Linker-Preserved Iron Metal–Organic Framework-Based Lateral Flow Assay for Sensitive Transglutaminase 2 Detection in Urine Through Machine Learning-Assisted Colorimetric Analysis

检出限 组织谷氨酰胺转胺酶 基质(化学分析) 色谱法 尿 再现性 氯化物 化学 线性范围 材料科学 生物化学 有机化学
作者
Mulya Supianto,Dong Kyu Yoo,Hagyeong Hwang,Han Bin Oh,Sung Hwa Jhung,Hye Jin Lee
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:9 (3): 1321-1330 被引量:24
标识
DOI:10.1021/acssensors.3c02250
摘要

A groundbreaking demonstration of the utilization of the metal-organic framework MIL-101(Fe) as an exceptionally perceptive visual label in colorimetric lateral flow assays (LFA) is described. This pioneering approach enables the precise identification of transglutaminase 2 (TGM2), a recognized biomarker for chronic kidney disease (CKD), in urine specimens, which offers a remarkably sensitive naked-eye detection mechanism. The surface of MIL-101(Fe) was modified with oxalyl chloride, adipoyl chloride, and poly(acrylic) acid (PAA); these not only improved the labeling material stability in a complex matrix but also achieved a systematic control in the detection limit of the TGM2 concentration using our LFA platform. The advanced LFA with the MIL-101(Fe)-PAA label can detect TGM2 concentrations down to 0.012, 0.009, and 0.010 nM in Tris-HCl buffer, urine, and desalted urine, respectively, which are approximately 55-fold lower than those for a conventional AuNP-based LFAs. Aside from rapid TGM2 detection (i.e., within 20 min), the performance of the MIL-101(Fe)-PAA-based LFA on reproducibility [coefficients of variation (CV) < 2.9%] and recovery (95.9-103.2%) along with storage stability within 25 days of observation (CV < 6.0%) shows an acceptable parameter range for quantitative analysis. A sophisticated sensing method grounded in machine learning principles was also developed, specifically aimed at precisely deducing the TGM2 concentration by analyzing immunoreaction sites. More importantly, our developed LFA offers potential for clinical measurement of TGM2 concentration in normal human urine and CKD patients' samples.
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
CrazyLion完成签到,获得积分10
1秒前
科研通AI6.3应助柴ab采纳,获得10
1秒前
4秒前
4秒前
5秒前
5秒前
招财发布了新的文献求助10
5秒前
了了了完成签到,获得积分10
6秒前
慈祥的怜翠完成签到 ,获得积分10
6秒前
愉快的真发布了新的文献求助10
6秒前
312完成签到,获得积分10
7秒前
yanyanmi完成签到 ,获得积分10
7秒前
hrbbdhr发布了新的文献求助30
9秒前
汉堡包应助312采纳,获得10
9秒前
10秒前
Lucas应助didi采纳,获得10
11秒前
蕨蕨发布了新的文献求助10
11秒前
12秒前
赘婿应助七彩螺旋采纳,获得10
12秒前
科研通AI6.3应助墨曦采纳,获得10
12秒前
hnlgdx发布了新的文献求助10
13秒前
Carlotta发布了新的文献求助30
13秒前
无花果应助SimmonsLI采纳,获得10
15秒前
zdp827完成签到 ,获得积分10
15秒前
hrbbdhr完成签到,获得积分10
15秒前
上官若男应助随机应变采纳,获得10
15秒前
chun发布了新的文献求助10
16秒前
CipherSage应助lynn采纳,获得50
18秒前
专注的玉米完成签到,获得积分10
18秒前
招财完成签到,获得积分10
18秒前
烟花应助袁小二采纳,获得10
19秒前
19秒前
隐形曼青应助Wiesen采纳,获得10
20秒前
猫毛发布了新的文献求助20
20秒前
21秒前
在水一方应助清秀小笼包采纳,获得10
22秒前
22秒前
leez发布了新的文献求助10
22秒前
Lxin完成签到 ,获得积分10
23秒前
英俊的铭应助chun采纳,获得10
24秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Pediatric Dermoscopy Trichoscopy & Onychoscopy 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
Additive Manufacturing Design and Applications (ASM Handbook, Volume 24A) 500
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 7570386
求助须知:如何正确求助?哪些是违规求助? 9150311
关于积分的说明 19570279
捐赠科研通 7155914
什么是DOI,文献DOI怎么找? 3263854
关于科研通互助平台的介绍 2429275
邀请新用户注册赠送积分活动 2253907