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Acetylcholinesterase (AChE) inhibition is a significant strategy for preventing Alzheimer's disease (AD) and neurodegenerative diseases. In this study, a dereplication system was utilized to rapidly identify and characterize acetylcholinesterase-interacting compounds by comparing UPLC-MS/MS profile screening approach and molecular docking analysis, derived from the extracts and fractions of the stem of Glycosmis parviflora (Sims) Little. Eleven potential AChE inhibitors were isolated and identified from the ethyl acetate extract of G. parviflora, including an undescribed alkaloid (9), namely glybomine D, eight known alkaloids (1−8), a flavonoid (10), and a phytosterol (11). The inhibitory potential of these compounds against AChE was assessed, with O-methylglycosolone (6), 1,3-dimethoxy-2-hydroxy-10-methyl-9(10H)-acridinone (1), skimmianine (4) and arborine (2), regarded as effective inhibitors, yielding IC50 values of 39.81 μM, 41.53 μM, 49.40 μM, and 59.92 μM, respectively. Notably, O-methylglycosolone exhibited the highest potency. Four of these potent AChE inhibitors exhibited mixed-type inhibition. However, O-methylglycosolone (6), 1,3-dimethoxy-2-hydroxy-10-methyl-9(10H)-acridinone (1), and arborine (2) were first reported modulating with acetylcholinesterase activity. Furthermore, molecular docking revealed O-methylglycosolone (6) superior binding affinity (–23.749 kcal/mol) compared to other compounds, mainly by interacting with the peripheral anionic site of AChE, which forms hydrogen bonds and hydrophobic forces may play an important role, interaction with amino acid residues such as Tyr341, Tyr72, Ser293, and Arg296 in the active cavity, which is crucial for effective and selective inhibition of AChE activity. ADMET predictions suggest that arborine (2), skimmianine (4), and O-methylglycosolone (6) demonstrate favorable permeability across the blood–brain barrier, while 1,3-dimethoxy-2-hydroxy-10-methyl-9(10H)-acridinone (1) exhibits comparatively reduced permeability. These findings highlight the potential of these compounds as natural AChE inhibitors for treating neurodegenerative diseases.

The landforms and human activities play important roles in quantifying surface water and groundwater interactions (SGIs) for water resources management. The study uses the groundwater and surface water flow (GSFLOW) model to quantify the dynamics of SGIs in the Pingtung Plain groundwater basin (PPGB) in southern Taiwan. Specifically, the study uses a physical-based numerical model to quantify the spatial and seasonal variations of water cycles influenced by complex fluvial landform conditions and human activities. Results of the model calibrations show good agreement with the data obtained from the available groundwater monitoring network and the selected stream stations. The basin-scale water budgets show highly nonuniform precipitation in the study area, and over 80% annual precipitation is from wet seasons in the PPGB. With high permeable surficial deposits in the PPGB, the year-averaged surface runoff and infiltration are approximately 57% and 40% of the total precipitation. The fluvial landforms with the high slope in the PPGB lead to 70% of annual surface runoff that becomes the streamflow, and the interflow dominates water interactions near streambeds. Results show that the interflow rate in the wet seasons is 200% more than that in the dry seasons. The net groundwater discharge to the streams is relatively small as compared to the interflow. Only 10% of the river flow is from the net groundwater discharge. In the PPGB, The pumping-induced variations of groundwater levels are insignificant as compared with the factor of the natural landforms. Because of the relatively small area of the proposed artificial lake, the contribution of the artificial lake on the local water budgets is insignificant, indicating the low impact of the artificial recharge lake on the surface water environment.