Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis.

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

Pseudotargeted lipidomics analysis of scoparone on glycerophospholipid metabolism in non-alcoholic steatohepatitis mice by LC-MRM-MS.

As the inflammatory subtype of nonalcoholic fatty liver disease (NAFLD), the progression of nonalcoholic steatohepatitis (NASH) is associated with disorders of glycerophospholipid metabolism. Scoparone is the major bioactive component in Artemisia capillaris which has been widely used to treat NASH in traditional Chinese medicine. However, the underlying mechanisms of scoparone against NASH are not yet fully understood, which hinders the development of effective therapeutic agents for NASH. Given the crucial role of glycerophospholipid metabolism in NASH progression, this study aimed to characterize the differential expression of glycerophospholipids that is responsible for scoparone's pharmacological effects and assess its efficacy against NASH. Liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) was performed to get the concentrations of glycerophospholipids, clarify mechanisms of disease, and highlight insights into drug discovery. Additionally, pathologic findings also presented consistent changes in high-fat diet-induced NASH model, and after scoparone treatment, both the levels of glycerophospholipids and histopathology were similar to normal levels, indicating a beneficial effect during the observation time. Altogether, these results refined the insights on the mechanisms of scoparone against NASH and suggested a route to relieve NASH with glycerophospholipid metabolism. In addition, the current work demonstrated that a pseudotargeted lipidomic platform provided a novel insight into the potential mechanism of scoparone action.

Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS.

Introduction: Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. Methods: Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. Results: A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy. Discussion: This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.

Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors.

Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.

Alkylresorcinol detection and identification in archaeological pottery using ultra-high-performance liquid chromatography-quadrupole/Orbitrap mass spectrometry.

RATIONALE: Alkylresorcinols (AR) are cereal-specific biomarkers and have recently been found in archaeological pots. However, their low concentrations and high susceptibility to degradation make them difficult to detect using conventional gas chromatography mass spectrometry (GC/MS). Here we describe the development of a more sensitive liquid chromatography mass spectrometry (LC/MS) method to detect these compounds. METHOD: A method based on the use of ultra-high-performance liquid chromatography (UHPLC) coupled to an Orbitrap mass analyser was established and validated for the detection of low-concentration ARs in pottery. During the preliminary experiments, UHPLC-Q/Orbitrap MS (ultra-high-performance liquid chromatography-quadrupole/Orbitrap mass spectrometry) was demonstrated to be more sensitive, and a wide range of AR homologues in cereal extracts were detected, unlike UHPLC-QTOFMS (ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry) and GC/MS. The developed method was utilised to profile AR homologue distribution in modern cereal samples and reanalyse AR-containing pots from the archaeological site of Must Farm. RESULTS: A highly sensitive LC/MS method with a limit of detection (LOD) of 0.02 µg/g and a limit of quantification (LOQ) of 0.06 µg/g was used to profile ARs in five modern cereal grains. The obtained LOD is 250 times lower than that obtained using the conventional GC/MS approach. AR 21:0 was the most abundant homologue in all four Triticum spp.-einkorn, emmer, Khorasan wheat and common wheat. Meanwhile, AR 25:0 was the predominant homologue in barley, potentially enabling differentiation between wheat and barley. The developed LC/MS-based method was successfully used to analyse ARs extracted from Must Farm potsherds and identified the cereal species most likely processed in the pots-emmer wheat. CONCLUSION: The described method offers an alternative and more sensitive approach for detecting and identifying ARs in ancient pottery. It has been successfully utilised to detect AR homologues in archaeological samples and discriminate which cereal species-wheat and barley-were processed in the pots.

REMEMProt: a resource of membrane-enriched proteome profiles, their disease associations, and biomarker status.

The differential expression of plasma membrane proteins is integrally analyzed for their diagnosis, prognosis, and therapeutic applications in diverse clinical manifestations. Necessarily, distinct membrane protein enrichment methods and mass spectrometry platforms are employed for their global and relative quantitation. First of its kind to explore, we compiled membrane-associated proteomes in human and mouse systems into a database named, Resource of Experimental Membrane-Enriched Mass spectrometry-derived Proteome (REMEMProt). It currently hosts 14,626 proteins (9,507 proteins in Homo sapiens; 5,119 proteins in Mus musculus) with information on their membrane-protein enrichment methods, experimental/physiological context of detection in cells or tissues, transmembrane domain analysis, and their current attribution as biomarkers. Based on these annotations and the transmembrane domain analysis in proteins or their binary/complex protein-protein interactors, REMEMProt facilitates the assessment of the plasma membrane localization potential of proteins through batch query. A cross-study enrichment analysis platform is enabled in REMEMProt for comparative analysis of proteomes using novel/modified membrane enrichment methods and evaluation of methods for targeted enrichment of membrane proteins. REMEMProt data are made freely accessible to explore and download at https://rememprot.ciods.in/.

Intercropping with maize and sorghum-induced saikosaponin accumulation in Bupleurum chinense DC. by liquid chromatography-mass spectrometry-based metabolomics.

Bupleuri Radix is an important medicinal plant, which has been used in China and other Asian countries for thousands of years. Cultivated Bupleurum chinense DC. (B. chinense) is the main commodity of Bupleuri Radix. The benefits of intercropping with various crops for B. chinense have been recognized; however, the influence of intercropping on the chemical composition of B. chinense is still unclear yet. In this study, intercropping with sorghum and maize exhibited little effect on the root length, root diameter, and single root mass of B. chinense. Only the intercropping with sorghum increased the root length of B. chinense slightly compared to the monocropping. In addition, 200 compounds were identified by UHPLC-Q-TOF-MS, and metabolomic combined with the Venn diagram and heatmap analysis showed apparent separation between the intercropped and monocropped B. chinense samples. Intercropping with sorghum and maize could both increase the saikosaponins, fatty acyls, and organic acids in B. chinense while decreasing the phospholipids. The influence of intercropping on the saikosaponin biosynthesis was probably related with the light intensity and hormone levels in B. chinense. Moreover, we found intercropping increased the anti-inflammatory activity of B. chinense. This study provides a scientific reference for the beneficial effect of intercropping mode of B. chinense.

Application of Magnetic Materials Combined with Echo® Mass Spectrometry System in Analysis of Illegal Drugs in Sewage.

The aim of this study is to solve the problems of the complicated pretreatment and high analytical cost in the detection technology of trace drugs and their metabolites in municipal wastewater. A high-performance magnetic sorbent was fsynthesized for the enrichment of trace drugs and their metabolites in wastewater to develop a magnetic solid-phase extraction pretreatment combined with the acoustic ejection mass spectrometry (AEMS) analytical method. The magnetic nanospheres were successfully prepared by magnetic nanoparticles modified with divinylbenzene and vinylpyrrolidone. The results showed that the linear dynamic range of 17 drugs was 1-500 ng/mL, the recovery was 44-100%, the matrix effect was more than 51%, the quantification limit was 1-2 ng/mL, and the MS measurement was fast. It can be seen that the developed magnetic solid-phase extraction (MSPE) method is a good solution to the problems of the complicated pretreatment and analytical cost in the analysis of drugs in wastewater. The developed magnetic material and acoustic excitation pretreatment coupled with mass spectrometry analysis method can realize the low-cost, efficient enrichment, and fast analysis of different kinds of drug molecules in urban sewage.

Identification of New Hepatic Metabolites of Miconazole by Biological and Electrochemical Methods Using Ultra-High-Performance Liquid Chromatography Combined with High-Resolution Mass Spectrometry.

The main objective of this study was to investigate the metabolism of miconazole, an azole antifungal drug. Miconazole was subjected to incubation with human liver microsomes (HLM) to mimic phase I metabolism reactions for the first time. Employing a combination of an HLM assay and UHPLC-HRMS analysis enabled the identification of seven metabolites of miconazole, undescribed so far. Throughout the incubation with HLM, miconazole underwent biotransformation reactions including hydroxylation of the benzene ring and oxidation of the imidazole moiety, along with its subsequent degradation. Additionally, based on the obtained results, screen-printed electrodes (SPEs) were optimized to simulate the same biotransformation reactions, by the use of a simple, fast, and cheap electrochemical method. The potential toxicity of the identified metabolites was assessed using various in silico models.

Untargeted Metabolomics Based on Ultra-High-Performance Liquid Chromatography Coupled with Quadrupole Orbitrap High-Resolution Mass Spectrometry for Differential Metabolite Analysis of Pinelliae Rhizoma and Its Adulterants.

The present study investigates the chemical composition variances among Pinelliae Rhizoma, a widely used Chinese herbal medicine, and its common adulterants including Typhonium flagelliforme, Arisaema erubescens, and Pinellia pedatisecta. Utilizing the non-targeted metabolomics technique of employing UHPLC-Q-Orbitrap HRMS, this research aims to comprehensively delineate the metabolic profiles of Pinelliae Rhizoma and its adulterants. Multivariate statistical methods including PCA and OPLS-DA are employed for the identification of differential metabolites. Volcano plot analysis is utilized to discern upregulated and downregulated compounds. KEGG pathway analysis is conducted to elucidate the differences in metabolic pathways associated with these compounds, and significant pathway enrichment analysis is performed. A total of 769 compounds are identified through metabolomics analysis, with alkaloids being predominant, followed by lipids and lipid molecules. Significant differential metabolites were screened out based on VIP > 1 and p-value < 0.05 criteria, followed by KEGG enrichment analysis of these differential metabolites. Differential metabolites between Pinelliae Rhizoma and Typhonium flagelliforme, as well as between Pinelliae Rhizoma and Pinellia pedatisecta, are significantly enriched in the biosynthesis of amino acids and protein digestion and absorption pathways. Differential metabolites between Pinelliae Rhizoma and Arisaema erubescens are mainly enriched in tyrosine metabolism and phenylalanine metabolism pathways. These findings aim to provide valuable data support and theoretical references for further research on the pharmacological substances, resource development and utilization, and quality control of Pinelliae Rhizoma.

Analysis of brominide disinfection by-products (DBPs) in aquaculture water using ultra-high performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-tof/MS).

BACKGROUND: halogenic disinfectants have been shown to produce toxic and carcinogenic disinfection by-products in the water disinfection process. Dibromohydantoin (DBDMH) is a commonly used water disinfectant in aquaculture. Aquaculture water has more complex matrix, and the analytical method for disinfection by-products (DBPs) have not been reported. Since the content of DBPs is related to the external conditions such as ultraviolet irradiation, temperatures, pH and humic acid. The semi-target screening method for mainly DBPs based on tracing mass spectrometry fragments of bromide and accurate mass of high resolution mass spectrometry was established by ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-tof/MS). Br-DBPs as a important class of DBPs from DBDMH, which quantification analysis methods were developed based on accurate mass of high resolution mass spectrometry. METHODS: through screening method to identify unknown Br-DBPs and quantitative analysis of the typical 4-bromophenol by-product of accurate mass was established. The conditions of the instrument parameters of mass spectrometry and SPE sample preparation procedure in complex real sample were optimized. The high efficiency method was demonstrated for the determination of Br-DBPs with a good linear correlation (R2 = 0.999) in the range of 0.500-200 µg L-1 and limit of detections (LODs) and limit of quantifications (LOQs) were 0.0250 ng L-1 and 0.0834 ng L-1, respectively. CONCLUSION: the developed screening and quantification analytical strategy for Br-DBPs is rapid, accurate and sensitivity applicable for environmental in aquaculture water monitoring.

Glycoproteomics: Charting new territory in mass spectrometry and glycobiology.

Glycosylation is an incredibly common and diverse post-translational modification that contributes widely to cellular health and disease. Mass spectrometry is the premier technique to study glycoproteins; however, glycoproteomics has lagged behind traditional proteomics due to the challenges associated with studying glycosylation. For instance, glycans dissociate by collision-based fragmentation, thus necessitating electron-based fragmentation for site-localization. The vast glycan heterogeneity leads to lower overall abundance of each glycopeptide, and often, ion suppression is observed. One of the biggest issues facing glycoproteomics is the lack of reliable software for analysis, which necessitates manual validation and serves as a massive bottleneck in data processing. Here, I will discuss each of these challenges and some ways in which the field is attempting to address them, along with perspectives on how I believe we should move forward.

Proteomic Profiling of Samples Derived from a Murine Model Following Cryptococcus neoformans Infection.

Proteomic profiling provides in-depth information about the regulation of diverse biological processes, activation of and communication across signaling networks, and alterations to protein production, modifications, and interactions. For infectious disease research, mass spectrometry-based proteomics enables detection of host defenses against infection and mechanisms used by the pathogen to evade such responses. In this chapter, we outline protein extraction from organs, tissues, and fluids collected following intranasal inoculation of a murine model with the human fungal pathogen Cryptococcus neoformans. We describe sample preparation, followed by purification, processing on the mass spectrometer, and a robust bioinformatics analysis. The information gleaned from proteomic profiling of fungal infections supports the detection of novel biomarkers for diagnostic and prognostic purposes.

Ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap mass spectrometry and molecular network analysis alongside network pharmacology to elucidate the active constituents and mechanism of action of Huahong tablet in treating pelvic inflammatory disease.

RATIONALE: Huahong tablet, a commonly used clinical Chinese patent medicine, shows good efficacy in treating pelvic inflammation and other gynaecological infectious diseases. However, the specific composition of Huahong tablets, which are complex herbal formulations, remains unclear. Therefore, this study aims to identify the active compounds and targets of Huahong tablets and investigate their mechanism of action in pelvic inflammatory diseases. METHODS: We utilised ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap mass spectrometry and the relevant literature to identify the chemical components of Huahong tablets. The GNPS database was employed to further analyse and speculate on the components. Potential molecular targets of the active ingredients were predicted using the SwissTargetPrediction website. Protein-protein interaction analysis was conducted using the STRING database, with visualisation in Cytoscape 3.9.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. Additionally, a traditional Chinese medicine-ingredient-target-pathway network was constructed using Cytoscape 3.10.1. Molecular docking validation was carried out to investigate the interaction between core target and specific active ingredient. RESULTS: A total of 66 chemical components were identified, and 41 compounds were selected as potential active components based on the literature and the TCMSP database. Moreover, 38 core targets were identified as key targets in the treatment of pelvic inflammatory diseases with Huahong tablets. GO and KEGG enrichment analysis revealed 986 different biological functions and 167 signalling pathways. CONCLUSION: The active ingredients in Huahong tablets exert therapeutic effects on pelvic inflammatory diseases by acting on multiple targets and utilising different pathways. Molecular docking confirmed the high affinity between the specific active ingredients and disease targets.

Insights into modifiers effects in differential mobility spectrometry: A data science approach for metabolomics and peptidomics.

Utilizing a data-driven approach, this study investigates modifier effects on compensation voltage in differential mobility spectrometry-mass spectrometry (DMS-MS) for metabolites and peptides. Our analysis uncovers specific factors causing signal suppression in small molecules and pinpoints both signal suppression mechanisms and the analytes involved. In peptides, machine learning models discern a relationship between molecular weight, topological polar surface area, peptide charge, and proton transfer-induced signal suppression. The models exhibit robust performance, offering valuable insights for the application of DMS to metabolites and tryptic peptides analysis by DMS-MS.

Advancing Simultaneous Extraction and Sequential Single-Particle ICP-MS Analysis for Metallic Nanoparticle Mixtures in Plant Tissues.

Engineered nanoparticles (ENPs) have been increasingly used in agricultural operations, leading to an urgent need for robust methods to analyze co-occurring ENPs in plant tissues. In response, this study advanced the simultaneous extraction of coexisting silver, cerium oxide, and copper oxide ENPs in lettuce shoots and roots using macerozyme R-10 and analyzed them by single-particle inductively coupled plasma-mass spectrometry (ICP-MS). Additionally, the standard stock suspensions of the ENPs were stabilized with citrate, and the long-term stability (up to 5 months) was examined for the first time. The method performance results displayed satisfactory accuracies and precisions and achieved low particle concentration and particle size detection limits. Significantly, the oven drying process was proved not to impact the properties of the ENPs; therefore, oven-dried lettuce tissues were used in this study, which markedly expanded the applicability of this method. This robust methodology provides a timely approach to characterize and quantify multiple coexisting ENPs in plants.

[Impurity profile analysis of amphotericin B using on-line two-dimensional high performance liquid chromatography-quadrupole time-of-flight mass spectrometry].

Amphotericin B (AmB) is a polyene-macrolide antimicrobial drug with a broad antibacterial spectrum and remarkable efficacy against deep fungal infections. It binds to ergosterol on the fungal cell membrane and alters its permeability, thereby destroying the membrane. AmB is a multicomponent antimicrobial medication that contains a wide range of impurities, rendering quality analysis extremely difficult. In the current Chinese Pharmacopoeia (Edition 2020) and European Pharmacopoeia (EP10.3), high performance liquid chromatography (HPLC) is applied to examine related substances in AmB. However, this technique presents a number of issues. For instance, the mobile phases used in the HPLC method described in both references contain nonvolatile inorganic salts, which cannot be coupled with a mass spectrometry (MS) detector. In addition, because the mobile phases used have a low pH, the component/impurities of AmB drug can easily be degraded or interconverted during the analytical process, leading to reduced analytical accuracy. Therefore, the accuracy and sensitivity of this method must be improved. In this study, a method based on on-line two-dimensional high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (2D HPLC-Q TOF/MS) was developed to analyze the impurity profile of AmB in accordance with the Chinese Pharmacopoeia (Edition 2020) and European Pharmacopoeia (EP10.3). The method combines on-line dilution and a multiple-capture HPLC system to achieve the efficient separation of AmB component/impurities. It also resolves the issue of poor solvent compatibility in 2D HPLC, increases the analytical flux, enhances the automation capability, reduces the mutual conversion of AmB and its impurities during the analytical process, and increases the detection sensitivity of the method. MS was also used to determine the structural inference of unstable components and impurities. An XBridge Shield C18 column (250 mm×4.6 mm, 3 µm) was used for first-dimensional-liquid chromatography with gradient elution using methanol-acetonitrile-4.2 g/L citric acid monohydrate solution (10∶30∶60, v/v/v, pH 4.7) as mobile phase A and methanol-acetonitrile-4.2 g/L citric acid monohydrate solution (12∶68∶20, v/v/v, pH 3.9) as mobile phase B. An Xtimate C8 column (10 mm×2.1 mm, 5 µm) was used as the trap column, and trapping and desalting were performed using 10 mmol/L ammonium formate aqueous solution containing 0.1% formic acid-acetonitrile (95∶5, v/v). An Xtimate C8 column (250 mm×2.1 mm, 5 µm) was used for second-dimensional-liquid chromatography with gradient elution using 10 mmol/L ammonium formate aqueous solution containing 0.1% formic acid-acetonitrile (95∶5, v/v) and 10 mmol/L ammonium formate aqueous solution containing 0.1% formic acid-acetonitrile (5∶95, v/v) as mobile phases. The data were collected in positive-ion mode. In this study, the structures of six impurities in amphotericin B were inferred, according to the fragmentation, the MS and MS2 spectra of each impurity. The developed method can be used to quickly and sensitively analyze the impurity profile of AmB. Furthermore, the research results on impurity profiles can be applied to guide improvements in AmB production.

Coupling imaging mass cytometry with Alcian blue histochemical staining for a single-slide approach.

Imaging mass cytometry (IMC) is a metal mass spectrometry-based method allowing highly multiplex immunophenotyping of cells within tissue samples. However, some limitations of IMC are its 1-µm resolution and its time and costs of analysis limiting respectively the detailed histopathological analysis of IMC-produced images and its application to small selected tissue regions of interest (ROI) of one to few square millimeters. Coupling on a single-tissue section, IMC and histopathological analyses could permit a better selection of the ROI for IMC analysis as well as co-analysis of immunophenotyping and histopathological data until the single-cell level. The development of this method is the aim of the present study in which we point to the feasibility of applying the IMC process to tissue sections previously Alcian blue-stained and digitalized before IMC tissue destructive analyses. This method could help to improve the process of IMC in terms of ROI selection, time of analysis, and the confrontation between histopathological and immunophenotypic data of cells.

Investigation of odor pollution by utilizing selected ion flow tube mass spectrometry (SIFT-MS) and principal component analysis (PCA).

Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban-rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances.