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This review highlights recent progress in exosome-based drug delivery for cancer therapy, covering exosome biogenesis, cargo selection mechanisms, and their application across multiple cancer types. As small extracellular vesicles, exosomes exhibit high biocompatibility and low immunogenicity, making them ideal drug delivery vehicles capable of efficiently targeting cancer cells, minimizing off-target damage and side effects. This review aims to explore the potential of exosomes in cancer therapy, with a focus on applications in chemotherapy, gene therapy, and immunomodulation. Additionally, challenges related to exosome production and standardization are analyzed, highlighting the importance of addressing these issues for their clinical application. In conclusion, exosome-based drug delivery systems offer promising potential for future cancer therapies. Further research should aim to enhance production efficiency and facilitate clinical translation, paving the way for innovative cancer treatment strategies.

A robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm is proposed in this paper for autoregressive eXogenous (ARX) models, addressing the challenges of heavy-tailed noise, as well as the difficulty in simultaneously processing multi-sensor information. First, for the potential outliers in industrial processes, the Student’s t-distribution is introduced to model the statistical characteristics of measurement noise, whose heavy-tailed property enhances the algorithm’s robustness. Second, a recursive framework is integrated into the Expectation-Maximization (EM) algorithm to satisfy the real-time requirement of dynamic system identification. Through a recursive scheme of the Q-function and sufficient statistics, model parameters are updated in real-time, allowing them to adapt to time-varying system characteristics. Finally, by exploiting the redundancy and complementarity of multi-sensor data, a multi-sensor information fusion mechanism is designed that adaptively calculates the weight of each sensor based on the noise variances. This mechanism effectively fuses multi-source observation information and mitigates the impact of single-sensor failure or inaccuracy on identification performance. Numerical examples and simulations of the continuous stirred-tank reactor (CSTR) demonstrate the validity of the proposed RMSREM algorithm.

Due to its high carbon content, lignin, particularly for lignin-containing solid waste, is considered an excellent raw material for the preparation of carbon materials like biochar. To produce high-quality lignin-based biochar (LGBCs), lignin extracted from black liquor was employed to prepare biochar at various pyrolysis temperatures (300~600 °C). The physicochemical properties of LGBCs were assessed using scanning electron microscopy, N2 adsorption/desorption, Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction. Furthermore, the adsorption capability and potential mechanism of LGBCs in removing Cd(II) were investigated as well. The results indicate that LGBCs produced at higher pyrolysis temperatures exhibit rougher surfaces and more developed pore structures, which facilitate the exposure of numerous active adsorption sites. The adsorption of Cd(II) by LGBCs generally follows the order of LG-300C < LG-400C < LG-500C < LG-600C. According to the Langmuir adsorption isotherm model, the theoretical maximum adsorption capacity of LG-600C for Cd(II) is calculated to be 18.54 mg/g. Adsorption mechanism analysis reveals that the complexation interaction, dependent on the surface functional groups, plays a crucial role in the adsorption of Cd(II) by LGBCs prepared at higher pyrolysis temperatures. This study demonstrates that, by controlling the pyrolysis temperature during biochar preparation, high-quality lignin-based biochar can be readily obtained.

Systemic inflammation biomarkers have emerged as promising tools for predicting infection-related complications in orthopedic surgery. However, its predictive value for surgical site infection (SSI) after closed pilon fractures remains underexplored. This study aimed to develop and validate a nomogram that integrates systemic inflammation biomarkers and conventional clinical predictors to estimate the risk of SSI after closed pilon fracture surgery. We retrospectively analyzed data from patients aged ≥18 years with closed pilon fractures treated surgically at a tertiary orthopedic center between January 2020 and December 2023. Systemic inflammation response index (SIRI) and other candidate biomarkers were calculated from peripheral blood samples collected upon admission. The diagnosis of SSI was based on CDC criteria, determined through inpatient records and routine 12-month postoperative follow-up. Restricted cubic spline (RCS) curves were used to assess dose-response relationships between biomarkers and SSI. Multivariable logistic regression was performed to identify independent predictors and construct a nomogram. Model performance was evaluated using discrimination, calibration, and decision curve analysis (DCA). Temporal validation was performed in an independent cohort from the same center (January 2024 to December 2024), and external validation was conducted in an independent cohort from another institution (August 2024 to September 2025) using identical eligibility criteria. Among 1314 patients included in model development, 57 cases (4.34%) of SSI were recorded. RCS analysis revealed a near-linear association between SIRI and SSI risk, with a threshold of 2.01 used for stratification. Multivariable analysis identified SIRI ≥ 2.01, BMI, surgical delay ≥ 6 days, Tscherne classification grade 3, prolonged surgical duration, and elevated fasting blood glucose (FBG) as independent predictors. The nomogram demonstrated good discrimination in the development cohort (AUC = 0.765) and maintained performance in temporal validation (AUC = 0.788) and external validation (AUC = 0.779). This study identified SIRI as a novel and independent systemic inflammation biomarker associated with SSI after closed pilon fracture. We further developed a nomogram combining SIRI and conventional clinical factors and validated it in both temporal and external cohorts, which may support individualized perioperative decision-making after further prospective multicenter validation.

Temperature is a major environmental factor that influences growth, development, metabolism, and physiological performance in fish. Grass carp (Ctenopharyngodon idella) is a highly productive fish in freshwater culture. To understand the molecular mechanism of grass carp under heat stress, we used RNA-Seq to analyze the liver and brain transcriptome of 12 libraries constructed from high-temperature (36 °C) and control (28 °C) groups. We obtained 42.49 and 42.57 GB of clean data from six liver and six brain libraries, respectively, and identified 2,534 genes that were differentially expressed in liver tissue and 1622 in brain tissue (P < 0.05). According to KEGG analysis, significant differences occurred in the expression of genes involved in metabolic and immune pathways, such as the cAMP signaling pathway, apoptosis, calcium signaling pathway, lipid metabolism, and protein processing in endoplasmic reticulum and peroxisome proliferator-activated receptor signaling pathways. This study revealed that high temperature enhanced lipid metabolism, reduced fatty acid synthesis, and disrupted the immune system of grass carp. These results investigated the molecular regulation of heat stress in grass carp and provided valuable information for the healthy culture of grass carp under high temperatures.

Background Genetic diversity within a species reflects population evolution, ecology, and ability to adapt. Genome-wide population surveys of both natural and introduced populations provide insights into genetic diversity, the evolutionary processes and the genetic basis underlying local adaptation. Grass carp is the most important freshwater foodfish species for food and water weed control. However, there is as yet no overall picture on genetic variations and population structure of this species, which is important for its aquaculture. Results We used 43,310 SNPs to infer the population structure, evidence of local adaptation and sources of introduction. The overall genetic differentiation of this species was low. The native populations were differentiated into three genetic clusters, corresponding to the Yangtze, Pearl and Heilongjiang River Systems, respectively. The populations in Malaysia, India and Nepal were introduced from both the Yangtze and Pearl River Systems. Loci and genes involved in putative local selection for native locations were identified. Evidence of both positive and balancing selection was found in the introduced locations. Genes associated with loci under putative selection were involved in many biological functions. Outlier loci were grouped into clusters as genomic islands within some specific genomic regions, which likely agrees with the divergence hitchhiking scenario of divergence-with-gene-flow. Conclusions This study, for the first time, sheds novel insights on the population differentiation of the grass carp, genetics of its strong ability in adaption to diverse environments and sources of some introduced grass carp populations. Our data also suggests that the natural populations of the grass carp have been affected by the aquaculture besides neutral and adaptive forces.

Antibiotics are widely used in human medical, livestock, and aquaculture fields. Most antibiotics are water-soluble and cannot be fully absorbed by humans or animals. If feces or wastewater containing antibiotics are improperly treated or discharged directly into surface water or groundwater, it will undoubtedly have an impact on aquatic organisms. The Ganjiang River is the largest river in Jiangxi Province and the largest tributary of Poyang Lake Basin. Jinjiang River, a tributary of Ganjiang River, is a typical livestock and poultry breeding area in the Poyang Lake Basin, along which many townships and counties are distributed. Gao’an and Shanggao counties are important agricultural and animal husbandry production areas in Jiangxi Province. In this paper, automatic solid phase extraction-ultra high performance liquid chromatography-mass spectrometry (SPE-UPLC-MS/MS) technology was used to simultaneously detect 27 antibiotics in 5 categories of macrolides, tetracyclines, quinolones, nitroimidazoles and sulfonamides in water. Based on this method, the concentrations and distributions of these antibiotics were analyzed. Ecological risk assessment of the Jinjiang River Basin was conducted using the ecological risk quotient method, aiming to supplement antibiotic data in the Jinjiang River Basin and provide scientific basis for local ecological environment management. The research results indicate that from 2019 to 2021, two years later, there was an increase in the use of Sulfadiazine and Roxithromycin in the Jinjiang River Basin, while the usage of Ciprofloxacin and Oxytetracycline was relatively low. In 2021, out of the 27 antibiotics, 24 were detected in surface water, 20 in groundwater, and all in wastewater. Among them, Sulfamethoxazole was the most widely used antibiotic, primarily in livestock and poultry farming. Gao’an City, a key breeding area in the Jinjiang River Basin, exhibited the highest concentration of Sulfamethoxazole at 409.96 ng·L -1 , which far exceeds other antibiotics and warrants significant attention. A comparison of surface water concentrations between the Jinjiang River and 12 other regions revealed higher overall pollution levels of Roxithromycin and Sulfamethoxazole. Furthermore, according to the ecological risk assessment results, only Sulfamethoxazole poses a moderate risk to aquatic organisms.

The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R2 of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management.

The main purpose of this article is to study the generalized Kudryashov’s equation with truncated M-fractional derivative, which is commonly used to describe the propagation of wide pulses in nonlinear optical fibers. By employing the complete discriminant system of fourth-order polynomials, various types of explicit solutions are systematically classified, which include periodic solutions, the trigonometric functions, the double-period solutions, and the elliptic function solutions. Additionally, a series of 2D, 3D, and contour plots are generated to visually depict the spatial distribution and evolution of various solutions. This not only advances the development of nonlinear equations in theory but also provides valuable guidance in practical applications.

Neoantigen (nAg) vaccines can induce anti-tumor specific immunity, and tumor killing promotes further antigen diffusion, which is expected to improve prognosis. However, the mutation of cancer cells under the selective pressure of vaccines and the immunosuppressive tumor microenvironment make the therapeutic effect unsatisfactory. Here, we develop a nanovaccine (nAg-MRDE/Mn) that can deliver nAg and induce in situ cancer vaccination to synergistically promote a personalized immune response, enhance antigen diffusion, and improve the microenvironment by modulating immunosuppressive cells and activating the innate immune response. Experiments show that nAgs are presented by dendritic cells and expressed by T cells, which cooperate with in situ vaccination to stimulate specific immunity. Cells involved in immunosuppression, such as M2 macrophages and regulatory T cells, are down-regulated, while M1 macrophages and natural killer cells are increased. In addition, the hydrogel loaded with chemokines and nAg-MRDE/Mn inhibits postoperative tumor recurrence, and the combination of nAg-MRDE/Mn and αPD-1 improves the therapeutic effect of αPD-1. This study validates the clinical potential of this strategy and provides ideas for improving neoantigen vaccines. Mutation of cancer cells under the selective pressure of vaccines and the immunosuppressive tumor microenvironment limit therapeutic efficacy of cancer neoantigen vaccine. Here this group develops a nanovaccine delivering nAg and induce in situ cancer vaccination to synergistically promote a personalized immune response, enhance antigen diffusion, and improve the microenvironment by modulating immunosuppressive cells and activating the innate immune response.