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.

Qualitative analysis of the ingredients absorbed into blood and their metabolites of Xihuang pill (XHP) were conducted using high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS/MS) technology. Network pharmacology was used to explore the potential anticancer mechanisms of the ingredients against glioma, and their specific mechanisms were validated through molecular docking and experimental verification. SD rats were intragastrically administered with XHP, and rat serum samples were collected. Ingredients absorbed into blood and their metabolites were identified based on the retention time of chromatographic peaks, accurate molecular mass, characteristic fragment ions, and comparisons with reference substances and literature data. PharmMapper and SwissTarget Prediction databases were used to obtain the targets of the XHP-medicated serum, while GeneCards, OMIM, PharmGKB, TTD, and DrugBank databases were used to obtain glioma disease targets. The "component-target" network relationship diagram was constructed using Cytoscape 3.9.1 software. The protein-protein interaction (PPI) network diagram was constructed using the STRING database, and the targets were analyzed using GO and KEGG analyses. Molecular docking was used to verify the binding ability of core targets with their corresponding compounds in XHP-medicated serum. The potential mechanism of the anti-glioma effect of 11-keto-β-boswellic acid (KBA), a representative component of XHP-medicated serum, was verified using CCK-8 and Western blot assays. A total of 40 compounds were identified in the XHP-medicated serum, including 28 prototype components and 12 metabolites. The network pharmacology results showed that elemonic acid, 3-acetyl-β-boswellic acid, KBA, α-boswellic acid, and other 5 compounds might be the active ingredients of XHP-medicated serum in the treatment of glioma. Glutathione reductase (GSR), glucose-6-phosphate dehydrogenase (G6PD), ATP-citrate lyase (ACLY), aldo-keto reductase family 1 member B1 (AKR1B1) and glutaredoxin (GLRX) were identified as key targets, involving pathways such as glutathione metabolism and the pentose phosphate pathway. Further cell experiments showed that KBA significantly inhibited the proliferation of T98G cells with an IC₅₀ of 30.96 μmol·L^-1, and KBA (30 μmol·L^-1) significantly downregulated the protein expression levels of GSR in T98G cells. In summary, XHP-medicated serum may exert its anti-glioma effect by regulating GSR and G6PD-targeted pathways involved in glutathione metabolism. These results provide valuable evidence for further investigating the mechanism of XHP in treating glioma. The animal welfare and experimental procedures were approved by the Ethical Committee of Laboratory Animals at Nanjing University of Chinese Medicine (approval No. ACU221001).

Acute suppurative thyroiditis(AST) is a rare thyroid disease, mostly caused by infections such as Staphylococcus aureus, and it is difficult to distinguish from subacute thyroiditis(SAT) at the beginning of the disease. Here we report the clinical data of a young male patient who was initially misdiagnosed as SAT, but was clinically diagnosed as AST with DNSIs accompanied by LS. The clinical features and treatment, combined with related literature, aim to enhance clinicians' understanding of this disease.