Search Results (127)

In this study, an integrated analytical method coupling ultra-high-performance liquid chromatography–photodiode array detection–quadrupole time-of-flight mass spectrometry with total antioxidant capacity determination (UHPLC-PDA-Q-TOF/MS-TACD) was developed for the rapid screening and identification of antioxidants in complex natural extracts. The system enables simultaneous chromatographic separation, mass spectrometric characterization, and on-line activity assessment by combining 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) and ferric ion reducing antioxidant power (FRAP) assays. When applied to Rosa rugosa samples from five different origins, the approach efficiently separated the extract and successfully localized antioxidants directly from the chromatographic profile. A total of 86 compounds were identified, including flavonoids, tannins, and phenolic acids. Among them, 60 exhibited free radical scavenging capacity and 59 showed reducing activity. Activity verification experiments showed that all seven compounds exhibited good antioxidant activity. The IC₅₀ values of gallic acid, ellagic acid, quercetin 3-O-rhamnoside, and rutin were 0.019, 0.025, 0.043, and 0.046 mM, respectively, which were significantly better than the positive control (vitamin C). This method provides methodological and technical support for the rapid discovery of antioxidant components in complex natural products. Full article

Diterpenoid diester alkaloids (DDAs) are the primary toxic constituents in aconite herbs, while also being the key pharmacologically active components. Consequently, establishing rapid enrichment and highly sensitive analytical methods for DDAs is of critical importance. Herein, we developed and constructed a gas–liquid microextraction (GLME) device, which enables the rapid and selective enrichment of DDAs from complex matrices. The enriched extract can be directly analyzed by high-resolution Orbitrap mass spectrometry without requiring any further pretreatment. A comparative analysis of six commonly used Aconitum herbs medicines and their processed derivatives was conducted. Notably, GLME enhanced the mass spectrometric signals of DDAs by 3–4 orders of magnitude, facilitating the identification of 27 alkaloids, including 3 potential new compounds (15-Ethyl-13-deoxyanhydroaconitine, 13-Hydroxy-15-ethylanhydroaconitine and 8-eicosapentaenoic-benzoylmesaconine). It was found that among the tested samples, the DDAs response intensity of raw Caowu was the highest, and the DDA signals decreased significantly after processing. This result chemically validates the detoxification efficacy of traditional methods. The proposed GLME-MS strategy has the advantages of being green, economical, easy to operate, and highly selective (>1000-fold), which provides a technical reference for the rapid detection, safety assessment, and quality control of Aconitum herbs. Full article

Introduction: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, ranks as the third leading cause of mortality globally. Patients diagnosed with HCC exhibit a dismal prognosis mostly due to the emergence of symptoms in the advanced stages of the disease. Moreover, conventional biomarkers demonstrate insufficient efficacy in the early detection of HCC, hence highlighting the need for the identification of novel and more effective biomarkers. Methods: In this paper, we investigate methods for integration of multi-omics data we generated by both untargeted and targeted mass spectrometric analysis of serum samples from HCC cases and patients with liver cirrhosis. Specifically, the performances of several feature selection methods are evaluated on their abilities to identify a panel of multi-omics features that distinguish HCC cases from cirrhotic controls. Results: The integrative analysis identified key molecules associated with liver including such as leucine and isoleucine as well as SERPINA1, which is involved in LXR/RXR Activation and Acute Response signaling. A new method that uses recursive feature selection in conjunction with a transformer-based deep learning model as an estimator led to more promising results compared to other deep learning methods that perform disease classification and feature selection sequentially. Conclusions: The findings in this study reinforce the importance of adapting or extending deep learning models to support robust feature selection, especially for integration of multi-omics data with limited sample size to avoid the risk of overfitting and the need for evaluation of the multi-omics features discovered in this study via blood samples from a larger and independent cohort to identify robust biomarkers for HCC. Full article

Early diagnosis of lung cancer, essential for reducing its high mortality rate, is currently challenging, partly due to the lack of specific biomarkers. Here, we attempted to develop a noninvasive and potentially sensitive screening method based on the proteomic analysis of unstimulated and stimulated saliva samples, collected by passive drooling and salivary swabs, respectively, from healthy heavy smokers enrolled in a nonprofit screening project. Protein content analyzed before and after sample cryopreservation for various periods and the associated two-dimensional electrophoresis revealed that protein extraction after short-term cryopreservation prevented the loss of detectable proteins. Mass spectrometric analysis of these electrophoretically resolved proteins revealed the presence of salivary proteins whose levels may be dysregulated in various types of lung cancer. Finally, in pilot experiments conducted on stimulated saliva from a patient with a lung cancer nodule, we detected altered content or selective presence of proteins involved in lung carcinogenesis, such as serpin B3 or the proteins S100A14 and aldoketoreductase-A1, respectively. While acknowledging that these findings require further validation, we believe that the use of saliva and related proteomic analyses may contribute to the identification of potential early lung cancer biomarkers, which could hopefully improve clinical management of the tumor and patient survival. Full article

Poplar leaves (Populi folium) are a herbal remedy traditionally used for the treatment of rheumatic diseases and prostate inflammation. The aim of our study was a comprehensive identification of secondary metabolites occurring in the leaves of Populus alba, Populus × candicans, and Populus nigra, in order to search for a source of raw plant material rich in active compounds. Total salicylate (TSC), flavonoid (TFC), and phenolic compound (TPC) contents were determined, and the antioxidant potential was assessed using DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline- 6-sulfonic acid) diammonium salt), and FRAP (ferric reducing antioxidant power) assays as well as 2D-TLC (two-dimensional thin layer chromatography) bioautography using DPPH, riboflavin-light-NBT (nitro blue tetrazolium chloride), and xanthine oxidase inhibition tests. Secondary metabolites present in the analyzed poplar leaves were identified under the developed HPLC-DAD-ESI/MS (high performance liquid chromatography with photodiode array detection and electrospray ionization mass spectrometric detection analysis conditions and using the 2D-TLC method. Among the 80 identified compounds, 13 were shown for the first time in the genus Populus. The most diverse and similar set of flavonoids characterized the leaves of P. × candicans and P. nigra, while numerous salicylic compounds were present in the leaves of P. alba and P. × candicans. All analyzed leaves are a rich source of phenolic compounds. The highest flavonoid content was found in the leaves of P. × candicans and P. nigra, while the leaves of P. alba were characterized by the highest content of salicylates. All examined poplar leaves demonstrated antioxidant potential in all the assays used, which decreased in the following order: P. nigra, P. × candicans, P. alba. Full article

Drug conjugates, in which chemotherapeutic or cytotoxic agents are coupled to targeting or delivering macromolecules like peptides or proteins via a linker, revolutionize cancer treatment. While protein-drug and antibody-drug conjugates have already secured a role in clinical oncology, peptide–drug conjugates (PDCs) are emerging as a promising alternative, offering enhanced efficacy and fewer side effects compared to the free drug molecules. Comprehensive chemical and biological investigation of PDCs is crucial during drug development and optimization, paving the way for the next generation of targeted therapies. Anthracycline-containing peptide conjugates have emerged as promising candidates in targeted cancer therapies due to their ability to deliver cytotoxic agents directly to tumor cells. However, their structural complexity poses significant analytical challenges, particularly in mass spectrometric characterization. Accurate identification and quantification of these conjugates are critical for assessing their stability, efficacy, and mechanism of action. This article explores the major difficulties encountered during mass spectrometry (MS) analysis of anthracycline-peptide conjugates, focusing on ionization issues, fragmentation behavior, and challenges of detection from biological matrix. Full article

Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation. Full article

Betalains, which contain nitrogen and are water soluble, are the pigments responsible for many traits of plants and biological activities in different organisms that do not produce them. To better annotate and identify betalains using a spectral library and fingerprint, a database catalog of 140 known betalains (112 betacyanins and 28 betaxanthins) was made in this work to simplify betalain identification in mass spectrometry analysis. Fragmented peaks obtained using MassFrontier, along with chemical structures and protonated precursor ions for each betalain, were added to the database. Product ions made in MS/MS and multistage MS analyses of betanin, beetroot extract, and red pitaya extract revealed the fingerprint of betalains, distinctive ions of betacyanin, betacyanin derivatives such as decarboxylated and dehydrogenated betacyanins, and betaxanthins. A distinctive ion with m/z 211.07 was found in betaxanthins. By using the fingerprint of betalains in the analysis of red pitaya extracts, the catalog of betalains in red pitaya was expanded to 86 (31 betacyanins, 36 betacyanin derivatives, and 19 betaxanthins). Four unknown betalains were detected to have the fingerprint of betalains, but further research will aid in revealing the complete structure. Taken together, we envisage that the further use of the fingerprint of betalains will increase the annotation coverage of identified molecules in studies related to revealing the biological function of betalains or making technologies based on these natural colorants. Full article

Mexico is considered one of the countries with the greatest diversity of the Salvia genus. A significant percentage of its species are known for their use in traditional medicine, highlighting their use as an analgesic. The objective of this work was to determine the chemical composition of the methanolic extracts of S. cinnabarina, S. lavanduloides and S. longispicata through untargeted metabolomics, as well as the in vivo evaluation of the antinociceptive effect and acute oral toxicity. The chemical profiling was performed using ultra-high performance liquid chromatography coupled with a high-resolution mass spectrometry (UPLC-ESI^+/−-MS-QTOF) system and tentative identifications were performed using a compendium of information on compounds previously isolated from Mexican species of the genus. Pharmacological evaluation was carried out using the formalin test and OECD guidelines. The analysis of the spectrometric features of the mass/charge ratios of the three salvias shows that a low percentage of similarity is shared between them. Likewise, the putative identification allowed the annotation of 46 compounds, mainly of diterpene and phenolic nature, with only four compounds shared between the three species. Additionally, the extracts of the three salvias produced a significant antinociceptive effect at a dose of 300 mg/kg administered orally and did not present an acute oral toxicity effect at the maximum dose tested, indicating a parameter of LD₅₀ > 2000 mg/kg. The exploration of the chemical profile of the three salvias by untargeted metabolomics shows that, despite being species with antinociceptive potential, they have different chemical profiles and therefore different active metabolites. Full article

(1) Background: Chronic rhinosinusitis (CRS) is a common chronic inflammation of the nasal mucosa and the paranasal sinuses. The pathogenesis of chronic rhinosinusitis (CRS) is multifactorial and, as of yet, not well understood. (2) Methods: Nasal lavage fluid samples were collected from patients diagnosed with chronic sinusitis with nasal polyposis (CRSwNP) (n = 10) and individuals without sinusitis (control group) (n = 10) who had no nasal complaints. In the present study, we used an untargeted label-free LC-MS/MS mass spectrometric approach combined with bioinformatics and network pathway analysis to compare the changes in the proteomic profiles of the CRSwNP group and the control group. Data from LC-MS/MS underwent univariate and multivariate analyses. (3) Results: The proteomic analyses revealed distinct differences in the abundances of nasal lavage fluid proteins between the CRSwNP and control groups: a total of 234 proteins, 151 up- and 83 down-regulated in CRSwNP. Functional Gene Ontology (GO) analysis showed that dysregulated proteins were involved in airway inflammatory reaction, immune response, and oxidative stress. The biomarkers were evaluated using the Receiver Operating Characteristic (ROC) curve; an Area Under the Curve (AUC) of 0.999 (95% CI) identified potential biomarkers between the CRSwNP and control group. EMILIN-3 and RAB11-binding protein RELCH were down-regulated, and Macrophage migration inhibitory factor and deoxyribonuclease-1 were up-regulated, in CRSwNP compared to the control group. (4) Conclusions: These differentially expressed proteins identified in CRSwNP are involved in airway inflammatory reaction, immune response, and oxidative stress. In particular, the identification of increased interleukin-36 gamma (IL-36γ), which contributes to inflammatory response, and a decrease in SOD, in this group are notable findings. In the future, several of these proteins may prove useful for exploring the pathogenesis of nasal polyps and chronic sinusitis or as objective biomarkers for quantitatively monitoring disease progression or response to therapy. Full article

Current immunoassay techniques for analyzing clinically relevant parathyroid hormone (PTH) circulating fragments cannot distinguish microheterogeneity among structurally similar molecular species. This hinders the identification of molecular species and the capture of target analyte information. Since structural modifications are important in disease pathways, mass spectrometry can detect, identify, and quantify heterogeneous ligands captured by antibodies. We aimed to create a sensitive and selective multiple reaction monitoring–mass spectrometric immunoassay analysis (MRM-MSIA)-based method for detecting and quantifying PTH fragments or proteoforms for clinical research. Our study established MRM transitions using triple-quadrupole tandem mass spectrometry for the signature peptides of five PTH fragments. This method was validated according to FDA guidelines, employing the mass spectrometric immunoassay (MSIA) protocol to bolster detection selectivity and sensitivity. This validated approach was applied by analyzing samples from type 2 diabetes mellitus (T2DM) patients with and without vitamin D deficiency. We found serum PTH fragments associated with vitamin D deficiency in patients with and without T2DM. We developed and validated the MRM-MSIA technique specifically designed for the detection and quantification (amino acid (aa38–44), (aa45–51), and (aa65–75)) of these fragments associated with vitamin D deficiency and T2DM. This study is the first to accurately quantify plasma PTH fragments using MRM-MSIA, demonstrating its potential for clinical diagnostics. Full article

Cyanogenic glycosides are naturally occurring compounds found in numerous plant species, which can release toxic hydrogen cyanide upon hydrolysis. The quantification of cyanogenic glycosides is essential for assessing their potential toxicity and health risks associated with their consumption. Liquid chromatographic techniques coupled with various detectors have been widely used for the quantification of cyanogenic glycosides. In this review, we discuss recent advances in chromatographic quantification methods for cyanogenic glycosides, including the development of new stationary phases, innovative sample preparation methods, and the use of mass spectrometry. We also highlight the combination of chromatographic separation with mass spectrometric detection for the identification and quantification of specific cyanogenic glycosides and their metabolites in complex sample matrices. Lastly, we discuss the current challenges and future perspectives in the development of reliable reference standards, optimization of sample preparation methods, and establishment of robust quality control procedures. This review aims to provide an overview of recent advances in chromatographic quantification methods for cyanogenic glycosides and their applications in various matrices, including food products, biological fluids, and environmental samples. Full article

Cardiovascular drugs have been a burning topic in the field of environmental analytical chemistry in the last few decades. Growing modern healthcare has led to the widespread use of pharmaceuticals. Among these, antihypertensives (sartans, angiotensin-converting enzyme inhibitors) and lipid-regulating drugs (fibrates and statins) are the most frequently consumed and, thus, excreted into wastewater. Their chemical fate during conventional and advanced wastewater treatment, such as ozonation, remains unclear. Analytical chemistry, providing sample pretreatment followed by instrumental analysis, has a tremendous role in water treatment evaluation, mostly from the perspective of parent contaminants’ removals and also assessment of transformation pathways. Ultrasensitive liquid chromatography–mass spectrometry (LC-MS) systems provide many opportunities. By carefully using planned workflows for chromatographic and mass-spectrometric data processing, i.e., suspect and non-target screening approaches, LC-MS allows for the identification and structural elucidation of unknown, predicted, suspected or selected transformation products. Accordingly, some examples and case studies on selected cardiovascular drugs in this review are presented to show the applicability of the used analytical approaches and workflows. Full article

The acrylation degree of vegetable oils plays a relevant role in determining the mechanical properties of the resulting polymers. Both epoxide and acrylate functionalities participate in polymerization reactions, producing various types of chemical bonds in the polymer network, which contribute to specific properties such as molecular size distribution, crosslinking degree, and glass transition temperature (Tg). The accurate identification of epoxide and acrylated groups in triglyceride molecules helps to predict their behavior during the polymerization process. A methodology based on analytical spectrometric techniques, such as direct infusion, mass spectrometry with electrospray ionization, and ultra-high-performance liquid chromatography, is used in combination with FTIR and ¹H NMR to characterize the epoxy and acrylic functionalities in the fatty chains with different numbers of carbon atoms of partially acrylated triglycerides obtained by a non-catalytic reaction. Full article