Advanced Imaging and Ai Reveal Smoking-Relanted Toxins in Placenta Samples

Rice University Scientists and Collaborators at Baylor College of Medicine (BCM) have demonstrated a new method for detecting the presence of dangerous chemicals from tobacco smoke in human placea with unprecedented Speed ​​and Precision.

The Research Team Used A Combination of Light-Based Imaging Techniques and Machine Learning (ML) Algorithms to Identify and Label Polycyclic Aromatic Hydrocarbons (PAHS) and Their Derivatives (PACS)-Toxic Compounds Generated Through The Incomplete Combus of Organic Materials. Exposure to these chemicals during pregnancy can result in negative health outcomes Such as prefers Birth, Low Birth Weight and Developmental Problems.

“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,” Said Oara Neumann, A Rice Research Scientist Who is the Fist Author On A Study Published Proceedings of the National Academy of Sciences.

“The Findings Reveal That Machine-Learning-Enhanced Vibrational Spectroscopy Can Acurately Distinguish Between Placntal Samples From Smokers and Nonsmokers.”

The New Method Was Used to Analyze the Placentas of Women Who Reported Smking During Pregnancy and Self-Reported Nonsmokers, Confirm that Pahs and Pacs Were Only in the Samples Collected From Smokers.

The Findings Offer A Critical Tool for Environmental and Health Monitoring, Enabling the Identification and Labeling of Harmful toxins Associated with Smoking as WELL AS Phherces Such AS Wildfires, Conflagations, Superfund Sites and Other High-Police Environments and Contamrated Products.

“Measuring Levels of Environmental Chemicals in the Placenta Can Give Us Insight into the Exposures That Both Mom and Baby Experienced During Pregnancy,” Said Melissa Suter, An Assistant Professor of Obstetrics and Gynecology at BCM. “This information can help us Understand How these Chemicals Can Affect the Pregnancy and the Baby’s Development and Help Scientists Inform Public Health Measures.”

The Research Relied on Surface-Enhanced Spectroscopy, A Method that uses Specially Designed Nanomaterials to Amplified the Way That Specific Light Wavelengths Interact with Targeted Compounds.

In this Case, The Researchers Leveged the Special Optical Properties of Gold Nanoshells Designed in the Nanoengineered Photonics and Plasmonics Research Group Led by Naomi Halas, University Teacher and Stanley C. Moore Professor of Electric and Computer Engineering at Rice.

“We Combined Two Complementary Techniques-Surface-Enhanced Rampestroscopy and Surface-Enhanced Infrared Absorption-O GENERATE HIGHLY DETAILED VIBRATIONAL SIGNATURES OF THE MOLECULES IN THE PLACENTAL SAMPLES,” SAID HALAS, WHY IS THE COLLECTING AUTHOR ON THE STUDY.

Halas, together with Peter Nordlander, the Wiess Chair in Physics and Astronomy and professor of electrical and computer engineering and materials science and nanoengineering at Rice, have made significant contributions to plasmonics, the study of light-induced collective oscillations of free electrons on the surface of Metallic Nanoparticles.

Surface-Enhanced Spectroscopy Leverages Plasmonics to Make Possible the In-Deptth Study of Molecular Structures with Very High Resolution at the Trace Concentrations Found in Biological and Environmental Samples.

The Integration of ML Algorithms – Characteristic Peak Extraction (Cape) and Characteristic Peak Similarity (Capsim) – Subvened Subtle Patterns in the Data That Woldise Have Gone Undeteced. Cape Identified Key Chemical Signatures from the Complex Datasets, While Capsim Matched these Signals to Known Pah Chemical Signatures. This outcome showcases The Transformive Impact of Computational Tools for Medical and Public Health Applications.

Ankit Patel, Assistant Professor of Electrical and Computer Engineering at Rice and Assistant Professor of Neuroscience at BCM, Said That Ml Served to “Tune Out the ‘Noise’ In The Data.”

“It’s Like the So-Called ‘Cocktail-Party Effect,'” Patel Said. “Pictures the noisy and chrowded room with lots of people talking at once. We are able to focus our attention on private conversation only by tuning out the reset –in the same way, Machine learning is able to parse Through the Spectral Data Associated with PAHS AND PACS MUCH MORE Effectively Than Humans Can. “

Subsequent Experiments Validated The Research Findings, Confirming That the New Method Provides Functional Alternative to Traditional, More Labo-and Time-Intensive Techniques.

Beyond Smoking-Relanted Exposure, The Research Could Enable Monitoring Exposure Toxins Affon Natural Disasters or Industrial Accidents, Equipping Health Care Providers with Foss and More Relable Way to Assess Risk and Potentially Improve Fetal and Material Health Officers.

“This New Method Offers An Unprecedend Level of Detail,” Said Bhagavatula Moorthy, The Kurt Randerath MD endowed Chair and Professor of Pediatrics and Neonatology at BCM.

“This Research Lays The Groundwork for Expanding UltraSensitive Pah- and Pac-Detection Technology in Biological Fluid Such As Blood and Urine As Well As in the Environmental Monitoring of Pahs, Pacs and Oter Hazardous Chemicals in Air, Water and Soil, Thereby Aiding in Human Risk Assessment. “

Other Rice Co-Authors Include Computer Science Doctoral Alum Yilong Ju, Who Developed The ML Algorithm, and andres Sanchez-Alvarado, an electric and computer engineering ph.d. Student in the halas Research Group Who was part of the team that conducted the experiment .