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The series of biochemical reactions, metabolic pathways, and regulatory interactions that occur during the storage of meat are the main causes of meat loss and waste. The volatile compounds produced by these reactions, such as hydrogen sulfide, acids, and amines, can directly indicate changes in the freshness of meat during storage and sales. In this study, a one-pot hydrothermal method based on a surface control strategy was used to develop nanoparticles of silver with different reactivities, which were further immobilized in agar powder to develop a colorimetric sensor array. Due to the different chemical interactions with various volatile compounds, the colorimetric sensor array exhibited distinct color changes. The study demonstrates significant differences between 12 different volatile compounds and provides a quantitative and visual method to reveal rich detection indicators. The colorimetric sensor array is an economical and practical multi-analyte identification method. It has many potential applications such as food packaging, anti-counterfeiting, health monitoring, environmental monitoring, and optical filters.

Surimi gel quality is crucial for seafood product texture and water retention, yet conventional processing often fails to maximize the potential of underutilized species like Hypomesus olidus. This study investigated the effects of ultrasonic power (100, 200, 400, 800 W) and time (5, 10, 15, and 20 min) on gel properties to establish optimal processing conditions. Results demonstrated that moderate ultrasonic treatment (200 W, 10 min) significantly enhanced gel quality, yielding a dense, uniform network with improved (p < 0.05) functionality: thr water-holding capacity increased by 35.88%, gel strength increased by 143.75%, and textural properties (hardness, cohesiveness, springiness, gumminess, chewiness) improved by 124.02%, 25%, 8.69%, 201.29%, 188.08% while maintaining color stability (1.59% whiteness increase). These improvements were attributed to optimized protein cross-linking and network formation. These findings provide a scientific basis for the ultrasonic processing of Hypomesus solidus surimi, offering practical parameters for industrial applications to enhance product quality efficiently. Future research should explore scaling effects and synergistic processing methods.

Herein, we design and prepare large-area silver nanoparticle (Ag NP) films based on evaporation-induced self-assembly, which offers the visual and real-time detection of chilled broiler meat freshness. The color change is based on the fact that an increase in the biogenic amine (BA) concentration causes a change in the absorption wavelength of Ag NPs caused by aggregation and etch of the Ag NPs, resulting in a yellow to brown color change, thus enabling a naked-eye readout of the BA exposure. The Ag NP films exhibit a rapid, sensitive, and linear response to BAs in a wide detection range of 2 µM to 100 µM. The Ag NP films are successfully applied as a quick-response, online, high-contrasting colorimetric sensor for visual detection of the freshness of chilled broiler meat.

Monitoring food quality throughout the food supply chain is critical to ensuring global food safety and minimizing food losses. Here we find that simply by mixing an aqueous solution of sugar-stabilized Ag+ and amines in an open vessel leads to the generation of Ag NPs and an intelligent evaluation system based on a colorimetric Ag+ probe is developed for real-time visual monitoring of food freshness. The self-assembly reaction between methylamine (MA) generated during meat storage and the colorimetric Ag+ probe produces different color changes that indicate changes in the quality of the meat. The colorimetric Ag+ probe was integrated into food packaging systems for real-time monitoring of chilled broiler meat freshness. The proposed evaluation system provides a versatile approach for detecting biogenic amines and monitoring chilled broiler meat freshness and it has the advantages of high selectivity, real-time and on-site measurements, sensitivity, economy, and safety and holds great public health significance.

[...]this alternative unemployment rate is positively correlated with China's GDP and reflects the changing economic development periods in China.

Transition metal-catalysed C–H hydroxylation is one of the most notable advances in synthetic chemistry during the past few decades and it has been widely employed in the preparation of alcohols and phenols. The site-selective hydroxylation of aromatic C–H bonds under mild conditions, especially in the context of substituted (hetero)arenes with diverse functional groups, remains a challenge. Here, we report a general and mild chelation-assisted C–H hydroxylation of (hetero)arenes mediated by boron species without the use of any transition metals. Diverse (hetero)arenes bearing amide directing groups can be utilized for ortho C–H hydroxylation under mild reaction conditions and with broad functional group compatibility. Additionally, this transition metal-free strategy can be extended to synthesize C7 and C4-hydroxylated indoles. By utilizing the present method, the formal synthesis of several phenol intermediates to bioactive molecules is demonstrated. Transition metal-catalysed C–H hydroxylation is one of the most notable synthetic advances to access alcohols and phenols. Here, the authors report a metal-free, mild C–H hydroxylation of (hetero)arenes via boron-mediated chelation.

Mesenchymal stem cells (MSCs) have attracted interest for their potential to alleviate liver injury. Here, the protective effect of MSCs on carbon tetrachloride (CCl 4 )-induced acute liver injury (ALI) was investigated. In this study, we illustrated a novel mechanism that ferroptosis, a newly recognized form of regulated cell death, contributed to CCl 4 -induced ALI. Subsequently, based on the in vitro and in vivo evidence that MSCs and MSC-derived exosomes (MSC-Exo) treatment achieved pathological remission and inhibited the production of lipid peroxidation, we proposed an MSC-based therapy for CCl 4 -induced ALI. More intriguingly, treatment with MSCs and MSC-Exo downregulated the mRNA level of prostaglandin-endoperoxide synthase 2 ( Ptgs2 ) and lipoxygenases ( LOXs ) while it restored the protein level of SLC7A11 in primary hepatocytes and mouse liver, indicating that the inhibition of ferroptosis partly accounted for the protective effect of MSCs and MSC-Exo on ALI. We further revealed that MSC-Exo-induced expression of SLC7A11 protein was accompanied by increasing of CD44 and OTUB1. The aberrant expression of ubiquitinated SLC7A11 triggered by CCl 4 could be rescued with OTUB1-mediated deubiquitination, thus strengthening SLC7A11 stability and thereby leading to the activation of system X C − to prevent CCl 4 -induced hepatocyte ferroptosis. In conclusion, we showed that MSC-Exo had a protective role against ferroptosis by maintaining SLC7A11 function, thus proposing a novel therapeutic strategy for ferroptosis-induced ALI. Schematic diagram of the protective effect of MSC-derived exosomes on maintaining SLC7A11 function during ferroptosis involved in CCl 4 -induced ALI.

A bstract The Higgs triplet model (HTM) extends the Standard Model (SM) by one complex triplet scalar (also known as the type-II seesaw model), offering a simple and viable way to account for nonzero neutrino masses. On the other hand, the nontrivial couplings of the triplet to the gauge fields and to the SM Higgs field are expected to influence the topological vacuum structure of the SM, and consequently, the energy and the field configuration of the electroweak sphaleron. The sphaleron process plays a crucial role in dynamically generating the baryon asymmetry of the Universe. In this work, we study the vacuum structure of the gauge and Higgs fields and calculate the saddle-point sphaleron configuration in the HTM. The coupled nonlinear equations of motion of the sphaleron are solved using the spectral method. We find the inclusion of the triplet scalar could in principle significantly change the sphaleron energy compared with the SM. Nevertheless, at zero temperature, the current stringent experimental constraint on the vacuum expectation value of the triplet suppresses the difference. Interestingly, we find that there still exists some narrow parameter space where the sphaleron energy can be enhanced up to 30% compared with the SM case.

Background Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. Methods The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). Results The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p <0.05. Conclusion KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

This study explored methods for integrating intangible cultural heritage (ICH) elements into modern furniture design, using Qianci as an example. Through symbolic semantics analysis, the cultural external layers of form, color, craftsmanship, and composition and the cultural internal layers of symbolic meaning, ideology, religious beliefs, and moral ethics of Qianci symbols were interpreted, extracting core design elements suitable for modern furniture design. Based on the Analytic Hierarchy Process (AHP), a hierarchical model was established to integrate intangible cultural heritage symbols with furniture design, quantitatively analyzing the importance of key elements in the design process. The results indicate that cultural symbolism, form design, cultural element integration, decorative patterns, cultural heritage value, and environmental friendliness are key indicators in furniture design. Accordingly, two thematic furniture design proposals were developed and evaluated, confirming the innovative potential and application value of Qianci symbols in modern furniture design. This study provides new perspectives and practical examples for the revitalization of intangible cultural heritage elements and the diversification of furniture design.