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Chromatography is a cornerstone methodology employed for quality evaluation of traditional Chinese medicine (TCM). However, the complexity of chromatographic data, where signals from multiple compounds overlap and interfere, often impedes the accurate identification of chemically significant features. This study proposed an integrated approach that combines multidimensional chromatographic fingerprinting with machine learning to trace the molecular origins of characteristic compounds in a representative TCM formula, Fufang E'jiao Jiang (FEJ). Following comprehensive chemical profiling, we constructed multi-dimensional datasets from chromatographic fingerprints, including TLC and LC-HRMS, with each dataset encompassing over 1700 features derived from retention time, m/z, and RGB values. Machine learning algorithms, such as random forest, were employed to select discriminative features, leading to the identification of 5 patterns in FEJ and 7 patterns in its intermediate products, primarily identified as ginsenosides. A simulation model further verified the significance of these features, showing that a single compound's chromatographic spot could effectively represent sample characteristics. We also introduced modified entropy values and obstacle factors to evaluate and weight the selected features. As a result, lobetyolin and ginsenoside Rf were recognized as key quality-related markers in FEJ and its intermediates, respectively. Experimental verification showed that this method can effectively deconvolute overlapping chromatographic signals and identify key quality-related features, providing an efficient and scalable computational framework for quality control in complex systems. In summary, this strategy is based on a general data structure and modular algorithm design, and hopefully to be applied to any sample system with complex chromatographic fingerprints (such as drug, environmental or food samples), without relying on specific domain knowledge.

A novel loose-multilayer polydimethylsiloxane/reduced graphene oxide composite (lmPDMS/rGO) was synthesized and immobilized onto a stainless steel wire to fabricate a solid-phase microextraction (SPME) fiber. The fiber exhibited a uniform multilayer structure, high thermal stability, mechanical robustness, and excellent electrical conductivity. It also demonstrated outstanding durability, remaining effective for up to 150 uses. Moreover, the fiber showed remarkable extraction efficiency for amphetamine-type stimulants (ATSs) in urine samples under an applied electric field. Analyte separation and quantification were performed using gas chromatography with a nitrogen phosphorus detector. Key extraction parameters (applied voltage, extraction time, stirring speed, and pH) were systematically optimized. Under the optimized conditions, calibration curves (5-500 ng L^-1) showed coefficients of determination >0.990, with relative recoveries of 82 %-105 %. Limits of detection (S/N = 3) and quantification (S/N = 10) ranged from 0.5 to 2.7 ng L^-1 and 1.8-9.1 ng L^-1, respectively. Intra- and inter-fiber RSDs were 3.9 %-6.2 % and 4.2 %-10.7 %, respectively. This study offers a promising strategy for designing functional PDMS-based composites with well-defined structure-performance relationships for enhanced SPME applications.

We developed the first fully automated rotating disk extraction system with flow control that uses hydrophobic natural deep eutectic solvents to preconcentrate polycyclic aromatic hydrocarbons from drinking water. The core innovation lies in the seamless combination of a porous polyetherimide disk, pre-coated with a thymol/carvone natural deep eutectic solvents layer, with an automated dynamic flow platform, uniting the advantages of rapid mass transfer via disk rotation and the precision, reproducibility, and scalability of flow-based automation. Systematic optimization of key operational parameters, including rotation speed, sample flow rate, extraction time, and eluent volume, enabled highly efficient analyte capture and release, achieving enrichment factors up to 6 in static mode, with detection limits as low as 0.1-6.9 μg L^-1. The automated setup demonstrated high analytical performance, delivering intra-day and inter-day precision (RSD ≤6.4 % and ≤12 %, respectively) and recovery rates of 85-114 % across diverse real-world matrices, including tap water, bottled water, and black tea infusions. Beyond experimental validation, all-atom classical molecular dynamics simulations provided mechanistic insight, revealing strong π-π interactions between analytes and thymol that drive efficient phase transfer within the NADES layer. Environmental sustainability was quantitatively assessed using the AGREEprep metric, yielding a favorable score of 0.71, largely due to the substitution of hazardous solvents with green NADES and the minimized solvent footprint enabled by automation. Overall, this study delivers a robust, automated, and environmentally responsible platform, establishing a new method for liquid-phase microextraction that integrates automation and green chemistry. This platform offers broad potential for sensitive, scalable monitoring of environmental, food, and industrial samples.

3D printing has revolutionized analytical chemistry by allowing the development of miniaturized and high-precision devices. In bioanalysis, sample collection and pre-treatment can be facilitated using 3D printing combined with flow analysis and mass spectrometry. Hence, a customized 3D-printed device was designed for sampling, clean-up, and target retrieval using filter paper as a sample collection material and sorbent. This device was integrated into a flow network for fully automatic extraction and applied to detect three anticoagulants in human urine. Different printing materials, designs and other factors related to fused deposition modeling 3D printing such as the infill percentage were evaluated to achieve the configuration that allowed the implementation of sampling and separation procedures in the same device. After establishing the final design of the device, several parameters such as the eluent composition and the hydrodynamic conditions were studied to enhance the recovery of the target analytes, namely apixaban, rivaroxaban, and warfarin. The processed samples were analyzed by UHPLC-MS/MS in positive ionization mode using a BEH C18 column. The method demonstrated good linearity (r² > 0.998) for quantification of the target analytes at concentrations ranging from 0.20 to 20 μg L^-1 for apixaban and warfarin, and from 0.50 to 20 μg L^-1 for rivaroxaban. LOD and LOQ values of 0.06-0.2 μg L^-1 and 0.1-0.5 μg L^-1, respectively, for undiluted urine were obtained. The method was found to be accurate (97.0-102 %) and precise (CV ≤ 6.0 %). This new approach, according to the scores obtained by applying the AGREEprep (0.70), AGREE (0.65) and BAGI (70) metrics, can be described as environmentally friendly, practical and suitable for bioanalytical purposes.

Neutrophil gelatinase-associated lipocalin (NGAL) is significantly released in the serum and urine of acute kidney injury (AKI) patients as early as 2 h after the onset of renal damage, making it a potential candidate as an early biomarker for AKI monitoring and prognosis. An electrochemical aptasensor for NGAL detection was developed within a wide linear range of 5.0-320.0 ng/mL in PBS solution and a limit range of 5.0-25.0 ng/mL in artificial urine. Limits of detection (LODs) of square wave voltammetry (SWV) mode in PBS and artificial urine were at 0.81 and 4.59 ng/mL, respectively, while electrochemical impedance spectroscopy (EIS) measurements were observed at 0.48 and 5.27 ng/mL in PBS and artificial urine samples, respectively. Particularly, the dual measurement mode integrated with a multiple linear regression (MLR) model achieved an improved LOD of 3.27 ng/mL in urine matrix. NGAL aptasensor offered admirable recovery performance in a range of 93.0% and 126.0% with the relative standard deviations (RSDs) between 1.162% and 4.625%. The aptasensor was highly selective for NGAL across urinary matrices, while it tolerated potential interferences (i.e., IGFBP-7, BSA, ascorbic acid, and glucose) and offered stable performance for up to 14-21 days of storage. Its label-free, portable, and sensitive performance makes it a promising candidate for NGAL point-of-care testing (POCT) applications, enabling rapid and reliable detection of AKI status in clinical and field settings.

To improve agricultural productivity and protect crops from pests, farmers often use pesticides. However, the widespread use of toxic organophosphate pesticides (OPPs) necessitates rapid, accurate, and cost-effective detection methods. This study detects Ethion, Malathion, and Phosalone in agricultural samples using Portable Ion Mobility Spectrometry (P-IMS) data combined with the N-mode PLS (N-PLS) algorithm. The N-PLS model demonstrated high accuracy, with adjusted R² values of 0.991 (Ethion), 0.999 (Malathion), and 0.998 (Phosalone), along with strong performance in RMSEP, log-Likelihood, and RPD metrics. The model reliably determined pesticide concentrations and avoided overfitting. Test sample recoveries ranged from 99.5 % to 100.5 %, highlighting the N-PLS method's second-order advantage and its effectiveness as a robust tool for OPP detection in agriculture.

Liquid biopsy has emerged as a promising alternative for glioma detection; however, the blood-brain barrier restricts the abundance of circulating tumor cells (CTCs) in peripheral blood, posing a significant challenge for sensitive CTCs-based diagnostics of glioma. To address this limitation, we present an Angiopep2 (Ang-2) peptide linked DNA walker assay for colorimetric glioma cell detection with high sensitivity and specificity. This system integrates Ang-2 and a cholesterol labeled tracker (cholesterol-tracker) as dual-targeting ligands, coupled with a DNA walker-mediated signal amplification strategy for enhanced sensitivity and specificity. Compared to conventional immunoassays relying on monoclonal antibodies, our platform demonstrates superior performance due to its preferential binding to intact glioma cells and the signal amplification induced by the λ-exonuclease-driven DNA walking mechanism. The biosensor achieved an exceptionally low detection limit of 0.12 cells/mL and exhibited strong anti-interference capabilities across multiple cell lines. Furthermore, the biosensor maintained ≥96 % of initial signal retained after 21 days at 4 °C (96.5 % ± 0.35, n = 3), with only marginal signal attenuation observed. Clinical validation using constructed serum samples revealed excellent agreement in glioma concentration calculated by the proposed sensing platform and added glioma cell concentrations. Collectively, our findings demonstrate that this sensing platform represents a rapid, sensitive, specific, and portable technology for the detection of U251 glioblastoma cells, with potential applications in clinical diagnostics of glioma.

Enantiomeric differentiation of chiral diols remains analytically challenging, particularly for aliphatic diols and complex mixtures. We report a ¹⁹F NMR-based three-component derivatization system combining fluorinated arylboronic acids with chiral amino alcohols to enable sensitive, high-resolution chiral analysis. By optimizing fluorine position and amino alcohol structure, we achieved excellent enantiomeric resolution across a range of diols, with resolution values up to 98.88. In some cases, simplified ¹⁹F signal patterns were rationalized by computationally predicted thermodynamic preferences among diastereomeric adducts. The method accurately determined enantiomeric excess in both model mixtures and crude Sharpless asymmetric dihydroxylation products, closely matching chiral HPLC results. Notably, it enables simultaneous detection of multiple diols without signal overlap, outperforming conventional ¹H NMR methods. This approach highlights the potential of boronic acid-amino alcohol systems as versatile platforms for high-throughput chiral analysis using ¹⁹F-{¹H} NMR.

Residual host cell proteins (HCPs) in biopharmaceutical production processes not only compromise drug efficacy but also pose risks to patient safety and product stability, particularly when high-risk HCPs are present. Continuous monitoring of HCPs content and species during downstream purification is therefore critical. Although liquid chromatography-mass spectrometry-based proteomics has emerged as a promising approach for HCP identification, its application is hindered by the substantial dynamic range disparity between high-abundance therapeutic proteins and trace-level HCPs (at parts-per-million, ppm, concentrations). Here, we developed an innovative workflow that eliminates conventional therapeutic protein pre-separation steps for increasing HCP detection. By integrating a two-stage native digestion strategy with molecular weight cutoff filtration, efficient HCP separation and enrichment were achieved. Mass spectrometry data were acquired in data-independent acquisition mode and processed using Spectronaut software for spectral library construction. This integrated approach enabled sensitive detection of HCPs down to 0.5 ppm and continuous quantitative tracking of critical impurities such as the high-risk protein PLBD2, thereby providing real-time monitoring of antibody purification processes and supporting process optimization. Comparative studies with ELISA demonstrated superior sensitivity and specificity of our approach, while systematic method validation confirmed its compliance with bioanalytical requirements, establishing the robustness of the proposed methodology.

Microfluidic sensing platforms have emerged as essential tools for addressing global needs across various sectors, including environmental monitoring and clinical diagnostics. These applications require devices with selective, sensitive, and portable capabilities that offer multifunctionality. This review focuses on microfluidic devices as an effective tool for environmental and clinical applications. We present the evolution of these platforms, examining their transition from classical to innovative designs and their critical roles in enabling precise measurements. Particular attention is given to various patterns featuring operational units, such as integrated valving systems for controlling fluid flow, reagent storage units, separation or delay zones, and detection units. In addition, the utilization of innovative materials, transitioning from polydimethylsiloxane-based devices to transparent film-based alternatives to overcome the limitations of traditional microfluidic systems, is covered. Here, heavy metals, pesticides, microorganisms, nutrients, polyfluoroalkyl substances, and gases are presented as target analytes for environmental analysis, whereas analyses of nucleic acids, proteins, antigens, and antibiotics are demonstrated for clinical diagnostics. Future perspectives and challenges in advancing microfluidic-based sensing platforms are also discussed.