有些实在是看不懂，只能模仿

library(httr)

baseUrl="https://eutils.ncbi.nlm.nih.gov/"

pubmedAction=list(

  base="entrez/eutils/index.fcgi",

  search="entrez/eutils/esearch.fcgi", #搜索接口

  fetch="entrez/eutils/efetch.fcgi", #获取数据接口

  summary="entrez/eutils/esummary.fcgi" #获取数据接口（fetch可返回多种数据格式）

)

#搜索文章的参数

searchArticleParam=list(

  retstart=0, #起始位置

  retmax=20, #每次取的数量

  usehistory='Y',#是否使用历史搜索

  querykey='',

  webenv='',

  term='(cell[TA]) AND 2017[DP]',#提交pubmed的词， 

  total_num=0, #总记录

  total_page=1, #总页数

  page_size=20, #每页数目

  current_page=1 #当前所在页数

)

postSearchUrl=paste(baseUrl,pubmedAction$search,sep="") #拼接搜索地址

r <- POST(postSearchUrl, 

          body = list(

            db='pubmed',

            term=searchArticleParam$term,

            retmode='json',

            retstart=searchArticleParam$retstart,

            retmax=searchArticleParam$retmax,

            usehistory=searchArticleParam$usehistory,

            rettype='uilist'

          )

)

stop_for_status(r) #清除http状态字符串

data=content(r, "parsed", "application/json") 

#data里面存储了所有数据

esearchresult=data$esearchresult

# $count=562,$retmax=20, $retstart=0,$querykey=1, $webenv=NCID_1_30290513_130.14.18.34_9001_1515165012_617859421_0MetA0_S_MegaStore_F_1

count = esearchresult$count

print(count)

#获得title和abstract

#这里使用了上面搜索返回的querkey,webnv，可以加快速度。下面的POST中可以不加上这参数

searchArticleParam$total_num=esearchresult$count

searchArticleParam$querykey=esearchresult$querykey

searchArticleParam$webenv=esearchresult$webenv

pubmedidStr="29275861,29275860"; #多个pubmedid之间用“,”连接

postFetchUrl=paste(baseUrl,pubmedAction$fetch,sep="")

r2 <- POST(postFetchUrl, 

          body = list(

            db='pubmed',

            id=pubmedidStr,

            retmode='xml', #返回xml格式的，这个接口不支持json格式

            usehistory=searchArticleParam$usehistory,

            querykey=searchArticleParam$querykey,

            webenv=searchArticleParam$webenv

          )

)

stop_for_status(r2)

library(xml2)

data2=content(r2, "parsed", "application/xml")

article=xml_children(data2)

#xml_length(article)为里面文章的数量

count=length(article)

cnt=1

while(cnt<=count){ #循环将title和abstract输出

  title=xml_find_first(article[cnt],".//ArticleTitle") #找到第一个ArticleTitle节点

  abstract=xml_find_first(article[cnt],".//AbstractText")

  print(xml_text(title))

  print(xml_text(abstract))

  cnt = cnt + 1

}

#上面提供了两个pubmedid，因此将会打印出两个title和abstract

第一个结果

第二个结果

[1] "Natural Killer Cells Control Tumor Growth by Sensing a Growth Factor."

[1] "Many tumors produce platelet-derived growth factor (PDGF)-DD, which promotes cellular proliferation, epithelial-mesenchymal transition, stromal reaction, and angiogenesis through autocrine and paracrine PDGFRβ signaling. By screening a secretome library, we found that the human immunoreceptor NKp44, encoded by NCR2 and expressed on natural killer (NK) cells and innate lymphoid cells, recognizes PDGF-DD. PDGF-DD engagement of NKp44 triggered NK cell secretion of interferon gamma (IFN)-γ and tumor necrosis factor alpha (TNF-α) that induced tumor cell growth arrest. A distinctive transcriptional signature of PDGF-DD-induced cytokines and the downregulation of tumor cell-cycle genes correlated with NCR2 expression and greater survival in glioblastoma. NKp44 expression in mouse NK cells controlled the dissemination of tumors expressing PDGF-DD more effectively than control mice, an effect enhanced by blockade of the inhibitory receptor CD96 or CpG-oligonucleotide treatment. Thus, while cancer cell production of PDGF-DD supports tumor growth and stromal reaction, it concomitantly activates innate immune responses to tumor expansion."

[2] "Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes."

[2] "The immune system can mount T cell responses against tumors; however, the antigen specificities of tumor-infiltrating lymphocytes (TILs) are not well understood. We used yeast-display libraries of peptide-human leukocyte antigen (pHLA) to screen for antigens of \"orphan\" T cell receptors (TCRs) expressed on TILs from human colorectal adenocarcinoma. Four TIL-derived TCRs exhibited strong selection for peptides presented in a highly diverse pHLA-A∗02:01 library. Three of the TIL TCRs were specific for non-mutated self-antigens, two of which were present in separate patient tumors, and shared specificity for a non-mutated self-antigen derived from U2AF2. These results show that the exposed recognition surface of MHC-bound peptides accessible to the TCR contains sufficient structural information to enable the reconstruction of sequences of peptide targets for pathogenic TCRs of unknown specificity. This finding underscores the surprising specificity of TCRs for their cognate antigens and enables the facile indentification of tumor antigens through unbiased screening."

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