Analytical science advances最新文献

The widespread use of pesticides in modern agriculture necessitates sensitive and sustainable methods to monitor their residues in environmental waters. This study reports the development and optimization of a dispersive liquid–liquid microextraction (DLLME) method combined with high performance liquid chromatography with diode array detector (HPLC–DAD) for the simultaneous determination of multiclass pesticides (metalaxyl, benalaxyl, chlorpyrifos, endrin, 4,4′-DDT and bifenthrin). Key extraction variables—including the type and volume of extraction/disperser solvents, pH, salt addition and vortex speed/time—were systematically evaluated. The optimized method employed tetrachloroethylene as the extraction solvent, acetonitrile as the disperser, pH 7, 3% w/v NaCl, a vortex speed of 1200 rpm and an extraction time of 80 s. Under these conditions, excellent enrichment factors, recoveries (87%–108%) and precision (intradays: 2.8%–8.6%; interday: 4.2%–8.6%) were achieved. The correlation coefficients (r²) exceeded 0.9977, and the limits of detection ranged from 0.3 to 1.3 µg/L. Compared to conventional extraction techniques, the proposed DLLME method provides faster analysis and uses less solvent. This approach provides a robust, sensitive and environmentally friendly alternative for monitoring multiclass pesticide residues in diverse water matrices.

Ligand-independent activation poses a significant challenge for synthetic receptors, limiting their dynamic range. Given the difficulties and inconsistencies in evaluating this crucial aspect in many studies, we utilized a dual-luciferase assay to assess the background activity of the SynNotch receptor. These receptors are modular, orthogonal constructs designed to detect and respond to specific extracellular signals by expressing a targeted protein. In this study, we engineered an anti-CD19 SynNotch receptor by incorporating a nanobody into the antigen-binding domain, leveraging the advantageous properties of camelid VHHs. We then evaluated the performance of the final construct, focusing on its functionality and ligand-independent activation using the dual-luciferase assay. The results revealed that although reporter expression significantly increased in the presence of the antigen, there was also a high level of background expression, which could hinder the performance of the SynNotch receptor. Additionally, the SynNotch receptor exhibited a 24-h delay in reaching its peak activation level in response to the antigen and in returning to baseline levels in its absence. Therefore, the optimization of the SynNotch construct is essential to enhance the receptor's dynamic range.

The cover image is based on the article Matrix-assisted Laser Desorption Ionization Mass Spectrometry for Detection of α/β-Chain Changes From Human Hemoglobin: An Exploratory Study by Bin Hu et al., https://doi.org/10.1002/ansa.70037

Soil mineralogy, analysed by x-ray diffraction (XRD), is crucial to understanding its variability and influence on physical and chemical properties. However, soil analysis by XRD faces challenges, especially with large sample volumes and subjective interpretation. This study proposes a simplified methodology for sample preparation and soil analysis by XRD, which is associated with the principal component analysis (PCA). Two PCA models were built: one using semiquantitative percentages of minerals and another based on diffraction intensities. After analysing 90 samples from nine farms producing Coffea arabica, the results suggest greater efficiency in identifying clusters related to mineralogical composition, offering a fast and objective approach to soil characterization.

The rapid and direct drug quantification is indispensable for quality control units in the pharmaceutical sector to assure safe formulation and delivery of the right medicine to patients. Spectral features of Levofloxacin (LFX) were examined to provide a Fourier transform infrared spectroscopy (FTIR)-based rapid and pragmatic analytical method for direct quantification of LFX in solid formulations. A chemometric model was developed for frequency region of 1252.39–1218.84 cm⁻¹ which is linear over the range of 30%–90% (w/w) with a coefficient of determination (R²) 0.995 and meets the requirements of International Council for Harmonization (ICH) and AOAC for the development of the method. The limit of detection (LOD) and limit of quantification (LOQ) were found to 7.616% w/w and 23.079% w/w, respectively. Principal component analysis (PCA) was performed to identify adulteration or degradation of active pharmaceutical ingredients (APIs). It was observed that PC1 and PC2 explained 99.93% and 99.91% of the IR spectral variance respectively. Consequently, this method can be applied for routine analysis of LFX and quality control in pharmaceutical regulatory laboratories.

Medicinal plants are a global healthcare cornerstone, with traditional communities, particularly in Ethiopia, relying on indigenous knowledge for treating various ailments. This study aimed to identify, isolate, and elucidate the structures of chemical constituents from the chloroform and methanol extracts of Syzygium guineense root using IR, 1D, and 2D NMR spectroscopic techniques in South Ethiopia of Woliata Zone. Preliminary phytochemical screening confirmed the presence of various secondary metabolites, including alkaloids, flavonoids, terpenoids, polyphenols, saponins, tannins, glycosides, and steroids. The result indicated that the powdered root material was sequentially extracted with chloroform and methanol, yielding 5 g of chloroform extract (SGA) and 9 g of methanol extract (SGB). Subsequent chromatographic fractionation of the extracts led to the isolation of two pure compounds: SGB-3 (25 mg) from the methanol extract and SGA-24 (100 mg) from the chloroform extract. The structural elucidation of both compounds was performed using 1D and 2D NMR spectroscopy. The spectroscopic data (IR, ¹H NMR, ¹³C NMR, and DEPT) of SGB-3 indicated a diterpene lactone, characterized by hydroxyl, olefinic, and lactone functionalities. Partial characterization of SGA-24 through 1D and 2D NMR (¹H NMR, ¹³C NMR, DEPT, HMBC, HSQC) revealed its identity as a terpenoid with an oleanane Δ¹²-ene skeleton, showing close similarities to arjunolic acid. These findings contribute valuable scientific evidence to the phytochemistry of Syzygium guineense roots from South Ethiopia, supporting its traditional uses and identifying potential lead compounds for further pharmacological investigations.

Metabolomics is the study of the complete set of small molecules involved in the biochemical processes within a biological system. It links directly to metabolism, capturing the biochemical pathways and processes in a state-specific manner and aiming to understand the dynamic interactions within these metabolites, including how they reflect the physiological and environmental conditions as well as cellular and physiological responses. Presently, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is the highest performance mass spectrometry technology for untargeted metabolomic analysis, allowing the simultaneous detection of thousands of compounds in a single analysis. This analytical performance is due to its extreme mass resolution and exceptional mass accuracy, unmatched by any other type of mass spectrometer. This technology enables precise identification and differentiation of metabolites within complex biological samples, providing highly accurate molecular formulas, based on exact mass and fine isotopic distribution. This review focuses on new developments in FT-ICR-MS technology for metabolomic analysis, new methodologies and recent applications. It also addresses the current challenges and perspectives for the use of FT-ICR-MS in metabolomics.

The presence of pesticide residues in food crops poses serious health concerns, necessitating precise, rapid and accessible detection techniques. This study investigates the use of Raman spectroscopy combined with advanced data analysis techniques to detect and quantify Mancozeb residues in collard greens. The primary objective was to evaluate the viability of this approach for accurate pesticide residue monitoring in leafy vegetables. Raman spectral data were collected and preprocessed using a standard normalization technique to reduce spectral noise and enhance signal quality. Dimensionality reduction was achieved through a statistical method that extracted key spectral features and successfully differentiated control from treated samples, explaining a combined variance of 86% across the first two principal components. Graphical score plots revealed clear clustering patterns across various residue concentrations, ranging from 0.01 to 0.5 parts per million, with samples categorized according to regulatory residue limits. To further assess predictive capability, several machine learning models were developed for classification and quantification, including deep learning–based and ensemble-based approaches. Among these, the support vector model achieved the highest classification precision of 95% and demonstrated strong calibration and prediction accuracy. A convolutional neural network achieved 99% training accuracy and 98% testing accuracy, effectively recognizing complex spectral patterns. Statistical validation using analysis of variance confirmed that the observed model differences were significant, supporting the robustness of the selected algorithms. The proposed method accurately quantified Mancozeb residues within the tested range and demonstrated high sensitivity even at low concentration levels. This study highlights the potential of Raman spectroscopy, integrated with computational modelling, as a non-destructive, fast and cost-effective tool for pesticide residue detection in food safety applications.

Bridging the gap between microsampling techniques and standard blood matrices presents a groundbreaking opportunity in metabolic biomarker analysis, offering minimally invasive, patient-centric alternatives to traditional venipuncture. This review presents the current knowledge obtained from the comparison of biomarkers analysis in liquid blood, plasma or serum in parallel to blood microsamples by targeted and untargeted metabolic profiling assays. It aims to explore the analytical performance of these approaches compared to conventional blood collection, emphasizing its efficacy in the field. Key challenges such as haematocrit effect and validation studies which are necessary steps for standardization and widespread clinical adoption are pointed out through examples in different applications. This review underscores the critical steps to employ the full potential of microsampling technologies for their integration in metabolic biomarker discovery and clinical diagnostics.

Single-cell analysis provides critical insights into cellular heterogeneity, dynamic behaviours and microenvironmental interactions, driving advancements in precision medicine and disease mechanism research. However, traditional technologies face limitations due to low throughput, insufficient sensitivity and bottlenecks in multi-omics integration. Microdroplet printing technology, with its advantages in high-throughput single-cell encapsulation, picolitre-level reaction precision and oil-free phase contamination avoidance, has propelled single-cell analysis into a new era of high-throughput and high-dimensional resolution through deep integration with multimodal detection platforms. This review systematically elaborates on the theoretical framework, diverse technical systems and multi-dimensional application scenarios of microdroplet printing technology. It further dissects the deep coupling mechanisms between this technology and multimodal detection platforms such as including mass spectrometry, Raman spectroscopy, fluorescence and ultraviolet detectors, as well as its unique advantages in single-cell analysis. Such cross-technology integration has significantly accelerated innovation in fields such as single-cell drug screening and multi-omics analysis, marking a significant leap in the evolution of single-cell analytical methodologies.