A high-performance liquid chromatography—diode-array detection method was developed and validated to determine simultaneously eleven major alkaloids in Corydalis decumbens (Thunb.) Pers. The alkaloids detected were corlumidine, protopine, coptisine, tetrahydrojatrorrhizine, palmatine, berberine, sanguinarine, papaverine hydrochloride, tetrahydropalmatine, bicuculline, and corydaline. Chromatographic separation was achieved using a C-18 column with a mobile phase composed of A (0.2% acetic acid solution, adjusted with triethylamine to pH 5.0) and B (acetonitrile), with stepwise gradient elution. Ultraviolet diode-array detection was used; chromatograms were examined at the wavelength of 280 nm. The regression equations showed a good linear relationship between the peak area of each marker and concentration (r = 0.9994–0.9999). The recovery values ranged between 93.66% and 100.54%. The method was fully validated with respect to detection and quantification limits, precision, reproducibility, and accuracy. The described high-performance liquid chromatography (HPLC) method was successfully used for the differentiation and quantification of the eleven major alkaloids in C. decumbens (Thunb.) Pers. and can be considered an effective procedure for the analyses of this important class of natural compounds.
Authors:Qiang Ren, Tianrui Xia, Xian-Gao Quan, Lin Ding, and Hui-Yun Wang
Scutellaria barbata D. Don has been used as a traditional Chinese medicine for antitumor and anti-inflammatory. However, there were just a few investigations about S. barbata D. Don according to bioactivity-directed isolation and online identification for the chemical constituents. In this work, eight compounds were isolated from S. barbata D. Don. The three flavonoids indicated the cytotoxic activity against human leukemic Reh cell lines. In addition, the constituents of S. barbata D. Don were further characterized and identified by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). The UHPLC- Q-TOF-MS method was in negative ion mode. HPLC separation was performed on a Tosoh TSK gel ODS-100V (4.6 × 150 mm, 3.0 μm) column by gradient elution using water containing 0.3% formic acid and acetonitrile as mobile phase at a flow rate of 0.8 mL min−1. A total of 18 compounds, including 4 phenolic acids and 14 flavonoids were tentatively characterized and identified by means of the retention time, accurate mass, and characteristic fragment ions.
Authors:Yun Wang, Jianhong Chen, Yutian Li, Puling Li, Javed Iqbal, Ying Chen, Yinlian Ma, and Cun Zhang
A reliable and rapid high-performance liquid chromatography coupled with diode array detector method (HPLC–DAD) was established and validated to determine eight gingerol simultaneously in the rhizomes of Zingiber offcinale Rosc. The separation of eight compounds (4-hydroxy-3-methoxy-benzenebutanol,3,5-dihydroxy-1-(4-hydroxy-3-methoxyphenyl) decane, 3,5-dihydroxy-1-(3,4-dimethoxyphenyl) decane, 6-gingerol, 8-gingerol, 6-shogaol, 5-hydroxy-1-(4-hydroxy-3-methoxyphenyl)-1,4-decadien-3-one, and 10-gingerol) were performed on an Agilent TC(2) C18 (250 mm × 4.6 mm, 5 μm) at 30 °C using acetonitrile (A) and 1% formic acid aqueous solution (B) as the mobile phase with gradient elution (0–10 min, 20%–35% A; 10–28 min, 35%–55% A; 28–35 min, 55%–60% A; 35–55 min, 60%–70% A; 55.01–60 min, 100%–100% A). The detection wavelength was set at 280 nm, and the flow rate was 0.8 mL/min. Validation of the analytical method was performed by linearity, precision, and accuracy test. All compounds were quantified with good linear calibration curves (coefficient of determination R2, >0.9999). The method showed good precision with overall coefficients of variation between 0.56% and 0.84%. The range of recovery was from 95.50% to 104.14% for the analytes. This method was successfully applied to quantify eight gingerols in Z. offcinale Rosc from different regions in China, so it can provide quality assessment for this medicine.
Authors:Shunsen Huang, Xiaoxiong Lai, Ye Xue, Cai Zhang, and Yun Wang
Background and aims
Previous research has established risk factors for problematic smartphone use (PSU), but few studies to date have explored the structure of PSU symptoms. This study capitalizes on network analysis to identify the core symptoms of PSU in a large sample of students.
This research investigated 26,950 grade 4 students (male = 13,271) and 11,687 grade 8 students (male = 5,739) using the smartphone addiction proneness scale (SAPS). The collected data were analyzed using a network analysis method, which can provide centrality indexes to determine the core symptoms of PSU. The two networks from the different groups were compared using a permutation test.
The results indicated that the core symptoms of students' problematic smartphone use were the loss of control and continued excessive use across the two samples.
Discussion and conclusions
These findings suggest that loss of control is a key feature of problematic smartphone use. The results also provide some evidence relevant to previous research from the perspective of network analysis and some suggestions for future treatment or prevention of students' problematic smartphone use.
Authors:Shunsen Huang, Xiaoxiong Lai, Yajun Li, Yuhan Luo, and Yun Wang
Background and aims
To understand the interaction between problematic smartphone use (PSU) and related influencing factors (individual variables, family environment, and school environment) and to determine the most influential factors affecting the use of smartphones by juveniles to implement effective interventions in the future.
A total of 3,442 children and adolescents (3,248 actual participants (males = 1,638, average age = 12.27 ± 2.36)) were included in the study. This study measured juveniles’ PSU and its influencing factors: individual variables (4 factors), family environments (13 factors), and school environments (5 factors). This study employed a network analysis approach for data assessment.
This study found that there were several central influencing factors (such as self-control ability, loss of control, parent-child relationship, and peer attitudes towards smartphone use) and bridge factors (such as peer attitudes towards smartphone use, peer pressure for smartphone use, and fear of missing out).
Discussion and conclusions
Juveniles’ PSU included several core symptoms and critical influencing factors. Intervention based on these factors may be effective, timely, and inexpensive.