Rapid Fingerprinting of Urinary Volatile Metabolites and Point-of-Care Diagnosis of Phenylketonuria on a Patterned Nanorod Sensor Array with Multiplexed Surface-Enhanced Raman Scattering Readouts
文献类型:期刊论文
作者 | Li, Zheng3; Lu, Xiaohui3; Zhang, Zhiyang1![]() |
刊名 | ANALYTICAL CHEMISTRY
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出版日期 | 2024-08-29 |
卷号 | 96期号:36页码:14541-14549 |
ISSN号 | 0003-2700 |
DOI | 10.1021/acs.analchem.4c02822 |
通讯作者 | Li, Zheng(zhengli24@szu.edu.cn) |
英文摘要 | Phenylketonuria (PKU) is one of the most common genetic metabolic diseases, especially among newborns. Traditional clinical examination of newborn blood samples for PKU is invasive, laborious, and limited to hospitals and healthcare facilities. We reported herein a SERS-based sensor array with three thiophenolic nanoreceptors built on a patterned nanorod vertical array for rapid and inexpensive detection of characteristic volatile biomarkers indicative of PKU in the urine and accurate classification of newborn baby patients all performed on a hand-held SERS spectrophotometer. The well-ordered array was generated from the volatility-driven assembly of gold nanorods (AuNRs) into an upright and closely packed hexagonal configuration. The uniformly distributed nanowells between AuNRs offered an intense and aspect-ratio-dependent plasmonic field for the molecular enhancement of SERS outputs. The SERS-based detector was integrated into a test chip for regular monitoring of volatile phenylketone bodies in the spiked solution or patients' urine within 5 min, allowing the quantification of a wide variety of normal or abnormal metabolites at their physiologically relevant concentration range. The detection limits for common biomarkers of PKU, including phenylpyruvic acid, 4-hydroxyphenylacetic acid, and phenylacetic acid, were at a few mu M and well below the diagnostic thresholds. Moreover, the volatile headspace mixtures from a given urine sample could be fingerprinted by the sensor array and discriminated using machine-learning algorithms. Ultimately, the discrimination of baby patients among 26 cases of mild and classic PKU phenotypes and 17 cases of healthy volunteers could be realized with an overall accuracy of 97%. This hand-held SERS platform plays a pivotal role in advancing healthcare applications in quick screening of neonatal PKU through a facile urinary vapor test. |
WOS关键词 | GOLD |
WOS研究方向 | Chemistry |
语种 | 英语 |
WOS记录号 | WOS:001308487300001 |
资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province for Distinguished Young Scholars ; Taishan Scholar Program of Shandong Province ; Shenzhen University 2035 Program for Excellent Research |
源URL | [http://ir.yic.ac.cn/handle/133337/35623] ![]() |
专题 | 烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室 烟台海岸带研究所_山东省海岸带环境工程技术研究中心 |
通讯作者 | Li, Zheng |
作者单位 | 1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Shandong Res Ctr Coastal Environm Engn & Technol, CAS Key Lab Coastal Environm Proc & Ecol Remediat,, Yantai 264003, Peoples R China 2.Gansu Prov Peoples Hosp, Clin Study & Evidence Based Med Inst, Lanzhou 730000, Peoples R China 3.Shenzhen Univ, Inst Adv Study, Shenzhen 518060, Peoples R China 4.Univ Jinan, Sch Mat Sci & Engn, Jinan 250022, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Zheng,Lu, Xiaohui,Zhang, Zhiyang,et al. Rapid Fingerprinting of Urinary Volatile Metabolites and Point-of-Care Diagnosis of Phenylketonuria on a Patterned Nanorod Sensor Array with Multiplexed Surface-Enhanced Raman Scattering Readouts[J]. ANALYTICAL CHEMISTRY,2024,96(36):14541-14549. |
APA | Li, Zheng,Lu, Xiaohui,Zhang, Zhiyang,Yan, Shuoyang,&Yang, Yunli.(2024).Rapid Fingerprinting of Urinary Volatile Metabolites and Point-of-Care Diagnosis of Phenylketonuria on a Patterned Nanorod Sensor Array with Multiplexed Surface-Enhanced Raman Scattering Readouts.ANALYTICAL CHEMISTRY,96(36),14541-14549. |
MLA | Li, Zheng,et al."Rapid Fingerprinting of Urinary Volatile Metabolites and Point-of-Care Diagnosis of Phenylketonuria on a Patterned Nanorod Sensor Array with Multiplexed Surface-Enhanced Raman Scattering Readouts".ANALYTICAL CHEMISTRY 96.36(2024):14541-14549. |
入库方式: OAI收割
来源:烟台海岸带研究所
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