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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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S2CIE: semantic, syntactic, and context-based information extraction for AOP development.

Saurav Kumar1, Shubh Sharma2, Deepika Deepika1

  • 1IISPV, Departament d' Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain; German Federal Institute for Risk Assessment (BfR), Berlin, Germany.

Environment International
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

S2CIE is a new platform that extracts mechanistic information from biomedical literature to speed up Adverse Outcome Pathway (AOP) development. It significantly reduces retrieval time and increases evidence capture for regulatory science.

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Area of Science:

  • Toxicology
  • Computational Biology
  • Biomedical Informatics

Background:

  • Adverse Outcome Pathways (AOPs) are crucial for hazard identification and regulatory decision-making in Next-Generation Risk Assessment (NGRA).
  • Developing AOPs is time-consuming due to manual literature review and limitations of existing text mining tools.
  • Current tools often lack interactivity, operate on limited data, and miss contextually relevant studies.

Purpose of the Study:

  • To introduce S2CIE (Semantic & Syntactic Context-based Information Extraction), an interactive, real-time platform for extracting mechanistic information from biomedical literature.
  • To address the challenges of labor-intensive AOP development by providing an efficient and comprehensive information extraction solution.
  • To support regulatory science by accelerating evidence gathering for AOPs.

Main Methods:

  • S2CIE annotates PubMed abstracts (32 million) with grammatical, entity-type, and dependency information.
  • It enables custom extraction rules based on syntactic patterns and semantic retrieval via user-specific queries.
  • The platform integrates entity recognition, visual exploration tools, and supports near real-time querying.

Main Results:

  • Identified 110 chemicals linked to liver steatosis/cholestasis with 98.79% precision, extracting PPAR interactions.
  • Demonstrated increased evidence capture (42.2%) and reduced retrieval time (99%) compared to AOP-HelpFinder for AOP 220.
  • Showcased domain-agnostic applicability through large-scale extraction of post-translational modification events.

Conclusions:

  • S2CIE significantly accelerates the evidence gathering process for AOP development.
  • The platform enhances transparency and auditability, crucial for regulatory applications.
  • S2CIE is available as open-source software, promoting wider adoption in scientific research and regulatory science.