Caffeine may protect people from cancer-causing carcinogens by forming stacking complexes with polycyclic aromatic chemicals.
Researchers from the University of Gdansk (Gdansk, Poland) have used UV–vis spectrometry to demonstrate that methylxanthine alkaloids, such as caffeine, may shield people from cancer-causing carcinogens by forming pi-pi stacking complexes with polycyclic aromatic chemicals.
In 2003, the team, led by Jacek Piosik of the Department of Molecular and Cellular Biology at the University of Gdansk, first attempted to determine the role of caffeine in protecting against cancer; a phenomenon that was observed in earlier studies but was never fully understood. They concluded that caffeine, as well as pentoxifylline (PTX), quelled the mutagenic activity of polycyclic aromatic agents through stacking complexes.
Recently, Piosik, alongside two colleagues from the University, expanded this study beyond caffeine and pentoxifylline to encompass all methylxanthine alkaloids (MTX), and determined that MTX forms stacking complexes with heterocyclic aromatic amines (HCAs). HCAs most often occur when meat is cooked at a high temperature. Using the Ames test with Salmonella typhimurium TA98 strain and the recently developed mutagenicity assay based on bioluminescence of Vibrio harveyi A16 strain, the team demonstrated that the presence of MTX significantly reduced the mutagenic activity of HCAs.
The first study was published in the September 29, 2003, issue of the journal Mutation Research. The second study appeared in the February 2011 issue of Bioorganic Chemistry.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.