The interleukin-10 （IL-10） family is expressed in various types of cells and has a wide range of biological functions， and it plays an important role in the development and progression of hepatic fibrosis. Hepatic fibrosis is a chronic liver disease characterized by abnormal repair of hepatic tissues after injury， activation of hepatic stellate cells， and excessive accumulation of extracellular matrix. The IL-10 family members include IL-10， IL-19， IL-20， IL-22， IL-24， IL-26， IL-28， IL-29， and IL-35， with similarities in structure and function， and changes in their expression levels are closely associated with the progression of hepatic fibrosis. Moderate upregulation of the expression of IL-10 family members can help maintain the quiescent state of hepatic stellate cells， promote the transformation of macrophages to anti-inflammatory phenotype， and regulate the activity of natural killer cells， thereby inhibiting inflammatory response， regulating cell apoptosis and autophagy， and finally reversing the progression of hepatic fibrosis. This article discusses the mechanism of action of IL-10 family members and their application in traditional Chinese medicine and Western medicine therapies， in order to provide new thoughts for the treatment of hepatic fibrosis.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Liver failure （LF） is a severe clinical syndrome characterized by severe impairment or decompensation of liver function. At present， the key role of immune molecules in the pathogenesis of LF has been well established. These molecules not only directly participate in the pathological process of LF， but also influence the course of LF by modulating the behavior of immune cells. In addition， immune molecules can be used as potential biomarkers for evaluating the prognosis of LF. This article summarizes the role of immune molecules in LF and explores the therapeutic strategies based on these immune molecules， in order to provide new directions for the diagnosis and treatment of LF.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Metabolic diseases have seen a steady increase in incidence in recent years, becoming one of the main causes of sub-health status globally. Neutrophil extracellular traps(NETs) are reticular complexes containing DNA, which trap foreign microorganisms or induce an immune response. Current research indicates that NETs are widely active in various metabolic diseases and can cause severe damage to the body through multiple mechanisms, including promoting blood glucose elevation, damaging vascular endothelial cells, forming vascular embolisms, triggering intense inflammation, and promoting lipid accumulation. Therefore, intervening in NETs is an important approach to treating metabolic diseases. Research has shown a close relationship between the theory of spleen heat-turbid toxin theory and metabolic diseases-NETs mechanism. The basic pathogenesis include the internal accumulation of phlegm-dampness, qi stagnation and blood stasis, internal accumulation of dampness-heat, phlegm and blood stasis, and flourishing toxic heat. Various Chinese herbal medicines with the functions of dispelling dampness, resolving phlegm, promoting blood circulation to remove blood stasis, and clearing heat and toxins, along with their extracts and compound prescriptions, can treat metabolic diseases by regulating NETs and delaying disease progression. This paper systematically outlined the formation mechanisms of NETs, their connection to metabolic diseases, the theoretical basis in TCM, their roles in numerous metabolic diseases, and the current research status of TCM in regulating NETs to prevent and control metabolic diseases, aiming to provide effective reference ideas for developing therapeutic strategies for metabolic diseases.
Humans ; Extracellular Traps/metabolism* ; Metabolic Diseases/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Animals ; Neutrophils/metabolism* ; Medicine, Chinese Traditional