PubMed Scraper: Biomedical Research, Clinical Studies & Abstracts 🚀

A high-speed, zero-login scraper and medical intelligence radar for PubMed (NCBI / National Library of Medicine). Monitor clinical trials, peer-reviewed medical publications, drug discoveries, oncology breakthroughs, and healthcare literature across 36+ million biomedical records.

Built for pharmaceutical researchers, biotech startups, healthcare data scientists, clinical trial monitors, RAG pipelines, and Model Context Protocol (MCP) autonomous agents.

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🌟 Why Use PubMed Research Papers Radar?

• Real-Time Medical Intelligence: Extract newly indexed peer-reviewed studies and clinical trials the moment they are cataloged on PubMed.
• Precision Study Filtering: Filter publications by Article Type (Clinical Trials, Systematic Reviews, or Free Full Text open-access articles).
• Comprehensive Citation Extraction: Structured paper titles, complete multi-section abstracts (Background, Methods, Results, Conclusions), journal names, publication dates, author rosters, DOI links, and direct PubMed Central (PMC) full-text links.
• Zero Login & Zero Proxies Required: 100% reliable cloud runs with no authentication friction, captcha walls, or IP blocking.
• MCP & LLM Ready: Clean JSON output formatted specifically for medical RAG vector embeddings, literature review agents, and autonomous research workflows.

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🎯 Use Cases

1. Biotech & Pharma Competitive Intel: Track newly released clinical trial results for oncology, immunology, and gene therapies.
2. Medical RAG & AI Agent Ingestion: Stream medical abstracts and PMC articles directly into vector databases (Pinecone, Qdrant, Chroma).
3. Systematic Literature Reviews: Automate the screening phase for meta-analyses and evidence-based medicine syntheses.
4. Key Opinion Leader (KOL) Discovery: Identify leading authors, principal investigators, and institutions actively publishing in target disease areas.

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⚙️ Input Parameters

Parameter	Type	Default	Description
searchQuery	String	immunotherapy OR CRISPR	Disease, condition, drug name, or medical keyword query (e.g. GLP-1, melanoma, mRNA vaccine).
maxResults	Integer	25	Maximum number of papers to extract (1 to 200).
filterArticleType	Select	all	Filter by study type: all, clinical_trial, review, or free_full_text.
sortBy	Select	pub_date	Order criteria: pub_date (Newest first) or relevance (Best match).

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📊 Sample Output (JSON)

[
  {
    "pmid": "42898399",
    "title": "Theranostic peptide-selenium hydrogel targeting NETs-mitochondrial dysfunction-pyroptosis axis for chronic diabetic wound repair.",
    "journal": "Bioactive materials",
    "pubDate": "2027 Mar",
    "primaryAuthor": "Xiao Dan",
    "authors": [
      "Xiao Dan",
      "Hao Chen",
      "Si Li",
      "Ying Zhang"
    ],
    "abstractSnippet": "Chronic diabetic wounds are characterized by persistent inflammation, elevated oxidative stress, and impaired vascularization...",
    "abstract": "BACKGROUND: Chronic diabetic wounds are characterized by persistent inflammation...\n\nMETHODS: A multifunctional theranostic peptide-selenium hydrogel was synthesized...\n\nRESULTS: In vitro and in vivo studies demonstrated targeted reduction of NETosis and mitochondrial preservation...\n\nCONCLUSION: This platform offers a promising therapeutic avenue for diabetic ulcer management.",
    "pubmedUrl": "https://pubmed.ncbi.nlm.nih.gov/42898399/",
    "doi": "10.1016/j.bioactmat.2026.09.022",
    "doiUrl": "https://doi.org/10.1016/j.bioactmat.2026.09.022",
    "pmcId": "PMC13677966",
    "pmcUrl": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677966/",
    "publicationTypes": [
      "Journal Article"
    ],
    "meshTerms": [
      "Diabetic Foot",
      "Hydrogels",
      "Selenium",
      "Wound Healing"
    ]
  }
]

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🤖 Integration via Python API

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "searchQuery": "GLP-1 receptor agonist obesity",
    "maxResults": 20,
    "filterArticleType": "clinical_trial",
    "sortBy": "pub_date"
}

run = client.actor("authoritative_glen/pubmed-research-papers-radar").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item['pmid']}] {item['title']}")
    print(f"Journal: {item['journal']} ({item['pubDate']})")
    print(f"PubMed: {item['pubmedUrl']}\n")

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🛡️ Reliability & Support

Maintained with zero-downtime architecture. For questions, feature requests, or custom medical data feeds, visit the actor discussion tab on Apify Console.