Lulu Zhang

2 papers in the library · 9 citations · publishing 2020-2023

Papers

Elucidation of Pharmacological Mechanism Underlying the Anti-Alzheimer's Disease Effects of Evodia rutaecarpa and Discovery of Novel Lead Molecules: An In Silico Study.

Molecules (Basel, Switzerland) August 3, 2023 Lulu Zhang, Jia Xu, Jiejie Guo et al. 9 citations

Alzheimer's disease is a brain disease with an insidious onset and multiple factors. An in silico study of the traditional Chinese herb Evodia rutaecarpa (Wuzhuyu) found that its active compounds primarily target Alzheimer's disease rather than migraines, for which the herb is traditionally used. Behavioral experiments showed that E. rutaecarpa extract improved learning and memory impairments in AD model mice. Using pharmacology networking and molecular docking, the authors found that alkaloids in the herb bind to key nodes of AD, affecting pathways including serotonergic synapse signaling (SLC6A4), hormones (PTGS2, ESR1, AR), anti-neuroinflammation (SRC, TNF, NOS3), transcription regulation (NR3C1), and molecular chaperones (HSP90AA1). Specific compounds—graveoline, 5-methoxy-N,N-dimethyltryptamine, dehydroevodiamine, and goshuyuamide II—showed stronger binding affinities to key proteins than known preclinical and clinical drugs.

DOI: 10.3390/molecules28155846 Full text Alzheimer’s disease Evodia rutaecarpa Hormones Migraines +8

Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events.

Entropy (Basel, Switzerland) February 20, 2020 Xueya Yan, Lulu Zhang, Jinlian Li et al.

Surges in sympathetic activity near the end of nocturnal sleep may contribute to cardiovascular events. Analyzing heart rate variability (HRV) during the hypnopompic period (the transition from sleep to waking) helps predict cardiovascular disease (CVD). In 2,217 initially CVD-free subjects, those who later developed CVD showed significant alterations in hypnopompic HRV. Machine learning models using hypnopompic HRV metrics achieved 81.4% accuracy for short-term CVD prediction (within two years), a 10.7% improvement over long-term prediction. Removing HRV metrics reduced short-term predictive performance by over 6%. Entropy-based complexity measures of hypnopompic HRV contributed more to prediction than conventional HRV measures.

DOI: 10.3390/e22020241 Full text Xgboost Cardiovascular disease Heart rate variability Machine learning +1