Squares-discriminant Analysis Research Articles

Revealing metabolic and biochemical variations via 1H NMR metabolomics in streptozotocin-nicotinamide-induced diabetic rats treated with metformin

The increasing prevalence of lean diabetes has prompted the generation of animal models that mimic metabolic disease in humans. This study aimed to determine the optimum streptozotocin-nicotinamide (STZ-NA) dosage ratio to elicit lean diabetic features in a rat model. It also used a proton nuclear magnetic resonance (1H NMR) urinary metabolomics approach to identify the metabolic effect of metformin treatment on this novel rat model. Three different STZ-NA dosage regimens (by body weight: Group A: 110 mg/kg NA and 45 mg/kg STZ; Group B: 180 mg/kg NA and 65 mg/kg STZ and Group C: 120 mg/kg NA and 60 mg/kg STZ) were administered to Sprague-Dawley rats along with oral metformin. Group A diabetic rats (A-DC) showed favorable serum biochemical analyses and a more positive response toward oral metformin administration relative to the other STZ-NA dosage ratio groups. Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed that glucose, citrate, pyruvate, hippurate, and methylnicotinamide differentiating the OPLS-DA of A-MTF rats (Group A diabetic rats treated with metformin) and A-DC model rats. Subsequent metabolic pathway analyses revealed that metformin treatment was associated with improvement in dysfunctions caused by STZ-NA induction, including carbohydrate metabolism, cofactor metabolism, and vitamin and amino acid metabolism. In conclusion, our results identify the best STZ-NA dosage ratio for a rat model to exhibit lean type 2 diabetic features with optimum sensitivity to metformin treatment. The data presented here could be informative to improve our understanding of non-obese diabetes in humans through the identification of possible activated metabolic pathways in the STZ-NA-induced diabetic rats model.

A first characterization of the microbiota-resilience link in swine

BackgroundThe gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.ResultsThis study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.ConclusionOur comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations.7uyXcYriZkKxPgmfB9NKtxVideo

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Using resampled nSight-2 hyperspectral data and various machine learning classifiers for discriminating wetland plant species in a Ramsar Wetland site, South Africa

Mapping wetland ecosystems at the species level provides critical information for understanding the nutrient cycle, carbon sequestration, retention and purification of water, waste treatment and pollution control. However, wetland ecosystems are threatened by climate variability and change and anthropogenic activities; thus, their assessment and monitoring have become critical to inform proper management interventions. Contemporary studies show that satellite-based Earth observation (EO) has significant potential for achieving this task. While many multispectral EO data are freely and readily available, its broad spectral bands limit its utility in differentiating subtle differences among similar plant species. In contrast, hyperspectral data has a high spectral resolution, which is superior in discerning minute differences in similar plant species. However, this data is associated with high dimensionality and multicollinearity, which negatively affect the performance of traditional, parametric classification algorithms. To this end, machine algorithms are often preferred to classify hyperspectral data due to their robustness to various data distributions and noise. The current study compared the performance of three advanced machine learning classifiers, i.e., Support Vector Machine (SVM), Random Forest (RF), and Partial Least Squares Discriminant Analysis (PLS-DA), in discriminating four dominant wetland plant species, i.e., Crocosmia sp., Grasses, Agapanthus sp. and Cyperus sp. using simulated hyperspectral data from an upcoming sensor, i.e., nSight-2. The results revealed that SVM is superior, with an overall accuracy of 93.18% (and class-wise accuracies > 85%). In comparison, there were minor differences in the performances of RF and PLS-DA, i.e., 84.09% and 83.63%, respectively. Overall, the results demonstrated that all the evaluated classifiers could achieve acceptable mapping accuracies. However, SVM is more robust, providing exceptional accuracies, and should be considered for operational mapping once the sensor is in space.

Integration of transcriptomics and metabolomics identify biomarkers of aberrant lipid metabolism in ulcerative colitis

BackgroundThe incidence of ulcerative colitis (UC) continues to rise globally, but effective therapeutic targets are still lacking. In recent years, numerous studies have indicated that lipid therapies could offer a novel perspective for UC treatment. Given the absence of prior research utilizing high-throughput data to identify target genes associated with lipid metabolism, we conducted this work. MethodsThe training set for this study was derived from four datasets within the Gene Expression Omnibus (GEO), encompassing a total of 357 UC patients. We employed four machine learning methods (LASSO, SVM, RF, and Boruta) to jointly identify core biomarkers in these patients, whose aberrant expression needed to be validated in independent datasets and in dextrose sulfate sodium salt (DSS)-induced UC mouse models. Regarding metabolomics, we detected abnormal oxidized lipids in the serum of UC mouse using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in conjunction with orthogonal partial least squares-discriminant analysis (OPLS-DA). ResultsPhospholipase A2 Group IIA (PLA2G2A) was first identified as a possible biomarker for UC, with AUC values of 0.810 and 1.000 in the two validation sets, while in animal models the gene showed similarly significant up-regulation in damaged intestinal mucosa. Further analysis of this gene showed that it was positively correlated with 17 immune cell types and histological severity. Additionally, we pioneered the development of a lipid metabolism score in UC research, which outperformed all individual genes in terms of disease diagnostic efficacy (AUC values of 0.980 and 1.000 for the two validation sets, respectively). Finally, the metabolomics study also identified 31 significantly abnormal oxidized lipids, including 12-HHT and DHA. ConclusionsPLA2G2A is a key therapeutic target for UC, and oxidized lipids such as 12-HHT can serve as potential serologic indicators for diagnosis.

LC-MS-based metabolomics reveals metabolite dynamic changes of beef after superchilling early post-mortem

To explore the underlying mechanisms by which superchilling (SC, −3 °C within 5 h of slaughter) improves beef tenderness, an untargeted metabolomics strategy was employed. M. Longissimus lumborum (LL) muscles from twelve beef carcasses were assigned to either SC or very fast chilling (VFC, 0 °C within 5 h of slaughter) treatments, with conventional chilling (CC, 0 ∼ 4 °C until 24 h post-mortem) serving as the control (6 per group). Biochemical properties and metabolites were investigated during the early post-mortem period. The results showed that the degradation of μ-calpain and caspase 3 occurred earlier in SC treated sample, which might be attributed to the accelerated accumulation of free Ca2+. The metabolomic profiles of samples from the SC and CC treatments were clearly distinguished based on partial least squares-discriminant analysis (PLS-DA) at each time point. It is noteworthy that more IMP and 4-hydroxyproline were found in the comparison between SC and CC treatments. According to the results of metabolic pathways analysis and the correlation analysis between traits related to tenderness and metabolites with significant differences (SC vs. CC), it can be suggested that the tenderization effect of the SC treatment may be related to the alteration of arginine and proline metabolism, and purine metabolism in the early post-mortem phase.

Characterization and classification of oleogels and edible oil using vibrational spectroscopy in tandem with one-class and multiclass chemometric methods

Oleogel represents a promising healthier alternative to act as a substitute for conventional fat in various food products. Oil selection is a crucial factor in determining the technological properties and applications of oleogels due to their distinct fatty acid composition, molecular weight, and thermal properties, as well as the presence of antioxidants and oxidative stability. Hence, the relevance of monitoring oleogel properties by non-destructive, eco-friendly, portable, fast, and effective techniques is a relevant task and constitutes an advance in the evaluation of oleogels quality. Thus, the present study aims to classify oleogels rapidly and reliably, without the use of chemicals, comparing two handheld near infrared (NIR) spectrometers and one portable Raman device. Furthermore, two different multivariate methods are compared for oleogel classification according to oil type. Three types of oleogels were prepared, containing 95 % oil (sunflower, soy, olive) and 5 % beeswax as a structuring agent, melted at 90 °C. Polarized light microscopy (PLM) images were acquired, and fatty acid composition, peroxide index and free fatty acid content were determined using official methods. A total of 240 oleogel and 92 oil spectra were obtained for each instrument. After spectra pretreatment, Principal Component Analysis (PCA) was performed, and two classification methods were investigated. The Data Driven - Soft Independent Modelling of Class Analogy (DD-SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) models demonstrated 95 % to 100 % of accuracy for the external test set. In conclusion, the use of vibrational spectroscopy using handheld and portable instruments in tandem with chemometrics showed to be an efficient alternative for classifying oils and oleogels and could be extended to other food samples. Although the classification of vegetable oils by NIR is widely used and known, this work proposes the classification of different types of oil in oleogel matrices, which has not yet been explored in the literature.

Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis.

Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6). Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM. The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.

Geochemistry of hydrothermal and stream sedimentary rutile in the Tiegelongnan porphyry-epithermal Cu (Au) deposit, Tibet: A tool for exploration

Rutile is a common and stable mineral in porphyry Cu deposits, and morphology and geochemistry of rutile are recognized as a potential indicator mineral for exploration. In this study, we investigate physical features and chemical composition of hydrothermal rutile in the Tiegelongnan porphyry-epithermal system, as well as rutile in stream sediments nearby to establish a method to apply rutile as an exploration proxy. Four stages of hydrothermal rutile have been identified. Potassic alteration stage rutile (Rt1) is associated with biotite, with elongated and acicular shape and small grain size (20–50 μm). Rutile from lower temperature phyllic alteration (Rt2), quartz veins (Rt3), and advanced argillic alteration (Rt4), has characteristic sector zoning and larger grain size (50–150 μm). The sector zoning is more commonly present in Rt2, Rt3, Rt4, that are associated with high-grade Cu mineralization, and is associated with incorporation of W, Fe, V, Sn, and Zr. The earlier rutile (Rt1) also has a geochemical signature different from the other three types of rutile, characterized by significantly lower Al, Cu, Zr, Nb, Sn, Hf, and higher Sc and Fe contents. Low-temperature hydrothermal alteration stage rutile (Rt2, 3, 4) share many geochemical similarities, but Rt3 has higher Nb and Ta contents (median = 3371.0 and 176.0 ppm, respectively), whereas Rt4 has higher Sb content (median = 24.3 ppm).Rutile trace element data from this study and literature are integrated to develop a Partial Least Square-Discriminant Analysis (PLS-DA) multivariate statistics plots that can discriminate porphyry Cu deposit rutile from orogenic gold and VHMS deposits, as well as rutile from different alteration stages. The PLS-DA plots predict that stream sedimentary gold-bearing rutile (RtSAu) adjacent to the Tiegelongnan deposit are derived from the deposit, and mainly from low-temperature phyllic and advanced argillic alteration stages. This is consistent with geochemical similarity between the RtSAu and the Tiegelongnan low-temperature hydrothermal rutile (Rt2 + Rt4). This is further confirmed by a U-Pb age of 125.3 ± 4.7 Ma (n = 24, 2σ) for RtSAu, which is consistent with the main porphyry mineralization age of ∼ 120 Ma in the Tiegelongnan deposit. We infer that the RtSAu may have formed during the ∼120 Ma epithermal system at Tiegelongnan, which has already largely eroded. In general, our findings prove the capability of using rutile, by combing the texture, geochemical compositions, geochronology, PLS-DA tools, and their spatial distribution, as a proxy mineral for the exploration of porphyry Cu deposits.

Evaluation of aroma characteristics of Penaeus vannamei with different drying methods using HS-SPME-GC-MS, MMSE-GC-MS, and sensory evaluation

The effects of microwave drying (MD), hot air drying (HAD), vacuum hot air drying (VD), and vacuum freeze drying (VFD) on the volatile profiles of Penaeus vannamei were investigated. A total of 89 and 94 volatile compounds were identified by headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC–MS) and monolithic material sorptive extraction gas chromatography–mass spectrometry (MMSE-GC–MS), respectively. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and variable influence on projection (VIP) models were utilized to select characteristic volatiles and key marker compounds (e.g., octanal, 1-octen-3-ol, 2-methyl-butanal, 2-ethyl-furan, and trimethyl-pyrazine) to discriminate among four drying methods. Based on synthesis of odor descriptions and sensory evaluation, it was found that P. vannamei via MD, HAD, and VD greatly reduced the fishy and generated roasted, fatty, and smoked odors. This study systematically analyzed the aroma characteristics of four traditional dried P. vannamei products, which may provide theoretical guidance for industrial production.

Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS)

Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field.

Geographic origin classification and simultaneous determination of methylxanthines in vietnamese tea using chemometrics based on the near infrared reflectance spectroscopy

This paper reported the results of classification of geographic origin and simultaneous analysis of three methylxanthines (caffeine, theobromine, theophylline) in Vietnamese tea samples by the infrared reflectance spectrophotometry coupled with chemometrics. The spectral range was 10,000-4,000cm-1 and each spectrum was measured at 2 cm-1 intervals. For the purpose of geographic origin classification, this study used FT-NIR spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis-Discriminant Analysis (PCA-DA). The ability to determine the origin of tea samples in the prediction set of PLS-DA model is 100%. Using the same IR spectral database combined with the partial least squares (PLS), three methylxanthines in tea samples are also quickly quantified. The PLS model based on the spectra of 24 tea samples in which the contents of 3 analytes were determined by high performance liquid chromatography- HPLC) were applied for simultaneous determination of caffeine, theobromine and theophylline in samples. The determination of methylxanthines in 7 tea samples in test set gave the good accuracy of the PLS model. The correlation coefficients (R2) in the prediction set were of 0.9582, 0.8894 and 0.9303 for theobromine, theophylline, and caffeine, respectively. This work demonstrated that infrared reflectance spectrophotometry combined with chemometrics could be applied to rapidly classify the geographic origin and simultaneous determination of main contents in green tea.

Impact of aroma-enhancing microorganisms on aroma attributes of industrial Douchi: An integrated analysis using E-nose, GC-IMS, GC–MS, and descriptive sensory evaluation

In order to enhance the aromatic profile of industrial Douchi, a comprehensive investigation was undertaken to assess the impact of aroma-enhancing microorganisms on the sensory attributes of Douchi. This evaluation utilized a combination of analytical techniques, including electronic nose analysis, gas chromatography-ion mobility spectrometry (GC-IMS), gas chromatography-mass spectrometry (GC–MS), and descriptive sensory analysis (DA). Both GC-IMS and GC–MS revealed significant changes in the volatile composition of Douchi following the addition of aroma-enhancing microorganisms (p < 0.05). Partial least squares-discriminant analysis (PLS-DA) identified benzaldehyde, benzene acetaldehyde, 3-octanone, and ethyl 2-methylbutyrate as significant differentiating volatile compounds. Additionally, compared to the control group, the sensory attributes of sourness in Douchi were significantly reduced (p < 0.001), while the attributes of wine-like and sweetness were notably enhanced (p < 0.05) when the ratio of G. candidum to C. versatilis was 1:1 (GCC group). By calculating the odor-activity values (OAVs) of key volatiles, it can be hypothesized that this aroma improvement of Douchi may be attributed to an increase in the typical volatiles (3-methyl-1-butanol, 1-octen-3-ol, 3-octanol, and 3-octanone) and ethyl 2-methylbutanoate with high OAVs (2340849.64 ∼ 16695327.86), as well as to decreases in the musty acetophenone. In conclusion, the aroma profile of Douchi was significantly enhanced when G. candidum and C. versatilis were added at a ratio of 1:1. This study provides valuable insights into the development of aroma enhancers for improving the sensory profile of Douchi.

Metabolic and elemental profiling as potential discriminating features among the black mahlab seeds (Monechma ciliatum) grown in three different regions.

Black mahlab (Monechma ciliatum) seed is a rich source of metabolites and minerals and is usually believed to have a similar composition between different areas of cultivation. Until now, no studies have assessed changes in black mahlab seeds (BMSs) to determine those constituents that help to discriminate them according to geographical origin. The present study attempted to compare the metabolomics and elemental profiles of BMSs of different geographical origins and identified the potential markers using ultrahigh-performance liquid chromatography quadrupole Orbitrap tandem mass spectrometry (UHPLC-Q-Orbitrap-MS2 ), and inductively coupled plasma mass spectrometry (ICP-MS) techniques and established the chemometric model to identify the potential markers and discriminate them according to cultivation sites. In this work, data from metabolites analysis by UHPLC-Q-Orbitrap-MS2 and multi-elemental data obtained from ICP-MS were combined with chemometrics for tracing the geographical origin of BMSs. Principal component analysis (PCA) was used to evaluate the overall grouping of samples. In contrast, partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed for authentication. PLS-DA and OPLS-DA models were fully validated (R2 Y and Q2 values > 0.5). Variable importance of various projections was applied to obtain valuable data about differential elements (seven markers were identified) and metabolites (23 markers were identified) with high discrimination potential. The outcomes presented in this study serve as an appropriate framework for developing novel discrimination approaches in food origin screening.

Determination of Antitumor Active Ingredients in Agarwood Essential Oil by Gas Chromatography-Mass Spectrometry (GC-MS) and Grey Relational Analysis

Agarwood essential oil (AEO) holds significant medicinal and economic value. While its antitumor activity is reported in the literature, the active ingredients remain unexplored. In this study, the chemical composition of AEO extracted by steam distillation (SD) and fungal fermentation-assisted SD (FFSD) was evaluated by gas chromatography-mass spectrometry (GC-MS). The antitumor activity was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Subsequently, the key active ingredients and targets of antitumor activity were evaluated using grey relational analysis and network pharmacology. The relative content of active ingredients in the AEOs extracted from different Aquilaria trees was compared. GC-MS tentatively identified 32 compounds, primarily sesquiterpenoids and aromatic compounds. Eight components with relative content differences were screened using orthogonal partial least squares-discriminant analysis (OPLS-DA), indicating that α-costal, guaiol, β-santalol may play important roles in the antitumor activity. Both SD and FFSD-extracted AEOs demonstrated the ability to inhibit the proliferation and survival of four cancer cell lines, with slightly stronger inhibitory effects observed in AEO extracted by SD. α-Costal, aristolone, 10-epi-γ-eudesmol, γ-eudesmol, agarospirol, hinesol, guaiol, β-santalol, and 6-isopropenyl-4,8a-dimethyl-1,2,3,5,6,7,8,8a-octahydro-naphthalen-2-ol were antitumor ingredients of AEO. The relative content of total active ingredients in agarwood (Aquilaria malaccensis) essential oil was higher, suggesting higher efficacy. This study confirmed the antitumor activity of AEO and identified its active ingredients. These findings provide a basis for further research on the antitumor mechanism of AEO.

UPLC-MS based metabonomics revealed the protective effects of Buyang Huanwu decoction on ischemic stroke rats.

Storke is a leading cause of death and disability affecting million people worldwide, 80% of which is ischemic stroke (IS). Recently, traditional Chinese medicines (TCMs) have received great attentions in treating IS due to their low poisonous effects and high safety. Buyang Huanwu Decoction (BHD), a famous and classical Chinese prescription, has been used for treating stroke-induced disability for centuries. Yet, its underlying mechanism is still in fancy. We first constructed an IS model by middle cerebral artery occlusion (MCAO). Then, a metabonomics study on serum samples was performed using UHPLC-QTOF/MS, followed by multivariate data analysis including principal components analysis (PCA) and orthogonal partial least squares-discriminate analysis (OPLS-DA). Metabolic profiling of PCA indicated metabolic perturbation caused by MCAO was regulated by BHD back to normal levels, which is in agreement with the neurobehavioral evaluations. In the OPLS-DA, 12 metabolites were screened as potential biomarkers involved in MCAO-induced IS. Three metabolic pathways were recognized as the most relevant pathways, involving one carbon pool by folate, sphingolipid metabolism and inositol phosphate metabolism. BHD significantly reversed the abnormality of 7 metabolites to normal levels. This is the first study to investigate the effect of BHD on IS at the metabolite level and to reveal the underlying mechanisms of BHD, which is complementary to neurobehavioral evaluation. In a broad sense, the current study brings novel and valuable insights to evaluate efficacy of TCMs, to interpret the action mechanisms, and to provide the theoretical basis for further research on the therapeutic mechanisms in clinical practice.

Multi-index evaluation of different parts of Amomum villosum

To investigate the quality differences between the seeds and husks of Amomum villosum and explore the rationality of using the seeds without husks, this study determined the content of protocatechuic acid, vanillic acid, epicatechin, quercitrin, volatile oil, water extract, and ethanol extract. The 2,2-diphenyl-1-picrylhydrazyl(DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS), and hydroxyl radical scavenging activities were determined to evaluate the antioxidant activities of seeds and husks. The quality differences between the seeds and husks were assessed through orthogonal partial least squares-discriminant analysis(OPLS-DA) and analytic hierarchy process(AHP) combined with the entropy weight method(EWM). Significant differences(P&lt;0.05) were observed in all 10 indicators between the seeds and husks. The levels of epicatechin, quercetin, and volatile oil were higher in the seeds, whereas those of protocatechuic acid, vanillic acid, water extract, and ethanol extract were higher in the husks. The seeds showed stronger scavenging ability against DPPH and ABTS radicals, while the husks showed a stronger scavenging effect on hydroxyl radicals. OPLS-DA significantly discriminated between the seeds and husks. Furthermore, volatile oil, water extract, DPPH radical scavenging rate, quercitrin, ABTS radical scavenging rate, hydroxyl radical scavenging rate, and vanillic acid were selected as the main differential indicators by variable importance in projection(VIP). Comprehensive scores calculated by AHP combined with EWM indicated that the seeds were superior to husks in terms of overall quality. However, there are still some dominant components and a certain antioxidant effect in the husks. Therefore, it is suggested to using Amomi Fructus with a certain amount of husks or utilizing the husks for other purposes.

Qualitative and semiquantitative analyses of the chemical components of the seed coat and kernel of Ziziphi Spinosae Semen

Ziziphi Spinosae Semen refers to the dried seed of Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou. The seed is composed of a reddish brown coat and a yellow kernel. A comparative study was conducted to investigate differences in the chemical composition and their relative contents between the seed coat and kernel of Ziziphi Spinosae Semen. First, the chemical compounds found in the seed coat and kernel were characterized and identified using ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). The analytical results tentatively identified 57 chemical compounds based on reference-compound comparison, literature retrieval, and chemical-database (e. g., MassBank) searches; these compounds included 14 triterpenes, 23 flavonoids, 7 alkaloids, 6 carboxylic acids, and 7 other types of compounds. The mass error of the identified compounds was within the mass deviation range of 5×10-6 (5 ppm). Next, two methods of multivariate statistical analysis, namely, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), were used to compare the differential compounds between the two seed parts. A total of 17 differential compounds were screened out via OPLS-DA based on a variable importance in projection (VIP) value of >5. The results revealed that betulinic acid, betulonic acid, alphitolic acid, and jujuboside Ⅰ mainly existed in the seed coat whereas the 13 other compounds, such as spinosin, jujuboside A, and 6‴-feruloylspinosin, mainly existed in the seed kernel. Therefore, these 17 differential compounds can be used to distinguish between the two seed parts. Finally, a semiquantitative method was established using UPLC and a charged aerosol detector (CAD) with inverse gradient compensation in the mobile phase. Six representative compounds with different types were selected to examine the CAD response consistency: magnoflorine (alkaloid), spinosin (flavone), 6‴-feruloylspinosin (flavone), jujuboside A (triterpenoid saponin), jujuboside B (triterpenoid saponin), and betulinic acid (triterpenoid acid). The results showed that the relative standard deviation (RSD) of the average response factors at different levels of these six compounds was 7.04% and that their response intensities were similar. Moreover, each compound in the fingerprint demonstrated good response consistency, and the peak areas obtained directly reflected the contents of each compound. Based on the semiquantitative fingerprints obtained, betulinic acid and oleic acid were considered the main components of the seed coat. The betulinic acid content in the seed coat was approximately 7 times higher than that in the seed kernel. Spinosin, jujuboside A, linoleic acid, betulinic acid, and oleic acid were the main components of the seed kernel. The spinosin content in the seed kernel was 18 times higher than that in the seed coat. In addition, the jujuboside A content in the seed kernel was 24 times higher than that in the seed coat. The proposed method can accurately determine the main components and compare the relative contents of these components in different seed parts. In summary, this study identified the differences in chemical components between the seed coat and kernel of Ziziphi Spinosae Semen and clarified the main components and their relative contents in these parts. The findings can not only provide a basis for the identification of chemical compounds and quality research on different parts of Ziziphi Spinosae Semen but also promote the development and utilization of this traditional Chinese medicine.

A study on the development and the application strategy of FT-MIRS-based models for the diagnosis of subclinical mastitis and milk quality classification in buffaloes

Buffalo mastitis detection and buffalo milk quality are of great importance to the world dairy industry. Somatic cell count (SCC) can be employed to assess mammary gland health and milk quality in dairy cows, but SCC detection is costly, and the detection instrument is expensive. The purpose of this study was to develop high and low SCC identification models using Fourier-transform mid-infrared spectrum (FT-MIRS), which could be used to diagnose subclinical mastitis (SCM) in buffalo and to determine the milk quality grade at a low cost. The dataset contained 899 buffalo milk samples collected from two regions in China. Firstly, the samples were divided into positive group above the threshold (SCM or unqualified milk) and negative group below the threshold (healthy or qualified milk) with SCC = 200 × 103 cells/mL (SCM in buffalo), 400 × 103 cells/mL (EU standard for raw cow milk, ES), 500 × 103 cells/mL (Indian standard for raw buffalo milk, IS), and 750 × 103 cells/mL (US standard for raw cow milk, US) as thresholds, respectively. Then, with FT-MIRS as predictive variables, predictive models were developed using Partial Least Squares Discriminant Analysis (PLSDA), Random Forest (RF), and Gradient Boosting Machine (GBM). The main results were as follows: the AUCval of the diagnostic model of SCM in buffalo using SCM criteria as a threshold was 0.84 (PLSDA). The AUCval values of the three buffalo milk quality classification models with ES, IS, and US as thresholds were 0.76 (PLSDA), 0.78 (GBM), and 0.84 (PLSDA), respectively. The predictive models established in this paper had a weak predictive ability for positive samples, and the “stepwise discriminant analysis” was recommended to improve the model application effect: The models were applied to classify samples as positive and negative, and then the samples with higher predictive probability were selected. Finally, the remaining samples were further differentiated using the reference methods of SCC detection. To conclude, FT-MIRS has the potential to predict buffalo mammary gland health status and buffalo milk quality grade, reduce the cost of SCC detection, and improve work efficiency, which will lay the foundation for rapid buffalo milk quality classification by raw milk regulatory authorities and rapid diagnosis of SCM in buffalo by farms.

Metabolomic changes in culture media with varying passage numbers of pig muscle stem cell culture for cultured meat production

Selecting the primary cells in an optimal state for cultured meat production is a crucial challenge in commercializing cultured meat. We investigated the metabolomic changes in culture media according to passage numbers for indirectly assessing the state of primary cells. Pig skeletal muscle stem cells (PSCs) harvested from the biceps femoris muscles of 7-d-old crossbred pigs (Landrace × Yorkshire × Duroc, LYD) were used for cell characterization. Fresh media (FM) and spent media (SM) of PSCs during passages 1 to 3 in vitro culture were prepared for metabolomics analysis. SM was collected on the third day of proliferation for each passage of PSCs. Cell characterization analysis revealed that the proliferation rate was highest at passage 2; however, a significant loss of expression of myogenic marker genes was observed at passage 3. Based on metabolomic profiles of culture media, FM and SM groups (SM1, SM2, and SM3) were clearly separated by partial least squares-discriminant analysis. A total of seven differentially abundant metabolites (DAMs) were identified from FM and SM for each passage, based on the following criteria: P < 0.05, fold change > 1.5 or < 0.66, and a variable importance in projection score > 1.5. All seven DAMs and their interconnected metabolites might be primarily used as substrates for energy production and most of them were relatively abundant in SM3. Among the seven DAMs, the three potential biomarkers (γ-glutamyl-L-leucine, cytosine, and ketoleucine), which showed significant changes exclusively in SM3, each had an area under the curve value of 1. Therefore, monitoring the levels of these key metabolites in culture media could serve as a quality control measure for cultured meat production by enabling the indirect detection of suboptimal PSCs based on their proliferation ability.