亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Feature‐based molecular networking with MS2LDA to profile compounds in Lanbuzheng based on ultra‐high‐performance liquid chromatography‐quadrupole Exactive Orbitrap high‐resolution mass spectrometry

轨道轨道 质谱法 化学 色谱法 化学成分 注释 高分辨率 计算机科学 人工智能 遥感 地质学
作者
Xiaoming Kong,Guanghuan Tian,Tong Wu,Shaowei Hu,Jie Zhao,Fuzhu Pan,JingTong Liu,Yi Ouyang,Liying Tang,Hongwei Wu
出处
期刊:Journal of Separation Science [Wiley]
卷期号:47 (16): e2400248-e2400248 被引量:10
标识
DOI:10.1002/jssc.202400248
摘要

Lanbuzheng (Geum japonicum Thunb. var. chinense Bolle), a plant found in Southwest China, is a traditional Chinese medicine that promotes hematopoiesis and antioxidant functions. Many of its chemical constituents remain unknown, posing challenges both to understanding its pharmacological mechanisms and to conducting quality control research. In this work, ultra-high performance liquid chromatography coupled with quadrupole Exactive Orbitrap high-resolution mass spectroscopy was used for profiling the composition of Lanbuzheng. Using positive ion mass spectrometry data enriched from Lanbuzheng extract, feature-based molecular networking (FBMN) was constructed and associated with Mass2Motifs substructures using MS2LDA. Prediction and validation of unknown constituents of Lanbuzheng using a custom-built compound library, SIRIUS, and network annotation propagation, achieved a semi-automated annotation of the molecular network. Based on the custom-built library comprising 206 compounds and the FBMN clustering results, the constituents in Lanbuzheng primarily include tannins, triterpenes, flavonoids, and phenolics. Using only 65 pre-identified compounds as references, 210 unknown compounds were annotated in various polarity regions of Lanbuzheng. Results of the current work indicate that molecular networks enable the efficient annotation of compounds in complex systems, laying the groundwork for the preliminary identification of pharmacologically active constituents of Lanbuzheng.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
3秒前
科研通AI6.2应助tdda采纳,获得10
4秒前
金匀完成签到,获得积分10
6秒前
TRNA发布了新的文献求助10
7秒前
10秒前
张欢馨应助王永文采纳,获得10
14秒前
Guigui发布了新的文献求助10
18秒前
TRNA完成签到,获得积分20
20秒前
香蕉觅云应助Jane采纳,获得10
20秒前
渡人舟应助提米橘采纳,获得50
20秒前
万万万发布了新的文献求助10
22秒前
领导范儿应助TRNA采纳,获得10
23秒前
CipherSage应助科研通管家采纳,获得10
25秒前
金匀关注了科研通微信公众号
27秒前
雪白曼文完成签到,获得积分10
29秒前
37秒前
37秒前
无花果应助onlyan采纳,获得10
39秒前
40秒前
金匀发布了新的文献求助10
42秒前
打打应助张少伟采纳,获得10
44秒前
英姑应助张少伟采纳,获得20
44秒前
sanvva应助罗赛采纳,获得10
45秒前
wanci应助万万万采纳,获得10
46秒前
慈溪的通稿完成签到,获得积分10
51秒前
激动的水蓝完成签到,获得积分10
52秒前
53秒前
渡人舟应助提米橘采纳,获得50
54秒前
渡人舟应助提米橘采纳,获得10
54秒前
渡人舟应助提米橘采纳,获得10
54秒前
渡人舟应助提米橘采纳,获得10
54秒前
渡人舟应助提米橘采纳,获得50
55秒前
渡人舟应助提米橘采纳,获得10
55秒前
渡人舟应助提米橘采纳,获得50
55秒前
万万万发布了新的文献求助10
58秒前
不安访风完成签到 ,获得积分10
1分钟前
科科完成签到,获得积分10
1分钟前
星辰大海应助芮精致采纳,获得10
1分钟前
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Health Psychology 600
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 500
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
Variations: A More Diverse Picture of Contemporary Art 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7591578
求助须知:如何正确求助?哪些是违规求助? 9168851
关于积分的说明 19625680
捐赠科研通 7170188
什么是DOI,文献DOI怎么找? 3267461
关于科研通互助平台的介绍 2432327
邀请新用户注册赠送积分活动 2259836