BIOSENSORS & BIOELECTRONICS
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2015
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IF 6.409
Xiaoli Qin,Yan Yin,Huijing Yu,Wenjuan Guo,Meishan Pei
An ultrasensitive electrochemical aptasensor for the quantitative detection of kanamycin antibiotic was fabricated based on a novel signal amplification strategy. This aptasensor was developed using thionine functionalized graphene (GR-TH) and hierarchical nanoporous (HNP) PtCu alloy as biosensing substrates for the first time. HNP-PtCu alloy with controllable bimodal ligament/pore distributions was successfully prepared by two-step dealloying of a well-designed PtCuAl precursor alloy combined with an annealing operation. GR-TH composite was synthesized by one-step reduction of graphene oxide (GO) in TH solution. Greatly amplified sensitivity was achieved by using GR-TH/HNP-PtCu composite owing to its large specific surface and good electron-transfer ability. Under the optimized conditions, the proposed aptasensor exhibited a high sensitivity and a wider linearity to kanamycin in the range 5×10 −7 –5×10 −2 μg mL −1 with a low detection limit of 0.42 pg mL −1 . This aptasensor also displayed a satisfying electrochemical performance with good stability, selectivity and reproducibility. The as-prepared aptasensor was successfully used for the determination of kanamycin in animal derived food.
研究领域:
分析化学
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生物传感技术
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食品安全检测
✓ 已验证
DOI
高影响因子
Advanced Healthcare Materials
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2025
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IF 9.6
Kexin Meng,Yao Shen,Dongni Hou,Hanbing Hu,Shouzhi Yang,Ziyue Zhang,Ayizekeranmu Yiming,Dingyitai Liang,Weibin Tian,Ludan He,Shuoyan Wei,Ying Wang,Jie Shen,Yuanlin Song,Sai Gu,Haiyang Su,Jian Zhou,Kun Qian
Rapid and accurate recognition of severe community-acquired pneumonia (CAP) would facilitate the optimal intervention. Currently, the diagnosis of severe CAP is commonly based on the criteria established by Infectious Disease Society of America (IDSA)/American Thoracic Society (ATS), which include 2 primary and 9 secondary criteria, making the process cumbersome and time-consuming. Here, a porous PtCu alloy-assisted laser desorption/ionization mass spectrometry (LDI MS) is designed for the extraction of plasma metabolic fingerprints (PMFs), coupling with machine learning for the diagnosis of severe CAP. The PtCu alloys with optimal particle size exhibit excellent sensitivity, reproducibility, and universality for metabolite detection, due to the porous structure, promising photoelectric effect, and improved melting surface structure. Further, the nanoplatform successfully records the PMFs within seconds, using only 0.5 µL native plasma. Machine learning of PMFs on 69 individuals produces a diagnostic model with an area under curve (AUC) of 0.832. Particularly, a three metabolic biomarker panel demonstrates enhanced diagnostic efficiency (AUC of 0.846), outperforming reported biomarkers (AUC of 0.560–0.770). Notably, the diagnosis can be completed in ≈35 min. The work affords a rapid and precise method for CAP management through metabolite analysis.
研究领域:
医学诊断
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代谢组学
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材料科学
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人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）