Search Results (898)

Rheum tanguticum is one of the primary rhubarb species used for food and medicinal purposes, and it has recently been gaining more attention and recognition. This research represents the first attempt to use stable isotopes and elemental analysis via IRMS to identify the geographical origin of Rheum tanguticum. A grand total of 190 rhubarb samples were gathered from 38 locations spread throughout the provinces of Gansu, Sichuan, and Qinghai in China. The carbon content showed a decreasing trend in the order of Qinghai, followed by Sichuan, and then Gansu. Nitrogen content was notably higher, with Qinghai and Sichuan displaying similar levels, while Gansu had the lowest nitrogen levels. Significant differences were noted in the δ¹³C (−28.9 to −26.5‰), δ¹⁵N (2.6 to 5.6‰), δ²H (−120.0 to −89.3‰), and δ¹⁸O (16.0‰ to 18.8‰) isotopes among the various rhubarb cultivation areas. A significant negative correlation was found between %C and both longitude and humidity. Additionally, δ¹³C and δ¹⁵N isotopes were negatively correlated with longitude, and δ¹⁵N showed a negative correlation with humidity as well. δ²H and δ¹⁸O isotopes exhibited a strong positive correlation with latitude, while significant negative correlations were observed between δ²H and δ¹⁸O isotopes and temperature, precipitation, and humidity. The LDA, PLS-DA, and k-NN models all exhibited strong classification performance in both the training and validation sets, achieving accuracy rates between 82.1% and 91.7%. The combination of stable isotopes, elemental analysis, and chemometrics provides a reliable and efficient discriminant model for accurately determining the geographical origin of R. tanguticum in different regions. In the future, the approach will aid in identifying the geographical origin and efficacy of rhubarb in other studies. Full article

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. Full article

Detection and characterization of newly synthesized cannabinoids (NSCs) is challenging due to the lack of availability of reference standards and chemical data. In this study, a binary classification system was developed and validated using partial least square discriminant analysis (PLS-DA) by utilizing readily available mass spectral data of known drugs to assist in the identification of previously unknown NCSs. First, a binary classification model was developed to discriminate cannabinoids and cannabinoid-related compounds from other drug classes. Then, a classification model was developed to discriminate classical (THC-related) from synthetic cannabinoids. Additional models were developed based on the most abundant functional groups including core groups such as indole, indazole, azaindole, and naphthoylpyrrole, as well as head and tail groups including 4-fluorobenzyl (FUB) and 5-Fluoropentyl (5-F). The predictive ability of these models was tested via both cross-validation and external validation. The results show that all models developed are highly accurate. Additionally, latent variables (LVs) of each model provide useful mass to charge (m/z) for discrimination between classes, which further facilitates the identification of different functional groups of previously unknown drug molecules. Full article

Background: Concerns have been raised regarding the effects of perfluoroalkyl substance (PFAS) exposure on cardiovascular diseases (CVD), but clear evidence linking PFAS exposure to CVD is lacking, and the mechanism remains unclear. Objectives: To study the association between PFASs and CVD in U.S. population, and to reveal the mechanism of PFASs’ effects on CVD. Methods: To assess the relationships between individual blood serum PFAS levels and the risk of total CVD or its subtypes, multivariable logistic regression analysis and partial least squares discriminant analysis (PLS-DA) were conducted on all participants or subgroups among 3391 adults from the National Health and Nutrition Examination Survey (NHANES). The SuperPred and GeneCards databases were utilized to identify potential targets related to PFAS and CVD, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of intersection genes were performed using Metascape. Protein interaction networks were generated, and core targets were identified with STRING. Molecular docking was achieved using Autodock Vina 1.1.2. Results: There was a positive association between Me-PFOSA-AcOH and CVD (OR = 1.28, p = 0.022), especially coronary heart disease (CHD) (OR = 1.47, p = 0.007) and heart attack (OR = 1.58, p < 0.001) after adjusting for all potential covariates. Me-PFOSA-AcOH contributed the most to distinguishing between individuals in terms of CVD and non-CVD. Significant moderating effects for Me-PFOSA-AcOH were observed in the subgroup analysis stratified by sex, ethnicity, education level, PIR, BMI, smoking status, physical activity, and hypertension (p < 0.05). The potential intersection targets were mainly enriched in CVD-related pathways, including the inflammatory response, neuroactive ligand–receptor interaction, MAPK signaling pathway, and arachidonic acid metabolism. TLR4 was identified as the core target for the effects of Me-PFOSA-AcOH on CVD. Molecular docking results revealed that the binding energy of Me-PFOSA-AcOH to the TLR4-MD-2 complex was −7.2 kcal/mol, suggesting that Me-PFOSA-AcOH binds well to the TLR4-MD-2 complex. Conclusions: Me-PFOSA-AcOH exposure was significantly associated with CVD. Network toxicology and molecular docking uncovered novel molecular targets, such as TLR4, and identified the inflammatory and metabolic mechanisms underlying Me-PFOSA-AcOH-induced CVD. Full article

Nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice monovarietal extra virgin olive oils (EVOOs), grown in two zones that are different in altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet-developed data-processing called MARA-NMR (Multiple Assignment Recovered Analysis by Nuclear Magnetic Resonance). This method, taking around one-hour of experimental time per sample, faces the possible quantification of different class of compounds at different concentration ranges, which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices, and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-device analytical strategies. Monovarietal EVOO’s composition is mainly affected by pedoclimatic conditions, in turn relying upon the nutritional properties, quality, and authenticity. Data collection, analysis, and statistical processing are discussed, touching on the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions. Full article

Humulus lupulus, or hops, is a vital ingredient in brewing, contributing bitterness, flavor, and aroma. The female plants produce strobiles rich in essential oils and acids, along with bioactive compounds like polyphenols, humulene, and myrcene, which offer health benefits. This study examined the aromatic profiles of five hop varieties grown in Brazil versus their countries of origin. Fifty grams of pelletized hops from each strain were collected and analyzed using HS-SPME/GC-MS to identify volatile compounds, followed by statistical analysis with PLS-DA and ANOVA. The study identified 330 volatile compounds and found significant aromatic differences among hops from different regions. For instance, H. Mittelfrüher grown in Brazil has a fruity and herbaceous profile, while the German-grown variety is more herbal and spicy. Similar variations were noted in the Magnum, Nugget, Saaz, and Sorachi Ace varieties. The findings underscore the impact of terroir on hop aromatic profiles, with Brazilian-grown hops displaying distinct profiles compared to their counterparts from their countries of origin, including variations in aromatic notes and α-acid content. Full article

Growth performance is an important economic trait of broilers but the related serum metabolomics remains unclear. In this study, we utilized non-targeted metabolomics using ultra-high-performance liquid phase tandem mass spectrometry (UHPLC-MS/MS) to establish metabolite profiling in the serum of Chinese Wumeng black-bone chickens. The biomarker metabolites in serum associated with growth performance of chickens were identified by comparing the serum metabolome differences between chickens that significantly differed in their weights at 160 days of age when fed identical diets. A total of 766 metabolites were identified including 13 differential metabolite classes such as lipids and lipid-like molecules, organic acids and their derivatives, and organoheterocyclic compounds. The results of difference analysis using a partial least squares discriminant analysis (PLS-DA) model indicated that the low-body-weight group could be differentiated based on inflammatory markers including prostaglandin a2, kynurenic acid and fatty acid esters of hydroxy fatty acids (FAHFA), and inflammation-related metabolic pathways including tryptophan and arachidonic acid metabolism. In contrast, the sera of high-body-weight chickens were enriched for riboflavin and 2-isopropylmalic acid and for metabolic pathways including riboflavin metabolism, acetyl group transfer into mitochondria, and the tricarboxylic acid (TCA) cycle. These results provide new insights into the practical application of improving the growth performance of local chickens. Full article

Tropical fruits such as cherimoya, soursop, and pineapple share sugars (glucose, fructose, and sucrose) in common but may differ in the content of other phytochemicals. In the present work, confocal Raman spectroscopy and partial least squares discriminant analysis (PLS-DA) were used to establish a classification model among the three fruits and to evaluate the effect of pre-processing methods on the model’s performance. The Raman spectra showed that glucose was present in the fruits in the 800–900 cm⁻¹ band and the 1100–1200 cm⁻¹ band. While sucrose was present in the bands of 1131.22 cm⁻¹, 1134.44 cm⁻¹, and 1133.37 cm⁻¹ in the three fruits, fructose was present in the bands of 1464.22 cm⁻¹, 1467.44 cm⁻¹, and 1464.22 cm⁻¹ in cherimoya, soursop, and pineapple. The accuracy of the PLS-DA model varied according to the pre-processing methods used. The Savitzky–Golay first derivative method produced a model with 98.69–100% and 100% precision on the training and prediction data, respectively. Full article

The herbaceous peony (Paeonia lactiflora Pall.) plant is world-renowned for its ornamental, medicinal, edible, and oil values. As global warming intensifies, its growth and development are often affected by high-temperature stress, especially in low-latitude regions. Superoxide dismutase (SOD) is an important enzyme in the plant antioxidant systems and plays vital roles in stress response by maintaining the dynamic balance of reactive oxygen species (ROS) concentrations. To reveal the members of then SOD gene family and their potential roles under high-temperature stress, we performed a comprehensive identification of the SOD gene family in the low-latitude cultivar ‘Hang Baishao’ and analyzed the expression patterns of SOD family genes (PlSODs) in response to high-temperature stress and exogenous hormones. The present study identified ten potential PlSOD genes, encoding 145–261 amino acids, and their molecular weights varied from 15.319 to 29.973 kDa. Phylogenetic analysis indicated that PlSOD genes were categorized into three sub-families, and members within each sub-family exhibited similar conserved motifs. Gene expression analysis suggested that SOD genes were highly expressed in leaves, stems, and dormancy buds. Moreover, RNA-seq data revealed that PlCSD1-1, PlCSD3, and PlFSD1 may be related to high-temperature stress response. Finally, based on the Quantitative Real-time PCR (qRT-PCR) results, seven SOD genes were significantly upregulated in response to high-temperature stress, and exogenous EBR and ABA treatments can enhance high-temperature tolerance in P. lactiflora. Overall, these discoveries lay the foundation for elucidating the function of PlSOD genes for the thermotolerance of herbaceous peony and facilitating the genetic breeding of herbaceous peony cultivars with strong high-temperature resistance. Full article

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ¹³C, δ¹⁵N, δ²H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication. Full article

Endometrial cancer is becoming increasingly common, highlighting the need for improved diagnostic methods that are both effective and non-invasive. This study investigates the use of urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine samples were collected from endometrial cancer patients (n = 77), patients with benign uterine tumors (n = 23), and control gynecological patients attending regular checkups or follow-ups (n = 96). These samples were analyzed using synchronous fluorescence spectroscopy to measure the total fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between control, benign, and malignant samples. These spectral markers demonstrated potential clinical applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA) was employed to reduce data dimensionality and enhance class separation. Additionally, machine learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid, accurate, and non-invasive alternative to current methods. Full article

The gardenia flower not only has extremely high ornamental value but also is an important source of natural food and spices, with a wide range of uses. To support the development of gardenia flower products, this study used headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) technology to compare and analyze the volatile organic compounds (VOCs) of fresh gardenia flower and those after using four different drying methods (vacuum freeze-drying (VFD), microwave drying (MD), hot-air drying (HAD), and vacuum drying (VD)). The results show that, in terms of shape, the VFD sample is almost identical to fresh gardenia flower, while the HAD, MD, and VD samples show significant changes in appearance with clear wrinkling; a total of 59 volatile organic compounds were detected in the gardenia flower, including 13 terpenes, 18 aldehydes, 4 esters, 8 ketones, 15 alcohols, and 1 sulfide. Principal component analysis (PCA), cluster analysis (CA), and partial least-squares regression analysis (PLS-DA) were performed on the obtained data, and the research found that different drying methods impact the VOCs of the gardenia flower. VFD or MD may be the most effective alternative to traditional sun-drying methods. Considering its drying efficiency and production cost, MD has the widest market prospects. Full article

Lately, the inclusion of additional lactic acid bacteria (LAB) strains to cheeses is becoming more popular since they can affect cheese’s nutritional, technological, and sensory properties, as well as increase the product’s safety. This work studied the effect of Lactiplantibacillus pentosus L33 and Lactiplantibacillus plantarum L125 free cells and supernatants on feta cheese quality and Listeria monocytogenes fate. In addition, rapid and non-invasive techniques such as Fourier transform infrared (FTIR) and multispectral imaging (MSI) analysis were used to classify the cheese samples based on their sensory attributes. Slices of feta cheese were contaminated with 3 log CFU/g of L. monocytogenes, and then the cheese slices were sprayed with (i) free cells of the two strains of the lactic acid bacteria (LAB) in co-culture (F, ~5 log CFU/g), (ii) supernatant of the LAB co-culture (S) and control (C, UHT milk) or wrapped with Na-alginate edible films containing the pellet (cells, FF) or the supernatant (SF) of the LAB strains. Subsequently, samples were stored in air, in brine, or in vacuum at 4 and 10 °C. During storage, microbiological counts, pH, and water activity (a_w) were monitored while sensory assessment was conducted. Also, in every sampling point, spectral data were acquired by means of FTIR and MSI techniques. Results showed that the initial microbial population of Feta was ca. 7.6 log CFU/g and consisted of LAB (>7 log CFU/g) and yeast molds in lower levels, while no Enterobacteriaceae were detected. During aerobic, brine, and vacuum storage for both temperatures, pathogen population was slightly postponed for S and F samples and reached lower levels compared to the C ones. The yeast mold population was slightly delayed in brine and vacuum packaging. For aerobic storage at 4 °C, an elongation in the shelf life of F samples by 4 days was observed compared to C and S samples. At 10 °C, the shelf life of both F and S samples was extended by 13 days compared to C samples. FTIR and MSI analyses provided reliable estimations of feta quality using the PLS-DA method, with total accuracy (%) ranging from 65.26 to 84.31 and 60.43 to 89.12, respectively. In conclusion, the application of bioprotective LAB strains can result in the extension of feta’s shelf life and provide a mild antimicrobial action against L. monocytogenes and spoilage microbiota. Furthermore, the findings of this study validate the effectiveness of FTIR and MSI techniques, in tandem with data analytics, for the rapid assessment of the quality of feta samples. Full article

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC. Full article

Amyloidosis diagnosis relies on Congo red staining with immunohistochemistry and immunofluorescence for subtyping but lacks sensitivity and specificity. Laser-microdissection mass spectroscopy offers better accuracy but is complex and requires extensive sample preparation. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy offers a promising alternative for amyloidosis characterization. Cardiac tissue sections from nine patients with amyloidosis and 20 heart transplant recipients were analyzed using ATR-FTIR spectroscopy. Partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) models were used to differentiate healthy post-transplant cardiac tissue from amyloidosis samples and identify amyloidosis subtypes [κ light chain (n = 1), λ light chain (n = 3), and transthyretin (n = 5)]. Leave-one-out cross-validation (LOOCV) was employed to assess the performance of the PLS-DA model. Significant spectral differences were found in the 1700–1500 cm⁻¹ and 1300–1200 cm⁻¹ regions, primarily related to proteins. The PLS-DA model explained 85.8% of the variance, showing clear clustering between groups. PCA in the 1712–1711 cm⁻¹, 1666–1646 cm⁻¹, and 1385–1383 cm⁻¹ regions also identified two clear clusters. The PCA and the HCA model in the 1646–1642 cm⁻¹ region distinguished κ light chain, λ light chain, and transthyretin cases. This pilot study suggests ATR-FTIR spectroscopy as a novel, non-destructive, rapid, and inexpensive tool for diagnosing and subtyping amyloidosis. This study was limited by a small dataset and variability in measurements across different instruments and laboratories. The PLS-DA model’s performance may suffer from overfitting and class imbalance. Larger, more diverse datasets are needed for validation. Full article