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Innovative Method Detects Tobacco Chemicals in Human Placenta
Researchers from Rice University and Baylor College of Medicine (BCM) have unveiled a groundbreaking technique for the rapid and precise detection of harmful chemicals from tobacco smoke in human placenta. This advancement holds significant implications for maternal and fetal health.
The team employed a fusion of light-based imaging methods and machine learning algorithms to accurately identify and categorize polycyclic aromatic hydrocarbons (PAHs) and their derivatives (PACs). These toxic substances are produced during the incomplete combustion of organic materials and have been linked to adverse pregnancy outcomes, including preterm births, low birth weights, and developmental issues.
“Our study addresses a vital concern in maternal and fetal health by enhancing our capability to detect dangerous compounds like PAHs and PACs in placental tissues,” stated Oara Neumann, a research scientist at Rice and lead author of a publication in the Proceedings of the National Academy of Sciences. “Our findings highlight that machine learning-enhanced vibrational spectroscopy can distinctly differentiate between placental samples from smokers and nonsmokers.”
Using this advanced method, researchers analyzed placenta samples from women who reported smoking during pregnancy alongside those of self-reported nonsmokers. The results confirmed the exclusive presence of PAHs and PACs in the placentas of smokers, providing a valuable tool for environmental health monitoring. This technology can also be applied to identify pollutants from various sources, including wildfires, industrial incidents, and high-contamination environments.
“Assessing the levels of environmental pollutants in the placenta offers insights into the exposures experienced by both the mother and the developing fetus,” explained Melissa Suter, an assistant professor in BCM’s obstetrics and gynecology department. “This data can aid in understanding the potential effects these chemicals may have on pregnancy and fetal development, informing better public health initiatives.”
The research hinged on surface-enhanced spectroscopy, a technique that utilizes specially engineered nanomaterials to amplify light interactions with targeted compounds. The team capitalized on the distinctive optical characteristics of gold nanoshells crafted by the Nanoengineered Photonics and Plasmonics research group at Rice, led by Naomi Halas, a University Professor at Rice.
“We innovatively combined surface-enhanced Raman spectroscopy and surface-enhanced infrared absorption to create detailed vibrational profiles of the molecules present in placental samples,” mentioned Halas, who also served as the study’s corresponding author.
Halas, along with Peter Nordlander, the Wiess Chair in Physics and Astronomy and professor of engineering at Rice, has been instrumental in advancing the field of plasmonics—the examination of light-induced collective oscillations of free electrons in metallic nanoparticles. By leveraging plasmonics, surface-enhanced spectroscopy enables researchers to investigate molecular structures at extremely high resolutions even at trace concentrations found in biological and environmental specimens.
The integration of machine learning algorithms, specifically characteristic peak extraction (CaPE) and characteristic peak similarity (CaPSim), allowed the team to uncover subtle data patterns that might otherwise remain hidden. CaPE enabled the identification of critical chemical signatures from complex data sets, while CaPSim helped relate these signals to recognized PAH chemical signatures. This demonstrates the substantial impact of computational techniques on medical and public health research.
Ankit Patel, an assistant professor at Rice involved in the study, compared the machine learning function to the “cocktail party effect,” where one can focus on a single conversation amid the noise of many. “Machine learning effectively filters through spectral data pertaining to PAHs and PACs more proficiently than human analysis,” Patel noted.
Following thorough testing, the researchers confirmed that their innovative method serves as an efficient alternative to traditional detection techniques that are often labor-intensive and time-consuming. This research has the potential to enhance monitoring of environmental toxins following natural catastrophes or industrial spills, providing healthcare practitioners with quicker, more reliable risk assessments that could lead to improved maternal and fetal health outcomes.
“This new approach presents an unparalleled level of analytical detail,” stated Bhagavatula Moorthy, the Kurt Randerath MD Endowed Chair and Professor of Pediatrics and Neonatology at BCM. “This study paves the way for the broader application of ultrasensitive detection technology for PAHs, PACs, and other hazardous substances in biological fluids like blood and urine, as well as in the environmental monitoring of dangerous chemicals in air, water, and soil, thus enhancing human health risk evaluations.”
The research team also included Yilong Ju, a doctoral computer science graduate who developed the machine learning algorithm, and Andres Sanchez-Alvarado, a Ph.D. student involved in the experimental work, both from Rice University.
This research received funding from the National Institutes of Health (P42ES027725), the Welch Foundation (C-1220, C-1222), and Rice’s Smalley-Curl Institute. The views expressed in this study do not necessarily represent those of the funding agencies.
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