N‐doped luteolin‐based carbon dots as a novel matrix for the analysis of small molecules by MALDI‐TOF MS

Inhibition and disaggregation effect of flavonoid-derived carbonized polymer dots on protein amyloid aggregation

In this research, four water-insoluble flavonoid compounds were utilized and reacted with arginine to prepare four carbonized polymer dots with good water-solubility in a hydrothermal reactor. Structural characterization demonstrated that the prepared carbonized polymer dots were classic core-shell structure. Effect of the prepared carbonized polymer dots on protein amyloid aggregation was further investigated using hen egg white lysozyme and human lysozyme as model protein in aqueous solution. All of the prepared carbonized polymer dots could retard the amyloid aggregation of hen egg white lysozyme and human lysozyme in a dose-depended manner. All measurements displayed that the inhibition ratio of luteolin-derived carbonized polymer dots (CPDs-1) was higher than that of the other three carbonized polymer dots under the same dosage. This result may be interpreted by the highest content of phenolic hydroxyl groups on the periphery. The inhibition ratio of CPDs-1 on hen egg white lysozyme and human lysozyme reached 88 % and 83 % at the concentration of 0.5 mg/mL, respectively. CPDs-1 also could disaggregate the formed mature amyloid fibrils into short aggregates.

Serum lipidomic profiling for liver cancer screening using surface-assisted laser desorption ionization MS and machine learning

The liver is a major organ in metabolism, and alterations in serum lipids are associated with liver disorders. Here, a rapid, easy, and reliable screening technique based on lipidomic profiling was developed using machine learning and surface-assisted laser desorption ionization mass spectrometry (SALDI MS) for liver cancer diagnosis. A graphitized carbon matrix (GCM) was created for serum lipid profiling in SALDI MS and demonstrated a better performance for neutral lipids analysis than conventional organic matrices. The fingerprint of serum lipids, including triacylglycerols (TGs), diacylglycerols (DGs), cholesteryl esters (CEs), glycerophospholipids (GPs), and other components, could be directly obtained by GCM-assisted LDI MS without extraction. Five machine learning methods were applied to distinguish liver cancer (LC) patients from healthy controls (HC) and chronic hepatitis B (CHB) patients. The best diagnostic performance was attained by linear discriminant analysis (LDA), which has a confusion matrix accuracy of 98.3 %. The receiver operating characteristic (ROC) curve for liver cancer exhibited an area under the curve (AUC) of 0.99, indicating a high degree of prediction accuracy. One-way ANOVA analysis revealed that numerous TGs were down-regulated in LC group. The results demonstrated the viability of GCM-assisted LDI MS as a valuable diagnostic tool for liver cancer.