Per- and Polyfluoroalkyl Substances (PFAS), a family of more than 4,700 chemical molecules, are known for their non-stick and waterproof properties. These versatile compounds find their application in a variety of industries, including cosmetics, textiles, and automobiles. PFAS exhibit remarkable resistance to degradation, which has led to their pervasive distribution in the environment and consequent human exposure. The effects of such exposure are far-reaching, including adverse effects on fertility, developmental abnormalities, cholesterol levels, immune function, and an increased risk of certain cancers. In addition to their toxicological effects, PFAS have become a global concern as regulators and researchers strive to understand the extent of their impact and develop effective methods for detection and mitigation.
This collection of articles brings together research on the contamination and effects of PFAS in air, soil, and water, shedding light on the complex challenges posed by these persistent substances in our environment. Overall, this collection of articles seeks to advance researchers' knowledge of the dynamics, effects, and detection of PFAS.
How feasible is it to integrate this Bayesian graph-theory methodology into existing commercial chromatography software workflows for semi-automated data curation in routine analytical laboratories?
Gabriel Vivó-Truyols outlines the benefits and challenges of combining graph theory with Bayesian statistics to automate peak assignment and alignment.
This week, Chromatography Online’s coverage spanned lipidomics-based early cancer detection, career paths in separation science, machine learning and uncertainty-driven approaches to LC method development, new EU wastewater rules for pharmaceutical micropollutants, automated Separation Quality Factor (SQF) evaluation in chromatography data systems, and solutions to GLP-1 peptide analysis challenges.
Gabriel Vivó-Truyols from the Spanish Council for Scientific Research, Madrid, Spain. discusses his work on using graph theory and Bayesian statistics for peak assignment and chromatographic alignment.
Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC–qTOF–MS) reveal metabolic shifts in tricuspid regurgitation.