Current Sample Preparation Methodologies for Determination of Catecholamines and Their Metabolites

样品制备 色谱法 化学 样品(材料) 电流(流体) 环境化学 物理 热力学
作者
Nian Shi,Xin-Miao Bu,Manyu Zhang,Bin Wang,Xin-Li Xu,Xuezhong Shi,Dilshad Hussain,Xia Xu,Di Chen
出处
期刊:Molecules [Multidisciplinary Digital Publishing Institute]
卷期号:27 (9): 2702-2702 被引量:11
标识
DOI:10.3390/molecules27092702
摘要

Catecholamines (CAs) and their metabolites play significant roles in many physiological processes. Changes in CAs concentration in vivo can serve as potential indicators for the diagnosis of several diseases such as pheochromocytoma and paraganglioma. Thus, the accurate quantification of CAs and their metabolites in biological samples is quite important and has attracted great research interest. However, due to their extremely low concentrations and numerous co-existing biological interferences, direct analysis of these endogenous compounds often suffers from severe difficulties. Employing suitable sample preparation techniques before instrument detection to enrich the target analytes and remove the interferences is a practicable and straightforward approach. To date, many sample preparation techniques such as solid-phase extraction (SPE), and liquid-liquid extraction (LLE) have been utilized to extract CAs and their metabolites from various biological samples. More recently, several modern techniques such as solid-phase microextraction (SPME), liquid–liquid microextraction (LLME), dispersive solid-phase extraction (DSPE), and chemical derivatizations have also been used with certain advanced features of automation and miniaturization. There are no review articles with the emphasis on sample preparations for the determination of catecholamine neurotransmitters in biological samples. Thus, this review aims to summarize recent progress and advances from 2015 to 2021, with emphasis on the sample preparation techniques combined with separation-based detection methods such capillary electrophoresis (CE) or liquid chromatography (LC) with various detectors. The current review manuscript would be helpful for the researchers with their research interests in diagnostic analysis and biological systems to choose suitable sample pretreatment and detection methods.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
1秒前
beenest完成签到,获得积分10
1秒前
吕不韦发布了新的文献求助10
1秒前
sxguo发布了新的文献求助10
1秒前
yuhanz发布了新的文献求助10
2秒前
轻松小之发布了新的文献求助10
3秒前
3秒前
烟花应助干净千青采纳,获得10
3秒前
小叮当完成签到,获得积分10
4秒前
方家竞发布了新的文献求助30
5秒前
缥缈三颜完成签到,获得积分10
7秒前
7秒前
7秒前
7秒前
9秒前
9秒前
day_on发布了新的文献求助10
10秒前
beenest发布了新的文献求助30
10秒前
小也完成签到 ,获得积分10
12秒前
12秒前
14秒前
飞飞飞发布了新的文献求助10
15秒前
15秒前
风趣煎蛋发布了新的文献求助10
16秒前
16秒前
uouuo完成签到 ,获得积分10
17秒前
冰子完成签到 ,获得积分0
17秒前
17秒前
最好的发布了新的文献求助10
19秒前
FatheadCarp发布了新的文献求助10
19秒前
20秒前
21秒前
王也夫发布了新的文献求助10
21秒前
John完成签到 ,获得积分10
23秒前
小白白完成签到 ,获得积分10
24秒前
24秒前
欧阳发布了新的文献求助10
26秒前
ahosre发布了新的文献求助10
26秒前
27秒前
28秒前
高分求助中
【请各位用户详细阅读此贴后再求助】科研通的精品贴汇总(请勿应助) 10000
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 1000
Global Eyelash Assessment scale (GEA) 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4044305
求助须知:如何正确求助?哪些是违规求助? 3582113
关于积分的说明 11385405
捐赠科研通 3309190
什么是DOI,文献DOI怎么找? 1821364
邀请新用户注册赠送积分活动 893691
科研通“疑难数据库(出版商)”最低求助积分说明 815809