利用mof衍生的双金属纳米立方杂交纳米片精确检测碳青霉烯耐药和高致病性肺炎克雷伯菌

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Dumei Ma, Yongqi Wang, Jiacheng Ye, Chao Xin, Chuan-Fan Ding, Yinghua Yan
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引用次数: 0

摘要

碳青霉烯耐药和高毒力代表肺炎克雷伯菌的两种不同的进化途径,在临床环境中提出了重大挑战。特别值得关注的是结合这两种特征的趋同菌株,使及时诊断和治疗复杂化。在此,我们提出了一种新的mof衍生的双金属纳米立方杂化纳米片(表示为Pt-G@Cu5Zn8C@Au),旨在增强激光解吸/电离质谱(LDI-MS),以区分会聚菌株与其他变体。这种新型材料是通过原始mof热解合成的,其特征是碳基体内均匀分布的Cu和Zn协同金属位点,解决了当前纳米材料在微生物细胞中原位提取代谢指纹的关键限制,如有限的灵敏度(如非晶硅、TiO2和金属纳米颗粒)或相对较弱的导电性和稳定性(mof基材料)。利用这种先进的基质,快速提取了248株肺炎克雷伯菌分离株的代谢指纹图谱,鉴定出23个vip评分最高的峰,作为区分会聚菌株与其变体的潜在生物标志物。结合机器学习,该预测模型使用SVM模型区分碳青霉烯敏感分离株(CS_cKP)和高毒分离株(hvKP)的准确率为100%,而使用KNN/NB模型区分碳青霉烯耐药分离株(CR_cKP)的准确率为78.26%。这些发现突出了我们的测定在区分会聚菌株及其变体方面的高准确性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Precise Detection of Carbapenem-Resistant and Hypervirulent Klebsiella pneumoniae Using MOF-Derived Bimetallic Nanocube Hybrid Nanosheet

Precise Detection of Carbapenem-Resistant and Hypervirulent Klebsiella pneumoniae Using MOF-Derived Bimetallic Nanocube Hybrid Nanosheet
Carbapenem resistance and hypervirulence represent two distinct evolutionary pathways in Klebsiella pneumoniae, posing significant challenges in clinical settings. Of particular concern are convergent strains that combine both traits, complicating timely diagnosis and treatment. Herein, we present a novel MOF-derived bimetallic nanocube hybrid nanosheet (denoted Pt-G@Cu5Zn8C@Au) designed to enhance laser desorption/ionization mass spectrometry (LDI-MS) in distinguishing convergent strains from other variants. The novel material, synthesized through the pyrolysis of pristine MOFs, features uniformly distributed Cu and Zn synergistic metal sites within the carbon matrix, addressing critical limitations of current nanomatrices for in situ extraction of metabolic fingerprints from microbial cells, such as limited sensitivity (e.g., amorphous silicon, TiO2, and metal nanoparticles) or relatively weak conductivity and stability (MOF-based materials). Utilizing this advanced matrix, the metabolic fingerprints of 248 K. pneumoniae isolates were rapidly extracted, identifying 23 top VIP-score peaks as potential biomarkers for differentiating convergent strains from their variants. Combined with machine learning, the prediction model achieved 100% accuracy in distinguishing convergent strains from carbapenem-sensitive isolates (CS_cKP) or hypervirulent isolates (hvKP) using the SVM model, while achieving 78.26% accuracy in differentiating them from carbapenem-resistant isolates (CR_cKP) with the KNN/NB models. These findings highlight the high accuracy and efficacy of our assay in distinguishing convergent strains from their variants.
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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