Multivariate Statistical Analysis Research Articles

Construction and application of a synthetic microbial community in reduced salinity fermentation of raw-materials based broad bean paste

Raw-materials based broad bean paste (rmBBP) is popular among consumers. Though high salinity can select functional microbial community for fermentation, it also impaired the fermentation efficiency of rmBBP and unfavored human health. Therefore, this study aimed to construct a synthetic microbial community based on key functional microbes for reduced salt rmBBP fermentation based on the elucidation of the microbial community succession during fermentation. Based on the tracked determination and multivariate statistical analysis of the physicochemical parameters, metabolites and the structure of microbial community, salinity and microbial interactions were key drivers for the succession of microbial community. Aspergillus, Staphylococcus, Weissella and Tetragenococcus were predicted to play key roles during fermentation. After that, core species were isolated from rmBBP mash and their salinity tolerance and metabolic characteristics were evaluated. Finally, a synthetic microbial community (Aspergillus oryzae, Staphylococcus carnosus, Tetragenococcus halophilus and Weissella confusa) was constructed and applied in reduced-salt rmBBP fermentation. The bioaugmentation contributed to the accumulation of volatile metabolites while effectively maintaining the acidity during rmBBP fermentation when salinity was reduced from 12% to 10%. In general, this study developed a synthetic microbial community with desired characteristics for successful reduced-salinity rmBBP fermentation.

Chemical analysis of different parts from agarwood columns by artificially agarwood-inducing method based on GC–MS and UPLC-TOF-MS

Agarwood is resin-containing wood produced by plants that have been injured. It is widely used in herbal medicine, incense, decorative items, and so on. In this study, we conducted resin area statistical analysis, determined starch particle and reducing sugar contents, and performed multivariate statistical analysis of chemical composition by GC–MS and UPLC-Q-TOF-MS to explore the different components in sections cut from an agarwood column, designated as A1–A4. The results showed that after stimulation by Agar-Bit inducer, the internal phloem parenchyma cells of the column started to form agarwood, and then starch granules were converted into soluble reducing sugars and agarwood resin. Section A1 showed rapid loss of starch granules, resulting in higher contents of reducing sugars and resin. The resin areas of agarwood in the respective sections were different, gradually decreasing on going from A1 to A4. Total numbers of metabolites of 87 and 63 were identified by GC–MS and UPLC-Q-TOF-MS, respectively. Of these, 10 and 16 metabolites with significant differences (variable importance projection >1) were selected through multivariate statistical analysis. These metabolites included chromones, sesquiterpenes, alkanes, and fatty acids. Among them, 6-methoxy-2-(2-phenylethyl)chromone and 6,7-dimethoxy-2-(2-phenylethyl)chromone were significant markers detected by both GC–MS and UPLC-Q-TOF-MS, which may be essential substances responsible for differences in the agarwood-forming capacities of the cut sections. In conclusion, there has been limited research on the different agarwood-forming capacities of agarwood columns. Here, we explored the differences in various sections of agarwood through chemical analysis to provide a more comprehensive and in-depth understanding of its constitution.

Determining Risk Factors for Detachment After Endothelial Keratoplasties: Nine-Year Review of a Single Institution.

To determine roles of patient history, donor tissue characteristics, tissue preparation methods, and surgeon technique for graft detachment requiring rebubbling after Descemet stripping automated endothelial keratoplasty (DSAEK) and Descemet membrane endothelial keratoplasty (DMEK) procedures. Retrospective analysis of all eyes undergoing first-time DSAEK or DMEK at the study institution between 2013 and 2022. Data were collected regarding recipients' history, donors' medical history, tissue preparation methods, intraoperative details, and postoperative clinical outcomes. Multivariate statistical analysis was conducted to identify risk factors for graft detachment necessitating rebubbling. Of 1240 eyes meeting inclusion criteria, 746 (60.2%) underwent DSAEK, and 494 (39.8%) underwent DMEK. DSAEK procedures had 12.5% rebubbling rate, whereas DMEK procedures had 18.4% rebubbling rate (P = 0.005). Sub50-DSAEK (<50 μm) grafts had 16.0% rebubble rate, whereas sub100-DSAEK (51-99 μm) and >100 μm DSAEKs had rebubble rate of 9.9% and 9.5%, respectively (P = 0.006). Significant risk factors for DSAEK graft detachment included history of retinal surgery (OR = 2.59), preloaded tissue (OR = 2.70), forceps insertion (OR = 2.33), use of sub50-DSAEK lenticules versus sub100-DSAEK (OR = 2.44) and >100 μm DSAEK (OR = 2.38) lenticules, and donor history of noninsulin-dependent diabetes mellitus (OR = 4.18). DMEK risk factors included recipient history of cancer (OR = 2.51) and use of higher SF6 gas concentration (OR = 1.09). Although rebubbled DMEK eyes had comparable refractive outcomes to nonrebubbled eyes (P >0.05), rebubbled DSAEK eyes had worse refractive outcomes at all time points (P < 0.05). Graft detachments in DSAEK and DMEK are influenced by various factors, including donor tissue characteristics, tissue preparation, and surgical technique. Identifying and understanding these factors can potentially improve postoperative outcomes.

Socioeconomic determinants of foodborne disease prevalence in Ecuador: A principal component analysis study

Foodborne diseases are a global health problem. Every year, millions of people die worldwide from these diseases. It has been determined that the high prevalence of these diseases is related to unfavorable socioeconomic conditions of the population. In this study, the relationship between foodborne diseases and socioeconomic conditions of the population was determined using principal component analysis as a multivariate statistical analysis technique. In this study, the socioeconomic variables of each Ecuador province and the prevalence of foodborne diseases (hepatitis A, salmonella, shigellosis and typhoid fever) during the years 2018 and 2019 were considered. The results show the relationship between foodborne diseases and the socioeconomic conditions of the population, as well as identifying regions more vulnerable to present high levels of prevalence of foodborne diseases, thus facilitating the implementation of social investment programs to reduce the prevalence of these diseases.

Effects of land use patterns on seasonal water quality in Chinese basins at multiple temporal and spatial scales

Diverse land use patterns exhibit varying effects on water quality across different seasons and spatial scales. However, current studies on the correlation between land use and water quality in single small-scale basins no longer meet the needs of regional coordinated development. Simultaneous comparative analysis of multiple large-scale basins can promote water environmental protection in different basins, but there is currently limited relevant research. In this study, the data from 86 sampling points across seven major river basins in China were analyzed. Multivariate statistical analysis and the redundancy analysis (RDA) were employed to investigate the influence of land use patterns on water quality at different seasons and spatial scales. The results indicated notable differences in water quality across various seasons and locations. Except for higher pH and permanganate index (COD) concentrations in the wet season in the Songhua River Basin and higher COD concentrations in the Pearl River Basin, the concentrations of all parameters in other basins are higher in the dry season. PH and COD exhibited considerable spatial variations within basins, while dissolved oxygen (DO) and ammonia nitrogen (NH4+-N) showed smaller variations. RDA showed that land use had a more pronounced effect on water quality during the dry season in the Yangtze, Liao and Pearl River Basins, while the impact was greater in the wet season in the four basins of the Yellow, Huai, Hai and Songhua River Basins. At the spatial scale, the 2000 m buffer zone had the most significant impact within the Yangtze, Hai and Liao River Basins, while the 1000 m buffer zone had the greatest impact in the Huai and Pearl River Basins. For the Yellow and Songhua River Basins, the buffer zone was 500 m during the wet season and 1000 m and 2000 m during the dry season, respectively. The research findings can offer a scientific foundation for the development of basin-specific land management policies and water quality protection measures in different basins from a multi-scale perspective.

Spatial metabolomics method to reveal the differences in chemical composition of raw and honey-fried Stemona tuberosa Lour. by using UPLC-Orbitrap Fusion MS and desorption electrospray ionization mass spectrometry imaging.

Stemona tuberosa Lour. (ST) is a significant traditional Chinese medicine (TCM) renowned for its antitussive and insecticidal properties. ST is commonly subjected to processing in clinical practice before being utilized as a medicinal substance. Currently, the customary technique for processing ST is honey-fried. Nevertheless, the specific variations in chemical constituents of ST before and after honey-fried remain unclear. This work aimed to analyze the variations in chemical constituents of ST before and after honey-fried and to study the distribution of differential markers in the roots. UPLC-Orbitrap Fusion MS combined with molecular network analysis was used to analyze the metabolome of ST and honey-fried ST (HST) and to screen the differential metabolites by multivariate statistical analysis. Spatial metabolomics was applied to study the distribution of differential metabolites by desorption electrospray ionization mass spectrometry imaging (DESI-MSI). The ST and HST exhibited notable disparities, with 56 and 61 chemical constituents found from each, respectively. After processing, the types of alkaloids decreased, and 12 differential metabolites were screened from the common compounds. The notable component variations were epibisdehydro-tuberostemonine J, neostenine, tuberostemonine, croomine, neotuberostemonine, and so forth. MSI visualized the spatial distribution of differential metabolites. Our research provided a rapid and effective visualization method for the identification and spatial distribution of metabolites in ST. Compared with the traditional method, this method offered more convincing data supporting the processing mechanism investigations of Stemona tuberosa from a macroscopic perspective.

Discriminating Craft and Industrial Beers by Untargeted Gas Chromatography-Mass Spectrometry (GC-MS) Metabolomics Combined with Multivariate Statistical Analysis

Developing robust analytical methodologies for the discrimination of craft and industrial beer presents a significant yet formidable challenge. The identification and differentiation of products necessitate the utilization of reliable analytical methods, which are indispensable for ensuring market stability and safeguarding consumer interests. In this study, representative samples with a significant market share were selected for headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis to compare the volatile substance characteristics of craft and industrial beers. The findings suggested that alcohols and esters constituted the primary volatiles. Craft beer exhibited a higher abundance of both types and concentrations compared to industrial beer. Subsequently, unsupervised and supervised chemometric techniques were employed for further analysis of the data matrix. An orthogonal partial least squares discriminant analysis (OPLS-DA) method was established to effectively discriminate between industrial and craft beers, enabling the identification of characteristic volatile markers. The untargeted GC-MS metabolomics coupled with multivariate statistical analysis holds promise for the authentication of craft and industrial beers.

Tracing the origin of isatidis radix based on multivariate data fusion combined with DBN classification algorithm

In this study, multidimensional characterization data such as chromaticity value, texture and compositional content of Isatidis Radix from different regions (Anhui; Hubei; Shaanxi; Xinjiang) were collected. By multivariate statistical analysis, 44 characterization factors (VIP >1, P < 0.05) were selected to distinguish the origin of Isatidis Radix. In addition, a unique artificial intelligence algorithm was created and optimized by merging 44 characterization factors with the deep belief network (DBN) classification algorithm. Compared with the traditional discriminant analysis method, the accuracy of this new method was significantly improved, and the discrimination rate of Isatidis Radix origin reached 100 %, and the traceability accuracy of Isatidis Radix also reached 100 %. This study supports the development of intelligent algorithms based on data fusion to track the origin of more agricultural products.

MTORC1 activation in presumed classical monocytes: observed correlation with human size variation and neuropsychiatric disease.

Gain of function disturbances in nutrient sensing are likely the largest component in human age-related disease. Mammalian target of rapamycin complex 1 (mTORC1) activity affects health span and longevity. The drugs ketamine and rapamycin are effective against chronic pain and depression, and both affect mTORC1 activity. Our objective was to measure phosphorylated p70S6K, a marker for mTORC1 activity, in individuals with psychiatric disease to determine whether phosphorylated p70S6K could predict medication response. Twenty-seven females provided blood samples in which p70S6K and phosphorylated p70S6K were analyzed. Chart review gathered biometric measurements, clinical phenotypes, and medication response. Questionnaires assessed anxiety, depression, autism traits, and mitochondrial dysfunction, to determine neuropsychiatric disease profiles. Univariate and multivariate statistical analyses were used to identify predictors of medication response. mTORC1 activity correlated highly with both classical biometrics (height, macrocephaly, pupil distance) and specific neuropsychiatric disease profiles (anxiety and autism). Across all cases, phosphorylated p70S6K was the best predictor for ketamine response, and also the best predictor for rapamycin response in a single instance. The data illustrate the importance of mTORC1 activity in both observable body structure and medication response. This report suggests that a simple assay may allow cost-effective prediction of medication response.

Phytochemical fingerprint revealing antibacterial and antioxidant activities of endemic banana cultivars in Southeast Asia

This study explores the bioactive secondary metabolite profiles of the peels of three major cultivars of bananas (Musa acuminata and Musa balbisiana). These cultivars are primarily grown in Southeast Asia and are widely consumed due to their rich nutritional and fiber content. The research utilizes advanced analytical techniques, specifically HPLC-DAD-q-TOF-MS/MS, in conjunction with both univariate and multivariate statistical analyses, to analyze the ethanolic extracts of the banana peels. This study identifies phenolic acids, flavonoids, and proanthocyanidins as significant contributors to the differentiation of the cultivars. The secondary metabolites rutin, chlorogenic acid, and gentisic acid are pinpointed as the key discriminants. Moreover, the research demonstrates a synergistic contribution of certain phytochemicals to the antioxidant and antibacterial properties of the banana peel extracts. The fingerprint profiling tools introduced in this study offer a reliable method for identifying metabolite biomarkers for the discrimination of banana cultivars.

The Light-Intensity-Affected Aroma Components of Green Tea during Leaf Spreading.

Leaf spreading is a key processing step that affects the aroma formation of green tea. The effects of a single-light wavelength on the aroma and taste of tea have been extensively studied. Less attention has been paid to the effect of different complex light intensities on the formation of green tea's volatile aroma during leaf spreading. The current study was designed to evaluate how leaf spreading under different complex light intensities relates to the quality of green tea. Using headspace solid-phase micro-extraction and gas chromatography-mass spectrometry (HS-SPME/GC-MS), volatile flavor compounds in green tea were analyzed during leaf spreading in five different light conditions. Multivariate statistical analysis and odor activity values (OAVs) were used to classify these samples and identify key odors. Eight distinct groups, including ninety volatile compounds, were detected. The most prevalent volatile compounds found in green tea samples were hydrocarbons and alcohols, which accounted for 29% and 22% of the total volatile compounds, respectively. Fourteen volatile compounds (OAV > 1) were identified as key active differential odorants. The chestnut-like aroma in green tea was mostly derived from 3-methyl-butanal and linalool, which were significantly accumulated in medium-intensity light (ML).

A comprehensive foodomics analysis of rambutan seed oils: Focusing on the physicochemical parameters, lipid concomitants and lipid profiles

A foodomics approach was employed to systematically characterize and compare the quality parameters, antioxidant activity, minor-components, fatty acid composition, and lipid profiles of the seed oils from the three most popular rambutan varieties in China. The total lipid content ranged from 23.40 to 25.77 g/100 g. The fatty acids 9cC18:1 (39.84%–40.92%) and C20:0 (28.45%–30.23%) were identified as the dominant ones, which are uncommon among higher plants. All oil samples exhibited low AI and TI values. BR-7 exhibited the highest levels of squalene (21.48 mg/kg), cholesterol (144.43 mg/kg), and tocopherol (17.42 mg/kg), and the lowest levels of polyphenols (24.21 mg GAE/kg). Additionally, a total of 807 lipid species were identified, with TAG, DGTS, and PE being the predominant ones. Multivariate statistical analyses revealed significant variations in lipid profiles among the varieties, particularly in glycerophospholipids and sphingolipids. Fifty-seven distinct lipids were identified as potential markers for distinguishing between rambutan varieties. Furthermore, a hypothetical scenario was developed by linking relevant lipid metabolism pathways. These findings establish a theoretical framework for comprehending rambutan seed oil in depth and unlocking its high-value potential.

A multidisciplinary approach using hydrogeochemistry, δ15NNO3 isotopes, land use, and statistical tools in evaluating nitrate pollution sources and biochemical processes in Costa Rican volcanic aquifers

Nitrate pollution threatens the Barva and Colima multi-aquifer system, the primary drinking water source in the Greater Metropolitan Area of Costa Rica. In addressing nitrate contamination dynamics, this study proposes an integrated approach by combining multivariate statistical analyses, hydrochemical parameters, sewage discharge, and regional land-use and land-cover patterns to assess the extent and degree of contamination, dominant biogeochemical processes, and refine the interpretation of nitrate sources previously derived solely from δ15NNO3 information. Over seven years (2015–2022), 714 groundwater samples from 43 sites were analyzed for nitrate and major ions, including two sampling campaigns for dissolved organic and inorganic carbon, nitrite, ammonium, FeTotal, MnTotal, and δ15NNO3 analyses. The findings presented elevated nitrate concentrations in urban and agricultural/urban areas, surpassing the Maximum Concentration Levels on several occasions, and oxidizing conditions favoring mineralization and nitrification processes in unconfined Barva and locally confined Upper Colima/Lower Colima aquifers. Similar nitrate contents and spatial patterns in agricultural and urban zones in the shallow Barva aquifer suggest comparable contributions from nitrogen fertilizers and urban wastewaters despite the gradual increase in urban land cover and the reduction of agricultural areas. Isotopic analyses and dissolved organic carbon (DOC) indicate a shift in nitrate sources from agricultural to urban areas in both Barva and Colima aquifers. Principal Component and Hierarchical Cluster Analyses link land use, nitrate sources, and water quality. Three distinct sample clusters aligned with forest/grassland, agricultural/urban, and urban land use, emphasizing the impact of anthropogenic activities on groundwater quality, even in the deeper Colima aquifers. The study challenges nitrate isotope mixing models, enhancing accuracy in identifying pollution sources and assessing the spatial extent of contamination by incorporating DOC and other hydrochemical parameters. Similar outcomes, with and without the use of nitrate isotopes, reinforce the usefulness of the integrated approach, providing a practical and cost-effective alternative.

Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.

Detection of cytotoxic effects of carboplatin in human nasopharyngeal carcinoma epithelioid cell line CNE-1 by Raman spectroscopy combined with multivariate statistical analysis

Raman spectroscopy combined with multivariate analysis was used to examine the molecular mechanism of carboplatin toxicity in human nasopharyngeal carcinoma epithelioid cell line CNE-1. Multivariate analysis was performed through principal component analysis combined with linear discriminant analysis, through which a diagnostic algorithm that could discriminate between the control and treatment groups was generated. Compared with the control group (0 μmol/L, 0 hour), the 2 protein Raman peaks (1002 cm−1 and 1659 cm−1) and the 2 deoxyribonucleic acid Raman peaks (1303 cm−1 and 1338 cm−1) in the treatment group decreased with increasing carboplatin concentrations as well as with increasing treatment durations. Using principal component analysis combined with linear discriminant analysis, the discrimination accuracy, sensitivity, and specificity between the control group and the cell groups treated at 12 and 16 μmol/L carboplatin for 24 hours were all 100%, suggesting that 16 μmol/L is suitable as the therapeutic dosing. Using the same method, the discrimination accuracy, sensitivity, and specificity between the control group and the cell groups that were treated with 12 μmol/L carboplatin for 24 and 36 hours were all 100%, indicating that 24 hours could be suitable treatment duration. These results suggest that Raman spectroscopy combined with multivariate statistical analysis could be a useful tool for assessing carboplatin-induced cytotoxicity in human nasopharyngeal carcinoma cells.

Integration of High-Resolution LC-Q-TOF Mass Spectrometry and Multidimensional Chemical-Biological Analysis to Detect Nanomolar-Level Acetylcholinesterase Inhibitors from Different Parts of Zanthoxylum nitidum.

Zanthoxyli radix is a popular tea among the elderly, and it is believed to have a positive effect on Alzheimer's disease. In this study, a highly effective three-step strategy was proposed for comprehensive analysis of the active components and biological functions of Zanthoxylum nitidum (ZN), including high-resolution LC-Q-TOF mass spectrometry (HRMS), multivariate statistical analysis for heterogeneity (MSAH), and experimental and virtual screening for bioactivity analysis (EVBA). A total of 117 compounds were identified from the root, stem, and leaf of ZN through HRMS. Bioactivity assays showed that the order of acetylcholinesterase (AChE) inhibitory activity from strong to weak was root > stem > leaf. Nitidine, chelerythrine, and sanguinarine were found to be the main differential components of root, stem, and leaf by OPLS-DA. The IC50 values of the three compounds are 0.81 ± 0.02, 0.14 ± 0.01, and 0.48 ± 0.01 μM respectively, indicating that they are potent and high-quality AChE inhibitors. Molecular docking showed that pi-pi T-shaped interactions and pi-lone pairs played important roles in AChE inhibition. This study not only explains the biological function of Zanthoxyli radix in alleviating Alzheimer's disease to some extent, but also lays the foundation for the development of stem and leaf of ZN.

Comprehensive identification of key quality markers of Buzhong Yiqi Oral Liquid based on multi-dimensional characteristic networks

Traditional Chinese Medicine (TCM) formulations often contain a complex mixture of compounds, making it challenging to evaluate their quality. Buzhong Yiqi Oral Liquid (BYOL), a well-known TCM formulation, has been used to treat gastrointestinal diseases and fatigue for a long time. However, the effective substances for its therapeutic effects remain unclear. This study aimed to establish a novel quality control approach for BYOL using a multidimensional feature network to identify quality markers (Q-markers). The multidimensional characteristic network was constructed by a “Spider-Web” model, including the compatibility of ingredients, traceability of compounds from raw materials to the final product, effectiveness, measurability, and stability. Multivariate statistical analysis was applied to quantify the individual dimensions of the feature network, and candidate compounds were identified based on regression regions within the network. Using the multidimensional characteristic network, 12 components were identified as key Q-markers for BYOL, consisting of calycosin-7-O-β-D-glucoside, formononetin, astragaloside IV, liquiritin, quercetin, hesperidin, glycyrrhizic acid, isorhamnetin, liquiritin apioside, hesperidin, luteolin, and nobiletin. The identified Q-markers offered valuable insights for future research on medicinal substances and quality control of BYOL.

A strategy to distinguish similar traditional Chinese medicines by liquid chromatography-mass spectrometry, electronic senses, and gas chromatography-ion mobility spectrometry: Marsdeniae tenacissimae Caulis and Paederiae scandens Caulis as examples.

Marsdeniae tenacissimae Caulis (MTC), a popular traditional Chinese medicine, has been widely used in the treatment of tumor diseases. Paederiae scandens Caulis (PSC), which is similar in appearance to MTC, is a common counterfeit product. It is difficult for traditional methods to effectively distinguish between MTC and PSC. Therefore, there is an urgent need for a rapid and accurate method to identify MTC and PSC. The aim is to distinguish between MTC and PSC by analyzing the differences in nonvolatile organic compounds (NVOCs), taste, odor, and volatile organic compounds (VOCs). Liquid chromatography-mass spectrometry (LC-MS) was utilized to analyze the NVOCs of MTC and PSC. Electronic tongue (E-tongue) and electronic nose (E-nose) were used to analyze their taste and odor respectively. Gas chromatography-ion mobility spectrometry (GC-IMS) was applied to analyze VOCs. Finally, multivariate statistical analyses were conducted to further investigate the differences between MTC and PSC, including principal component analysis, orthogonal partial least squares discriminant analysis, discriminant factor analysis, and soft independent modeling of class analysis. The results of this study indicate that the integrated strategy of LC-MS, E-tongue, E-nose, GC-IMS, and multivariate statistical analysis can be effectively applied to distinguish between MTC and PSC. Using LC-MS, 25 NVOCs were identified in MTC, while 18 NVOCs were identified in PSC. The major compounds in MTC are steroids, while the major compounds in PSC are iridoid glycosides. Similarly, the distinct taste difference between MTC and PSC was precisely revealed by the E-tongue. Specifically, the pronounced bitterness in PSC was proven to stem from iridoid glycosides, whereas the bitterness evident in MTC was intimately tied to steroids. The E-nose detected eight odor components in MTC and six in PSC, respectively. The subsequent statistical analysis uncovered notable differences in their odor profiles. GC-IMS provided a visual representation of the differences in VOCs between MTC and PSC. The results indicated a relatively high relative content of 82 VOCs in MTC, contrasted with 32 VOCs exhibiting a similarly high relative content in PSC. In this study, for the first time, the combined use of LC-MS, E-tongue, E-nose, GC-IMS, and multivariate statistical analysis has proven to be an effective method for distinguishing between MTC and PSC from multiple perspectives. This approach provides a valuable reference for the identification of other visually similar traditional Chinese medicines.

Study on the enhanced efficacy mechanism of vinegar-processed Cyperus rotundus in the treatment of primary dysmenorrhea.

The enhanced efficacy of vinegar-processed Cyperus rotundus (VCR) in treating primary dysmenorrhea (PD) has been observed. However, the active components and potential mechanisms of synergy are still unclear. The objective of this study was to develop a method that combines bionic technology, plant metabolomics and network pharmacology to discover the active components and potential mechanisms underlying the enhanced therapeutic effects of VCR for PD. Vinegar processing alters the flavor of C.rotundus, leading to changes in its properties. The acidic nature of vinegar enhances the selectivity of the medicine toward the liver, thereby improving its ability to soothe the liver, regulate qi and provide pain relief. Through gas chromatography-mass spectrometry and multivariate statistical analysis, 30 key differential components between raw C.rotundus and VCR have been screened and identified. These differential components primarily exert their therapeutic effects in treating PD by modulating targets such as interleukin-6, TNF, TP53 and PTGS2, as well as pathways including the estrogen signaling pathway, ovarian steroidogenesis, the TNF signaling pathway and the HIF-1 signaling pathway. The findings of this study serve as a reference for the application of VCR in compound formulas and clinic practiceal. Furthermore, the methodology employed in this study provides research insights for the processing of other Chinese medicines.

Military expenditure and income inequality in European NATO Member States

The purpose of this investigation was to empirically assess the association between military expenditure and income inequality in the 19 European member states of NATO between 2011 and 2022. To achieve this, the authors carried out multivariate statistical analysis using Kaplan-Meier life tables and survival estimation techniques. The results highlighted a trend in the countries analysed that income inequality tended to decrease when investment in the military increased during the period under consideration. The association manifested itself in the shortest time in the group of countries with the lowest military spending per capita, while emerging in the longer term in the countries with the highest. Furthermore, under high threat of war, the association tends to be shortest in relative terms in countries with the lowest military expenditure per capita. The results also revealed that the association between military spending and income inequality is more pronounced in the smallest countries by population and those with the lowest military expenditure per capita. The authors believe that this investigation will enrich scientific knowledge with new insights.