Analyst最新文献

Inhalation therapies often combine budesonide, glycopyrronium bromide, and salbutamol sulphate, necessitating analytical methods capable of their simultaneous quantification. Conventional UHPLC methods typically address these active pharmaceutical ingredients (APIs) individually, leading to inefficiencies in development, validation, and regulatory alignment. Analytical Quality by Design (AQbD) has transformed pharmaceutical analytical method development through systematic risk assessment and structured optimization, but its application in the bioanalytical domain remains limited. The challenge addressed in this study is the creation of a single robust UHPLC method that meets both pharmaceutical and bioanalytical requirements within regulatory frameworks. A unified UHPLC method was developed using AQbD principles, employing Design of Experiments to identify and optimize critical method parameters. The optimized method achieved baseline separation of all three APIs on a YMC UltraHT Hydrosphere C18 (2.1 × 100 mm; 2.0 μm) column under gradient elution with methanol and 0.1% formic acid in 10 mM ammonium formate. Robustness was established through a defined Method Operable Design Region. The method was first validated under ICH Q2 (R2) for pharmaceutical applications, confirming accuracy, precision, and sensitivity. Subsequently, it was extended to the bioanalytical domain and validated under ICH M10 guidelines in simulated lung fluid, demonstrating reproducibility in complex matrices. This dual validation highlights the method's versatility and regulatory robustness, underscoring AQbD's ability to unify pharmaceutical and bioanalytical method development into a single lifecycle appropriate platform. This study demonstrates the first AQbD-driven UHPLC method validated under both ICH Q2 (R2) and ICH M10, bridging pharmaceutical and bioanalytical applications. Extending AQbD principles into bioanalysis provides a regulatory-relevant framework that enhances robustness, lifecycle flexibility, and compliance. The work establishes a unified strategy for inhalation therapies and beyond, supporting broader adoption of science- and risk-based analytical development.

Raman spectroscopy enables label-free and non-destructive structural analysis of biomolecules; however, its application is limited by the inherently weak Raman signals, which necessitate high concentrations of biomolecules for detection. In our previous study, we developed a liquid–liquid phase separation (LLPS)-assisted Raman method, in which biomacromolecules are concentrated into PEG-induced droplets, enabling the acquisition of high signal-to-noise (S/N) Raman spectra from dilute solutions with small volumes, such as 30 μM and 50 μL. We demonstrate here its broad analytical utility for several applications, including real-time monitoring of catalytic reactions such as RNA degradation and quantitative detection of protein-small molecule interactions exemplified by the avidin–biotin system. Furthermore, small molecules such as amino acids, monosaccharides and supersulfides were successfully concentrated, allowing their Raman spectra to be obtained with markedly improved S/N ratios. This technique thus provides a simple, highly sensitive and versatile analytical platform for Raman-based biochemical studies, with wide potential applications in analyzing biomolecular structures and intermolecular interactions, as well as diagnostics.

Salmonella is a widely distributed foodborne pathogen that poses a serious threat to public health. Traditional detection methods suffer from limitations such as being time-consuming, complex, and reliant on expensive equipment, making them unsuitable for rapid and on-site detection. In recent years, nanomaterial-based optical biosensors have emerged as a research hotspot in Salmonella detection due to their high sensitivity, strong specificity, fast response, and portability. These sensors offer new technological approaches for real-time monitoring and early warning of pathogens. This paper reviews the research progress of nanomaterial-based optical biosensors for Salmonella detection over the past five years, systematically summarizing the design principles, performance characteristics, and applications of different biorecognition elements and optical signal components in Salmonella detection. It also highlights the selection and integrated design strategies of combining different biorecognition elements with optical signal components to improve detection sensitivity, shorten detection time, and enhance specificity, providing a theoretical reference for researchers in this field. Finally, the paper analyzes the strengths and weaknesses of current technologies and discusses future development directions of nanomaterial-based optical biosensors for Salmonella detection, aiming to advance the technology and provide more efficient and reliable solutions for public health security.

Correction for ‘Highly-selective and sensitive plasmon-enhanced fluorescence sensor of aflatoxins’ by Tetyana Sergeyeva et al., Analyst, 2022, 147, 1135–1143, https://doi.org/10.1039/D1AN02173G.

Understanding dynamic metabolic processes is central to elucidating cellular function and disease mechanisms. Glycolysis and glutaminolysis are particularly important, as they support bioenergetic and biosynthetic pathways, and their dysregulation is strongly linked to disorders. Raman spectroscopy provides a powerful, non-invasive approach for probing cellular dynamics, and recent advances in instrumentation and computational analysis have enhanced its sensitivity, enabling detection of subtle metabolic variations in complex environments. In this study, Raman spectroscopy combined with two-dimensional correlation spectroscopy (2D-COS) was applied to investigate metabolic responses of cells exposed either to glucose alone or glucose supplemented with glutamine, with emphasis on glutamine's effect on overall metabolic dynamics. Cells were starved for 2 h and then exposed to nutrients, after which they were fixed at 15 minute intervals for up to 2 h and spectroscopically monitored to evaluate the kinetic evolution of the metabolic response. To validate the approach, simulated datasets were initially used to model simplified metabolic pathway dynamics, which confirmed that 2D-COS could reliably track the kinetic evolution of simulated variables, even in the presence of high background interference. Analysis of cellular spectra revealed systematic temporal changes across biomolecular bands, suggesting partial synchronisation of metabolic responses, with oscillatory patterns observed under glucose-only conditions. In contrast, glucose–glutamine samples showed accelerated and amplified metabolic variability, with stronger correlations and additional variable bands, particularly linked to nucleic acid vibrations. Overall, these findings demonstrate the utility of Raman 2D-COS for resolving intracellular metabolic dynamics from complex datasets, offering new opportunities for advancing diagnostics and therapeutic interventions.

Carbon monoxide (CO) is a key signaling molecule in mammals, playing essential roles in cell protection and the maintenance of cellular homeostasis and exhibiting great therapeutic potential for various diseases. Moreover, dysregulation of endogenous CO metabolism has been closely linked to the development of multiple pathological conditions. Tricarbonyl chloride (glycine) ruthenium (CORM-3) can release low concentrations of CO within biological systems and demonstrates anti-inflammatory and cardioprotective properties. As a CO-releasing agent, CORM-3 offers significant potential for the treatment of various clinical conditions. Therefore, the development of effective analytical methods for detecting the CO-releasing agent CORM-3 is crucial for monitoring its levels and elucidating its specific biological mechanisms in vivo. Herein, we report a near-infrared fluorescent probe incorporating a benzothiazole fluorophore with an allyl ether moiety as the reactive site. The probe enables rapid and selective detection of CORM-3 by releasing the fluorophore. It exhibits excellent sensing performance, including fast response time, high selectivity and sensitivity, a large Stokes shift, and a turn-on fluorescence signal at 660 nm. Importantly, the probe effectively avoids issues related to heavy metal ion involvement and potential interference from nitroreductase, which are common limitations in existing methods. It has been successfully applied for visual detection of CORM-3 in living cells. Furthermore, the probe can be fabricated into test strips and, together with a smartphone-based RGB analysis application, allows for rapid and convenient on-site detection of CORM-3. These results demonstrate that the probe serves as a powerful tool for tracking CORM-3 in biological samples.

Streptococcus pneumoniae (S. pneumoniae) is a major cause of respiratory infections, requiring rapid, sensitive, and accessible diagnostic tools for early intervention, especially in resource-limited settings. Herein, we developed a microfluidic immunosensor integrated with a 0.4 μm pore-size filter membrane and antibody-functionalized Au@Pt nanozymes for the ultrasensitive colorimetric detection of S. pneumoniae in saliva samples. The device operates through four sequential steps: bacterial enrichment, immunoprobe binding, impurity removal, and nanozyme-catalyzed TMB oxidation with a distinct blue signal. The system achieved a detection limit of 21 cfu mL⁻¹, representing a 21-fold improvement over HRP-based sensors and a 476-fold enhancement compared to conventional ELISA kits. It showed high accuracy with recovery rates of 89.2–112.5% in spiked saliva samples and showed excellent agreement with qPCR results (R² > 0.98). The modular and cost-effective microfluidic chip, combined with smartphone-based signal readout, highlights its strong potential for point-of-care testing in under-resourced settings.

Achieving high geometric symmetry in solid-state nanopores is crucial for consistent signal response and molecular recognition. However, dielectric breakdown-based nanopore fabrication often yields asymmetric pore structures, particularly in thin membranes where local electric field fluctuations are pronounced. In this work, we systematically investigate the influence of membrane thickness and surface charge modification on nanopore symmetry. By introducing silane-based functional groups with distinct charge characteristics, we demonstrate that positively charged surfaces significantly reduce asymmetry. Phase-resolved analysis of the breakdown current–voltage curves reveals that positive surface charge minimizes voltage fluctuations throughout the breakdown process, as quantified by the relative standard deviation. Breakdown voltage is also increased, indicating enhanced dielectric robustness. All-atom molecular dynamics simulations show that positive modification promotes electron and ion accumulation near the membrane interface, stabilizing the local electric field and guiding more deterministic breakdown paths. These findings offer mechanistic insights and practical strategies to enhance the precision, reproducibility, and controllability of nanopore fabrication, paving the way for improved nanopore-based sensing applications.

The rapid and sensitive detection of trace organic pollutants in water is crucial for ensuring environmental safety. Traditional detection methods struggle to meet the demands of large-scale, real-time, and on-site detection. This paper reviews recent advances in the application of machine learning (ML) in spectral detection methods for trace organic pollutants. It introduces techniques such as data augmentation, intelligent feature extraction, and model construction, as well as their application in different spectral techniques, for example, generative adversarial networks (GANs) for data augmentation, convolutional neural networks (CNNs) for feature extraction, and random forests (RF) for classification and identification. It focuses on exploring the combination of different spectral techniques and ML methods, such as the antibiotic database established by combining surface-enhanced Raman spectroscopy (SERS) and CNNs, and the classification of microplastics using infrared spectroscopy combined with RF. Through these combinations, ML enhances the sensitivity, selectivity, and robustness of detection. Furthermore, it provides an in-depth analysis of model interpretability methods and cross-laboratory validation frameworks, emphasizing the importance of building standardized detection processes and evaluation systems. Looking ahead, research in this field will focus on more efficient ML algorithms, deep integration of hardware and algorithms, and the expansion of application scenarios, to build an AI-driven autonomous decision-making system for pollutant detection and treatment.

An innovative multi-color colorimetric method integrated with smartphone detection was designed with great sensitivity and specificity for the determination of mercury ions (Hg²⁺) in aqueous samples based on platinum nanoparticles (PtNPs) and gold nanorods (AuNRs). The peroxidase-mimicking activity of PtNPs triggered the conversion of 3,3′,5,5′-tetramethylbenzidine (TMB) into its oxidized form (TMB²⁺), and the resulting TMB²⁺ could quantitatively etch AuNRs, inducing a distinct color transition from blue-green to pale red. By forming metallophilic interactions with the PtNPs, Hg²⁺ effectively suppressed the etching process of the AuNRs by inhibiting PtNPs’ peroxidase-like catalytic activity. Based on this principle, a correlation allowed for the quantification of Hg²⁺ by measuring the average color intensity (I_c) calculated from red, green, and blue (RGB) values of the reaction solution, exhibiting a linear response ranging from 10 to 400 nM with a detection limit (LOD) of 5.1 nM. The method also exhibited outstanding selectivity and anti-interference capability against competing metal ions. Robust practical applicability was confirmed in complex real water samples with satisfactory spike-and-recovery results. Moreover, the proposed sensor integrated AuNRs served as a multi-color indicator for Hg²⁺-modulated peroxide-mimicking enzyme activity of PtNPs, with smartphone-based RGB analysis, presenting a rapid and cost-effective solution for field-based environmental monitoring of mercury contamination.