Microchimica Acta最新文献

A combined "immunochromatographic surface-enhanced Raman scattering (SERS) sensor-deep learning" detection strategy is proposed. Specifically, we first prepared an immunochromatographic SERS sensor with excellent surface enhancement capability for the specific recognition of neutrophil gelatinase-associated lipocalin (NGAL) molecules in serum, significantly enhancing the detection sensitivity of the target molecule. Subsequently, we integrated deep learning algorithms to perform preprocessing, feature extraction, and pattern recognition analysis on complex SERS spectral signals, overcoming the limitations of traditional algorithms in handling high-dimensional nonlinear data. This approach enables rapid, sensitive, and highly specific detection of NGAL. The platform achieves a detection limit as low as 0.1059 ng/mL with a broad linear detection range. For clinical validation, serum samples from 14 volunteers were analyzed. Six deep learning models were employed to classify the acquired clinical spectral data, with the Residual Network (ResNet) model achieving a classification accuracy of 98.81%, a loss value of only 0.0877, and an area under the receiver operating characteristic curve (AUC) of 99.97%. In addition, in the analysis of new samples, the classification and prediction of data from patients and normal individuals were successfully achieved. The proposed "immunoassay surface-enhanced Raman scattering sensor - deep learning" combined detection strategy has demonstrated extraordinary potential in the rapid, sensitive and specific detection of NGAL, providing crucial technical support for the intelligent discrimination of chronic kidney disease.

An on-chip electromembrane extraction integrated with solid-phase microextraction (EME-SPME) was developed for the determination and monitoring of anticancer drugs, including imatinib, irinotecan, and mitomycin C, in various biological fluids. The extraction device incorporated an electrospun polyacrylonitrile (PAN)/MOF-303 nanocomposite fiber, which functioned as the extraction phase. Simultaneous analyte migration under an applied electric field and adsorption onto the nanocomposite surface enabled efficient preconcentration within the compact microfluidic platform. Under optimized conditions, the proposed EME-SPME-HPLC-UV method exhibited low limits of detection ranging from 0.1 to 1.5 µg L^-1. Satisfactory linearity was achieved across the ranges 0.5-1000 ng mL^-1 for imatinib and 5-1000 ng mL^-1 for both irinotecan and mitomycin C, with coefficients of determination (R²) ≥ 0.9938. The method also demonstrated acceptable precision, with relative standard deviations (RSDs) ≤ 7.5%. The applicability of the system was investigated through the extraction of target analytes from human urine and plasma samples, yielding relative recoveries between 78 and 110%. These results highlight the potential of the developed EME-SPME platform as a sensitive, precise, and environmentally friendly technique for therapeutic drug monitoring of anticancer agents in complex biological matrices.

A dual-mode ratiometric fluorescent nanosensor was developed for ultrasensitive detection of glucose via simultaneous measuring pH and O₂ changes during enzymatic oxidation. The nanosensor integrates pH-sensitive fluorescent probe FITC and O₂-sensitive probe RuDP into hybrid nanoparticle core, with glucose oxidase (GOx) immobilized poly-L-lysine shell. During GOx-catalyzed reaction with glucose, the nanosensor gives rise of a decrease in pH-sensitive fluorescence due to acidification and an increase in O₂-sensitive fluorescence owing to oxygen depletion, enabling a dual-mode readout under single-excitation wavelength. The emission ratio between O₂-sensitive peak and pH-sensitive peak, employs signal amplification and results in high sensitivity, compared to single O₂ and pH testing. The ratiometric fluorescent signal exhibits a strong linear response in the 0-1 mM glucose range with a detection limit of 7.3 µM. The sensor shows robust performance and high reproducibility in DMEM and human sweat. In vivo imaging of zebrafish further confirms the photostability, and sensitivity of nanosensor, highlighting their utility for glucose detection. This study presents the first ratiometric fluorescence amplification strategy employing dual pH/O₂ testing for real-time glucose detection. The proposed nanosensor promotes non-invasive glucose detection in biomedical research and dual-mode ratiometric amplification strategies for monitoring metabolic targets.