ArticleProceedings of the National Academy of Sciences of the United States of America2025
Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- SERS and SEIRA Detection of Polycyclic Aromatic Hydrocarbons and their Derivatives in Murine Tissues: Bioaccumulation and Clearance.Journal of Raman spectroscopy : JRS · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated health risks. PAHs/PACs are the most dangerous chemicals found in tobacco smoke, and cigarette use during pregnancy can convey these molecules to the developing fetus through the placenta. This exposure is associated with many negative health outcomes, from premature birth to sudden infant death syndrome and adverse neurodevelopmental disorders. This study demonstrates the use of surface-enhanced Raman and surface-enhanced infrared absorption spectroscopies for direct detection of PAHs/PACs in human placental tissue. We applied two spectroscopy-informed machine learning algorithms, Characteristic Peak Extraction (CaPE) and Characteristic Peak Similarity (CaPSim), to identify the specific PAHs and PACs present in the placenta of women who smoked tobacco cigarettes in pregnancy compared to spectra of the placenta from self-reported nonsmokers. CaPE and CaPSim analysis enabled a clear distinction between these two groups. Independent verification was accomplished by detecting PAH-DNA and PAC-DNA adducts in the smoking group by means of a
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