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<PubmedArticle><MedlineCitation Status="MEDLINE" Owner="NLM" IndexingMethod="Automated"><PMID Version="1">39506244</PMID><DateCompleted><Year>2024</Year><Month>11</Month><Day>07</Day></DateCompleted><DateRevised><Year>2024</Year><Month>11</Month><Day>07</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">2045-7634</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><Issue>21</Issue><PubDate><Year>2024</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Cancer medicine</Title><ISOAbbreviation>Cancer Med</ISOAbbreviation></Journal><ArticleTitle>Autoimmune Diseases and Risk of Non-Hodgkin Lymphoma: A Mendelian Randomisation Study.</ArticleTitle><Pagination><StartPage>e70327</StartPage><MedlinePgn>e70327</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/cam4.70327</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Non-Hodgkin lymphoma (NHL) is one of the most common haematologic malignancies in the world. Despite substantial efforts to identify causes and risk factors for NHL, its aetiology is largely unclear. Autoimmune diseases have long been considered potential risk factors for NHL. We carried out Mendelian randomisation (MR) analyses to examine whether genetically predicted susceptibility to ten autoimmune diseases (Beh&#xe7;et's disease, coeliac disease, dermatitis herpetiformis, lupus, psoriasis, rheumatoid arthritis, sarcoidosis, Sj&#xf6;gren's syndrome, systemic sclerosis, and type 1 diabetes) is associated with risk of NHL.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Two-sample MR was performed using publicly available summary statistics from cohorts of European ancestry. For NHL and four NHL subtypes, we used data from UK Biobank, Kaiser Permanente cohorts, and FinnGen studies.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Negative associations between type 1 diabetes and sarcoidosis and the risk of NHL were observed (odds ratio [OR] 0.95, 95% confidence interval [CI]: 0.92-0.98, p&#x2009;=&#x2009;5&#x2009;&#xd7;&#x2009;10<sup>-3</sup>, and OR 0.92, 95% CI: 0.85-0.99, p&#x2009;=&#x2009;2.8&#x2009;&#xd7;&#x2009;10<sup>-2</sup>, respectively). These findings were supported by the sensitivity analyses accounting for potential pleiotropy and weak instrument bias. No significant associations were found between the other eight autoimmune diseases and NHL risk.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">These findings suggest that genetically predicted susceptibility to type 1 diabetes, and to some extent sarcoidosis, might reduce the risk of NHL. However, future studies with different datasets, approaches, and populations are warranted to further examine the potential associations between these autoimmune diseases and the risk of NHL.</AbstractText><CopyrightInformation>&#xa9; 2024 The Author(s). Cancer Medicine published by John Wiley &amp; Sons Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Shi</LastName><ForeName>Xiaoting</ForeName><Initials>X</Initials><Identifier Source="ORCID">0000-0002-5256-0646</Identifier><AffiliationInfo><Affiliation>Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Yale Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wallach</LastName><ForeName>Joshua D</ForeName><Initials>JD</Initials><Identifier Source="ORCID">0000-0002-2816-6905</Identifier><AffiliationInfo><Affiliation>Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ma</LastName><ForeName>Xiaomei</ForeName><Initials>X</Initials><Identifier Source="ORCID">0000-0001-9472-8032</Identifier><AffiliationInfo><Affiliation>Department of Chronic Diseases Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Rogne</LastName><ForeName>Tormod</ForeName><Initials>T</Initials><Identifier Source="ORCID">0000-0002-9581-7384</Identifier><AffiliationInfo><Affiliation>Yale Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Chronic Diseases Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Community Medicine and Global Health, University of Oslo, Oslo, Norway.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>UL1 TR001863</GrantID><Acronym>TR</Acronym><Agency>NCATS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Cancer Med</MedlineTA><NlmUniqueID>101595310</NlmUniqueID><ISSNLinking>2045-7634</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D057182" MajorTopicYN="Y">Mendelian Randomization Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001327" MajorTopicYN="Y">Autoimmune Diseases</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008228" MajorTopicYN="Y">Lymphoma, Non-Hodgkin</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020022" MajorTopicYN="Y">Genetic Predisposition to Disease</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020641" MajorTopicYN="N">Polymorphism, Single Nucleotide</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003922" MajorTopicYN="N">Diabetes Mellitus, Type 1</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055106" MajorTopicYN="N">Genome-Wide Association Study</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016017" MajorTopicYN="N">Odds Ratio</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Mendelian randomisation</Keyword><Keyword MajorTopicYN="N">autoimmune diseases</Keyword><Keyword MajorTopicYN="N">non&#x2010;Hodgkin lymphoma</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="medline"><Year>2024</Year><Month>11</Month><Day>8</Day><Hour>7</Hour><Minute>47</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2024</Year><Month>11</Month><Day>8</Day><Hour>7</Hour><Minute>46</Minute></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2024</Year><Month>9</Month><Day>12</Day></PubMedPubDate><PubMedPubDate PubStatus="received"><Year>2024</Year><Month>6</Month><Day>7</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2024</Year><Month>9</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2024</Year><Month>11</Month><Day>7</Day><Hour>0</Hour><Minute>52</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">39506244</ArticleId><ArticleId IdType="doi">10.1002/cam4.70327</ArticleId></ArticleIdList><ReferenceList><Title>References</Title><Reference><Citation>R. 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This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR&#x2009;+&#x2009;algorithm (DeLong p-value&#x2009;=&#x2009;3&#x2009;&#xd7;&#x2009;10<sup>-5</sup>). For T2D, the EHR&#x2009;+&#x2009;algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values&#x2009;=&#x2009;0.03 and 1&#x2009;&#xd7;&#x2009;10<sup>-4</sup>, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.</AbstractText><CopyrightInformation>&#xa9; 2024. The Author(s).</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y" EqualContrib="Y"><LastName>Szczerbinski</LastName><ForeName>Lukasz</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15-276, Bialystok, Poland.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Clinical Research Centre, Medical University of Bialystok, 15-276, Bialystok, Poland.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y" EqualContrib="Y"><LastName>Mandla</LastName><ForeName>Ravi</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Cardiology Division, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y" EqualContrib="Y"><LastName>Schroeder</LastName><ForeName>Philip</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Porneala</LastName><ForeName>Bianca C</ForeName><Initials>BC</Initials><AffiliationInfo><Affiliation>Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Josephine H</ForeName><Initials>JH</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine, Harvard Medical School, Boston, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Florez</LastName><ForeName>Jose C</ForeName><Initials>JC</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine, Harvard Medical School, Boston, MA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mercader</LastName><ForeName>Josep M</ForeName><Initials>JM</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA. mercader@broadinstitute.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA. mercader@broadinstitute.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA. mercader@broadinstitute.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine, Harvard Medical School, Boston, MA, USA. mercader@broadinstitute.org.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Udler</LastName><ForeName>Miriam S</ForeName><Initials>MS</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA. MUDLER@mgh.harvard.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA. MUDLER@mgh.harvard.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA. MUDLER@mgh.harvard.edu.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine, Harvard Medical School, Boston, MA, USA. MUDLER@mgh.harvard.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Manning</LastName><ForeName>Alisa K</ForeName><Initials>AK</Initials><AffiliationInfo><Affiliation>Programs in Metabolism and Medical &amp; Population Genetics, Broad Institute of Harvard and MIT, 415 Main St., Cambridge, MA, 02142, USA. amanning@broadinstitute.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA. amanning@broadinstitute.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine, Harvard Medical School, Boston, MA, USA. amanning@broadinstitute.org.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA. amanning@broadinstitute.org.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>11-22-PDFPM-03</GrantID><Agency>American Diabetes Association</Agency><Country/></Grant><Grant><GrantID>1-19-ICTS-068</GrantID><Agency>American Diabetes Association</Agency><Country/></Grant><Grant><GrantID>K23 DK131345</GrantID><Acronym>DK</Acronym><Agency>NIDDK NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>K23DK114551</GrantID><Acronym>DK</Acronym><Agency>NIDDK NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>Fund for Medical Discovery Clinical Research Award</GrantID><Agency>Massachusetts General Hospital ECOR</Agency><Country/></Grant><Grant><GrantID>K24 HL157960</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U01HG011723</GrantID><Acronym>HG</Acronym><Agency>NHGRI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>2022063</GrantID><Acronym>DDCF</Acronym><Agency>Doris Duke Charitable Foundation</Agency><Country>United States</Country></Grant><Grant><GrantID>AMP CMD RFP 2</GrantID><Agency>Foundation for the National Institutes of Health</Agency><Country/></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2024</Year><Month>11</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Sci Rep</MedlineTA><NlmUniqueID>101563288</NlmUniqueID><ISSNLinking>2045-2322</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003924" MajorTopicYN="Y">Diabetes Mellitus, Type 2</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D057286" MajorTopicYN="Y">Electronic Health Records</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020412" MajorTopicYN="Y">Multifactorial Inheritance</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003922" MajorTopicYN="Y">Diabetes Mellitus, Type 1</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015995" MajorTopicYN="N">Prevalence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008137" MajorTopicYN="N">Longitudinal Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016022" MajorTopicYN="N">Case-Control Studies</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2024</Year><Month>4</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2024</Year><Month>9</Month><Day>29</Day></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2024</Year><Month>11</Month><Day>8</Day><Hour>6</Hour><Minute>17</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2024</Year><Month>11</Month><Day>7</Day><Hour>0</Hour><Minute>27</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2024</Year><Month>11</Month><Day>6</Day><Hour>23</Hour><Minute>50</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">39505999</ArticleId><ArticleId IdType="doi">10.1038/s41598-024-74730-9</ArticleId><ArticleId IdType="pii">10.1038/s41598-024-74730-9</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Coppola, L. et al. 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File added
import xml.etree.ElementTree as ET
def xml_to_obj(xml_element):
res = {}
for child in xml_element:
if child.text:
res[child.tag] = child.text
else:
child_dict = xml_to_obj(child)
if child.tag in res:
if isinstance(res[child.tag], list):
res[child.tag].append(child_dict)
else:
res[child.tag] = [res[child.tag], child_dict]
else:
res[child.tag] = child_dict
return res
def parseXmlFile(filename):
tree = ET.parse(filename)
root = tree.getroot()
return xml_to_obj(root)
\ No newline at end of file
from requests import get
from parser.xmlParser import parseXmlFile
FILENAME = "pubmedData.xml"
#term = "diabetes+type+1+OR+diabetes+type+2+OR+mental+health"
term = "diabetes+type+1"
date_min = "2024/11/07"
date_max = "2024/11/08"
url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term={term}&retmode=json&mindate={date_min}&maxdate={date_max}&usehistory=y"
response = get(url)
search_res = response.json()
query_key = search_res["esearchresult"]["querykey"]
webenv = search_res["esearchresult"]["webenv"]
print(f"Query key: {query_key}")
print(f"Web env: {webenv}")
url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&query_key={query_key}&WebEnv={webenv}"
response = get(url)
with open(f"data/{FILENAME}", "w", encoding="utf-8") as file:
file.write(response.text)
obj = parseXmlFile(f"data/{FILENAME}")
print(obj)
\ No newline at end of file
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