Lin Zhang

Ginger has been reported to potentially treat Alzheimer’s disease (AD), but the specific compounds responsible for this biological function and their mechanisms are still unknown. In this study, a combination of network pharmacology, molecular docking, and dynamic simulation technology was used to screen active substances that regulate AD and explore their mechanisms. The TCMSP, GeneCards, OMIM, and DisGeNET databases were utilized to obtain 95 cross-targets related to ginger’s active ingredients and AD as key targets. A functional enrichment analysis revealed that the pathways in which ginger’s active substances may be involved in regulating AD include response to exogenous stimuli, response to oxidative stress, response to toxic substances, and lipid metabolism, among others. Furthermore, a drug-active ingredient–key target interaction network diagram was constructed, highlighting that 6-Gingerol is associated with 16 key targets. Additionally, a protein–protein interaction (PPI) network was mapped for the key targets, and HUB genes (ALB, ACTB, GAPDH, CASP3, and CAT) were identified. Based on the results of network pharmacology and cell experiments, 6-Gingerol was selected as the active ingredient for further investigation. Molecular docking was performed between 6-Gingerol and its 16 key targets, and the top three proteins with the strongest binding affinities (ACHE, MMP2, and PTGS2) were chosen for molecular dynamics analysis together with the CASP3 protein as the HUB gene. The findings indicate that 6-Gingerol exhibits strong binding ability to these disease targets, suggesting its potential role in regulating AD at the molecular level, as well as in abnormal cholinesterase metabolism and cell apoptosis, among other related regulatory pathways. These results provide a solid theoretical foundation for future in vitro experiments using actual cells and animal experiments to further investigate the application of 6-Gingerol.

In this study, we aimed to establish a multi-residue analytical method for the simultaneous detection of chemical contaminants in eggs. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we developed an analytical method that can separate 244 compounds (including β-agonists (25), imidazole and benzimidazoles (31), sulfonamides (22), antihistamines (10), β-lactam (5), insecticides (7), quinolones (24), non-steroidal anti-inflammatory drugs (13), and steroidal hormones (38)) within 30 min. A new enhanced matrix removal-lipid (EMR-Lipid) material was used as a purified sorbent in the QuEChERS clean-up method. Excellent linearity (r > 0.9905) was achieved. Additionally, recoveries ranged between 51.33% and 118.28%, with repeatability (RSD_r) and reproducibility (RSD_wR) in the range of 1.01–14.22% and 1.08–14.96%, respectively. In all of the compounds, low limits of quantification (LOQs) ≤ 5 μg kg⁻¹ were found. Meanwhile, the detection limit (CCα) and detection capability (CCβ) were 1.88–40.60 μg kg⁻¹ and 2.85–407.19 μg kg⁻¹, respectively. In conclusion, the evaluated method was shown to provide reliable screening, quantification, and identification of 244 multi-class chemicals in eggs and was successfully applied in real samples.

A rapid and simplified sample preparation method was developed for the simultaneous determination of 26 beta-agonists in swine muscle using a multi-functional filter (MFF) based on quick, easy, cheap, effective, rugged, and safe methods (QuEChERS). MFF integrated the cleanup and filter procedures, thereby significantly improving the efficiency of sample preparation compared with traditional solid-phase extraction. The sample was processed via enzymatic hydrolysis, purified with the optimized MFF containing 150 mg magnesium sulfate, 50 mg PSA, and 50 mg C18, and then analyzed using ultra-high performance liquid chromatography-tandem mass spectrometry. All procedures can be completed in 6.5 h. Good linearity (R² > 0.99) was detected in all analytes. The recoveries ranged from 71.2% to 118.6%, with relative standard deviations (RSDs) of less than 18.37% in all spiked concentrations. The limits of detection (LOD) and the limits of quantitation (LOQ) were 0.01–0.10 and 0.10–0.50 μg/kg, respectively. The decision limit (CCα) and detection capacity (CCβ) values fluctuated in the range of 3.44–25.71 and 6.38–51.21 μg/kg, respectively. This method is a good alternative for detecting beta-agonist residues in swine muscle and can be successfully applied to the national risk monitoring of agro-product quality and safety in China.