Scientists from Rice University and Baylor College of Medicine have developed a method to detect hazardous chemicals in the human placenta, according to recent research. The study was part of the Superfund Research Program (SRP) and focused on identifying polycyclic aromatic hydrocarbons (PAHs) and their derivatives (PACs), which are harmful compounds formed through incomplete combustion.
The team utilized light-based imaging techniques combined with machine learning algorithms to analyze placental samples. This new approach allows for quick and precise detection of these toxic substances, which are linked to adverse health effects like preterm birth and developmental issues.
Dr. Oara Neumann, a Rice research scientist and first author of the study published in the Proceedings of the National Academy of Sciences, stated, “Our work addresses a critical challenge in maternal and fetal health by improving our ability to detect harmful compounds like PAHs and PACs in placenta samples.” The research showed that machine-learning-enhanced vibrational spectroscopy could effectively differentiate between samples from smokers and nonsmokers.
The findings have significant implications for environmental and health monitoring. They enable the identification of toxins related not only to smoking but also other sources such as wildfires or high-pollution environments. Dr. Melissa Suter from Baylor emphasized that measuring chemical levels in the placenta provides insights into exposures during pregnancy, aiding public health measures.
Dr. Bhagavatula Moorthy from Texas Children's Hospital noted that this research paves the way for expanding detection technology beyond placentas to biological fluids like blood and urine. It could also be used for environmental monitoring, assisting in human risk assessment.
The research relied on surface-enhanced spectroscopy using nanomaterials designed by Dr. Naomi Halas’s group at Rice University. Halas explained they combined surface-enhanced Raman spectroscopy with infrared absorption to produce detailed molecular signatures.
Machine learning played a crucial role in analyzing data efficiently. Dr. Ankit Patel likened it to focusing on a conversation amid noise: “Machine learning is able to parse through the spectral data associated with PAHs and PACs much more effectively than humans can.”
This innovative method offers an alternative to traditional techniques, potentially improving maternal-fetal health outcomes by providing faster assessments after exposure events like natural disasters or industrial accidents.
Other contributors include Yilong Ju, who developed the ML algorithm, and Andres Sanchez-Alvarado from Rice University’s Halas group. The study received support from various institutions including the National Institutes of Health.
