Reliable discrimination between wild and farmed fish is important for aquatic product traceability and fishery resource conservation. In this study, Hypophthalmichthys molitrix (H. molitrix) was used as a model species to investigate whether muscle fatty acid profiles could distinguish production origin. Fatty acid compositions were determined by GC–MS, and ten machine learning algorithms were applied to construct discrimination models. OPLS-DA identified six candidate discriminative fatty acids, among which C22:6n3 and C16:1 showed relatively stable contributions across multiple models. After feature selection, most machine learning models showed improved classification performance. BayesNet exhibited relatively balanced and stable performance, achieving ACC of 93.6% in the training set and 91.2% in the test set, with a test-set AUC of 0.98. These results suggest that fatty acid profiling combined with chemometrics and machine learning approaches has potential for distinguishing wild from farmed H. molitrix. However, further validation using larger cross-seasonal and multi-regional datasets is still required.

Metal-organic frameworks (MOFs) are promising chiral separation materials owing to their well-defined pore structures and tunable functionality. In this work, mesoporous/microporous NU-1000 was functionalized with L-Cysteine (L-Cys) via solvent-assisted ligand exchange to produce L-Cys-NU-1000, which was applied as a stationary phase in capillary electrochromatography (CEC). The resulting L-Cys-NU-1000@capillary system achieved baseline separation of five chiral amino acid enantiomers with excellent intra-day, inter-day, and batch reproducibility (RSDs < 5%). Notably, the system retained high enantioselectivity even after 100 consecutive injections. This study represents the first application of NU-1000 in chiral separation, opening new avenues for its use and laying a foundation for broader future applications. The system demonstrates distinct advantages for quality control of amino acids, offering high efficiency, robustness, and reproducible enantiomeric separation crucial for food and pharmaceutical analysis.

Triple-negative breast cancer (TNBC) presents high heterogeneity, strong invasiveness, lack of clear molecular targets, and a propensity for immune evasion. Herein, a pH-responsive, targeted nanoplatform-Veliparib@MnCl2 PEG-E5 nanoparticles (VMP NPs)-is developed for enhanced antitumor immune response via mechanotransduction. VMP NPs are constructed via self-assembly of a poly (ADP-ribose) polymerase (PARP) inhibitor (Veliparib) and Mn2 + coordination core, and functionalized with a C-X-C chemokine receptor type 4 (CXCR4)-targeting 1,2-Distearoyl-sn-glycero-3-phosphoethanolamine-polyethylene glycol-E5 peptide shell. These NPs exhibit uniform particle size distribution, biocompatibility, stability, and efficient CXCR4-mediated cellular uptake. They inhibit the proliferation and migration of MDA-MB-231 cells. In a co-culture system with NK-92 cells, VMP NPs enhance NK-92 cell immune responses, increasing cytotoxicity and promoting the secretion of IFN-gamma, TNF-alpha, perforin, and granzyme B, thereby activating the NK cell-mediated extrinsic apoptotic pathway. Additionally, VMP NPs upregulate TNF-related apoptosis-inducing ligand (TRAIL) receptor 2 expression on MDA-MB-231 cells, sensitizing cells to TRAIL-mediated apoptosis and activating the intrinsic apoptotic pathway synergistically. In a murine model of tumors, this coordinated mechanism enables efficient tumor targeting, marked growth suppression, and a pro-inflammatory tumor microenvironment via mechanotransduction, with little detectable systemic toxicity. These findings illustrate the potential of this multifunctional nanoplatform as a promising and well-tolerated strategy for targeted TNBC immunotherapy.

Laos and its cross-border regions with China remain highly endemic for foot-and-mouth disease (FMD). With the increasing cross-border trade under the Belt and Road Initiative, the risk of FMD virus (FMDV) introduction into China via live cattle trade may have increased; however, integrated quantitative assessments tailored to the China-Laos live-cattle trade interface remain limited. To address the limitations of passive outbreak reporting in endemic settings, this study integrated official outbreak records and literature-derived serological exposure data from 2005 to 2025 to construct a comprehensive risk analysis framework combining maximum entropy (MaxEnt) ecological niche modeling and stochastic scenario tree risk assessment. Monte Carlo simulations were utilized to quantitatively evaluate the risk of viral introduction through legal and illegal trade pathways, as well as the effectiveness of various intervention strategies. The FMD habitat suitability model indicated that predicted suitability was most strongly associated with wind speed seasonality and distance to roads. Approximately 36.2% of the territory in Laos was classified as suitable habitats, characterized by a belt-like distribution along transportation networks. Quantitative assessment identified illegal live-cattle trade as the dominant modeled pathway for potential FMDV introduction events. The mean per-batch probability of a potential FMDV introduction event through the illegal-trade pathway was 0.0679, and the modeled expected annual number of potential FMDV introduction events under the baseline scenario was 61.7. Graded scenario-response analysis showed progressive reductions in the expected annual number of potential FMDV introduction events as annual illegal-entry volume, source infection probability, or transport-stage evasion decreased. In contrast, reducing batch size while holding total annual illegal-entry volume constant increased this endpoint because shipment frequency increased. Increasing destination-herd vaccination coverage and vaccine antibody qualification targets reduced the expected annual number of potential FMDV introduction events, especially when both components were improved jointly. In conclusion, illegal live-cattle trade may represent a major pathway for transboundary FMDV introduction. Future prevention and control strategies should shift from isolated border interception to comprehensive whole-chain risk governance.

BACKGROUND:Cronobacter is a group of opportunistic pathogens frequently associated with food contamination, particularly in powdered infant formula, posing a serious health risk to neonates and young children. Iron acquisition systems are critical for bacterial survival and virulence, with the iucABCD-iutA operon encoding the aerobactin siderophore synthesis pathway playing a central role. METHODS:Transcriptomic analysis comparing iron-limited and iron-replete conditions was performed to identify iron-responsive genes in Cronobacter. Single-, double-knockout, and corresponding complementation strains targeting iucC and iucD were constructed and systematically evaluated for siderophore production, intracellular iron accumulation, and host cell interaction. RESULTS:Transcriptomic analysis revealed global changes in gene expression under iron-limited conditions, particularly in iron acquisition-related pathways, with iucC and iucD significantly upregulated. Deletion of iucC and iucD markedly reduced siderophore production and total intracellular iron content, and significantly impaired bacterial adhesion to and invasion of Caco-2 cells. CONCLUSION:These findings highlight the essential roles of iucC and iucD in Cronobacter iron acquisition and pathogenicity and suggest that the aerobactin synthesis pathway may represent a potential target for future anti-infective strategies.

The Yangtze River Basin, one of the world’s most biodiverse freshwater ecosystems, has experienced a sharp decline in fishery resources and a continuous decrease in biodiversity in recent years owing to long-term, high-intensity human activities. To facilitate holistic conservation and restoration of the Yangtze River’s ecological environment, China implemented a “Ten-Year Fishing Ban” policy in 2020, with a complete prohibition of commercial fishing from January 1, 2021. However, during the enforcement of this ban, some merchants engaged in illegal fishing of wild aquatic products and misrepresented them as farmed products for profit, severely undermining law enforcement and ecological recovery. Hypophthalmichthys molitrix, an economically important freshwater fish species in China, is frequently involved in illegal fishing. Owing to the high morphological similarity between wild and farmed individuals, traditional identification methods are inadequate to meet the demands of high-precision and high-efficiency regulatory oversight. Therefore, developing a scientific and reliable technique to distinguish between wild and farmed H. molitrix is critical and urgent. To address this, this study focused on H. molitrix. The aim was to establish an accurate method for discriminating between wild and farmed individuals by systematically comparing differences in the fatty acid composition of their muscle tissues and integrating various machine-learning algorithms. Wild H. molitrix samples were collected from different sections of the Yangtze River and from commercially available farm samples. Fatty acids in the muscle tissue were analyzed using gas chromatography-mass spectrometry, and their relative contents were calculated using the area normalization method to obtain comprehensive fatty acid profile data. Subsequently, a discrimination model system incorporating six typical machine learning algorithms—Bayes Net, Logistic Regression, k-Nearest Neighbors (k-NN), Adaptive Boosting M1 (AdaBoost.M1), Random Forest, and Decision Table—was constructed. The performance of each algorithm in discriminating between the wild and farmed H. molitrix strains was systematically evaluated. A correlation-based feature selection subset evaluator (CFS subset interval) algorithm was introduced to screen fatty acid variables to further enhance the model performance and generalization capability. This process ultimately identified the seven most discriminative fatty acids: C16:1, C17:0, C20:2, C20:3n3, C20:4n6, C20:5n3, and C22:6n3. The six aforementioned machine learning models were reconstructed based on the selected key fatty acids. Their performance was systematically compared across multiple metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The results indicated that feature dimensionality reduction significantly improved the overall discriminative ability of all models. The model built on the AdaBoost.M1 algorithm demonstrated optimal performance, achieving an accuracy of 90.5% (sensitivity, 89.5%; specificity, 91.3%) on the training set and 81.0% (sensitivity, 100.0%; specificity, 63.6%) on the test set. The AUC values for the development and test sets were as high as 0.97 and 0.94, respectively, indicating excellent fit and generalization stability. This model can be considered as the optimal choice for discriminating between wild and farmed H. molitrix. Other algorithms, such as k-NN and Bayes Net, also showed significant performance improvements after feature selection, further validating the crucial role of the seven selected fatty acids in distinguishing wild and farmed H. molitrix. In summary, we systematically analyzed the fatty acid profiles of muscle tissues from wild and farmed H. molitrix. By combining effective feature selection with multiple machine-learning algorithms, a reliable discrimination model for wild and farmed H. molitrix was successfully constructed. This method not only provides a robust scientific basis and practical tool for the traceability of aquatic products through technical pathways, but also offers technical support for the effective implementation of the fishing ban policy in the Yangtze River Basin and for the scientific conservation and ecological restoration of fishery resources, demonstrating the broad application prospects and practical applications.

Cell-free DNA (cfDNA), a damage-associated molecular pattern, plays a pivotal role in initiating and perpetuating inflammatory responses in a wide range of diseases. Recent advances in nanotechnology have enabled the design of diverse nanomaterials that can bind or degrade cfDNA, offering promising therapeutic avenues. This review systematically summarizes cfDNA-targeting nanomaterials, including cationic polymers, metal-based constructs, and inorganic frameworks for efficient adsorption. Catalytic platforms such as deoxyribonuclease (DNase)-loaded nanocarriers and DNase-mimetic nanozymes are also highlighted for their potential in direct cfDNA degradation. Furthermore, we discuss combinatorial therapeutic strategies that integrate cfDNA clearance with anti-inflammatory therapy, immunomodulation, and reactive oxygen species scavenging to achieve synergistic inflammation control. The therapeutic relevance of these strategies is explored across multiple inflammatory disease models, including autoimmune diseases, infection-induced inflammation, acute injuries, and chronic inflammatory conditions. By bridging nanotechnology and immunopathology, cfDNA-targeted nanomedicine represents a powerful paradigm for precise and durable inflammatory resolution. Finally, we outline current challenges in clinical translation and discuss future directions for the rational design and optimization of cfDNA-scavenging nanotherapeutics.

Peanut-induced allergic reactions are characterized by rapid onset, high morbidity, and mortality. The current stage to reduce the allergenicity of peanut protein involves various processing methods, including ultrasonication, which is widely used in food processing to disrupt protein structure. In this study, peanut crude protein was treated with ultrasonication and fed to mice. Results showed a significant reduction and alleviated tissue damage in the intestine, lung, and spleen. Additionally, inflammatory factors such as TSLP, IL-33, and so forth were significantly reduced in mice. Furthermore, ultrasound-purified Ara h 2 protein treatment in cells showed a significant reduction in cellular inflammation through the MAPK and NF-κB pathways. This study also found that ultrasonication altered the structure of the Ara h 2 protein, which may be the main reason for its reduced sensitization.

Nucleic acid amplification tests are widely used for molecular diagnostics. However, primer-dimer formation and nonspecific amplification may compromise analytical accuracy and increase the risk of false-positive results. Here, temperature-gated PNA@AuNRs (peptide nucleic acid-modified gold nanorods) were introduced to improve amplification specificity. A microwave-assisted modification method efficiently immobilized PNA onto AuNRs, enabling directional primer capture at low temperature and controlled release near polymerase-optimal conditions. PNA@AuNRs-LAMP achieved a limit of quantification (LOQ) of 102 copies/μL with strong linearity (R2 = 0.991), while PNA@AuNRs-qPCR reached 10 copies/μL with excellent linearity (R2 = 0.998). Mechanistic analysis demonstrated strong PNA-primer affinity and a distinct thermal-start effect at 60 °C, absent in traditional LAMP. In spiked equine serum and nasal samples, PNA@AuNRs effectively eliminated nonspecific amplification and improved detection accuracy. These results highlight PNA@AuNRs-gated amplification as a promising strategy for high-specificity molecular diagnostics.

Gliadin is an allergenic protein present in wheat gluten and serves as the primary causative agent of celiac disease. In this study, we developed an aptasensor based surface-enhanced Raman scattering (SERS) platform, to achieve rapid and highly sensitive detection of gliadin in food products. The platform was constructed with an aptamer-modified Au enhanced silicon chip as the capture substrate (Gli1@Au@Si-substrate) and gold nanoflowers (AuNFs) were coated with a Prussian blue (PB) shell layer and then functionalized with aptamers to serve as the detection label (Gli1@PB@AuNF). Gli4@Au@Si-substrate can effectively recognize and capture target gliadin. Gli1@PB@AuNF Label can afford strong SERS activity in the Raman silent region at 2150 cm(-1). The preparation of Gli4@Au@Si-substrate and Gli1@PB@AuNF label was systematically investigated using variety characterization methods. As expected, this aptasensor exhibited a favorable detection performance for Gliadin. And good linearity with a detection limit as low as 0.0793 mu g/mL was obtained. Excellent specialty, sensitivity reproducibility, and stability were also exhibited in the result. Furthermore, this method was applied to detect gliadin in various food products, and the results were consistent with the product manual. This aptasensor provides a novel approach for gliadin detection and facilitating standardized allergen labeling in the food industry.

Musk deer are forest-dwelling artiodactyls of high conservation value, but their wild populations remain under severe conservation pressure due to poaching driven by the demand for natural musk, together with habitat fragmentation and habitat loss. Efficient non-invasive image-based monitoring is therefore important for musk deer conservation; however, fine-grained detection of musk deer and visually similar artiodactyls in ecological images remains difficult because of background camouflage, vegetation occlusion, and high inter-class similarity. In this study, a fine-grained wildlife image dataset was constructed, and an RT-DETR-based framework, termed DSCF-DET, was proposed for automated detection in complex natural scenes. DSCF-DET integrates three task-oriented modules: DRPBlock for receptive-field-aware feature extraction, SASTE for sparse spatial encoding, and CBAFusion for cross-level feature fusion. On the constructed dataset, DSCF-DET achieved 91.4% precision, 86.2% recall, 88.7% F1-score, and 86.4% mAP50. Compared with RT-DETR-r18, it improved these metrics by 8.6, 12.1, 10.5, and 12.4 percentage points, respectively, while maintaining moderate model complexity. Visualization results showed more target-focused feature responses and reduced background-related activations. Cross-dataset experiments on an independent public wildlife dataset further suggested potential applicability to broader wildlife detection scenarios. These results indicate that DSCF-DET provides a computationally balanced approach for ecological image screening and intelligent musk deer monitoring.

Background Efficient enantiomer separation is crucial in drug analysis and biomedical research. The strategy of combining capillary electrochromatography (CEC) with metal-organic frameworks (MOFs) has attracted much attention due to its high specific surface area and tunable chiral recognition sites. However, conventional MOF-based CEC systems suffer from poor stability and limited enantioselectivity. Results In this study, a triazole-functionalized amino acid metal-organic framework (TAMOF) was synthesized by an environmentally friendly method, and a TAMOF@capillary chiral column was constructed by using a preanchoring strategy to immobilize it on the inner wall of a capillary tube. The system successfully achieved the baseline separation of five chiral amino acids with high resolution, good reproducibility and long-term stability. Significance and novelty The pre-anchored immobilization strategy proposed in this study provides a universal approach for the stable loading of MOF materials in microseparation systems. In addition, the TAMOF-functionalized CEC chiral columns provide a cost-effective and scalable platform for enantiomer separation, which exhibits significant potential for applications in the pharmaceutical and biomedical fields.

Chiral separation is vital in medicine because the two versions of a chiral drug can act differently, and one might cause toxicity. Therefore, efficient enantioseparation is essential in pharmaceutical quality control. In this study, we modified the amino site of l-lysine (L-Lys) with a triazole ring. The triazole amino acid ligand (NL) with self-assembly potential was designed independently. Then NLMOF was synthesized by coordination with Cu (II) under green ambient stirring conditions. The NLMOF (triazole-modified metal-organic framework) was uniformly integrated into a capillary column through a post-modification method and utilized in open tubular capillary electrochromatography (OT-CEC) for enantioseparation. 10 chiral amino acids and 1 chiral drug was successfully separated in NLMOF@OT-CEC method with excellent repeatability (RSD < 5 %) and stability (cycles > 100). Furthermore, when compared with similar MOFs (metal-organic frameworks) chiral stationary phase, NLMOF demonstrates advantages in terms of mild preparation conditions, cost-effectiveness, and environmental sustainability, making it a promising candidate for industrial-scale production.

Pangolins, the world's most trafficked wild mammals, face a dire need for immediate protection against illegal trade. Harnessing the cutting-edge advancements in artificial intelligence, particularly within deep learning, offers a beacon of hope for their survival. This groundbreaking study tackles this urgent call by curating a comprehensive dataset of pangolin images and pioneering a state-of-the-art pangolin detection model, built upon an enhanced Yolov8 architecture. Through innovative enhancements such as integrating bifpn into the network neck, incorporating the Triplet Attention mechanism into the backbone network, and leveraging the Slideloss loss function to prioritize challenging samples, our refined model surpasses the limitations of the original Yolov8s model. Notably, our improved model showcases a remarkable 13.6 % reduction in FLOP and 50.0 % in parameters, while elevating mAP to an impressive 87.0 % and shrinking model size to a mere 14.3 MB. These enhancements culminate in unparalleled recognition accuracy and model efficiency, empowering wildlife managers with superior tools for monitoring and identifying pangolins. This groundbreaking advancement not only facilitates the crucial task of pangolin conservation but also signifies a significant stride towards safeguarding our planet's precious biodiversity.

Diabetic kidney disease (DKD) is a microvascular complication of diabetes with high morbidity and mortality, necessitating effective treatment. In this study, the Loureirin B analogue (LB-A) was utilized to treat DKD in mice. The results demonstrated that LB-A effectively prevent the progression of DKD in mice, significantly lowering fasting blood glucose levels and reducing proteinuria levels. Additionally, there was a significant decrease in oxidase content in the kidneys of mice, accompanied by an increase in antioxidant oxidase content, resulting in a decrease in ROS levels, mitigating oxidative stress state through modulation of Cxcl1. Cell experiments further confirmed that reducing Cxcl1/Cxcr2 axis activation prevented the onset of DKD induced by high glucose exposure and affected the therapeutic effect of LB-A as well. These findings provide evidences to support that LB-A may mitigate oxidative stress by modulating the Cxcl1 signaling pathway, thereby contributing to renal protection in the context of DKD treatment.

Loop-mediated isothermal amplification (LAMP) holds great promise for rapid nucleic acid detection. However, its application is hindered by the notable occurrence of false positives. In this work, gold nanorod (AuNR)-mediated hot start effect, which adsorbs primers and controls the release of single-stranded DNA (ssDNA) primers at about 50 °C, was demonstrated. The AuNR-mediated LAMP reaction for detecting African horse sickness virus (AHSV) showed high selectivity against other DNA fragments, even at high magnesium concentrations. Furthermore, the assay achieves excellent sensitivity with a detection limit of 100 copies/μL. Additionally, the method displays good repeatability from 50 to 65 °C (optimal), and the coefficient of variation of Tt at the same concentration of nucleic acids is less than 5%. Our study optimizes the LAMP reaction using AuNRs, thereby reducing false positives and offering great potential for clinical diagnosis.

Equine arteritis virus (EAV) and African horse sickness virus (AHSV) primarily infect equids, causing symptoms such as fever, edema, and internal hemorrhage, which can be fatal in severe cases. These diseases pose significant threats to the equine industry and socio-economic development. Traditional detection methods are timeconsuming, labor-intensive, and have limited scope of application. To address the demand for rapid on-site detection and visual analysis, we developed a method combining Recombinase Polymerase Amplification (RPA) and CRISPR-Cas13a/13b for dual detection, enabling rapid detection of both EAV and AHSV in a single tube. The method allows detection within one hour, and the results can be visually observed. Experimental results demonstrate that the method has a detection limit of 102 copies/mu L, with good sensitivity, and exhibits no cross-reactivity with other equine viruses, demonstrating high specificity. Furthermore, the dual detection platform was successfully applied to artificially simulated positive equine nasal swab samples, yielding results consistent with those obtained by quantitative real-time PCR (qPCR), with a concordance rate of 100 %. This study provides a new and efficient tool for the rapid diagnosis of equine pathogens, with broad application prospects, especially in both laboratory and field diagnostics. This holds significant implications for equine health and the sustainable development of international equestrian events.

Chirality is a fundamental property in nature, and chiral molecules are closely related to human health and the origin of life. Therefore, the exploration and preparation of optically active compounds of paramount importance. Membrane separation is a large-scale and continuous separation technique that has been developing quickly in recent years. It has many potential applications, particularly in chiral membrane separation technology, which is currently a hotspot for study. Depending on the types of membranes, chiral membranes can be divided into two categories: chiral solid membranes and chiral liquid membranes. Solid membranes outperform the others in terms of better mechanical performance and separation efficiency. This review presents in-depth summaries of chiral solid membranes made of different materials, and their applications in drug separation. It also providing insights into the potential for the future development of chiral solid membranes.

Coilia nasus is an endangered freshwater fish in China. Due to overfishing and ecological environment deterioration, its resources are rapidly declining and even facing the risk of extinction. Therefore, conservation measures must be taken immediately to ensure the sustainable resources of C. nasus. The purpose of this study is to predict the suitable habitats of C. nasus in the Yangtze River and to make a spatiotemporal and geographical analysis of the illegal fishing cases of C. nasus in the Yangtze River as part of the conservation efforts. In this study, the MaxEnt model was used to predict the suitable habitats of C. nasus, and the distribution characteristics of illegal fishing cases involving C. nasus were revealed through a spatiotemporal geographic analysis. Based on the collected data of 19 climatic variables, three topographic variables and one flow accumulation variable, we constructed the MaxEnt model, optimized the parameter settings of the MaxEnt software, and called the R package Kuenm to screen and determine the optimal parameters from the MaxEnt model with 1240 different parameter combinations, and predicted the suitable habitats of the C. nasus in the Yangtze River Basin. The results showed that the main breeding areas of C. nasus were mainly concentrated in Chongqing in the upper reaches of the Yangtze River and Jiangsu Province and Shanghai in the middle and lower reaches of the Yangtze River, which had suitable hydrological and topographic conditions for C. nasus survival. In addition, river flow accumulation and temperature are the main variables affecting the distribution of C. nasus in the Yangtze River. At the same time, we analyzed the spatial and temporal distribution of illegal fishing cases of C. nasus and found that there were relatively serious illegal fishing activities in Chongqing and Shanghai, especially in highly suitable habitats areas, where illegal fishing cases broke out frequently, and suggested that high-suitability areas should be prioritised as conservation areas of C. nasus.

This study developed an efficient method for identifying and quantitatively analyzing animal-origin milk powders using Raman spectroscopy combined with chemometrics. By employing the MultiClassClassifier model, the method achieved high accuracy in distinguishing various types of animal-origin milk powders, with sensitivity and specificity both exceeding 80% and an overall accuracy of 93%. Furthermore, the quantitative models based on partial least squares regression and support vector machine regression exhibited excellent linear correlations, with both root mean square error and mean relative error below 0.2. These models successfully quantified adulteration in camel, mare, and donkey milk powders in comparison to goat and cow milk powders. The study's approach not only holds significant promise for detecting adulteration in specialty milk powders but also demonstrates wide applicability in analyzing other powdered adulterants.