Orthogonal Projections To Latent Structures Discriminant Analysis Research Articles

Metabolic profiling of blood plasma in depression in pregnant women during early pregnancy.

The study aimed to perform metabolic profiling of serum samples using liquid chromatography with mass spectroscopy (LC-MS) and to explore potential biomarkers of early trimester depression. Using the Edinburgh Postnatal Depression Scale (EPDS), participants were randomly divided into study and control groups. Serum metabolic profiles of the two groups were analysed by using LC-MS. Differential metabolite and pathway analysis were identified by using orthogonal projections to latent structure-discriminant analysis (OPLS-DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Additionally, least absolute shrinkage and selection operator (LASSO) logistic and receiver operating characteristic (ROC) curve analyses were also conducted to explore potential biomarkers of antenatal depression (AD). The study included 41 participants, consisting of 16 subjects with AD and 25 controls. A total of 22 different metabolites were identified (p < .005), mainly affecting glycerophospholipid metabolism, linoleic acid metabolism, synthesis and degradation of ketone bodies, phenylalanine metabolism, and butanoate metabolism. The area under the ROC curve (AUC) for the LysoPC (24:0) was 0.858. This suggests that LysoPC (24:0) may be a potentially effective predictor of risk factors for AD. The study suggests that LysoPC (24:0) may be an effective and specific lipid biomarker for early trimester depression.

Untargeted Metabolomics Approach for the Discovery of Salinity-Related Alkaloids in a Stony Coral-Derived Fungus Aspergillus terreus

As a part of the important species that form coral reef ecosystems, stony corals have become a potential source of pharmacologically active lead compounds for an increasing number of compounds with novel chemical structures and strong biological activity. In this study, the secondary metabolites and biological activities are reported for Aspergillus terreus C21-1, an epiphytic fungus acquired from Porites pukoensis collected from Xuwen Coral Reef Nature Reserve, China. This strain was cultured in potato dextrose broth (PDB) media and rice media with different salinities based on the OSMAC strategy. The mycelial morphology and high-performance thin layer chromatographic (HPTLC) fingerprints of the fermentation extracts together with bioautography were recorded. Furthermore, an untargeted metabolomics study was performed using principal component analysis (PCA), orthogonal projection to latent structure discriminant analysis (O-PLSDA), and feature-based molecular networking (FBMN) to analyze their secondary metabolite variations. The comprehensive results revealed that the metabolite expression in A. terreus C21-1 differed significantly between liquid and solid media. The metabolites produced in liquid medium were more diverse but less numerous compared to those in solid medium. Meanwhile, the mycelial morphology underwent significant changes with increasing salinity under PDB cultivation conditions, especially in PDB with 10% salinity. Untargeted metabolomics revealed significant differences between PDB with 10% salinity and other media, as well as between liquid and solid media. FBMN analysis indicated that alkaloids, which might be produced under high salt stress, contributed largely to the differences. The biological activities results showed that six groups of crude extracts exhibited acetylcholinesterase (AChE) inhibitory activities, along with 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging and antibacterial activities. The results of this study showed that the increase in salinity favored the production of unique alkaloid compounds by A. terreus C21-1.

UPLC-MS/MS-based metabolomics and chemometrics unveil metabolic patterns and their regulatory pathways over the course of pumpkin seeds (Cucurbita maxima) germination

While pumpkin seeds (Cucurbita maxima) are regarded as agro-industrial waste, they serve as powerhouses packed with a plethora of valuable compounds with potential nutraceutical significance. The present work offers an integrated view of metabolites dynamics and their regulatory pathways over the course of pumpkin seeds germination and their potential entanglements on Alzheimerʼs disease (AD) mitigation through Ultra Performance Liquid Chromatography-mass spectrometry (UPLC-MS/MS) analysis and chemometrics. A total of 76 compounds spanning amino acids, phenolic acid, flavonoids, lignans, vitamins, fatty acids, phospholipids and phytosterols were annotated revealing changing trends during germination process. Coincidentally, orthogonal projection to latent structures-discriminant analysis (OPLS-DA) demonstrated a clear separation trend among the five pumpkin samples assuring their chemical heterogeneity. Complementarily, OPLS-DA models were constructed in different timepoint comparisons to pick out the significant metabolic features which were subsequently imported into MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) software for metabolic pathway enrichment analysis. Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed biosynthesis of unsaturated fatty acids, phenylpropanoid biosynthesis, glycerophospholipid metabolism and phenylalanine, tyrosine and tryptophan biosynthesis as the top predictive metabolic pathways associated with significant phytochemical shifts during germination process. Importantly, our efficacy experimental results dissected that 5 d and 7 d sprouted samples exerted noteworthy inhibitory potentials against acetylcholine esterase (AChE) and monoamine oxidase (MAO-B) enzymes. OPLS-derived coefficients plots portrayed that docosahexaenoic acid, oleic acid, lirioresinol A and tocotrienols dramatically augmented during germination were probably ascribed to promising AChE and MAO-B inhibitory activities. These findings offer a turning point for using pumpkin sprouts with an appreciable biochemical content to pursue the concept of nutritional therapy and accelerate perpetual influx of novel bioproducts to rectify AD.

Differential short-term and long-term metabolic and cytokine responses to infection of severe fever with thrombocytopenia syndrome virus.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV), which has a wide geographic distribution. The primary clinical manifestations of SFTS are fever and thrombocytopenia, with multiorgan failure being the leading cause of death. While most patients recover with treatment, little is known about the potential long-term metabolic effects of SFTSV infection. This study aimed to shed light on dysregulated metabolic pathways and cytokine responses following SFTSV infection, which pose significant risks to the short-term and long-term health of affected individuals. Fourteen laboratory-confirmed clinical SFTS cases and thirty-eight healthy controls including 18 SFTSV IgG-positive and 20 IgG-negative individuals were recruited from Taizhou city of Zhejiang province, Eastern China. Inclusion criteria of healthy controls included residing in the study area for at least one year, absence of fever or other symptoms in the past two weeks, and no history of SFTS diagnosis. Ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) was used to obtain the relative abundance of plasma metabolites. Short-term metabolites refer to transient alterations present only during SFTSV infection, while long-term metabolites persistently deviate from normal levels even after recovery from SFTSV infection. Additionally, the concentrations of 12 cytokines were quantified through fluorescence intensity measurements. Differential metabolites were screened using orthogonal projections to latent structures discriminant analysis (OPLS-DA) and the Wilcoxon rank test. Metabolic pathway analysis was performed using MetaboAnalyst. Between-group differences of metabolites and cytokines were examined using the Wilcoxon rank test. Correlation matrices between identified metabolites and cytokines were analyzed using Spearman's method. We screened 122 long-term metabolites and 108 short-term metabolites by analytical comparisons and analyzed their correlations with 12 cytokines. Glycerophospholipid metabolism (GPL) was identified as a significant short-term metabolic pathway suggesting that the activation of GPL might be linked to the self-replication of SFTSV, whereas pentose phosphate pathway and alanine, aspartate, and glutamate metabolism were indicated as significant long-term metabolic pathways playing a role in combating long-standing oxidative stress in the patients. Furthermore, our study suggests a new perspective that α-ketoglutarate could serve as a dietary supplement to protect recovering SFTS patients.

Revealing the 1H-NMR Profiling of Six Edible Mushrooms Consumed in the Northeastern Highlands of Puebla, Mexico.

The 1H-NMR metabolomics profiling of six edible mushrooms consumed in the northeastern highlands of Puebla, Mexico is presented. These fungi were morpho- and molecularly identified as Infundibulicybe squamulosa, Amanita jacksonii, Lepista nuda, Russula delica, Russula brevipes, and Lactarius indigo. The chemical profiling confirmed the presence of eight essential amino acids and their derivatives, six organic acids, six nucleosides, low amounts of reducing sugars, and valuable nutraceuticals such as betaine, carnitine, glycero-3-phosphocholine and O-acetylcarnitine which were differentially determined and quantified in the six mushrooms by qNMR. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) generated four different groups. Two of these groups were constituted by fungal species with phylogenic relationships whereas non-phylogenetic related species were separated from each other. The potential use of 1H-NMR metabolomics and chemometrics to group macromycetes and determine the nutritional and nutraceutical potential of these local foods is demonstrated.

Associations of Plasma and Fecal Metabolites with Body Mass Index and Body Fat Distribution in Children.

Childhood obesity continues to be a critical public health concern with far-reaching implications for the well-being. This study aimed to investigate the association between metabolites in plasma and feces and indicators including body mass index (BMI), BMI for age Z score (BMIZ), and body fat distribution among children aged 6-9 years in China. This cross-sectional study enrolled 424 healthy children, including 186 girls and 238 boys. Dual-energy X-ray absorptiometry (DXA) was used to determine the body fat content and regional fat distribution. Plasma and fecal metabolites were analyzed using targeted metabolomic technologies. A total of 200 plasma metabolites and 212 fecal metabolites were accurately quantified via ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). By using Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and random forest model, we discovered that 9 plasma metabolites and 11 fecal metabolites were associated with different weight statuses. After adjusting for potential covariates and false discovery rate (FDR) correction, multiple linear regression analyses revealed that plasma metabolites (fumaric acid, glycine, l-glutamine, methylmalonic acid, and succinic acid) and fecal metabolites (protocatechuic acid) were negatively associated (β: -1.373--0.016, pFDR: <0.001-0.031; β: -1.008--0.071, pFDR: 0.005-0.033), while plasma metabolites (isovaleric acid, isovalerylcarnitine, l-glutamic acid, and pyroglutamic acid) and fecal metabolites (3-aminoisobutanoic acid, butyric acid, N-acetylneuraminic acid, octanoylcarnitine, oleoylcarnitine, palmitoylcarnitine, stearoylcarnitine, taurochenodesoxycholic acid, and taurodeoxycholic acid) exhibited positive associations with BMI, BMIZ, and body fat distribution (β: 0.023-2.396, pFDR: <0.001; β: 0.014-1.736, pFDR: <0.001-0.049). Plasma and fecal metabolites such as glutamine may serve as a potential therapeutic target for the development of obesity.

Distinct Changes in Metabolic Profile and Sensory Quality with Different Varieties of Chrysanthemum (Juhua) Tea Measured by LC-MS-Based Untargeted Metabolomics and Electronic Tongue.

Chrysanthemum tea, a typical health tea with the same origin as medicine and food, is famous for its unique health benefits and flavor. The taste and sensory quality of chrysanthemum (Juhua) tea are mainly determined by secondary metabolites. Therefore, the present research adopted untargeted metabolomics combined with an electronic tongue system to analyze the correlation between the metabolite profiles and taste characteristics of different varieties of chrysanthemum tea. The results of sensory evaluation showed that there were significant differences in the sensory qualities of five different varieties of chrysanthemum tea, especially bitterness and astringency. The results of principal component analysis (PCA) indicated that there were significant metabolic differences among the five chrysanthemum teas. A total of 1775 metabolites were identified by using untargeted metabolomics based on UPLC-Q-TOF/MS analysis. According to the variable importance in projection (VIP) values of the orthogonal projections to latent structures discriminant analysis (OPLS-DA), 143 VIP metabolites were found to be responsible for metabolic changes between Huangju and Jinsi Huangju tea; among them, 13 metabolites were identified as the key metabolites of the differences in sensory quality between them. Kaempferol, luteolin, genistein, and some quinic acid derivatives were correlated with the "astringency" attributes. In contrast, l-(-)-3 phenyllactic acid and L-malic acid were found to be responsible for the "bitterness" and "umami" attributes in chrysanthemum tea. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the flavonoid and flavonol biosynthesis pathways had important effects on the sensory quality of chrysanthemum tea. These findings provide the theoretical basis for understanding the characteristic metabolites that contribute to the distinctive sensory qualities of chrysanthemum tea.

Thrombosis Rates and Genetic Thrombophilia Risk Among Patients With Advanced Germ Cell Tumors Treated With Chemotherapy

IntroductionMen with advanced germ cell tumors (GCT) treated with chemotherapy are at high risk of venous thromboembolism (VTE). Predictors of VTE may identify patients who would benefit from prophylactic anticoagulation. Patients and MethodsMen with advanced GCT (Stage IS, II, III) treated with chemotherapy were identified at 2 centers. High genomic risk was defined from a 5 single nucleotide polymorphism (SNP) germline panel. Logistic regression was used to evaluate the impact of genomic risk on VTE within 6 months of chemotherapy initiation. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) was used to build models to predict VTE based on clinical variables and an 86 SNP panel. ResultsThis 123-patient cohort experienced a VTE rate of 26% with an incidence of high genomic risk of 21%. Men with high genomic risk did not have a significantly higher VTE rate (31%, 8/26) than men with low genomic risk (25%, 24/97), unadjusted OR 1.4 (95% CI 0.5-3.5, P = .54). Incorporation of clinical variables (Khorana score, N3 status and elevated LDH) resulted in adjusted OR 2.1 (95% CI 0.7-6.5, P = .18). A combined model using clinical variables and 86 SNPs performed similarly (AUC 0.77) compared to clinical variables alone (AUC 0.72). ConclusionsA previously established 5-SNP panel was not associated with VTE among patients with GCT receiving chemotherapy. However, multivariable models based on clinical variables alone warrant further validation to inform prophylactic anticoagulation strategies.

Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined With Deep Learning for Classification of Idiopathic Recurrent Spontaneous Miscarriage (IRSM)

Idiopathic recurrent spontaneous miscarriage (IRSM) is characterized by the repeated losses of two or more clinically detected pregnancies before 24 week of gestation with the cause remaining unknown. We aim to ascertain whether attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with deep learning (DL) can accurately distinguish IRSM from controls. Endometrial tissue (internal lining of the uterine cavity) was collected from 20 women with IRSM and 20 controls during window of implantation. ATR-FTIR spectroscopy was used to acquire spectra (400–4000 cm−1) from three random locations across each specimen and averaged. Multivariate analysis was used to assess group differentiation. Furthermore, DL classifiers were implemented to predict IRSM. Hierarchical cluster analysis (HCA) demonstrated distinct clustering of the two groups. The principal component analysis (PCA) loading plot indicated contributions from amide I, II and III bands, symmetric methyl (CH3) bending of proteins, lipids, nucleic acids, asymmetric stretching of CH2 due to lipids, collagen, and glucose in discriminating between IRSM and controls. Supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA) exhibited optimized discrimination between the groups. Convolutional neural network (CNN) and variational autoencoder-artificial neural network (VAE-ANN) classifiers exhibited good performance and showed classification accuracy of 94% and 94% and F1 scores (mean of recall and precision) of 95% and 93%, respectively. The optimal area under the receiver operating characteristic curve (AUC) value of both CNN and VAE-ANN was 0.94. ATR-FTIR spectroscopy combined with DL approach is promising for understanding complex disease pathophysiology and enabling early prediction.

Geographical origin identification of winter jujube (Ziziphus jujuba ‘Dongzao’) by using multi-element fingerprinting with chemometrics

Winter jujube (Ziziphus jujuba ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.

The Shelf Life of Milk-A Novel Concept for the Identification of Marker Peptides Using Multivariate Analysis.

The quality of food is influenced by several factors during production and storage. When using marker compounds, different steps in the production chain, as well as during storage, can be monitored. This might enable an optimum prediction of food's shelf life and avoid food waste. Especially, proteoforms and peptides thereof can serve as indicators for exogenous influences. The development of a proteomics-based workflow for detecting and identifying differences in the proteome is complex and time-consuming. The aim of the study was to develop a fast and universal workflow with ultra-high temperature (UHT) milk as a proteinaceous model food with expectable changes in protein/peptide composition. To find an optimum shelf life without sticking to a theoretically fixed best-before date, new evaluation and analytical methods are needed. Consequently, a modeling approach was used to monitor the shelf life of the milk after it was treated thermally and stored. The different peptide profiles determined with high-resolution mass spectrometry (HRMS) showed a significant difference depending on the preparation method of the samples. Potential marker peptides were determined using orthogonal projections to latent structures discriminant analysis (OPLSDA) and principal component analysis (PCA) following a typical proteomics protocol with tryptic hydrolysis. An additional Python-based algorithm enabled the identification of eight potential tryptic marker peptides (with mass spectrometric structural indications m/z 885.4843, m/z 639.3500, m/z 635.8622, m/z 634.3570, m/z 412.7191, m/z 623.2967, m/z 880.4767, and m/z 692.4041), indicating the effect of the heat treatment. The developed workflow is flexible and can be easily adapted to different research questions in the field of peptide analysis. In particular, the process of feature identification can be carried out with significantly less effort than with conventional methods.

Investigation into the role of phenolic compounds in the protection of citrus against Phyllosticta citricarpa

IntroductionThe use of chemical fungicides to combat disease has made a substantial contribution to food quality and security. Nonetheless, their applications have been limited due to environmental and health concerns, unaffordability, and the fact that pathogens have acquired resistance to some of these fungicides. Alternative eco-friendly and safe control methods should be explored. The current study investigated the influence of citrus rind phenolic compounds against Phyllosticta citricarpa infection by metabolic profiling of two citrus cultivars with varying degrees of susceptibility to infection.MethodsChromatographic data obtained by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC) was subjected to multivariate data analysis to identify biomarkers associated with the tolerant cultivar. The identified biomarkers were tested in vitro against P. citricarpa.ResultsSeville oranges, a tolerant cultivar, displayed higher levels of phenolic content and lower total sugar content, that are both associated with lower susceptibility to citrus black spot infection. The generated Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA) models gave an overview of the data set and identified components that may be responsible for the differences in susceptibility between the two cultivars. Candidate biomarkers associated with tolerance were identified as naringin, neoeriocitrin, bruteiridin, melitidin, and lucenin-2.ConclusionNaringin, a major candidate biomarker was able to inhibit the growth of the pathogen at 10 000 ppm.

Characteristic flavor compounds in Guizhou green tea and the environmental factors influencing their formation: Investigation using stable isotopes, electronic nose, and headspace-gas chromatography ion migration spectrometry

The Guizhou province is a significant contributor to the production of green tea within China. The present study aims to examine the characteristic flavor compounds found in Guizhou green tea and explore the environmental factors that contribute to the development of these unique flavor compounds. Accordingly, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and electronic nose were employed in conjunction with odor activity value (OAV) analysis to develop an orthogonal projections to latent structures discriminant analysis (OPLS-DA) model that was capable of discerning between the three distinct habitats (QE, QM, QW) of Guizhou green tea based on their flavor constituents. The findings show that the primary constituents contributing to the flavor profile of Guizhou green tea consisted of 3-methyl-2-butenal, butanal, pentanal, linalool, 6-methyl-5-hepten-2-one, dimethyl disulfide, and dimethyl sulfide. Subsequently, the stable isotope technique was employed to examine the growth environmental factors of Guizhou green tea, based on the identification of the flavoring compounds that differentiate green tea from various habitats within the Guizhou province. Accordingly, temperature and sunshine were found to have the greatest impact on the flavor compounds present in Guizhou green tea.

A multiplex metabolomic approach for quality control of Spirulina supplement and its allied microalgae (Amphora & Chlorella) assisted by chemometrics and molecular networking

Microalgae species are of economic importance regarded as “green gold” being rich in bioactive compounds. Spirulina and Chlorella are the most popular microalgal species and are marketed as healthy food supplements. At the same time, Amphora holds potential as a source of healthy lipids and essential fatty acids. Yet, there are considerable variations in their reported chemical composition, and less is known about their compositional differences. A multiplexed metabolomic approach was adopted for the quality control (QC) of Spirulina supplements and to compare its constitutive metabolome to Chlorella and Amphora. The adopted protocol comprised gas chromatography-mass spectrometry (GC–MS), ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS), and ultraviolet–visible spectrophotometry (UV/Vis) for mapping their primary and secondary metabolome. Interestingly, UPLC-HRMS/MS analysis delineated the abundance of fatty acids in Amphora versus glycolipids enrichment in Spirulina, and porphyrins were the main pigments identified in Spirulina, with scarce occurrence in Chlorella. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) analysis of GC–MS data set revealed palmitic acid, 3-mannobiose, and glyceryl-glycoside as being most enriched in Spirulina, versus sucrose and leucine in Chlorella and Amphora, respectively. Despite being of low discriminatory potential, UV/Vis OPLS-DA modeling showed that Spirulina was distinguished with the UV absorbances of carotenoids and chlorophyll pigments, as indicated by its OPLS-DA derived S-plot. Our study provides a QC approach for the analysis of the microalgal species and poses alternative spectral and compositional markers for their discrimination.

Oxidative Stability and Pasting Properties of High-Moisture Japonica Brown Rice following Different Storage Temperatures and Its Cooked Brown Rice Flavor

The study proposed to investigate the impacts of storage temperatures (15, 20, 25 °C) on the oxidative stability (peroxide value, carbonyl value, malondialdehyde content) and sensory attributes (pasting properties, cooked brown rice flavor) of high-moisture japonica brown rice. According to the findings, the peroxide value, the carbonyl value, and the malondialdehyde content of high-moisture japonica brown rice stored at a temperature of 15 °C exhibited consistently low levels, and the pasting properties were favorable. In addition, 22 out of 51 flavor volatiles were screened as key differential volatile flavor compounds in cooked brown rice via a combination of ANOVA and orthogonal projections to latent structures-discriminant analysis (OPLS-DA). Among them, 3-heptylacrolein had an aroma of fat and mushroom, and its contents were higher at 15 °C and 20 °C. These findings could serve as a valuable reference for storing high-moisture japonica brown rice under low temperature conditions as well as for investigating the flavor characteristics of cooked brown rice derived from this variety.

Reflection of Physical Descriptive Parameters as Functional Phenes on Olive Oil Cross-border Mapping

The medicinal and nutritional benefits of olive oil are reflected in its extensive incorporation in many cultures in food and pharmaceutical products. This study suggests the use of colour-based models, viscosity and refractive index as parametric attributes to experiment and track down the authenticity of the oil through image analysis and accurate determination of refractive index and viscosity of oils from different regions.The combination of RGB measurement and chemometric methods (PCA, PCA-class) of olive oil samples collected from distinctive areas (Tunisia, Libya and Egypt) enabled the differentiation of olive oil samples per region of cultivation and was found to be accurate, precise (R2X 0.997) and successful in detecting the accuracy of the labelled information (traceability determination, freshness, purity, date and storage conditions) and could be extrapolated to study the position of harvested areas. The results also revealed a perfect discrimination between the defined categories based on the origin and age after performing the Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA) on the visible spectra of the samples reaching 90% of correct classifications.

Does Keeping Cows for More Lactations Affect the Composition and Technological Properties of the Milk?

This study investigated differences in the raw milk composition and technological properties between cows with different numbers of lactations. In total, 12 commercial herds were visited within a period of 12 weeks. On each farm, milk samples from five young cows (lactations 1-2) and five older cows (lactation ≥ 3) were collected. For each farm, milk samples from the young cows and the older cows, respectively, were pooled. The pooled milk samples were analyzed for gross composition and technological properties. Using principal component analysis (PCA) to assess the overall variation in milk quality attributes and the potential clustering of milk from young cows and older cows, respectively, an effect of breed, but no clear effect of lactation number, was observed. In contrast, one-way ANOVA showed higher plasmin activity (p = 0.002) in pooled milk from the older cows, whereas plasminogen-derived activity (p = 0.001) and total proteolysis (p = 0.029) were higher in milk from the young cows. Likewise, orthogonal projections to latent structure discriminant analysis (OPLS-DA) showed higher plasmin activity in milk from older cows, whereas younger cows had higher plasminogen-related activity and higher total proteolysis. To conclude, except for plasmin and plasminogen-related activities, there were no major differences in the composition and technological properties between milk from older cows and young cows.

Metabolomic profiling of Medicago sativa-derived fungal endophytes and evaluation of their biological activities.

This study aimed to discover the potential of Medicago sativa-derived fungal endophytes as a prospective source of bioactive metabolites. In the present study, three different strains of fungal endophyte Aspergillus terreus were isolated from leaves L, roots T and stems St of Medicago sativa to explore their biological and chemical diversity. These isolated fungi were exposed to different fermentation conditions by adding various chemical elicitors to their solid fermentation media. According to LC-HRESIMS-based metabolomics and multivariate analysis, each chemical treatment had a different effect on the chemical profiles of the fungi. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) proposed several compounds with anticancer action against MCF-7 (a human breast cancer cell line) and MDA-MB-231 (a human epithelial breast cancer cell line).

Breath Analysis for Lung Cancer Early Detection-A Clinical Study.

This clinical study presents a comprehensive investigation into the utility of breath analysis as a non-invasive method for the early detection of lung cancer. The study enrolled 14 lung cancer patients, 14 non-lung cancer controls with diverse medical conditions, and 3 tuberculosis (TB) patients for biomarker discovery. Matching criteria including age, gender, smoking history, and comorbidities were strictly followed to ensure reliable comparisons. A systematic breath sampling protocol utilizing a BIO-VOC sampler was employed, followed by VOC analysis using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). The resulting VOC profiles were subjected to stringent statistical analysis, including Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA), Kruskal-Wallis test, and Receiver Operating Characteristic (ROC) analysis. Notably, 13 VOCs exhibited statistically significant differences between lung cancer patients and controls. The combination of eight VOCs (hexanal, heptanal, octanal, benzaldehyde, undecane, phenylacetaldehyde, decanal, and benzoic acid) demonstrated substantial discriminatory power with an area under the curve (AUC) of 0.85, a sensitivity of 82%, and a specificity of 76% in the discovery set. Validation in an independent cohort yielded an AUC of 0.78, a sensitivity of 78%, and a specificity of 64%. Further analysis revealed that elevated aldehyde levels in lung cancer patients' breath could be attributed to overactivated Alcohol Dehydrogenase (ADH) pathways in cancerous tissues. Addressing methodological challenges, this study employed a matching of physiological and pathological confounders, controlled room air samples, and standardized breath sampling techniques. Despite the limitations, this study's findings emphasize the potential of breath analysis as a diagnostic tool for lung cancer and suggest its utility in differentiating tuberculosis from lung cancer. However, further research and validation are warranted for the translation of these findings into clinical practice.

Application of 1H NMR metabolomics analysis to sparkling wines aged with different types of sugar added to the liqueur de dosage

Over the past decade, 1H NMR metabolomic studies have become increasingly popular in wine research since they can provide a chemical fingerprint with information regarding authenticity, geographical origin, vintage, and wine composition. Comparatively few 1H NMR-based studies have evaluated sparkling wines. In the present study, quantitative 1H NMR metabolomics was applied to assess the composition of finished traditional method (bottle-fermented) sparkling wines aged for 18-months in cellar conditions (14 °C, 70% relative humidity) following liqueur d′expedition (dosage) additions with six sugar types (glucose, fructose, sucrose derived from sugar beets and sugar cane, maltose, and MCR Sucraisin®) and a zero-dosage control. No systematic trends were observed when comparing the metabolite profile across sugar types, nor between the zero-dosage control and treatments with sugar additions. Multivariate statistical approaches using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) did not effectively discriminate between dosage treatments nor wine age, while orthogonal projections to latent structures discriminant analysis (OPLS-DA) successfully discriminated between wines by age and was validated through permutation tests. This study presents the first application of 1H NMR metabolomics coupled with multivariate analysis to monitor the aging of sparkling wines.