|
1
|
Yang RP, Tang Q, Yi HL, Tao Z, Xiao X, Zhao AT, Huang Y. A supramolecular fluorescence sensor array for the differentiation of multiple anions and prediction of iodine in artificial urine using machine learning. Mikrochim Acta 2024; 191:401. [PMID: 38884887 DOI: 10.1007/s00604-024-06489-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/04/2024] [Indexed: 06/18/2024]
Abstract
The simultaneous discrimination and detection of multiple anions in an aqueous solution has been a major challenge due to their structural similarity and low charge radii. In this study, we have constructed a supramolecular fluorescence sensor array based on three host-guest complexes to distinguish five anions (F-, Cl-, Br-, I-, and ClO-) in an aqueous solution using anionic-induced fluorescence quenching combined with linear discriminant analysis. Due to the different affinities of the three host-guest complexes for each anion the anion quenching efficiency for each host-guest complex was likewise different, and the five anions were well recognized. The fluorescence sensor array not only distinguished anions at different concentrations (0.5, 10, and 50 µM) with 100% accuracy but also showed good linearity within a certain concentration range. The limit of detection (LOD) was < 0.5 µM. Our interference study showed that the developed sensor array had good anti-interference ability. The practicability of the developed sensor array was also verified by the identification and differentiation of toothpaste brands with different fluoride content and the prediction of the iodine concentration in urine combined with machine learning.
Collapse
Affiliation(s)
- Ru-Pei Yang
- Key Laboratory of Macrocyclic and Supramolecular Chemistry of Guizhou Province, Guizhou University, Guiyang, 550025, China
| | - Qing Tang
- Department College of Tobacco Science, Guizhou University, Guiyang, 550025, China
| | - Hong-Ling Yi
- School of Pharmaceutical Sciences, Guizhou University, Guiyang, 550025, China
| | - Zhu Tao
- Key Laboratory of Macrocyclic and Supramolecular Chemistry of Guizhou Province, Guizhou University, Guiyang, 550025, China
| | - Xin Xiao
- Key Laboratory of Macrocyclic and Supramolecular Chemistry of Guizhou Province, Guizhou University, Guiyang, 550025, China
| | - An-Ting Zhao
- Key Laboratory of Macrocyclic and Supramolecular Chemistry of Guizhou Province, Guizhou University, Guiyang, 550025, China
| | - Ying Huang
- The Engineering and Research Center for Southwest Bio-Pharmaceutical Resources of National Education Ministry of China, Guizhou University, Guiyang, 550025, China.
| |
Collapse
|
|
2
|
Chen H, Guo S, Zhuang Z, Ouyang S, Lin P, Zheng Z, You Y, Zhou X, Li Y, Lu J, Liu N, Tao J, Long H, Zhao P. Intelligent Identification of Cerebrospinal Fluid for the Diagnosis of Parkinson's Disease. Anal Chem 2024; 96:2534-2542. [PMID: 38302490 DOI: 10.1021/acs.analchem.3c04849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal-organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.
Collapse
Affiliation(s)
- Huiting Chen
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
| | - Siyun Guo
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zehong Zhuang
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Sixue Ouyang
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
| | - Peiru Lin
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zhiyuan Zheng
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yuanyuan You
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiang Zhou
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
| | - Yuan Li
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
| | - Jiajia Lu
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Ningxuan Liu
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jia Tao
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China
| | - Hao Long
- Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Peng Zhao
- NMPA Key Laboratory for Research and Evaluation of Drug Metabolism, Guangdong Provincial Key Laboratory of New Drug Screening, Guangdong Provincial Key Laboratory of Cardiac Function and Microcirculation, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
| |
Collapse
|
|
3
|
Zhang M, Dalapati R, Shi J, Liao C, Tian Q, Wang C, Yang X, Chen S, Porter MD, Zang L. Fluorescent sensor based on solid-phase extraction with negligible depletion: A proof-of-concept study with amines as analytes. Anal Chim Acta 2023; 1245:340828. [PMID: 36737131 DOI: 10.1016/j.aca.2023.340828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/21/2022] [Accepted: 01/11/2023] [Indexed: 01/14/2023]
Abstract
This paper describes the development and proof-of-concept testing of an easy-to-use trace analysis technique, namely F-SPE, by coupling fluorescent sensor with solid phase extraction (SPE). F-SPE is a two-step methodology that concentrates an analyte from a liquid sample onto a fluorophore-modified membrane and measures the amount of analyte from the extent the extracted analyte quenches the emission of the fluorophore. By applying the principle of negligible depletion (ND) intrinsic to SPE, the procedure of F-SPE for analyzing a sample can be markedly simplified while maintaining the ability to detect analytes at low limits of detection (LOD). The merits of this approach are demonstrated by impregnating a SPE membrane with a perylene diimide (PDI) fluorophore, N,N'-di(nonyldecyl)-perylene-3,4,9,10-tetracarboxylic diimide (C9/9-PDI), for the low-level detection of organic amines (e.g., aniline) and amine-containing drugs (e.g., Kanamycin). The sensing mechanism is based on the donor-acceptor quenching of PDI by amines, which, when coupled with the concentrative nature of SPE, yields LODs for aniline and Kanamycin of 67 nM (∼6 ppb) and 32 nM (∼16 ppb), respectively.
Collapse
Affiliation(s)
- Miao Zhang
- School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Shaanxi, Xi'an, 710021, China
| | - Rana Dalapati
- Nano Institute of Utah, University of Utah, Salt Lake City, UT, 84112, USA; Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, 84112, USA
| | - Jiangfan Shi
- Nano Institute of Utah, University of Utah, Salt Lake City, UT, 84112, USA; Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, 84112, USA
| | - Chenglong Liao
- School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Shaanxi, Xi'an, 710021, China
| | - Qingyun Tian
- School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Shaanxi, Xi'an, 710021, China
| | - Chuanyi Wang
- School of Environmental Science and Engineering, Shaanxi University of Science and Technology, Shaanxi, Xi'an, 710021, China
| | - Xiaomei Yang
- Nano Institute of Utah, University of Utah, Salt Lake City, UT, 84112, USA
| | - Shuai Chen
- Jiangxi Engineering Laboratory of Waterborne Coating, Jiangxi, Nanchang, 330013, China.
| | - Marc D Porter
- Nano Institute of Utah, University of Utah, Salt Lake City, UT, 84112, USA; Department of Chemical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
| | - Ling Zang
- Nano Institute of Utah, University of Utah, Salt Lake City, UT, 84112, USA; Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
| |
Collapse
|