Analytical Methods最新文献

The ultrasensitive detection of microRNAs in bodily fluids is crucial for early diagnosis of cancer. Herein, we design an electrochemical biosensor based on a dual-signal amplification strategy utilizing reversible addition-fragmentation chain transfer (RAFT) and ring-opening metathesis polymerization (ROMP) to achieve ultra-sensitive quantitative detection of miRNA-21. The sensing interface starts from a gold electrode carrying a thiol-immobilized peptide nucleic acid (PNA) capture probe. Once miRNA-21 hybridizes specifically with the PNA, we anchor the RAFT chain-transfer agent 2-(dodecylthiocarbonothioylthio)-2-methylpropionic acid (CDTPA) onto the duplex by taking advantage of Zr⁴⁺ coordination chemistry. With the azo initiator 2,2'-azobis[2-(2-imidazolin-2-yl) propane] dihydrochloride (VA-044), in solution, the monomer dicyclopentenyloxyethyl acrylate (DCPEA)-which bears a cyclopentene side group-undergoes RAFT polymerization, decorating the duplex with polymer chains that bristle with pendant cyclopentene rings. Subsequently, the ruthenium-based catalyst AquaMet ((4-((4-ethyl-4-methylpiperazin-1-ium-1-yl)methyl)-1,3-dimesitylimidazolidin-2-ylidene)dichloro(2-isopropoxybenzylidene) ruthenium(II)) chloride coordinates with the cyclopentene groups, catalyzing the ROMP of cyclopentene-based ferrocene (CFc), thereby forming a polymer brush rich in ferrocene electroactive molecules on the electrode surface, achieving efficient two-stage amplification of the electrochemical signal. Under optimized conditions, the sensor returns a linear response to miRNA-21 from 10^-17 M to 10^-13 M and reaches a detection limit as low as 4.72 aM. Signals from single-mismatched RNA, triple-mismatched RNA, and miRNA-320b were all strongly suppressed relative to the target, while the sensor also proved to be stable in one week and remarkably unfazed by complex biological backgrounds.

Methylmercury (CH₃Hg⁺) is the most toxic form of mercury found in fish, shellfish, and other aquatic organisms and poses significant risks to human health through the food chain. The present study reports the development of a fluorescence aptasensor for the quantification of CH₃Hg⁺ using aptamer-functionalized gold nanostars (AuNSs) and complementary DNA-functionalized alendronic acid-modified upconversion nanoparticles (ADA-UCNPs). The ADA-UCNPs-AuNSs fluorescence aptasensor employed an on-off detection mode and completed the detection within 60 min. The developed fluorescence aptasensor exhibited a good linear response to CH₃Hg⁺ over the concentration range of 0.5 to 20.0 µM, with a limit of detection of 0.37 µM. The aptasensor exhibited satisfactory accuracy and precision, with relative standard deviations within the evaluated CH₃Hg⁺ concentration range and recoveries of 91.00% to 99.20% in aquatic products. The newly constructed aptasensor also showed satisfactory selectivity and resistance to interference and maintained good stability, supporting its potential for practical application in food monitoring.

This work presents a downscaled and resource-efficient multi-analytical workflow for the parallel determination of polar indicators (aliphatic amines, short-chain dicarbonyls, malondialdehyde) and combustion tracers (PAHs) using micro-aliquots of PM₂.₅ filters (0.07 g, equivalent to ∼60 m³ of sampled air). By implementing dedicated chemical derivatization protocols (Fmoc-Cl for amines and acid-catalyzed DNPH for dicarbonyls) alongside direct solvent extraction for PAHs, the method minimizes sample consumption and solvent waste while achieving limits of detection in the low pg m^-3 to ng m^-3 range. Procedural optimization ensured robust intermediate precision (<12% RSD across target analytes) and controlled reagent hydrolysis artifacts. The analytical performance was validated through a three-day field study during a sharp meteorological transition, demonstrating that the downscaled strategy effectively resolves short-term diurnal shifts and local chemical transformations from minimal sample mass without requiring full-filter destruction.

The lack of robust early-stage molecular biomarkers severely limits the timely diagnosis of non-small cell lung cancer (NSCLC). To address this analytical challenge, we developed an integrated workflow combining serum N-glycan profiling with machine learning (ML) algorithms to explore the discriminatory patterns of serum N-glycan profiles between NSCLC and control groups and across disease stages. N-Glycans were analyzed using ultra-high-performance liquid chromatography with hydrophilic interaction chromatography and fluorescence detection (UHPLC-HILIC-FLD). The resulting high-dimensional chromatographic data from patient and control cohorts were subjected to statistical and ML analyses. Serum glycan profiling revealed marked dysregulation of the glycome. Twenty of the 28 N-glycan peaks showed significant cancer-associated alterations, with selected features also exhibiting stage-specific differences. Notably, relative quantitation showed that the agalactosylated core-fucosylated glycan G1 (H3N4F1) increased 2.55-fold in the cancer cohort. To evaluate the diagnostic utility of these glycomic profiles, nine ML models were trained and evaluated using 5-fold cross-validation. A neural network (NN) model demonstrated strong classification performance, achieving an area under the curve (AUC) of 0.93 for binary classification (NSCLC vs. healthy controls) and 0.91 for three-class classification. Furthermore, SHapley Additive exPlanations (SHAP) analysis elucidated the model decision-making process, identifying glycans G7 (H5N4), G23 (H6N5S3), and G2 (H3N5F1 and H5N2) as key features contributing to classification. These findings indicate that integrating UHPLC-HILIC-FLD serum glycomic profiling with explainable machine learning provides an exploratory analytical framework for identifying candidate glycan features associated with NSCLC status and stage.

Manganese ferrite (MnFe₂O₄) is a promising electrode material because of its redox-active Mn and Fe centers, chemical stability, and tunable spinel structure. However, its practical use in electrochemical sensing may be limited due to its poor electrical conductivity, particle aggregation, and limited access to active sites. This review discusses recent advances in the fabrication of pristine and hybrid MnFe₂O₄-based electrochemical sensors. It is understood that synthesis methods, surface morphology, crystallinity, defects, and interfacial composition influence sensing performance. Various preparation methods such as hydrothermal, sol-gel, co-precipitation, microwave-assisted, sonochemical, electrodeposition, and thermal decomposition have been briefly discussed. The analytical sensing performance of MnFe₂O₄ incorporated with carbon materials, polymers, metal-organic framework-derived materials, chitosan, and noble-metal nanoparticles (NPs) has been discussed for the detection of heavy metals, hydrogen peroxide, pharmaceuticals, pesticides, endocrine disruptors, and biomolecules. It is believed that MnFe₂O₄-based hybrid materials may exhibit improved conductivity, active surface area, analyte adsorption, and selectivity. Therefore, MnFe₂O₄-based hybrid materials demonstrate enhanced sensing performance. Subsequently, challenges and future perspectives have been discussed.

N-Acetylneuraminic acid (Neu5Ac) is a bioactive compound that supports health across all age groups and plays an irreplaceable physiological role in food fortification and disease prevention and control. Herein, N-acetylneuraminic acid-functionalized gold nanoparticles (Neu5Ac-AuNPs) signal probes with peroxidase-like catalytic activity were synthesized and, based on these probes, a novel paper-based colorimetric aptasensor integrated with a smartphone was developed for the intuitive and highly sensitive detection of Neu5Ac. The sensing mechanism relies on the competitive binding between free Neu5Ac and the Neu5Ac-AuNPs signal probes for the binding sites on the aptamer immobilized on the paper substrate. Coupled with a TMB-H₂O₂ colorimetric signal amplification system, the assay employs a smartphone to capture colorimetric images and extract grayscale values for quantitative analysis of Neu5Ac. The developed sensor achieved a limit of detection (LOD) of 4.99 µM and a limit of quantification (LOQ) of 5 µM and was successfully applied to milk and beef samples, yielding spiked recoveries ranging from 92.80% to 102.30%. This work provides a promising strategy for the simple, rapid, and reliable on-site detection of Neu5Ac in food matrices.

Cysteine (Cys) was an essential biothiol whose dysregulation is linked to neurotoxicity, liver injury, childhood growth retardation, and cardiovascular diseases, making its accurate detection critical for clinical diagnosis and biochemical research. Although excited-state intramolecular proton transfer (ESIPT)-based fluorescent probes offer advantages such as a large Stokes shift and low background interference, conventional ESIPT probes struggled to distinguish Cys from structurally similar homocysteine (Hcy) and glutathione (GSH). Herein, we rationally designed a novel ESIPT probe named LYB-Cys via Knoevenagel condensation, using 2-(2'-hydroxyphenyl)benzoxazole (HBO) as the electron donor and 2-(benzo[d]thiazol-2-yl)acetonitrile (BTAN) as the electron acceptor. The probe specifically responded to Cys through a conjugate addition-cyclization reaction, producing a ratiometric fluorescent signal with excellent selectivity over Hcy, GSH, and other interferents. Owing to this performance, LYB-Cys enabled quantitative Cys detection in milk and fetal bovine serum, superior to Ellman's method for thiol detection. It also provided a robust molecular platform that bridged fundamental probe design with practical applications in food quality control and biochemical analysis.

This review systematically examines and critically evaluates the development of solid-substrate colorimetric sensors for gaseous formaldehyde detection over the past two decades, with a focus on the effects of sensing-molecule reaction mechanisms, substrate properties, and testing conditions on sensing performance. Based on formaldehyde recognition mechanisms, the sensing systems are classified into five groups: 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole (AHMT), hydroxylamine sulfate, primary amines, electron-rich carbonyl compounds, and metal-based systems. Comparative analysis shows that AHMT, hydroxylamine sulfate, and electron-rich carbonyl compound systems can achieve low-concentration formaldehyde detection. The chemical properties of sensing molecules primarily determine formaldehyde recognition mechanisms and color response, whereas substrate pore structure, specific surface area, surface properties, and mass-transfer characteristics influence sensing-molecule immobilization, formaldehyde diffusion, and interfacial reactions. Further analysis indicates that substrate properties do not exhibit a simple monotonic relationship with sensing performance. Similar substrate characteristics may produce different effects across sensing systems, and these differences are jointly influenced by sensing-molecule properties, sampling methods, and temperature and humidity conditions. In addition, sampling mode, temperature, humidity, and signal readout methods can affect sensor performance and complicate comparisons among reported systems. Overall, solid-substrate colorimetric sensors offer simplicity, cost-effectiveness, and visual detection, but challenges remain in long-term stability, environmental adaptability, and standardized performance evaluation. Future research should focus on the synergistic design of sensing molecules and substrates, optimization of sensing layers and interfaces, and standardized methods for sampling, color-signal readout, and performance evaluation to improve data comparability, detection reliability, and practical applicability.

Hepatitis screening is clinically challenging. Conventional serological and biochemical assays rely on cumbersome procedures, require long detection times, and lack sufficient sensitivity to identify pathological changes. Surface-enhanced Raman scattering (SERS) enables rapid, label-free, and high-sensitivity molecular fingerprinting of biological fluids. It holds great promise for noninvasive serum biochemical analysis. Here, we report a machine learning-enhanced SERS platform based on a sugar-cube-like Ag nanocrystal (SC-AgN) substrate for accurate discrimination between healthy and DMN-induced hepatitis mouse serum. We fabricated the SC-AgN substrate with favourable uniformity, reproducibility and SERS enhancement to capture robust and well-resolved spectra from mouse serum samples. Clear spectral differences were identified in hepatitis serum, which correspond to protein denaturation, lipid peroxidation, and disturbed nucleic acid metabolism. Principal component analysis (PCA) was applied to reduce dimensionality, remove spectral noise, and eliminate redundant spectral features. The optimized spectral variables were further imported into a sparse partial least-squares discriminant analysis (SPLS-DA) model for precise sample classification. The finalized PCA-SPLS-DA model achieved an accuracy of 96.7%, a sensitivity of 100.0%, a specificity of 94.6%, and an AUC of 0.973 (95% CI, 0.945-0.992) for distinguishing DMN-induced hepatitis from healthy serum samples. This diagnostic performance outperforms that of conventional spectroscopic approaches. This work establishes a robust SC-AgN-based SERS diagnostic strategy integrated with the PCA-SPLS-DA model, which is expected to provide a rapid, low-cost and high-accuracy solution for hepatitis screening and auxiliary clinical diagnosis.

Antimicrobial resistance (AMR) is a global human health threat and wastewater analysis of antibiotics can help inform AMR policies. Accurate quantification of antibiotic use in urban populations is needed. This requires analytical methods capable of high sample throughput to support short-term laboratory antibiotic fate investigations where large sample numbers are generated, and sub-µg L^-1 quantification limits for wastewater monitoring studies. Therefore, a direct injection ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was developed for 11 representative antibiotics and metabolites with a 13 min run time and quantitation limits in the range of 0.0012-0.69 µg L^-1. Avoiding solid phase extraction simplified sample preparation whilst mitigating potential contamination or analyte loss, additional solvent use and single-use plastics. High throughput application of the method was demonstrated by assessing antibiotic stability in laboratory-scale gravity sewers (n = 6) with sampling intervals of 0, 0.5, 1, 2, 4, 6, 8, and 24 hours. Amoxicilloic acid, hydroxymetronidazole, trimethoprim and clarithromycin showed satisfactory short-term stability (4 h) for sampling influent wastewater at treatment plants for population use assessment. The sensitivity of the direct injection methodology was demonstrated by random influent wastewater monitoring (n = 5) at three treatment plants in North-East Scotland during August by 24 h pseudo flow-weighted composite sampling. This revealed amoxicillin, metronidazole, trimethoprim and clarithromycin use ranged from 263 to 3559 mg/1000 people per day. This was 0.5 to 1.8 times the corresponding average daily prescribed quantity (calculated from total August prescribing) within the treatment plant's catchment area, highlighting differences in prescription and consumption levels across locations. Findings demonstrated direct injection UHPLC-MS/MS to be ideal for supporting population use assessment of antibiotics through application to laboratory and monitoring wastewater studies.