Analytical techniques with high sensitivity and selectivity are essential to the quantitative analysis of clinical samples. Liquid chromatography coupled to tandem mass spectrometry is the gold standard in clinical chemistry. However, tandem mass spectrometers come at high capital expenditure and maintenance costs. We recently showed that it is possible to generate very similar results using a much simpler single mass spectrometry detector by performing enhanced in-source fragmentation/annotation (EISA) combined with correlated ion monitoring. Here we provide a step-by-step protocol for optimizing the analytical conditions for EISA, so anyone properly trained in liquid chromatography–mass spectrometry can follow and apply this technique for any given analyte. We exemplify the approach by using 2-hydroxyglutarate (2-HG) which is a clinically relevant metabolite whose d-enantiomer is considered an ‘oncometabolite’, characteristic of cancers associated with mutated isocitrate dehydrogenases 1 or 2 (IDH1/2). We include procedures for determining quantitative robustness, and show results of these relating to the analysis of dl-2-hydroxyglutarate in cells, as well as in serum samples from patients with acute myeloid leukemia that contain the IDH1/2 mutation. This EISA–mass spectrometry protocol is a broadly applicable and low-cost approach for the quantification of small molecules that has been developed to work well for both single-quadrupole and time-of-flight mass analyzers.

Riparian zones have significant impact on nitrate removal despite their small areas. Most research on riparian zones have been implemented at small scales. Direct measurement at large scale is infeasible, thus using models is a good alternative. This study introduces a modified SWAT model, referred as SWAT_LS. Two modifications were implemented: (i) adding hydrological routing from upland areas to riparian zones; and (ii) adding a module to simulate denitrification in riparian zones based on the Riparian Nitrogen Model. SWAT_LS was applied to the Odense river basin in Denmark, a densely tile-drained agricultural river basin. Compared to SWAT, SWAT_LS provides an equally good performance for streamflow, and a significant improvement in nitrate predictions. SWAT_LS predicts that current riparian zones remove only 4–17% of nitrate loads because 70% of the riparian areas are bypassed due to subsurface drainage implementation. This ability would dramatically increase to 25–85% if riparian zones are entirely undrained.