Quantitative analysis of 17 hypoglycemic drugs in fingerprints using ultra-high-performance liquid chromatography/tandem hybrid triple quadrupole linear ion trap mass spectrometry

Probabilistic quotient's work and pharmacokinetics' contribution: countering size effect in metabolic time series measurements

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.

Simultaneous Determination and Health Risk Assessment of Four High Detection Rate Pesticide Residues in Pu'er Tea from Yunnan, China

Four pesticides with a high detection rate in Pu'er tea have been determined by a QuEChERS (quick, easy, cheap, effective, rugged, safe) method with multiwalled carbon nanotubes (MWCNTs), and combined ultrahigh-performance liquid chromatography-triple quadrupole linear ion trap-tandem mass spectrometry (UHPLC-QTRAP-MS/MS). MWCNs have been compared with other common purification materials, and found to be superior. The matrix effect was systematically studied, and the results show that the MWCNs can quickly and effectively reduce matrix interference values, which were in the range from -17.8 to 13.8. The coefficients (R2) were greater than 0.99, with the limit of quantification ranging from 0.1 to 0.5 μg/kg, and the recovery rate ranging from 74.8% to 105.0%, while the relative standard deviation (RSD) ranged from 3.9% to 6.6%. A total of 300 samples, taken from three areas in which Yunnan Pu'er tea was most commonly produced, tested for four pesticides. The results show that the detection rate of tolfenpyrad in Pu'er tea was 35.7%, which is higher than other pesticides, and the lowest was indoxacarb, with 5.2%. The residual concentrations of chlorpyrifos, triazophos, tolfenpyrad and indoxacarb ranged from 1.10 to 5.28, 0.014 to 0.103, 1.02 to 51.8, and 1.07 to 4.89 mg/kg, respectively. By comparing with China's pesticide residue limits in tea (GB 2763-2021), the over standard rates of chlorpyrifos, tolfenpyrad, and indoxacarb were 4.35%, 0.87% and 0%, respectively. The risk assessment result obtained with the hazard quotient (HQ) method shows that the HQ of the four pesticides was far less than one, indicating that the risk is considered acceptable for the four pesticides in Pu'er tea. The largest HQ was found for tolfenpyrad, 0.0135, and the smallest was found for indoxacarb, 0.000757, but more attention should be paid to tolfenpyrad in daily diets in the future, because its detection rate, and residual and residual median were all relatively high.