Search Results (2,541)

Background/Objectives: In recent years, lipids have emerged as critical regulators of different disease processes, being involved in cancer pathogenesis, progression, and outcome. Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) has significantly expanded the technology’s reach, enabling spatially resolved profiling of lipids directly from tissue, including formalin-fixed paraffin-embedded (FFPE) specimens. In this context, MALDI matrix selection is crucial for lipid extraction and ionization, influencing key aspects such as molecular coverage and sensitivity, especially in such specimens with already depleted lipid content. Thus, in this work, we aim to explore the feasibility of mapping lipid species in FFPE clinical samples with MALDI-MSI using 6-aza-2-thiothymine (ATT) as a matrix of choice. Methods: To do so, ATT performances were first compared to those two other matrices commonly used for lipidomic analyses, 2′,5′-dihydroxybenzoic acid (DHB) and Norharmane (NOR), on lipid standards. Results: As a proof-of-concept, we then assessed ATT’s performance for the MALDI-MSI analysis of lipids in FFPE brain sections, both in positive and negative ion modes, comparing results with those obtained from other commonly used dual-polarity matrices. In this context, ATT enabled the putative annotation of 98 lipids while maintaining a well-balanced detection of glycerophospholipids (60.2%) and sphingolipids (32.7%) in positive ion mode. It outperformed both DHB and NOR in the identification of glycolipids (3%) and fatty acids (4%). Additionally, ATT exceeded DHB in terms of total lipid count (62 vs. 21) and class diversity and demonstrated performance comparable to NOR in negative ion mode. Moreover, ATT was applied to a FFPE glioblastoma tissue microarray (TMA) evaluating the ability of this matrix to reveal biologically relevant lipid features capable of distinguishing normal brain tissue from glioblastoma regions. Conclusions: Altogether, the results presented in this work suggest that ATT is a suitable matrix for pathology imaging applications, even at higher lateral resolutions of 20 μm, not only for proteomic but also for lipidomic analysis. This could enable the use of the same matrix type for the analysis of both lipids and peptides on the same tissue section, offering a unique strategic advantage for multi-omics studies, while also supporting acquisition in both positive and negative ionization modes. Full article

Avian influenza A viruses (IAVs) pose a persistent threat to the poultry industry, causing substantial economic losses. Although traditional vaccines have helped reduce the disease burden, they typically rely on multivalent antigens, emphasize humoral immunity, and require intensive production. This study aimed to establish a genetically matched host–cell system to evaluate antigen-specific immune responses and identify conserved CD8+ T cell epitopes in avian influenza viruses. To this end, we developed an MHC class I genotype (B21)-matched host (Lohmann VALO SPF chicken) and cell vector (DF-1 cell line) model. DF-1 cells were engineered to express the hemagglutinin (HA) gene of clade 2.3.4.4b H5N1 either transiently or stably, and to stably express the matrix 1 (M1) and nucleoprotein (NP) genes of A/chicken/South Korea/SL20/2020 (H9N2, Y280-lineage). Following prime-boost immunization with HA-expressing DF-1 cells, only live cells induced strong hemagglutination inhibition (HI) and virus-neutralizing (VN) antibody titers in haplotype-matched chickens. Importantly, immunization with DF-1 cells transiently expressing NP induced stronger IFN-γ production than those expressing M1, demonstrating the platform’s potential for differentiating antigen-specific cellular responses. CD8+ T cell epitope mapping by mass spectrometry identified one distinct MHC class I-bound peptide from each of the HA-, M1-, and NP-expressing DF-1 cell lines. Notably, the identified HA epitope was conserved in 97.6% of H5-subtype IAVs, and the NP epitope in 98.5% of pan-subtype IAVs. These findings highlight the platform’s utility for antigen dissection and rational vaccine design. While limited by MHC compatibility, this approach enables identification of naturally presented epitopes and provides insight into conserved, functionally constrained viral targets. Full article

The rabies virus (RABV) phosphoprotein (P protein) has multiple functions, including acting as the essential non-catalytic cofactor of the viral polymerase (L protein) for genome replication and transcription; the principal viral antagonist of the interferon (IFN)-mediated innate immune response; and the chaperone for the viral nucleoprotein (N protein). Although P protein is known to undergo phosphorylation by cellular kinases, the location and functions of the phosphorylation sites remains poorly defined. Here, we report the identification by mass-spectrometry (MS) of residues of P protein that are modified by phosphorylation in mammalian cells, including several novel sites. Analysis of P protein with phospho-mimetic and phospho-inhibitory mutations of three novel residues/clusters that were commonly identified by MS (Ser48, Ser183/187, Ser217/219/220) indicate that phosphorylation at each of these sites does not have a major influence on nuclear trafficking or antagonistic functions toward IFN signalling pathways. However, phosphorylation of Ser48 in the N-terminus of P protein impaired function in transcription/replication and in the formation of replication structures that contain complexes of P and N proteins, suggestive of altered interactions of these proteins. The crystal structure of P protein containing the S48E phospho-mimetic mutation indicates that Ser48 phosphorylation facilitates the binding of residues 41–52 of P protein into the RNA-binding groove of non-RNA-bound N protein (N⁰), primarily through the formation of a salt bridge with Arg434 of N protein. These data indicate that Ser48 modification regulates the cycling of P-N⁰ chaperone complexes that deliver N protein to RNA to enable transcription/replication, such that enhanced interaction due to S48E phospho-mimetic mutation reduces N protein delivery to the RNA, inhibiting subsequent transcription/replication processes. These data are, to our knowledge, the first to implicate phosphorylation of RABV P protein in conserved replication functions of the P gene. Full article

Background: Nutrition is of paramount importance during early development, since suboptimal growth in this period of life is linked to adverse long- and mid-term outcomes. This is particularly relevant for preterm infants, who fail to thrive during the first weeks of life and develop extrauterine growth restriction (EUGR). This group of premature babies represents an interesting population to investigate using a metabolomic approach to optimize nutritional intake. Aims: To analyse and compare the urinary metabolomic pattern at birth of preterm infants with and without growth restriction at 36 weeks of postmenstrual age or at discharge, searching for putative markers of growth failure. Methods: We enrolled preterm infants between 23 and 32 weeks of gestational age (GA) and/or with a birth weight <1500 g, admitted to the Neonatal Intensive Care Unit (NICU) at the Department of Women’s and Children’s Health of Padova University Hospital. We collected urinary samples within 48 h of life and performed untargeted metabolomic analysis using mass spectrometry. Results: Sixteen EUGR infants were matched with sixteen non-EUGR controls. The EUGR group showed lower levels of L-cystathionine, kynurenic acid, L-carnosine, N-acetylglutamine, xanthurenic acid, aspartylglucosamine, DL5-hydroxylysine-hydrocloride, homocitrulline, and L-aminoadipic acid, suggesting a lower anti-inflammatory and antioxidant status with respect to the non-EUGR group. Conclusions: Metabolomic analysis suggests a basal predisposition to growth restriction, the identification of which could be useful for tailoring nutritional approaches. Full article

Plant genetics and chemotaxonomic analysis are considered key parameters in understanding evolution, plant diversity and adaptation. Korean Peninsula has a unique biogeographical landscape that supports various aromatic plant species, each with considerable ecological, ethnobotanical, and pharmacological significance. This review aims to provide a comprehensive overview of the chemotaxonomic traits, biological activities, phylogenetic relationships and potential applications of Korean aromatic plants, highlighting their significance in more accurate identification. Chemotaxonomic investigations employing techniques such as gas chromatography mass spectrometry, high-performance liquid chromatography, and nuclear magnetic resonance spectroscopy have enabled the identification of essential oils and specialized metabolites that serve as valuable taxonomic and diagnostic markers. These chemical traits play essential roles in species delimitation and in clarifying interspecific variation. The biological activities of selected taxa are reviewed, with emphasis on antimicrobial, antioxidant, anti-inflammatory, and cytotoxic effects, supported by bioassay-guided fractionation and compound isolation. In parallel, recent advances in phylogenetic reconstruction employing DNA barcoding, internal transcribed spacer regions, and chloroplast genes such as rbcL and matK are examined for their role in clarifying taxonomic uncertainties and inferring evolutionary lineages. Overall, the search period was from year 2001 to 2025 and total of 268 records were included in the study. By integrating phytochemical profiling, pharmacological evidence, and molecular systematics, this review highlights the multifaceted significance of Korean endemic aromatic plants. The conclusion highlights the importance of multidisciplinary approaches including metabolomics and phylogenomics in advancing our understanding of species diversity, evolutionary adaptation, and potential applications. Future research directions are proposed to support conservation efforts. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

During the course of genome mining initiatives, we identified a marine-derived Micromonospora, assigned here as strain WMMD956; the genome of WMMD956 appeared to contain a number of features associated with everninomicins, well-known antimicrobial orthosomycins. In addition, LCMS-based hierarchical clustering analysis and principal component analysis (hcapca) revealed that WMMD956 displayed an extreme degree of metabolomic and genomic novelty. Dereplication of high-resolution tandem mass spectrometry (HRMS/MS) and Global Natural Product Social molecular networking platform (GNPS) analysis of WMMD956 resulted in the identification of several analogs of the previously known everninomicin. Chemical structures were unambiguously confirmed by HR-ESI-MS, 1D and 2D NMR experiments, and the use of MS/MS data. The isolated metabolites, 1–3, were evaluated for their antibacterial activity against methicillin-resistant Staphalococcus aureus (MRSA). Full article

The Junggar Basin contains a large amount of coal resources and is an important coal production base in China. The coal seam in Zhundong coalfield has a large single-layer thickness and high content of inertinite, but large particle Fe-sulphide minerals are associated with coal seams in some mining areas. A series of economic and environmental problems caused by the combustion of large-grained Fe-sulphide minerals in coal have seriously affected the economic, clean and efficient utilization of coal. In this paper, the ultra-thick coal seam of the Xishanyao formation in the Yihua open-pit mine of the Zhundong coalfield is taken as the research object. Through the analysis of coal quality, X-ray fluorescence spectrometer test of major elements in coal, inductively coupled plasma mass spectrometry test of trace elements, SEM-Raman identification of Fe-sulphide minerals in coal and LA-MC-ICP-MS test of sulfur isotope of marcasite, the coal quality characteristics, main and trace element characteristics, macro and micro occurrence characteristics of Fe-sulphide minerals and sulfur isotope characteristics of marcasite in the ultra-thick coal seam of the Xishanyao formation are tested. On this basis, the occurrence state and genesis of large particle Fe-sulphide minerals in the ultra-thick coal seam of the Xishanyao formation are clarified. The main results and understandings are as follows: (1) the occurrence state of Fe-sulphide minerals in extremely thick coal seams is clarified. The Fe-sulphide minerals in the extremely thick coal seam are mainly marcasite, and concentrated in the YH-2, YH-3, YH-8, YH-9, YH-14, YH-15 and YH-16 horizons. Macroscopically, Fe-sulphide minerals mainly occur in three forms: thin film Fe-sulphide minerals, nodular Fe-sulphide minerals, and disseminated Fe-sulphide minerals. Microscopically, they mainly occur in four forms: flake, block, spearhead, and crack filling. (2) The difference in sulfur isotope of marcasite was discussed, and the formation period of marcasite was preliminarily divided. The overall variation range of the δ³⁴S value of marcasite is wide, and the extreme values are quite different. The polyflake marcasite was formed in the early stage of diagenesis and the δ³⁴S value was negative, while the fissure filling marcasite was formed in the late stage of diagenesis and the δ³⁴S value was positive. (3) The coal quality characteristics of the thick coal seam were analyzed. The organic components in the thick coal seam are mainly inertinite, and the inorganic components are mainly clay minerals and marcasite. (4) The difference between the element content in the thick coal seam of the Zhundong coalfield and the average element content of Chinese coal was compared. The major element oxides in the thick coal seam are mainly CaO and MgO, followed by SiO₂, Al₂O₃, Fe₂O₃ and Na₂O. Li, Ga, Ba, U and Th are enriched in trace elements. (5) The coal-accumulating environment characteristics of the extremely thick coal seam are revealed. The whole thick coal seam is formed in an acidic oxidation environment, and the horizon with Fe-sulphide minerals is in an acidic reduction environment. The acidic reduction environment is conducive to the formation of marcasite and is not conducive to the formation of pyrite. (6) There are many matrix vitrinite, inertinite content, clay content, and terrigenous debris in the extremely thick coal seam. The good supply of peat swamp, suitable reduction environment and pH value, as well as groundwater leaching and infiltration, together cause the occurrence of large-grained Fe-sulphide minerals in the extremely thick coal seam of the Xishanyao formation in the Zhundong coalfield. Full article

The olive oil industry, a key component of Southern Europe’s agricultural sector, generates large amounts of by-products during processing, including olive leaves, branches, stones, and seeds. In the context of growing environmental concerns and limited natural resources—particularly in the Mediterranean regions—there is increasing interest in circular economy approaches that promote the valorization of agricultural residues. These by-products are rich in bioactive compounds, particularly phenolics such as oleuropein and hydroxytyrosol, which are well known for their antioxidant and anti-inflammatory activities. This study aimed to evaluate the phenolic content and antioxidant capacity of by-products from three olive cultivars using high-performance liquid chromatography with photodiode array detection (HPLC–PDA) and mass spectrometry (MS). The leaves and seeds, particularly from the “Cobrança” and a non-identified variety, presented the highest antioxidant activity, as well as the highest concentration of phenolic compounds, demonstrating once again the direct relationship between these two parameters. The identification of the compounds present demonstrated that the leaves and branches have a high diversity of phenolic compounds, particularly secoiridoids, flavonoids, phenylpropanoids, phenylethanoids, and lignans. An inverse relationship was observed between the chlorophyll and carotenoid content and the antioxidant activity, suggesting that phenolic compounds, rather than pigments, are the major contributors to antioxidant properties. Therefore, the by-products of the olive oil industry are a valuable source of sustainable bioactive compounds for distinct industrial sectors, such as the food, nutraceutical, and pharmaceutical industries, aligning with the European strategies for resource efficiency and waste reduction in the agri-food industries. Full article

Maize is a key crop in Ecuador for both human and animal consumption. Its vulnerability to fungal contamination and mycotoxins poses risks to food safety. The aim of this study was to analyze the occurrence of fungi and mycotoxins in maize grown in different regions of Ecuador (29 localities) and postharvest factors influencing contamination. Fungal identification was performed through culturing and morphological analysis. Analysis of multi-toxins was carried out using liquid chromatography coupled with mass spectrometry (LC-MS/MS). Statistical analyses included PCA and linear regression models. Fungal contamination was found in 93.3% of samples; mycotoxins were present in 90%. Fusarium and Aspergillus were dominant. Fumonisins (66.6%), zearalenone (30%), aflatoxins (16.7%), and trichothecenes B (13.3%) were the most prevalent. Co-occurrence of up to three mycotoxins per sample was observed, more frequent on the coast. Grain moisture and temperature were strongly correlated with contamination levels. The study reveals widespread contamination of Ecuadorian maize, with environmental and postharvest factors playing key roles. This poses a food safety concern, highlighting the need for improved storage and monitoring systems. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

Background/Objectives: Oxylipins, a family of oxygenated natural products derived from polyunsaturated fatty acids (PUFAs), play crucial roles in various physiological processes. Evaluating their levels in vivo helps to reveal their roles in health and disease. Because of the numerous isomers of oxylipins, it is essential to develop efficient and precise analytical methods for their identification and quantification. The objective of this study is to establish a quantitative method for oxylipin analysis and its application to the assessment of oxylipins in children’s plasma, with potential implications for diagnostic use in pediatric populations. Methods: A liquid chromatography–electrospray ionization–tandem mass spectrometry method was developed to quantify 64 oxylipins and four precursor PUFAs within 36 min. The limits of quantification ranged from 0.25 to 50 pg, with most analytes showing recoveries and matrix effects between 85 and 110% and between 90 and 110%, respectively. Intra- and inter-day precision values were within 15%. The established method was applied to plasma samples from children aged 9–12 years (boys = 181; girls = 161) in Hokkaido, Japan, to assess the relation between plasma oxylipin and PUFA levels and age, sex, and body mass index. Results: There was no significant correlation between oxylipin levels and age, sex, or body mass index. However, among the PUFAs, boys had higher eicosapentaenoic acid and arachidonic acid levels than those of girls, with a significant increase in eicosapentaenoic acid levels in the overweight group compared with those in the underweight group. Conclusions: We successfully developed a simple and highly selective method for the analysis of oxylipins in preadolescent children’s plasma samples. Thus, this study provides a foundation for broader application of the developed method to different biological samples in future studies. Full article

Black Chiloe’s giant garlic is a functional food produced by a mild Maillard reaction that contains relevant bioactive molecules like organosulfur compounds (OSCs) and (poly)phenols (PPs). Compared with raw garlic, black garlic has a higher content of PPs and S-allyl cysteine (SAC), a key OSC due to its bioactivities. The objective of the present work was to optimize by chemometric tools a green microwave-assisted extraction (MAE) of SAC and PPs present in black Chiloe’s giant garlic to detect and identify novel bioactive molecules with antioxidant and/or inhibitory activities over cyclooxygenase, α-glucosidase, and acetylcholinesterase enzymes. The MAE factors were optimized using a central composite design, establishing optimal PP and SAC yields at 67 °C, 0% ethanol, 12 min and 30 °C, 40% ethanol, 3 min, respectively. PP and SAC values were 9.19 ± 0.18 mg GAE/g DW and 2.55 ± 0.10 mg SAC/g DW. Applying effect-directed analysis using high-performance thin layer chromatography-bioassay and mass spectrometry, the bioactive molecules present in the MAE extract with antioxidant and inhibitory activities over cyclooxygenase, α-glucosidase, and acetylcholinesterase enzymes were identified as N-fructosyl-glutamyl-S-(1-propenyl)cysteine, N-fructosyl-glutamylphenylalanine, and Harmane. Full article

The agro-industrial sector produces large amounts of by-products that have a high environmental impact, so it has become essential to recover food waste at all levels. This is because it often contains bioactive molecules that can be a valuable source of new products such as animal feed, biopolymers, or products for human use, (e.g., cosmetics and nutraceuticals) due to its antioxidant, antimicrobial, and anti-inflammatory properties. Advanced analytical methodologies such as liquid chromatography coupled to mass spectrometry (LC-MS) are crucial for the characterisation of bioactive chemicals in these waste materials. LC-MS enables both targeted and untargeted metabolomic approaches, facilitating the identification and quantification of a wide range of secondary metabolites, including polyphenols, flavonoids, alkaloids, and terpenoids. The choice of extraction methodology is essential for the precise identification and quantification of these metabolites. This study provides an overview of LC-MS as an effective tool for analysing complex extracts derived from plant waste, discussing both methodological aspects and typical bioactive metabolites identified, and offering examples of their potential applications in cosmeceutics. Full article

Background: Liquid chromatography-mass spectrometry (LC-MS), widely used in metabolomics, is often limited by low ionization efficiency and ion suppression, which reduce overall metabolite detectability and quantification accuracy. To address these challenges, chemical isotope labeling (CIL) LC-MS has emerged as a powerful approach, offering high sensitivity, accurate quantification, and broad metabolome coverage. This method enables comprehensive profiling by targeting multiple submetabolomes. Specifically, amine-/phenol- and hydroxyl-containing metabolites are labeled using dansyl chloride under distinct reaction conditions. While this strategy provides extensive coverage, the sequential analysis of each submetabolome reduces throughput. To overcome this limitation, we propose a two-channel mixing strategy to improve analytical efficiency. Methods: In this approach, samples labeled separately for the amine/phenol and hydroxyl submetabolomes are combined prior to LC-MS analysis, leveraging the common use of dansyl chloride as the labeling reagent. This integration effectively doubles throughput by reducing LC-MS runtime and associated costs. The method was evaluated using human urine and serum samples, focusing on peak pair detectability and metabolite identification. A proof-of-concept study was also conducted to assess the approach’s applicability in putative biomarker discovery. Results: Results demonstrate that the two-channel mixing strategy enhances throughput while maintaining analytical robustness. Conclusions: This method is particularly suitable for large-scale studies that require rapid sample processing, where high efficiency is essential. Full article