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Stroke is the second most common cause of death in the world and a leading cause of disability. Ischemic stroke is the most common type of stroke (~87%), necessitating research into effective treatments. Chondroitin sulfate (CS) is a sulfated glycosaminoglycan (GAG) found in the central nervous system (CNS) that contains labile sulfate groups which, upon loss, leads to inaccurate structural annotations. Variable sulfation patterns have been implicated in several neurological diseases. Additionally, CS-GAG analysis is challenging due to labile sulfate groups and the presence of positional isomers. These isomers must be distinguished to develop effective targeted therapies. Currently, glycan mass spectrometry imaging (MSI) lacks soft ionization sources which impedes intact analysis of the labile sulfate modifications. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a soft ambient ionization technique capable of preserving labile species without chemical derivatization. In this work, IR-MALDESI with parallel reaction monitoring (PRM) was used to energetically resolve and characterize intact mono-sulfated CS-GAG positional isomers in healthy and ischemic stroke brain. Our results revealed that both positional isomers were upregulated in the stroke brain and their relative abundance remained constant across the tissue.

A novel ultrasound-assisted magnetic sorbent-wrapped stick dip extraction (UA-MSWS-DE) method was developed for the batch-scale determination of seven tyrosine kinase inhibitors (TKIs) in human plasma. The extraction device was fabricated by immobilizing octadecylphosphonic acid-functionalized magnetic nanoparticles onto flame-sealed glass capillaries embedded with magnets. Ultrasonic-assisted dip extraction was used to extract TKIs from diluted plasma, followed by quantification via liquid chromatography-tandem mass spectrometry (LC-MS/MS). Key extraction parameters were systematically optimized, yielding the following optimal conditions: sample pH 5.0 and volume 1.0 mL, sorbent particle size 20 nm and dosage 10 mg, extraction time 20 min, and desorption with 150 µL of ethanol for 8 min. The method demonstrated excellent linearity (R² ≥ 0.99), low limits of quantification (0.1-5.0 ng/mL), and good intra-/inter-day precision (RSDs < 14.95%). The accuracy in blinded plasma samples, tested at 0.25-25% of the studied concentration range, ranged from 87.13 to 112.33%. Compared to conventional magnetic dispersive solid-phase extraction, the UA-MSWS-DE strategy simplifies handling and is designed to facilitate potential parallel processing using only an ultrasonic bath. Five greenness metrics confirmed the method's high sustainability. Furthermore, the modular design enables adaptation to other analytes by modifying the sorbent coating, making UA-MSWS-DE a sensitive, green, and automation-ready method for therapeutic drug monitoring.

Microplastic (MP) pollutes our terrestrial and aquatic ecosystems due to their uncontrolled discharge into our environment. The analysis of MP contamination is still a challenge, although significant improvements are made for different environmental matrices. Using mass-based particle analysis methods such as thermal extraction and desorption-gas chromatography/mass spectroscopy (GC/MS) or pyrolysis GC/MS, essential parameters such as the MP's morphology, size, and shape cannot be obtained, which are indispensable to assess the hazard of the respective particles. Raman, micro-Fourier transform infrared, and attenuated total reflectance spectroscopy are particle-based analysis methods, which are time-consuming due to the high purification effort. Thus, novel, reliable, and time-efficient methods for MP analysis are required. Previously, studies showed the potential of frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify plastics' type, shape, size, and morphology, and distinguishing these from natural materials. However, only pure plastic granules were investigated, omitting that commodity plastics accumulating in our environment contain various additive, filler, or dye concentrations. To circumvent the dependency of additive, filler, and dye concentrations, we investigated the fluorescence spectra and lifetimes of three plastic types, individually composed with two fillers, three additives, and two dyes in six different concentrations. We heuristically modeled the dependency of the concentration on plastics' fluorescence lifetime using a logarithmic model with a high correlation and showed that identifying the plastic types is hardly possible when fillers, additives, or dyes are added in various concentrations because of their superimposing fluorescence lifetimes. However, further research has to be conducted to investigate different emission states of fluorescence to optimize the FD-FLIM method, as only one excitation wavelength and emission band was used for the investigations.

Carbohydrate-based biosensors represent a cutting-edge class of bioinspired diagnostic platforms that exploit the inherent specificity of glycan-protein interactions for pathogen detection. Carbohydrate-functionalized biosensing systems offer remarkable advantages in terms of sensitivity, selectivity, and biocompatibility, positioning them as compelling alternatives to conventional antibody- or nucleic acid-based assays. By mimicking natural recognition mechanisms, these interfaces enable rapid, scalable, and robust capture of microbial targets, even in complex biological matrices, thereby paving the way for detection platforms suitable for clinical diagnostics, environmental monitoring, and food safety applications. Recent advancements in glycan chemistry, nanotechnology, and surface functionalization, particularly the precise control of ligand density, orientation, and spatial arrangement, have significantly enhanced the performance of these biosensors. This review highlights the design principles, detection strategies, and emerging applications of carbohydrate-based biosensors targeting a broad spectrum of studied pathogens. It underscores their transformative potential in advancing point-of-care diagnostics and enhancing infectious disease surveillance.

Volatilomics is an emerging discipline aimed at characterizing volatile metabolites in diverse matrices. Recently, comprehensive two-dimensional gas chromatography (GC × GC) coupled with parallel flame ionization detection (FID) and mass spectrometry (MS) has gained attention for combining accurate quantification with unambiguous compound identification. Traditionally, FID and MS data are processed independently. This study addresses their integration by merging FID and MS chromatograms into a single fused chromatogram, enhancing pattern recognition during template matching and enabling large-scale quantitative volatilomics. Feature matching is guided by MS spectral similarity, minimizing mismatches and extracting FID responses for robust quantification. The contribution discusses the workflow designed to obtain combined detector signals and the challenges posed by dual parallel detection operated under both thermal and differential-flow modulation, dual-parallel second-dimension column configurations, and differences in acquisition frequency between detectors. From an application standpoint, chromatogram fusion directly responds to emerging analytical needs in volatilomics-supporting quantitative, high-throughput analysis across extended time frames through the FID channel, while ensuring the mandatory MS confirmation required for the reliable identification of fragrance allergens and regulated compounds. The resulting fused chromatogram consolidates complementary detector information within a single multidimensional chromatogram, improving data consistency, interpretability, and throughput. Overall, chromatogram-level fusion represents a key step toward integrated, multimodal analytical platforms for robust and scalable volatilomics workflows.

Isotope dilution with inductively coupled plasma mass spectrometry (ID ICP-MS) has been recognized as one of the most precise and accurate techniques for trace and ultra-trace elemental analysis, with applications spanning geochemistry, environmental monitoring, nuclear forensics, and toxicology. Despite its analytical robustness, the global distribution of ID ICP-MS research remains concentrated in economically developed regions. This study presents a scientometric assessment of worldwide publications on ID ICP-MS from 1980 to 2025, based on bibliographic data retrieved from Scopus and Web of Science. The results reveal that geochemically relevant elements-often used in geochronology-have historically driven the development and application of the technique, followed by toxic elements such as mercury (Hg), cadmium (Cd), and lead (Pb). Results also show that China and the USA lead in total publications, while European countries demonstrate strong internal collaboration networks. In contrast, Latin American and African countries exhibit a markedly lower number of contributions, suggesting significant limitations in access to instrumentation, isotopically enriched standards, and research infrastructure. These findings emphasize the need for initiatives that promote global access to ID ICP-MS, which could play a crucial role in enhancing analytical capacity and advancing scientific equity worldwide.

Traditional Chinese medicine (TCM), characterized by its multi-component, multi-target, and multi-pathway nature, presents considerable challenges in the identification of chemical constituents and elucidation of metabolic mechanisms. TCM samples encompass a wide range of materials, including crude herbal parts, processed products, in vitro cell cultures, and in vivo biological specimens, each contributing to the complexity of analysis. MS, as a pivotal analytical tool for uncovering the material basis of TCM, has been widely employed for compound identification and in vivo metabolic pathway analysis, owing to its high throughput, sensitivity, and resolution. However, the inherently high-dimensional, noisy, and complex nature of MS data poses significant limitations to traditional analytical methods in terms of data processing efficiency and structural identification accuracy. In recent years, artificial intelligence (AI) technologies, particularly machine learning (ML), and deep learning (DL) models, have demonstrated remarkable potential in spectral interpretation, structure prediction, and metabolic pathway modeling within the context of MS-based TCM research. This review systematically summarizes the latest advances in the application of AI in TCM MS analysis, with a particular focus on two key areas: the utilization of AI for rapid qualitative analysis of complex TCM compounds, including spectral preprocessing, feature extraction, structural attribution, and isomer differentiation; and the role of AI in metabolite identification and reconstruction of in vivo metabolic pathways (Alvarado, Sci Eng Ethics, 29(5):32, 2023), encompassing metabolite screening, network modeling, and multi-omics integration. Furthermore, we critically discuss current challenges impeding further progress, such as the lack of high-quality MS databases, limited interpretability of AI models, and insufficient capabilities for cross-modal data fusion. Finally, we propose future directions for the field, emphasizing the importance of building interpretable, generalizable, and integrative AI frameworks. In summary, the convergence of AI and MS technologies is reshaping the paradigm of TCM research from empirical investigation to data-driven intelligence, thereby opening new avenues for the modernization of TCM and precision pharmacological studies.

This study presents the development of a HiSorb microextraction method coupled with thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) for analyzing cannabidiol (CBD) in commercial hemp oils. Two HiSorb sorptive coatings were evaluated, triple-phase polydimethylsiloxane (PDMS)/carbon wide-range (CWR)/divinylbenzene (DVB) and double-phase PDMS/CWR, for their extraction efficiencies. The triple-phase HiSorb, combined with water-bath incubation for 2 h at 80 °C, delivered the highest CBD recovery. The developed HiSorb-TD-GC-MS method showed excellent linearity (R² = 0.9951), with a method limit of detection (LOD) of 30 µg/mL and a limit of quantification (LOQ) of 100 µg/mL. The total chromatographic runtime was 11.2 min. Precision was satisfactory, with intra-day RSD % ranging from 6.8 to 7.3% and inter-day RSD % ranging from 6.5 to 7.7%. Recoveries at three concentration levels for diluted sample (low 100 μg/mL, medium 300 μg/mL, high 500 μg/mL) ranged from 77.2 to 86.4%. No detectable formation of Δ⁹-THC was observed, confirming that the method does not induce isomerization of CBD during GC analysis. Greenness assessment yielded AGREE = 0.75 (analytical method) and AGREEprep = 0.54 (sample preparation). The developed method was applied to commercial hemp oil samples and revealed no significant statistical difference (p > 0.05) between the label claim and measured CBD concentration in all tested samples. The application of green analytical methods contributes to sustainability by minimizing environmental impact and enhancing resource efficiency.

Mimotopes of the porcine epidemic diarrhea virus (PEDV) spike protein (SP) were screened from a Fv-antibody library, and the neutralizing activity of screened mimotopes was analyzed using plaque assay and docking simulation. The screened Fv-antibody (mimotopes) corresponded to the variable region of the heavy chain of immunoglobulin G, composed of three complementarity-determining regions (CDRs) and four framework regions. The Fv-antibody library was constructed by randomizing the amino acid sequence of CDR3 and expressed on the outer membrane of E. coli using auto-display technology. Monoclonal anti-PEDV SP antibody was used as probes to screen Fv-antibodies mimicking PEDV SP from the library. Two screened Fv-antibodies (mimotopes of PEDV SP) with binding affinity to the monoclonal antibody were expressed as soluble proteins, and their binding affinity was estimated using a surface plasmon resonance biosensor. The neutralizing activity of PEDV SP mimotopes to prevent PEDV infection was calculated using a plaque assay based on the cytopathic effect. Additionally, molecular docking simulations were performed to examine the interactions of PEDV SP mimotopes with ACE2 receptor as well as APN which had been considered infection-related receptors for coronavirus.