To investigate the immunomodulatory effects of porcine skin collagen peptides on RAW264.7 cells,this study prepared Porcine skin collagen peptides via enzymatic hydrolysis and analyzed their amino acid composition and molecular weight distribution.The effects of porcine collagen peptides on RAW264.7 cells were systematically evaluated using a range of methods,including the MTT assay,neutral red phagocytosis assay,cell morphological observation,cytokine content measurement,and real-time quantitative PCR.These analyses assessed cell proliferation activity,phagocytic function,morphological changes,the production levels of NO,TNF-α,IL-2,and IL-12,as well as the mRNA expression of related immune factors(iNOS,TNF-α,IL-2,and IL-12).The results demonstrated that Porcine skin collagen peptides are primarily composed of glycine,proline,alanine,glutamic acid,aspartic acid,and arginine,with molecular weights mainly distributed between 500 and 5 000 Da.Porcine skin collagen peptides significantly promoted RAW264.7 cell proliferation and phagocytosis,induced morphological changes,enhanced NO,TNF-α,IL-2,and IL-12 production,and upregulated the gene expression of iNOS,TNF-α,IL-2,and IL-12.In conclusion,Porcine skin collagen peptides exhibit potential in activating macrophages and enhancing non-specific immune functions.

Fumonisins(FBs) have stable properties and are difficult to break down in their structure through conventional production and processing,resulting in severe residues in agricultural products.Among various detoxification methods,enzymatic detoxification is highly favored due to its safety,efficiency,and minimal environmental impact.In order to explore the detoxification effect of lipase products on fumonisin B1(FB1),this study evaluated the detoxification performance of three kinds of lipase products.Under the optimal conditions,the detoxification rate of the three kinds of lipase products on 1 000 ng/mL FB1 was close to 100%.Further,the highest detoxification rate was (88.00±0.19)% and (90.42±0.07)% respectively in the scenario application of three kinds of feed raw materials,namely,jade Rice noodles,peanut meal,and distillers dried grains with solids(DDGS),as well as gastrointestinal juice.Through liquid chromatography-mass spectrometry(LC-MS),it was found that the action mode of lipase product is a dual mechanism of "degradation adsorption",and the degradation product is C22H47NO5.The study on the detoxification effect of FB1 provides important theoretical support for the large-scale application of FB1 removal and has potential application prospects.

To address the safety hazards caused by histamine contamination in aquatic product processing,this study used rosemary leaves as raw material and extracted rosemary essential oil by atmospheric steam distillation,optimizing the extraction process through single-factor experiments and response surface methodology.The results showed that the optimal process conditions were a solid-to-liquid ratio of 1∶10(mg∶L),distillation time of 3.8 h,soaking time of 1.9 h,and raw material particle size of 19 mesh,under which the essential oil yield reached 2.18 %.The extracted essential oil was applied to the inhibition study of Morganella morganii,a high histamine-producing microorganism,and its antibacterial activity was systematically evaluated.Antibacterial experiments showed that 30 and 40 mg/mL concentrations of rosemary essential oil had inhibition zone diameters of (21.90±0.20) and (24.73±0.45) mm,respectively,and the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were 0.4 and 1.0 mg/mL,respectively.Further mechanism studies showed that rosemary essential oil destroyed the cell membrane structure,changed the osmotic pressure inside and outside the membrane,and caused the leakage of intracellular macromolecules such as RNA and proteins,thus significantly inhibiting the growth of Morganella morganii.This study provides theoretical basis and methodological support for the application of rosemary essential oil as a natural antibacterial agent in the control of histamine in aquatic products,and has positive significance for improving the safety of skipjack tuna products.

Plant immune elicitors are a class of substances capable of activating a plants innate immune system and enhancing its resistance to pathogenic microorganisms.They hold significant potential in green agriculture and sustainable crop protection.Compared to traditional chemical pesticides,immune elicitors offer advantages such as being non-toxic,environmentally friendly,and less likely to induce resistance,thereby helping to reduce pesticide usage,ensure food safety,and protect the ecological environment.In recent years,oligosaccharides have attracted increasing research interest due to their promising biological activity;however,challenges such as limited variety and inconsistent efficacy still hinder their practical application.This study aims to develop a plant immune elicitor that is abundant in source,cost-effective,and stable in quality.Using hyaluronic acid as the raw material,hyaluronic acid oligosaccharides were prepared through a low-cost process,and their eliciting effects were evaluated in an Arabidopsis thaliana-Pseudomonas syringae pv.tomato DC3000(Pst DC3000) infection system.The experimental results showed that the optimal pretreatment concentration of hyaluronic acid oligosaccharides was 25 mg/L.Pretreating Arabidopsis seedlings with this concentration prior to inoculation with Pst DC3000 significantly reduced the disease index,showing a 35.13% decrease compared to the untreated control group.These findings indicate that hyaluronic acid oligosaccharides can partially suppress pathogen infection and enhance plant resistance.

To explore the quality differences among various commercially available rose tea products,this study selected eight representative rose tea samples for experimentation.By testing indicators such as color,moisture content,total polyphenols,total flavonoids,and DPPH radical scavenging capacity,a quality evaluation system for different rose teas was established.Pearson correlation analysis was also conducted to compare the mass fraction of active substances and antioxidant capacity in rose tea samples.The results showed that the moisture content of Kushui rose was the lowest,while the brightness value(L*) and redness value(a*) of double-petal rose were the highest.The total polyphenol mass fraction was highest in Kushui rose,and the difference was significant compared with other samples(p<0.05).The total flavonoid mass fraction in Muhong rose was 14.03 mg/g.The total anthocyanin mass fraction in Damask rose and French rose was relatively low,and the difference among samples was not significant(p>0.05).Additionally,all rose tea samples demonstrated good antioxidant capacity.Muhong rose received a higher overall sensory evaluation score and was more acceptable to consumers.Pearson correlation analysis revealed a highly significant positive correlation between total polyphenols and total flavonoids in the tested samples(p<0.01).Overall,the variety of rose has a significant impact on the quality of rose tea.This study provides a theoretical basis for consumers to choose rose tea and promotes the upgrading and optimization of edible rose processing products.

Urate oxidase,a key enzyme in the biological purine metabolism pathway,reduces the level of uric acid in the body and alleviates and treats a range of diseases caused by high uric acid.In this paper,the literature related to urate oxidase was systematically analyzed and summarized using bibliometric methods.A systematic analysis of 2 240 urate oxidase-related papers published from 1980 to 2024 in the Web of Science Core Collection database revealed that researchers have been paying more attention to urate oxidase since 2000 and have conducted a large number of studies on it.The United States ranks as the country with the highest number of published articles,while Chongqing Medical University stands out as the institution with the most significant output.Sensors and Actuators B-Chemical and Biosensors & Bioelectronic were the journals with the highest number of articles(36) and citations(2 488),respectively.Analysis of the highly cited literature shows that the topics of highest interest related to urate oxidase are “hyperuricemia” “rasburicase” and “Structure and evolutionary significance of urate oxidase”.Keyword clustering(uric acid,urate oxidase,hyperuricemia,gout,electrochemical biosensor) reveals the complete chain of urate oxidase from the underlying metabolic mechanism(oxidation of uric acid into allantoin,hydrogen peroxide,and carbon dioxide),to pathological phenotypes(hyperuricemia,gout,etc.),and to technological innovations(biologic sensor applications),providing a systematic perspective for disease research,diagnostic tool development and precision therapy.The present study provides a systematic description of urate oxidase from a macroscopic point of view,which will provide a valuable guideline for subsequent research.

Short-term power load forecasting is a critical task for ensuring optimal scheduling and secure operation of power systems.Given the prevalent nonlinear,periodic,and fluctuating characteristics in load data,traditional single-kernel Support Vector Machine models exhibit limitations in both representational capacity and prediction accuracy.To address this issue,this paper proposes an Adaptive Multi-Kernel Support Vector Machine model that integrates Chaotic Particle Swarm Optimization and an adaptive kernel parameter adjustment mechanism,aiming to improve forecasting accuracy and robustness.Specifically,a weighted composite kernel is constructed by combining a linear kernel,a periodic kernel,and a radial basis function(RBF) kernel,wherein the kernel weights are globally optimized using CPSO to enhance multi-scale feature modeling capabilities.Meanwhile,an adaptive mechanism is introduced to dynamically update kernel parameters based on validation error feedback,enabling the model to better respond to nonstationary inputs.The prediction results show that the proposed CPSO-AMK-SVM model significantly outperforms traditional models in terms of prediction accuracy,stability,and generalization ability,indicating strong potential for practical applications.

Aiming at the problems of significant noise interference,blurred edge information and difficult spatial positioning in liver ultrasound images,a REC-UNet model is proposed to verify its segmentation accuracy in extracting liver parenchyma from liver ultrasound images.The REC-UNet model was constructed by integrating the dual attention mechanism with the ResNet50 residual network.587 liver ultrasound images were trained using a mixed loss function.Based on ablation experiments,the evaluation indicators of the improved model were compared with those of the mainstream network models.The mean intersection over union(MiOU),precision,recall rate and Dice coefficient of the improved model reached 91.67%,91.58%,93.23% and 91.58% respectively.The global Dice coefficient of this model was 5.10%,4.60%,4.44% and 6.13% higher than that of the U-Net,UNet++,Attention-UNet and FCN models,respectively.The REC-UNet model can precisely segment the liver parenchyma in ultrasound images of the liver,and its various segmentation indicators have significantly improved compared with other mainstream neural network models.

To address the issues of high computational cost in the backbone network,insufficient feature extraction capability,and limited accuracy in small target detection in the YOLOv11 model for printed circuit board(PCB) surface defect detection,an improved YOLOv11 model is proposed.The model introduces the C3k2_iRMB_Cascaded module,which integrates Cascaded Group Attention(CGA) and Inverted Residual Mobile Block(iRMB) to enhance multi-scale feature extraction while reducing computational complexity.A Selective Boundary Aggregation(SBA) module is adopted to construct a Re-Calibration Feature Pyramid Network(Re-Calibration FPN),addressing the semantic loss issue in traditional FPN through bidirectional feature fusion and an adaptive attention mechanism,thereby improving small target detection accuracy.A lightweight Detect_LSCD detection head is designed,incorporating shared convolution and a scale-adaptive scaling mechanism to enhance the robustness of multi-scale object detection.Experimental results demonstrate that the improved model achieves a 3.4% increase in PmA 0.5 and a 1.5% increase in PmA 0.5:0.95 compared with the original YOLOv11n model,effectively balancing detection accuracy and real-time performance,making it suitable for quality inspection requirements in modern electronic manufacturing systems.