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Inflammation and oxidative stress play key roles in the process of aging and age-related diseases. Since serine availability plays important roles in the support of antioxidant and anti-inflammatory defense system, we explored whether serine deficiency affects inflammatory and oxidative status in D-galactose-induced aging mice. Male mice were randomly assigned into four groups: mice fed a basal diet, mice fed a serine- and glycine-deficient (SGD) diet, mice injected with D-galactose and fed a basal diet, and mice injected with D-galactose and fed an SGD diet. The results showed that D-galactose resulted in oxidative and inflammatory responses, while serine deficiency alone showed no such effects. However, serine deficiency significantly exacerbated oxidative stress and inflammation in D-galactose-treated mice. The composition of fecal microbiota was affected by D-galactose injection, which was characterized by decreased microbiota diversity and downregulated ratio of Firmicutes/Bacteroidetes, as well as decreased proportion of Clostridium XIVa. Furthermore, serine deficiency exacerbated these changes. Additionally, serine deficiency in combination with D-galactose injection significantly decreased fecal butyric acid content and gene expression of short-chain fatty acid transporters (Slc16a3 and Slc16a7) and receptor (Gpr109a) in the brain. Finally, serine deficiency exacerbated the decrease of expression of phosphorylated AMPK and the increase of expression of phosphorylated NFκB p65, which were caused by D-galactose injection. In conclusion, our results suggested that serine deficiency exacerbated inflammation and oxidative stress in D-galactose-induced aging mice. The involved mechanisms might be partially attributed to the changes in the microbiota-gut-brain axis affected by serine deficiency.

The experiment developed the ultra-high-performance liquid chromatography–tandem mass spectrometry (UPLC/MS/MS) method for testing emamectin benzoate, and studied the metabolism of emamectin benzoate in rice plants and rice-growing environments via application of this testing method. The dissipation curve of emamectin benzoate standard substance was good at 0.5–200 μg L−1, and its correlation coefficient was greater than 0.99. In the concentration range of 0.1–50 μg kg−1, the average recovery rate of plants, soil, and field water was 82 %–102 %, and relative standard deviation (RSD) was between 0.3 % and 15.9 %. Half-lives in rice plants and soil were 0.8–2.8 days and 1.9–3.8 days, respectively, and emamectin benzoate was not detected in rice or rice hull. The experiment showed that emamectin benzoate is harmless to human health at the concentration recommended by the manufacturer.

An ultrahigh-performance liquid chromatography-tandem mass spectrometry method was developed and validated for the determination of lactoferrin in camel milk based on the signature peptide. The camel lactoferrin was purified by heparin affinity chromatography and then used to screen tryptic signature peptides. The signature peptide was selected on the basis of sequence database search and identified from the tryptic hydrolysates of purified camel lactoferrin by ultrahigh-performance liquid chromatography and quadrupole time-of-flight tandem mass spectrometry. The pretreatment procedures included the addition of isotope-labeled winged peptide and the disposal of lipids and caseins followed by an enzymatic digestion with trypsin. Analytes were separated on an Acquity UPLC BEH 300 C18 column and then detected on a triple-quadrupole mass spectrometer in 7 min. The limits of detection and quantification were 3.8 mg kg−1 and 11 mg kg−1, respectively. The recoveries ranged from 74.5% to 103.6%, with relative standard deviations below 7.7%. The validated method was applied to determine the lactoferrin in ten samples collected from Xinjiang Province.

The association between the magnetic properties of lake sediments and heavy metal(loid)s (HMs) is well-documented; however, their correlation with the chemical fractions of HMs remains under-investigated. Developing a robust workflow for predicting HMs risk utilizing various machine learning techniques in conjunction with magnetic analysis presents a complex challenge. This study assessed the predictive efficacy of nine machine learning models for determining the chemical fractions of HMs, employing magnetic parameters derived from sediment cores in a large, shallow lake. These models encompassed random forest, support vector machine, relevance vector machine, extreme gradient boosting, principal component regression, multivariate adaptive regression splines, gradient boosting with component-wise linear models, and lasso and elastic-net regularized generalized linear models. The support vector machine model demonstrated superior performance, achieving coefficient of determination values surpassing 0.8 in both training and testing phases. Through interpretable machine learning approaches, key drivers of HMs were identified among magnetic and physicochemical indicators. Magnetic susceptibility values, high coercivity remanent magnetization, ratios of anhysteretic remanent magnetization to magnetic susceptibility, and anhysteretic remanent magnetization to saturation isothermal remanent magnetization within specific ranges exhibited a positive correlation with Cd, Hg, and Sb. This research significantly advances our understanding of HMs risk assessment in lake sediments by leveraging accessible magnetic measurements within an interpretable machine learning framework.

Chlorothalonil and its toxic metabolite, 4-hydroxy-chlorothalonil, pose significant environmental and health risks. However, their simultaneous and accurate detection remains challenging due to their differing ionization efficiencies in mass spectrometry and the interference caused by enzymatic reactions in sulfur-rich vegetables. This study developed a UHPLC-MS/MS method for simultaneous detection of chlorothalonil and 4-hydroxy-chlorothalonil, using an atmospheric pressure chemical ionization (APCI) source, optimizing the probe temperature to 600 °C and a set of optimal chromatography parameters. A low-temperature and acidification synergistic enzyme inhibition strategy was developed, involving refrigerating samples and extraction reagents, acidifying with citric acid before sample homogenization, and extracting with formic acid/acetonitrile, significantly improving chlorothalonil recovery. Method validation demonstrated limits of detection (LOD) and quantification (LOQ) of 0.003 mg/kg and 0.01 mg/kg, respectively, with recoveries of 76.5–91.1% for chlorothalonil and 87.6–96.7% for 4-hydroxy-chlorothalonil. The method was successfully applied in monitoring the residue risks in sulfur-rich vegetables.

This study was aimed to identify the chemical compounds of Aedes aegypti that can be potentially used to develop pheromone-based vector control methods. In this study, we compared the chemical compounds collected from the organs of mosquitoes at different developmental stages in the life cycle. We also compared the composition and amount of extracts from the different tissues of male and female adult mosquito. Interestingly, we found large amount of C17-C20 ethyl and methyl esters in the wings of female and antennae of male mosquito. We also found that isopropyl esters, dodelactone, octadecenoic acid and medium-chain fatty acid increase drastically during the late larval stage (L4). Old adult mosquitoes showed remarkable increase in production of C16:1 and C18:1 methyl esters, as a first example of chemical signatures specifically associated with aging in the animals. This knowledge may open the ground to find new behaviorally-important molecules with the ability to control Aedes specifically.

Rapeseed is an important oilseed with proper fatty acid composition and abundant bioactive components. Canada and China are the two major rapeseed-producing countries all over the world. Meanwhile, Canada and Mongolia are major importers of rapeseed due to the great demand for rapeseed in China. To investigate the metabolites in rapeseeds from three countries, ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based metabolomics was employed to analyze rapeseeds from China, Canada, and Mongolia. As results, 67, 53, and 68 metabolites showed significant differences between Chinese and Canadian, Chinese and Mongolian, and Canadian and Mongolian rapeseeds, respectively. Differential metabolites were mainly distributed in the metabolic pathways including phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and ubiquinone and other terpenoid-quinone biosynthesis. Among the differential metabolites, contents of sinapate and sinapine were higher in Chinese rapeseeds, while the contents of brassicasterol, stigmasterol, and campestanol were higher in Canadian rapeseeds. These findings might provide insight into the metabolic characteristics of rapeseeds from three countries to guide processing and consumption of the products of rapeseed.

A novel matrix certified reference material (CRM) for clenbuterol in mutton (GBW 10216) was developed to assist measurement and risk monitoring of clenbuterol in mutton. The candidate CRM raw samples were obtained by oral administration of clenbuterol and investigating the pharmacokinetics of clenbuterol in sheep. A high-precision isotope dilution coupled with liquid chromatography tandem mass spectrometry (LC-ID-MS/MS) method was established and assigned the value of clenbuterol in mutton powder through combined detection of nine inter-laboratories. The certified value with expanded uncertainty was 21.1 ± 2.2 μg/kg (k = 2, 95% confidence) for clenbuterol in mutton. The prepared matrix CRM was sufficiently homogeneous between and within bottles. The long-term stability of clenbuterol in mutton powder was evaluated for 12 months at −20℃ and short-term stability for 7 days at 4℃ and 50℃. The uncertainties originating from characterization, homogeneity, and stability were systematically analyzed and evaluated. The prepared matrix CRM can be applied for proficiency testing and nationwide risk monitoring programs to guarantee the accuracy and comparability of clenbuterol measurement results in mutton.

In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters were analysed in the sedimentary column from the centre of Lake Taihu. The sedimentary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 124 years old. Surface layers of the column were found to contain significantly higher concentrations of Cd, Co, Cu, Pb, Sb, Ti, and Zn than the middle and bottom layers. The sedimentary core contained a substantial amount of ferrimagnetic minerals. Most of the TMs were present in the residual state, except for Mn and Pb. The chemical fractions of Cd exhibited the most significant variation with depth. The pollution load index (PLI) indicated moderate TMs pollution levels in the region, whereas the risk assessment code (RAC) classified Mn as being heavily polluted. Multiple linear regression (MLR) and random forest (RF), support vector machine (SVM), and XGBoost (1.7.7.1) machine learning models were used to simulate the RAC and total concentration of TMs, using physical and chemical indicators and magnetic parameters of the sediments as input variables. The MLR model outperformed RF, SVM, and XGBoost in simulating the CFs and total concentrations of most TMs in the sedimentary column, with R2 up to 0.668 and 0.87. The SHapley Additive exPlanations (SHAP) method reveals that χarm/χ is the dominant factor influencing the RAC of As in the XGBoost models. For the RAC of Co and Cu in RF models, C% and N% exhibit greater contributions.

Major depressive disorder (MDD), especially combined with anxiety, has a high incidence and low detection rate in China. Literature has shown that patients under major depression with anxiety (MDA) are more likely to nominate a somatic, rather than psychological, symptom as their presenting complaint. In the theory of Traditional Chinese Medicine (TCM), clinical symptoms of MDD patients are mainly categorized into two different syndrome patterns: Deficiency and Excess. We intend to use resting-state functional magnetic resonance imaging (rs-fMRI) to investigate their brain functional differences and hopefully to find their brain function mechanism. For our research, 42 drug-naive MDA patients were divided into two groups (21 for Deficiency and 21 for Excess), with an additional 19 unaffected participants in the normal control (NC) group. We took Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Scale (HAMA), and brain fMRI scan for each group and analyzed the data. We first used Degree Centrality (DC) to map the functional differences in brain regions, utilized these regions as seed points, and used a seed-based functional connectivity (FC) analysis to identify the specific functional connection between groups. The Deficiency group was found to have higher HAMD scores, HAMA scores, and HAMD somatic factor than the Excess group. In the DC analysis, significant decreases were found in the right precuneus of both the Deficiency and Excess groups compared to the NC group. In the FC analysis, the right precuneus showed significant decreased network connectivity with the bilateral cuneus, as well as the right lingual gyrus in the Deficiency group when compared to the NC group and the Excess group. Through our research, it was found that precuneus dysfunction may have a relationship with MDA and Deficiency patients have more severe physical and emotional symptoms, and we realized that a larger sample size and multiple brain mode observations were needed in further research.