Orthogonal Partial Least Squares Discriminant Analysis Research Articles

Identification of Volatile Organic Compounds and Analysis of Aroma Characteristics in Ten Pear Syrups.

Aroma in food plays an important role in food perception and acceptance, which depends on various mixtures of volatile organic compounds (VOCs). Moreover, VOCs are of great significance for aroma identification. In this study, headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) technology was used to determine the VOCs in 10 pear syrups. A total of 127 VOCs were quantitatively determined, including 9 common VOCs and 46 characteristic VOCs of 10 pear syrups. The pear syrups were divided into three categories by cluster analysis, and thirty-eight differential VOCs were obtained using orthogonal partial least squares discrimination analysis (OPLS-DA) and fourteen key VOCs were selected by odor activity value (OAV). It was revealed that the key and common aroma components of pear syrups were butanoic acid, methyl ester, 2-methyl-, methyl ester and Hexanoic acid, and ethyl ester. The characteristic and differential VOCs were 10-Undecen-1-ol, Hexadecanal, n-Propylacetate, Cyclohexanol, 5-methyl-2-(1-methylethyl)-, (1S,2R,5S)-, Methional, Disulfide, dimethyl, 8-Nonenoic acid, ethyl ester, Naphthalene, 1,2-dihydro-1,1,6-trimethyl-, 3H-Purin-6-amine, N,N,3-trimethyl-, 2-Octanol,2,6-dimethyl-, Furyl hydroxymethyl ketone, Heptane, 2,2,4,6,6-pentamethyl-, and Butanoic acid,2-methyl-,methyl ester. The above results showed that different pear syrups had rich diversity in aroma compounds, with some components being shared among them while others are exclusive to specific syrups.

Characterization of key aroma-active compounds in Zanthoxylum schinifolium by sensory evaluation and multiple instrumental analyses

The aroma characteristics of Zanthoxylum schinifolium from four different production areas in Sichuan were identified and compared. Aroma components were analyzed via sensory evaluation, gas chromatography–olfactometry–mass spectrometry (GC–O–MS), and comprehensive two-dimensional gas chromatography-olfactometry-mass spectrometry (GC × GC–O–MS). The results revealed that minty, spicy, woody, fresh, citrus, and herbal notes could be perceived, with the Jinyang sample exhibiting the most significant aroma profile. Compared with GC–O–MS, GC × GC–O–MS offers advantages in identifying aroma-active compounds, and it can identify more types and quantities of aroma-active compounds. In this study, 40 aroma-active compounds from seven different classes were identified via GC × GC–O–MS. Through aroma extraction dilution analysis (AEDA), 14 aroma-active compounds were quantitatively analyzed, and odor activity values (OAVs) were calculated. Additionally, orthogonal partial least squares discriminant analysis (OPLS-DA) was employed to identify four aroma-active compounds with variable importance in projection (VIP) scores greater than 1, indicating their potential as identification markers. This study enhances our understanding of the sensory characteristics and aroma-active compounds of Zanthoxylum schinifolium.

Metabolomic profiling reveals the step-wise alteration of bile acid metabolism in patients with diabetic kidney disease

BackgroundDiabetic kidney disease (DKD) is the major complication of diabetes concomitant with gut dysbiosis and glycometabolic disorder, which are strongly associated with bile acid (BA) metabolism. Yet studies investigating the BA metabolism involving in DKD pathogenesis are limited. This study aimed to explore the metabolomic profiling of BAs in DKD and analyze its association with DKD progression.MethodsAn ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was established to quantify BAs in the plasma, fecal and urine samples of patients with DKD or T2DM and healthy individuals (n = 30 for each group). The key BAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and receiver-operating characteristic (ROC) curve. Polynomial regression and Pearson’s correlation analyses were performed to assess the correlation between the key BAs and the clinical indicators reflecting DKD progression.ResultsMetabolomic profiling of 50 kinds of BAs presented the markedly step-wise alterations of BAs in plasma and feces as well as the little in urine of patients with DKD. Eight kinds of BAs in the plasma, eight kinds in the feces and three kinds in the urine were abnormally expressed, accompanying with the increased conjugated/unconjugated ratios of cholic acid, deoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid and hyocholic acid in the plasma, and of cholic acid, chenodeoxycholic acid and lithocholic acid in the feces. Moreover, the increased plasma level of glycochenodeoxycholic acid, and the increased fecal levels of glycolithocholic acid, 7-ketodeoxycholic acid and chenodeoxycholic acid-3-β-D-glucuronide are strongly correlated with the clinical indicators reflecting DKD progression, including eGFR, 24 h urinary protein and 24 h urinary microalbumin.ConclusionsOur study for the first time disclosed the specific alterations of BA metabolism reflecting the step-wise progression of DKD, providing the basis for early identification and therapeutical strategies for DKD.

Application of preoperative advanced diffusion magnetic resonance imaging in evaluating the postoperative recurrence of lower grade gliomas

BackgroundRecurrence of lower grade glioma (LrGG) appeared to be unavoidable despite considerable research performed in last decades. Thus, we evaluated the postoperative recurrence within two years after the surgery in patients with LrGG by preoperative advanced diffusion magnetic resonance imaging (dMRI).Materials and methods48 patients with lower-grade gliomas (23 recurrence, 25 nonrecurrence) were recruited into this study. Different models of dMRI were reconstructed, including apparent fiber density (AFD), white matter tract integrity (WMTI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), Bingham NODDI and standard model imaging (SMI). Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was used to construct a multiparametric prediction model for the diagnosis of postoperative recurrence.ResultsThe parameters derived from each dMRI model, including AFD, axon water fraction (AWF), mean diffusivity (MD), mean kurtosis (MK), fractional anisotropy (FA), intracellular volume fraction (ICVF), extra-axonal perpendicular diffusivity (De⊥), extra-axonal parallel diffusivity (De∥) and free water fraction (fw), showed significant differences between nonrecurrence group and recurrence group. The extra-axonal perpendicular diffusivity (De⊥) had the highest area under curve (AUC = 0.885), which was significantly higher than others. The variable importance for the projection (VIP) value of De⊥ was also the highest. The AUC value of the multiparametric prediction model merging AFD, WMTI, DTI, DKI, NODDI, Bingham NODDI and SMI was up to 0.96.ConclusionPreoperative advanced dMRI showed great efficacy in evaluating postoperative recurrence of LrGG and De⊥ of SMI might be a valuable marker.

Pre-ozonation coupled with forward osmosis with fertilizer as draw solution for simultaneous wastewater treatment and agricultural irrigation

Membrane-based processes have emerged as effective solutions for treating tannery wastewater. Persistent membrane fouling remains a significant obstacle to long-term operational sustainability, and residual pollutants often persist in the treated effluent. To address these challenges, we investigated an integrated ozone-forward osmosis (O3-FO) process, with a focus on evaluating the efficacy of pre-ozonation in alleviating membrane fouling, as well as exploring the possibilities of this integrated process for the reuse of tannery wastewater. Fertilizer, specifically 2 M Ca(NO3)2, served as the draw solution (DS) in this process. The findings reveal that the integrated system demonstrated remarkable pollutant retention capabilities. Notably, no metal was detected in the fertilizer. Therefore, the integrated process not only dilutes the fertilizer but also minimizes the risk of heavy metal contamination to both crops and soil. Furthermore, pre-ozonation effectively mitigated membrane fouling, resulting in an increase in membrane flux with a maximum increase of 20.98 % at 0.6 L/min. Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed pre-ozonation has the most significant impact on four parameters: water flux (Jw), chemical oxygen demand (COD), fouling resistance (Rf) and oxidation reduction potential (ORP). Meanwhile, ammonium nitrogen (NH4-N) variation, dissolved organic carbon (DOC) variation and ORP played an important role in the four systems. Multi-criteria decision analysis (MCDA) showed that the 0.2 L/min O3-FO system achieved the highest score (0.66), followed by 0.6 L/min (0.53), 0.4 L/min (0.41) and 0 L/min (0.29). This research offers a theoretical framework for the synergistic integration of advanced oxidation and membrane technology in the treatment of industrial wastewater.

The vaginal metabolomics profile with features of polycystic ovary syndrome: a pilot investigation in China.

Polycystic ovary syndrome (PCOS) is the most common metabolic disorder and reproductive endocrine disease, posing an elevated risk to women of reproductive age. Although metabolism differences in serum, amniotic fluid and urine have been documented in PCOS, there remains a paucity of evidence for vaginal fluid. This study aimed to identify the metabolic characteristics and potential biomarkers of PCOS in Chinese women of reproductive age. We involved ten newly diagnosed PCOS women who attended gynecology at Zhongda Hospital and matched them with ten healthy controls who conducted health check-up programs at Gulou Maternal and Child Health Center in Nanjing, China from January 1st, 2019 to July 31st, 2020. Non-targeted metabolomics based on ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was applied to differentially screen vaginal metabolites between PCOS group and healthy controls. Principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA) and enrichment analysis were used to observe differences, search for potential biomarkers and enrich related pathways. Among the 20 participants, a total of 195 different metabolites were detected between PCOS group and healthy control group. PCOS and control groups were effectively separated by vaginal fluid. Lipids and lipid-like molecules constituted the majority of differential metabolites. Notably, dopamine exhibited an increased trend in PCOS group and emerged as the most significant differential metabolite, suggesting its potential as a biomarker for identifying PCOS. The application of UHPLC-MS/MS based vaginal metabolomics methods showed significant differences between PCOS and non-PCOS healthy control groups, especially linoleic acid metabolism disorder. Most differential metabolites were enriched in pathways associated with linoleic acid metabolism, phenylalanine metabolism, tyrosine metabolism, nicotinate and nicotinamide metabolism or arachidonic acid metabolism. In this pilot investigation, significant metabolomics differences could be obtained between PCOS and healthy control groups. For PCOS women of reproductive age, vaginal metabolism is a more economical, convenient and harmless alternative to provide careful personalized health diagnosis and potential targets for therapeutic intervention.

Strategies for identifying adulterated Acanthopanax senticosus using portable mass spectrometry and chemometrics

Acanthopanax senticosus is widely cultivated in China, Japan, South Korea, and Russia, and is extensively researched worldwide. Illegal activities, particularly adulteration have been fostered due to its large market demand and high price. This study successfully identifies authentic and adulterated Acanthopanax senticosus samples using a portable mass spectrometer combined with chemometrics. Different data processing and feature selection methods were evaluated for authenticity and adulteration models, with SNV-VIP being selected as the optimal method for both models. Excellent classification performance was achieved using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), K-nearest Neighbors (KNN), Random Forest (RF), and Convolutional Neural Networks-Long Short-Term Memory-Attention (CNN-LSTM-Attention) models, with prediction accuracies for the authenticity model validation set being 1.00, 1.00, 1.00, and 1.00, respectively. The prediction accuracies for the adulteration model validation set were 0.86, 0.73, 0.89, and 0.92, respectively. Additionally, the models identified the most important ions based on variable importance. The combination of portable mass spectra (PMS) and chemometrics provides a simple, rapid, and reliable method for identifying adulteration in Acanthopanax senticosus samples. This study provides a new basis into the application of PMS for adulteration detection in the food field.

Dynamic evolution of volatile compounds during cold storage of sturgeon fillets analyzed by gas chromatography-ion mobility spectrometry and chemometric methods

This study investigated the changes in quality and volatile flavoring compounds in sturgeon fillets during refrigeration. Potential flavor compounds were identified using orthogonal partial least squares discriminant analysis (OPLS-DA) and gas chromatography–mass spectrometry (GC-IMS). The results showed that TVB-N content, TBARS values, total colony counts, and K-value increased with prolonged refrigeration, reaching spoilage thresholds after approximately eight days. A total of 33 volatile compounds (including monomers and dimers) were detected in sturgeon fillets during different refrigeration periods, including aldehydes, alcohols, ketones, acids, esters, ethers, and other compounds. OPLS-DA further revealed flavor differences in sturgeon fillets across different refrigeration stages, identifying 18 distinct volatile compounds. Correlation analysis showed that trans-2-Pentenal, 2-methylpropanal, and glutaraldehyde were associated with pleasant odor to the frozen sturgeon fillets in the early stage, while hexane nitrile and thiazole were the main substances that caused unpleasant odors.

Elemental fingerprinting based on ICP-MS/OES and machine learning: Sample digestion-free and standard-free strategy for geographical origin identification of Chinese liquor

The significant price disparity of Chinese baijiu originating from various regions has unfortunately partially given rise to the proliferation of counterfeit and substandard products, and this may cause disturbance in the market economy. Ensuring the traceability and differentiation of baijiu by its origin is therefore of paramount importance. In this study, the Baijiu samples were straightforward dilution without sample digestion, followed by the detection of elemental signal intensities using ICP-MS and ICP-OES without accurate quantification, to rapidly construct their multi-elemental fingerprints. Heatmap and HCA (hierarchical cluster analysis) were used for data visualization and tentative classification, respectively. PCA (principal component analysis) was implemented to corroborate the classification outcomes. Subsequently, the supervised OPLS-DA (orthogonal partial least squares discrimination analysis) was deployed to enhance the identification and predictive capabilities for the Baijiu samples. The unequivocal discrimination of the three distinct Baijiu origins with 100% classification accuracy. The validation values R2 and Q2 approached 1.0, indicating a great fit and prediction capability of the OPLS-DA model. The VIP plot evaluated the importance of elemental variables, revealing that the key contributions were primarily from elements K, Ca, Na, Mg, and Al. Collectively, these findings demonstrate the efficacy of chemometric techniques, when integrated with multi-elemental fingerprints based on their signal intensities only, in identifying the geographical origins of the Baijiu. This approach holds promise for applications in quality control and anti-counterfeiting measures in the baijiu industry.

High-pressure processing for pickled crabs: Safety, quality and flavor assessment and correlation analysis of spoilage indicators

Pickled aquatic products are often susceptible to spoilage, which can significantly affect their edibility and flavor. This study aimed to improve the safety and quality aspects of pickled crabs by treated with high-pressure processing (HPP, 400 MPa for 5 min). The physical, chemical, microbiological, and flavor properties of the crabs before and after HPP were assessed, correlation and cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted on specific indicators influencing crab spoilage. The results indicated that HPP effectively reduced the total viable count, psychrophilic bacteria count (PBC), and Escherichia coliform (E. coli) count during storage at 4 °C (reduced by 30.61%, 36.72%, 100%, day 0, respectively; 19.02%, 21.95%, 100%, day 42, respectively). Moreover, HPP treatment reduced the total volatile basic nitrogen (TVBN) content (35.33 mg/100 g to 31.81 mg/100 g) and the production of putrescine (1858.7 mg/kg to 1293.04 mg/kg) and cadaverine (753.47 mg/kg to 422.08 mg/kg) in the end of storage stage, which extend the shelf life of the pickled crab by 12 days. Furthermore, the application of HPP facilitated the penetration of ethanol into the crab meat, thereby enhancing the development of the characteristic pickled crab flavor. The OPLS-DA analyses indicated that TVBN, TBA, and histamine as deteriorative factors for quality deterioration are key factors determining spoilage of crab, which are significantly correlated with other corruption products. This study provides valuable insights into improving the safety and flavor of crab products, establishing a theoretical basis for ensuring quality control throughout the production process.

Reduced antiviral gene expression and elevated CXCL8 expression in peripheral blood are associated with severe hypoxemia in RSV-infected children.

The pathology of respiratory syncytial virus (RSV) infection remains unclear. An unbalanced immune response to RSV infection can lead to immunopathology, causing airway damage and impaired exchange of oxygen and carbon dioxide between the air and the bloodstream. We aimed to evaluate the association of the expression of inflammatory and antiviral genes in peripheral blood with severe hypoxemia in children with RSV infection seen in the hospital emergency room. We conducted a cross-sectional study on 121 RSV-infected children seen in hospital emergency rooms between 2015 and 2023. Total RNA was extracted from whole blood samples, and gene expression (IL-6, TNFα, CXCL8, ISG15, IFIT1, RIGI, IFNβ, CCL5, and CXCL10) was quantified using quantitative RT-PCR. The outcome variable was having severe hypoxemia (SpO2 ≤ 90%). The association analysis was performed using a volcano plot, adjusted logistic regression, and orthogonal partial least squares discriminant analysis (OPLS-DA). We found that 26 of 121 children had severe hypoxemia (SpO2 ≤ 90%). CXCL8 was overexpressed [fold changes (FC) > 2; q-value < 0.05], and ISG15, IFIT1, RIGI, IFNβ, CCL5, and CXCL10 were underexpressed (FC <0.5; q-value <0.05) in children with severe hypoxemia. These associations were ratified using adjusted logistic regression. The OPLS-DA showed that the gene expressions of CXCL8, ISG15, IFIT1, RIGI, and CXCL10 had values of variable importance in projection (VIP) ≥1, being the most relevant features. In conclusion, an imbalance favoring inflammation over antiviral defense may contribute to the pathogenesis of severe hypoxemia in RSV-infected children. These findings provide valuable insights into the pathology of RSV infection.

Comparison of Ultra-High-Pressure and Conventional Cold Brew Coffee at Different Roasting Degrees: Physicochemical Characteristics and Volatile and Non-Volatile Components

The impact of the roasting degree on ultra-high-pressure cold brew (UHP) coffee remains unclear, although it has been found that UHP technology accelerates the extraction of cold brew (CB) coffee. Therefore, this study investigated the effects of three different degrees of roasting (light, medium, and dark) on the physicochemical characteristics, volatile and non-volatile components, and sensory evaluation of UHP coffee. Orthogonal partial least-squares-discriminant analysis (OPLS-DA) and principal component analysis (PCA) were used to assess the effects of different roasting degrees. The results showed that most physicochemical characteristics, including total dissolved solids (TDSs), extraction yield (EY), total titratable acidity (TTA), total sugars (TSs), and total phenolic content (TPC), of UHP coffee were similar to those of conventional CB coffee regardless of the degree of roasting. However, the majority of physicochemical characteristics, non-volatile components, including the antioxidant capacity (measured based on DPPH and ABTS) and melanoidin, caffeine, trigonelline, and CGA contents increased significantly with an increase in roasting degree. The sensory evaluation revealed that as the roasting degree rose, the nutty flavor, astringency, bitterness, body, and aftertaste intensities increased, while floral, fruity, and sourness attributes decreased. The HS-SPME-GC/MS analysis showed that most volatile components increased from light to dark roasting. Moreover, 15 representative differential compounds, including hazelnut pyrazine, linalool, butane-2,3-dione, and 3-methylbutanal, were identified by calculating the odor-active values (OAVs), indicating that these contributed significantly to the odor. The PCA showed that the distance between the three roasting degree samples in UHP coffee was smaller than that in CB coffee. Overall, the effect of roasting degrees on UHP coffee was less than that on CB coffee, which was consistent with the results of physicochemical characteristics, volatile components, and sensory evaluation.

Serum metabolomics analysis of malnutrition in patients with gastric cancer: a cross sectional study

BackgroundAlthough malnutrition is common in cancer patients, its molecular mechanisms has not been fully clarified. This study aims to identify significantly differential metabolites, match the corresponding metabolic pathways, and develop a predictive model of malnutrition in patients with gastric cancer.MethodsIn this cross-sectional study, we applied non-targeted metabolomics using liquid chromatography-mass spectrometry to explore the serum fingerprinting of malnutrition in patients with gastric cancer. Malnutrition-specific differential metabolites were identified by orthogonal partial least-squares discriminant analysis and t-test and matched with the Human Metabolome Database and the LIPID Metabolites and Pathways Strategy. We matched the corresponding metabolic pathways of malnutrition using pathway analysis at the MetaboAnalyst 5.0. We used random forest analyses to establish the predictive model.ResultsWe recruited 220 malnourished and 198 non-malnourished patients with gastric cancer. The intensities of 25 annotated significantly differential metabolites were lower in patients with malnutrition than those without, while two others were higher in patients with malnutrition than those without, including newly identified significantly differential metabolites such as indoleacrylic acid and lysophosphatidylcholine(18:3/0:0). We matched eight metabolic pathways associated with malnutrition, including aminoacyl-tRNA biosynthesis, tryptophan metabolism, and glycerophospholipid metabolism. We established a predictive model with an area under the curve of 0.702 (95% CI: 0.651–0.768) based on four annotated significantly differential metabolites, namely indoleacrylic acid, lysophosphatidylcholine(18:3/0:0), L-tryptophan, and lysophosphatidylcholine(20:3/0:0).ConclusionsWe identified 27 specific differential metabolites of malnutrition in malnourished compared to non-malnourished patients with gastric cancer. We also matched eight corresponding metabolic pathways and developed a predictive model. These findings provide supportive data to better understand molecular mechanisms of malnutrition in patients with gastric cancer and new strategies for the prediction, diagnosis, prevention, and treatment for those malnourished.

Development of a Quality Evaluation Method for Allii Macrostemonis Bulbus Based on Solid-Phase Extraction–High-Performance Liquid Chromatography–Evaporative Light Scattering Detection Chromatographic Fingerprinting, Chemometrics, and Quantitative Analysis of Multi-Components via a Single-Marker Method

As a traditional Chinese medicine (TCM), Allii Macrostemonis Bulbus (AMB) is a key herb for the treatment of thoracic paralytic cardiac pain, but its quality evaluation method has not yet been fully clarified. In this study, chromatographic fingerprints of AMB were developed using solid-phase extraction–high-performance liquid chromatography–evaporative light scattering detection (SPE-HPLC-ELSD) to evaluate the quality of AMB from various origins and processing methods. This was achieved by employing chemical pattern recognition techniques and verifying the feasibility and applicability of the quality evaluation of AMB through the quantitative analysis of multi-components via a single-marker (QAMS) method. Through the analysis of the fingerprints of 18 batches of AMB, 30 common peaks were screened, and 6 components (adenosine, syringin, macrostemonoside T, macrostemonoside A, macrostemonoside U, and macrostemonoside V) were identified. Moreover, three differential markers (macrostemonoside A, macrostemonoside T, and macrostemonoside U) were screened out using chemometrics techniques, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Subsequently, a QAMS method was established for macrostemonoside T and macrostemonoside U using macrostemonoside A as an internal reference. The results demonstrate the method’s accuracy, reproducibility, and stability, rendering it suitable for the quality evaluation of AMB. This study provides a theoretical basis for drug quality control and the discovery of quality markers for AMB.

Analysis and discrimination of adhesive species using ATR-FTIR combined with Raman, and HS-GC-IMS together with multivariate statistical analysis

Identifying the species and origin of adhesives in criminal investigations aids in narrowing inquiry scope and supporting case detection. This study introduces two advanced combined analytical techniques for distinguishing adhesive species, including attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with Raman spectroscopy, and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) together with multivariate statistical analysis. ATR-FTIR categorized seven adhesives into three groups based on the base materials, with further differentiation achieved via Raman spectra. Analysis of volatile components identified 79 volatile organic compounds (VOCs), with esters being the most concentrated. The fingerprint profile clearly illustrated the characteristic fingerprint sequence and unique marker compounds of each adhesive, effectively enabling their differentiation. Multivariate statistical analysis methods, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap, and hierarchical cluster analysis (HCA), were utilized to visually interpret the classification of adhesives. This integrated analytical approach provides a comprehensive analysis of adhesive compositions, facilitating the diversification and precision of adhesive species identification, and broadening the scope for detecting and analyzing trace evidence in forensic science.

Unveiling the gut microbiota blueprint of schizophrenia: a multilevel omics approach.

Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia. Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial α-diversity and β-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software. The findings of the study indicated a lack of substantial evidence supporting significant disparities in α-diversity and β-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system. Our results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia.

LC–HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients

Schizophrenia (SCH) is a major mental illness that causes impaired cognitive function and long-term disability, so the requirements for reliable biomarkers for early diagnosis and therapy of SCH are essential. The objective of this work was an untargeted lipidomic study of serum samples from a Serbian cohort including 30 schizophrenia (SCH) patients and 31 non-psychiatric control (C) individuals by applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) and chemometric analyses. Principal component analysis (PCA) of all samples indicated no clear separation between SCH and C groups but indicated clear gender separation in the C group. Multivariate statistical analyses (PCA and orthogonal partial least squares discriminant analysis (OPLS-DA)) of gender-differentiated SCH and C groups established forty-nine differential lipids in the differentiation of male SCH (SCH-M) patients and male controls (C-M), while sixty putative biomarkers were identified in the differentiation of female SCH patients (SCH-F) and female controls (C-F). Lipidomic study of gender-differentiated groups, between SCH-M and C-M and between SCH-F and C-F groups, confirmed that lipids metabolism was altered and the content of the majority of the most affected lipid classes, glycerophospholipids (GP), sphingolipids (SP), glycerolipids (GL) and fatty acids (FA), was decreased compared to controls. From differential lipid metabolites with higher content in both SCH-M and SCH-F patients groups compared to their non-psychiatric controls, there were four common lipid molecules: ceramides Cer 34:2, and Cer 34:1, lysophosphatidylcholine LPC 16:0 and triacylglycerol TG 48:2. Significant alteration of lipids metabolism confirmed the importance of metabolic pathways in the pathogenesis of schizophrenia.

Alterations of Ceramides, Acylcarnitines, GlyceroLPLs, and Amines in NSCLC Tissues.

Abnormal lipid metabolism plays an important role in cancer development. In this study, nontargeted lipidomic study on 230 tissue specimens from 79 nonsmall cell lung cancer (NSCLC) patients was conducted using ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). Downregulation of sphingosine and medium-long-chain ceramides and short-medium-chain acylcarnitine, upregulation of long-chain acylcarnitine C20:0, and enhanced histamine methylation were revealed in NSCLC tissues. Compared with paired noncancerous tissues, adenocarcinoma (AC) tissues had significantly decreased levels of sphingosine, medium-long-chain ceramides (Cer d18:1/12:0 and Cer d16:1/14:0, Cer d18:0/16:0, Cer d18:1/16:0, Cer d18:2/16:0, Cer d18:2/18:0), short-medium-chain (C2-C16) acylcarnitines, LPC 20:0 and LPC 22:1, and significantly increased levels of the long-chain acylcarnitine C20:0, LPC 16:0, LPC P-16:0, LPC 20:1, LPC 20:2, glyceroPC, LPE 16:0, and LPE 18:2. In squamous cell carcinoma (SCC) tissues, sphingosine, Cer d18:2/16:0 and Cer d18:2/18:0, and short-medium-chain acylcarnitines had significantly lower levels, while long-chain acylcarnitines (C20:0, and C22:0 or C22:0 M), LPC 20:1, LPC 20:2, and N1,N12-diacetylspermine had significantly higher levels compared to controls. In AC and SCC tissues, the levels of LPG 18:0, LPG 18:1, and LPS 18:1 were significantly decreased, while the levels of ceramide-1-phosphate (C1P) d18:0/3:0 or LPE P-16:0, N1-acetylspermidine, and 1-methylhistamine were significantly increased than controls. Furthermore, an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model based on a 4-lipid panel was established, showing good discrimination ability between cancerous and noncancerous tissues.

Characterization of the Geographical Origin of the Traditional Chinese Fermented Soybean Douchi by Gas Chromatography-Mass Spectrometry (GC-MS) and Chemometrics

Douchi is a highly appreciated product due to its unique flavor, high nutritional value, and health benefits. Douchi from different regions exhibits distinct characteristics. The objective of this study was to investigate the volatile profiles in douchi produced from different geographical origins using gas chromatography-mass spectrometry (GC-MS) and chemometrics. The volatiles were extracted by headspace solid-phase micro-extraction (HS-SPME) and subsequently determined by GC-MS. A comparative analysis was further conducted to identify the variations in volatile compounds among the samples. The correlation between the samples was investigated by employing cluster analysis (CA) and principal component analysis (PCA). An orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established to discriminate samples from different regions and identify key differential compounds. A total of 46 volatile compounds were detected, among which terpenes, alcohols were the main compounds. Furthermore, 14 key differentiating compounds were screened out based on the OPLS-DA modle. The application of GC-MS combined with chemometrics offers a theoretical framework for douchi quality control and classification.

Authentication of Pacific white shrimp (Litopenaeus vannamei) reared in freshwater and seawater areas using fatty acid profiles combined with chemometrics

Fatty acid (FA) profiling in muscle and hepatopancreas of Litopenaeus vannamei was combined with k-nearest neighbor (KNN), and random forest (RF) to differentiate L. vannamei raised in freshwater and seawater areas. The FA profiles of the two tissues differed between L. vannamei cultured in freshwater and seawater. Moreover, six (C22:6n3, C20:3n3, C17:0, C18:3n3, C20:5n3, and C20:2) and seven (C22:6n3, C16:0, C18:3n3, C18:2n6, C20:2, C20:1, and C18:1n9) variables in muscle and hepatopancreas, respectively, were selected by orthogonal partial least squares-discriminant analysis (OPLS-DA) to create models for distinguishing L. vannamei cultured in freshwater and seawater. The most selected variables in muscle and hepatopancreas were related to salinity. Furthermore, using FA profiles from the two tissues, both KNN and RF had initial and cross-validated classification rates >93%, while the predictive classification rates of the models based on muscle FA profiles were higher than that of the models based on hepatopancreas FA profiles. Therefore, FA profiles in muscle were more effective than hepatopancreas FAs for identifying shrimps. FA profiling in muscle tissue represents a promising method for discriminating between L. vannamei cultured in freshwater and seawater.