现象也体现了中药多成分作用于多个靶点的治疗特点。从化合物角度来看, 有3.83%的化合物拥有超过20个作用靶点, 说明在这个网络中应存在着能够作用于清肺排毒汤大部分靶点的少数关键化合物。通过分析这个网络图的拓扑性质, 及对中心度值、亲中心度值、等级值3个值进行分析, 得到了在整个网络中起到了枢纽作用的化合物, 其中排名最靠前的5个化合物分别是槲皮素、山奈酚、木犀草素、β-谷固醇和柚皮苷。基于网络药理学算法理论, 从单个化合物-靶点网络的角度考虑, 这5个化合物可能代表这个组方的大部分疗效, 即可能是清肺排毒汤的主要疗效物质。从靶点的角度来看, 单个靶点受到越多的化合物影响, 则清肺排毒汤对这个靶点的调控性可能越高, 有13.55%的靶点有不小于10个的化合物于其交互, 其中有8个靶点链接了100个以上的化合物。
3 PPI网络
使用STRING网站对得到的潜在靶点进行分析, 得到了PPI网络图(图 2)。
其中每个节点的大小代表了其等级值(degree), 颜色由冷到暖代表了中心度值(betweenness centrality), 连线的粗细和颜色分别代表了Edge betweenness和combine-score。对PPI网络进行拓扑分析能够得到这个网络中的枢纽蛋白, 其中等级值排名前5位的分别为AKT1 (AKT serine/threonine kinase 1)、IL6 (interleukin 6)、MAPK8 (mitogen-activated protein kinase 8)、MAPK1 (mitogen-activated protein kinase 1)和JUN (jun proto-oncogene)。同时使用Cytoscape的MCODE功能对PPI网络进行了模块分析, 其中K-core值大于4的模块一共有5个, 表明其中的蛋白有着更为紧密的联系, 它们可能共同执行着某个生物过程, 如模块5与细胞周期调控有关、模块4与信号传导和免疫系统相关。在对模块1的分析中发现其包含大量白介素相关靶点和干扰素相关靶点, 能够调控免疫与炎症反应, 其中更有许多病毒和细胞启动子中存在的序列CREB1 (CAMP responsive element binding protein 1)和具有抗病毒活性和重要免疫调节功能的IFNG (interferon gamma) (图 3)。
4 GO富集分析与KEGG通路分析结果
使用DAVID网站对潜在靶点进行GO富集分析, 得到了P < 0.05的GO条目共计858个, 其中包含生物过程(biological process, BP)条目681个, 细胞组成(cell composition, CC)条目72个, 分子功能(molecular function, MF)条目105个, 各类别前20的通路如图 3所示。通过KEGG通路分析得到了P < 0.05的通路共122条, 包括non-small cell lung cancer、small cell lung cancer、HIF-1 signaling pathway、Toll-like receptor signaling pathway、T cell receptor signaling pathway等已报道[19]与肺炎相关的通路, 同样包括TNF signaling pathway、PI3K-Akt signaling pathway、MAPK signaling pathway、B cell receptor signaling pathway、apoptosis等与肺损伤保护相关的通路。其中排名前30的通路如图 4所示。
对KEGG分析的结果在DAVID网站中进行聚类分析, 使用Function Annotation Clustering功能, Classification Stringency设置为Medium, 可得到13类, 得分前5类如表 1所示。
5 清肺排毒汤各药材指标性成分与两种蛋白的分子对接结果
选取了清肺排毒汤中19种药材中24种化学成分与3CLpro和ACE2的对接结果如表 2所示。其中每个蛋白对接最好的前2位化合物的对接结果如图 5所示, 其中麦角甾醇(ergosterol)能够与2019-nCoV的3C类似蛋白酶形成氢键, 其他3种对接的最佳结果均未形成氢键。
讨论
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本文采用中药网络药理学方法, 在充分考虑化合物的成药性和物质的可存在性的基础上, 基于清肺排毒汤各药材中近千种已知化合物的OB与DL值, 结合各种药材的法定质量控制指标, 筛选整合分析并建立可靠的化合物库。以肺炎为疾病模型, 构建相应的药理网络。通过常规网络药理学算法得出主要核心化合物为槲皮素、山奈酚、木犀草素、β-谷固醇和柚皮苷, 该结果与部分文献[10]采用网络药理学筛选其他抗新型冠状病毒知名汤剂得出的核心化合物较为一致, 且文献对这些化合物进行分子对接评分, 其相应的结合能与临床抗病毒化学药物结合能极其接近, 但是不同的中药方剂筛选出来却得到同样的核心化合物, 理论上其合理性可能有待商榷, 可能缺乏处方特异性。因此在此基础上, 本文特别注重选用清肺排毒汤中所有药材(除生石膏、山药)的质量控制指标或文献报道的重要成分进行后续的分子对接分析。
为了进一步合理确证清肺排毒汤抗COVID-19的药效物质基础, 本文采用分子对接技术对清肺排毒汤组方药材的法定质量控制指标或文献报道的重要成分进行对接验证。在进行分子对接时, 一般认为配体与受体结合的构象稳定时能量越低, 发生的作用可能性越大。以结合能≤-5.0 kJ·mol-1作为筛选标准, 与2019-nCoV的3C类似蛋白酶(3CLpro)结合能小于-5.0的有百秋李醇(藿香)、柴胡皂苷b (柴胡)、麦角甾醇(猪苓)、紫菀酮(紫苑)、23-乙酰泽泻醇B (泽泻), 即该类化合物可能直接作用于新型冠状病毒3CLpro, 从而阻断病毒增殖; 与ACE2结合能小于-5.0的有百秋李醇(藿香)、款冬酮(冬花)、麦角甾醇(猪苓)、细辛脂素(细辛)、盐酸麻黄碱(麻黄)、紫菀酮(紫苑), 表明该类化合物直接作用于宿主人体细胞, 提高机体免疫力, 阻断病毒侵袭; 同时与3CLpro和ACE2结合能小于-5.0的有百秋李醇、麦角甾醇及紫菀酮, 即藿香、紫苑、猪苓可能同时具有较好的抗2019-nCoV及其诱导的COVID-19。本文基于临床有效处方清肺排毒汤中的化合物库中的质量标志物进行同一水平的横向结合能比较, 评价这些化合物抗新型冠状病毒肺炎的贡献大小, 对于后续的优化处方等二次开发可能具有一定的意义, 缺点在于没有评价其他的非质控指标的化合物, 其他化合物也可能具有类似的功效, 但是作者仍觉得中药发挥药效可能还是其中的主要物质, 其他含量很少的物质可能活性挺好, 但是基于物质的可存在性等因素, 它们发挥效应的贡献率可能微乎其微。当然上述所有结论均为基于分子模拟对接得到的结果, 其科学合理性有待进一步的临床验证。
综上所述, 本文采用生物信息学手段进行清肺排毒汤的抗2019-nCoV及COVID-19的初步预测, 除了生石膏、山药缺乏特征化合物, 其余的19种药材通过网络药理学和分子对接技术, 初步探索了清肺排毒汤抗2019-nCoV诱导的肺炎的生物学途径, 也初步探索了该方发挥药效的主要药效物质, 为后续临床中医遣方用药以及该方的优化提供了一定的参考, 但其结果的准确性仍需要临床进一步验证。
基金
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  • 十堰市科技局指导项目(19Y30)
文献
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2020年第55卷第3期
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doi: 10.16438/j.0513-4870.2020-0136
  • 接收时间：2020-02-18
  • 首发时间：2026-01-20
  • 出版时间：2020-03-12
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  • 收稿日期：2020-02-18
  • 修回日期：2020-02-22
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十堰市科技局指导项目(19Y30)
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    1.北京中医药大学中药学院, 北京 102488
    2.十堰市太和医院, 湖北医药学院附属医院, 湖北 十堰 442000

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*张晨宁, Tel:15972596857, E-mail:;
孙毅坤, Tel:15010706091, E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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