Analytical Chemistry最新文献

Nanozymes feature structural stability, functional diversity, and tunable activity, demonstrating promise in enzyme-nanozyme cascade systems for small molecule detection. However, the cascade performance is typically hindered by pH incompatibility and the cumbersome preparation of nanozymes. Herein, copper-doped zeolitic imidazolate framework-90 (Cu/Zn-ZIF-90) was developed through a facile synthesis within 10 min of reaction, which was established as a promising ascorbate peroxidase (APX)-like nanozyme for pH-adaptive cascades. Cu/Zn-ZIF-90 oxidizes ascorbic acid using H₂O₂ as cosubstrate and demonstrates higher affinity for H₂O₂ than most APX-like or POD-like nanozymes, enabling effective intermediate conversion in cascade reactions. Notably, Cu/Zn-ZIF-90 nanozyme exhibits optimal activity under neutral pH, resolving pH mismatch when coupled with acid-denatured oxidases. As proof of concept, we integrated APX-like Cu/Zn-ZIF-90 with choline oxidase to develop a one-step cascade fluorescence biosensor for choline detection. The sensor achieved a broad linear range (1–1000 μM) and a low detection limit (0.85 μM), outperforming most existing methods while demonstrating robust applicability in the analysis of complex food matrices. This work establishes APX-mimicking nanozymes as key components to overcome cascade pH barriers, enabling one-step small-molecule biosensing.

Early detection of breast cancer remains challenging due to limitations of current screening methods, including reduced sensitivity in dense tissue, false positives that lead to additional imaging and invasive biopsies. Untargeted metabolomics using noninvasive matrices such as urine has emerged as a promising complementary approach. In this study, a voltammetric electronic tongue consisting of 12 sensors, bare and modified with three isomeric conjugated polymers, was developed to transduce urinary metabolomic differences into electrochemical fingerprints. Performance was first evaluated on artificial urine and then tested on a larger set of clinical specimens. Differential pulse voltammetry signals were preprocessed to reduce dimensionality, analyzed by PCA and PLS-DA for pattern recognition and outlier detection, and classified into cancer and control groups using a range of linear, nonlinear, and ensemble-based supervised learning. On artificial urine, PCA showed clear separation, and gradient boosting achieved the highest test accuracy (96%). In clinical urine, separation by PCA was less pronounced, whereas PLS-DA and supervised models improved discrimination, with gradient boosting yielding 97% accuracy. Overall, the results show that the proposed electronic tongue captures clinically relevant urinary signatures and that supervised methods are advantageous when moving from artificial to real-world samples.

Identification of hemoglobin (Hb) variants is of significant value in the clinical diagnosis of hemoglobinopathies. Conventional methods used to identify Hb variants in clinical laboratories can narrow down the range of candidates for a Hb variant sample but are unable to pinpoint the exact Hb variant. In this study, Next-Generation Protein Sequencing (NGPS), a semiconductor chip-based single-molecule protein sequencing (SMPS) technology, was explored as a novel method to identify Hb variants. Two heterozygous Hb variant samples underwent NGPS analysis. Proteotypic peptides corresponding to Hb variants were successfully detected, enabling the identification of the samples as Hb Handsworth (Hb α-Handsworth subunit G18R) and Hb G-Accra (Hb β-G-Accra subunit D73N). The NGPS method has been demonstrated as a potential tool to identify Hb variants. Although there are still limitations to overcome for the wide adoption of NGPS, this exploration supports the potential use of NGPS and other SMPS technologies in clinical applications.