Optimization of Ultrasound-Assisted Extraction of Flavonoids from Celastrus hindsii Leaves Using Response Surface Methodology and Evaluation of Their Antioxidant and Antitumor Activities.

Celastrus hindsii is a potential source of flavonoids with biological activities. This study aimed to develop an ultrasound-assisted technique for extracting flavonoids from leaves of C. hindsii. Response surface methodology was employed to optimize the extraction conditions for maximizing the total flavonoid content (TFC). A maximum TFC of 23.6 mg QE/g was obtained under the extraction conditions of ultrasonic power of 130 W, extraction temperature of 40°C, extraction time of 29 min, and ethanol concentration of 65%. The flavonoid-rich extracts were then studied for their antioxidant and anticancer activities. The results showed that the C. hindsii leaf extract exhibited potent radical scavenging activities against DPPH (IC50 of 164.85 µg/mL) and ABTS (IC50 of 89.05 µg/mL). The extract also significantly inhibited the growth of 3 cancer cell lines MCF7, A549, and HeLa with the IC50 values of 88.1 µg/mL, 120.4 µg/mL, and 118.4 µg/mL, respectively. Notably, the extract had no cytotoxicity effect on HK2 normal kidney cell line. This study suggests that flavonoid-rich extract is a promising antioxidant and anticancer agent and that ultrasound-assisted extraction is an efficient method for extracting flavonoids from C. hindsii leaves.

Causality Assessment of Adverse Drug Reaction: Controlling Confounding Induced by Polypharmacy.

BACKGROUND: Post-marketing pharmaceutical surveillance, a.k.a. pragmatic clinical trials (i.e., PCT), plays a vital role in preventing accidents in practical treatment. The most important and difficult task in PCT is to assess which drug causes adverse reactions (i.e., ADRs) from clinical texts. The confounding (i.e., factors cause confusions in causality assessment) is generated by the polypharmacy (i.e., multiple drugs use), which makes most of existing methods poor for detecting drugs that capably cause observed ADRs. OBJECTIVE: We aim to improve the performance of detecting drug-ADR causal relations from clinical texts. To this end, a mechanism for reducing the impact of confounding on the detecting process is needful. METHODS: We proposed a novel model which is called the analogy-based active voting (i.e., AAV) for improving the ability of detecting causal drug-ADR pairs, in case multiple drugs are prescribed for treating the comorbidity. This model is inspired by the analogy principle which was proposed by Bradford Hill. RESULTS: The experimental results show the improvement of recognizing causal relations between drugs and ADRs that are confirmed by the SIDER. In addition, the proposed model is promising to detect infrequently observed causal drug-ADR pairs when the drug is not commonly used. CONCLUSION: The proposed model demonstrates its ability for controlling the polypharmacy-induced confounding, to improve the quality of causality assessment of ADRs. Additionally, this also shows that the analogy principle is applicable for the assessment.