Background: Si-Ni-San (SNS), a commonly used traditional Chinese medicine (TCM) formula, has potency against liver diseases, such as hepatitis and non-alcoholic fatty liver disease (NAFLD). However, the therapeutic efficacy and pharmacological mechanisms of action of SNS against liver fibrosis remain largely unclear.Methods: A carbon tetrachloride (CCl₄)-induced liver fibrosis mouse model was adopted for the first time to investigate the beneficial effects of SNS on liver fibrosis. The potential mechanisms of action of SNS were explored using the network pharmacology-based strategy and validated with the aid of diverse assays.Results: SNS treatment reduced collagen and ECM deposition, downregulated fibrosis-related factor (hyaluronic acid and laminin) contents in serum, maintained the morphological structure of liver tissue, and improved liver function in the liver fibrosis model. Based on network pharmacology results, apoptosis, inflammation and angiogenesis, together with the associated pathways (including VEGF, TNF, caspase, PPAR-γ and NF-κB), were identified as the mechanisms underlying the effects of SNS on liver fibrosis. Further in vivo experiments validated the significant mitigatory effects of SNS on inflammatory infiltration and pro-inflammatory cytokine contents (IFNγ, IL-1β and TGF-β1) in liver tissues of mice with liver fibrosis. SNS suppressed pathologic neovascularization as well as levels of VEGFR1, VEGF and VEGFR2 in liver tissues. SNS treatment additionally inhibited hepatic parenchyma cell apoptosis in liver tissues of mice with liver fibrosis and regulated apoptin expression while protecting L02 cells against apoptosis induced by TNF-α and Act D in vitro. Activation of hepatic stellate cells was suppressed and the balance between MMP13 and TIMP1 maintained in vitro by SNS. These activities may be associated with SNS-induced NF-κB suppression and PPAR-γ activation.Conclusion: SNS effectively impedes liver fibrosis progression through alleviating inflammation, ECM accumulation, aberrant angiogenesis and apoptosis of hepatic parenchymal cells along with inhibiting activation of hepatic stellate cells through effects on multiple targets and may thus serve as a novel therapeutic regimen for this condition.

Background: Tumor compression-induced pain (TCIP) is a complex pathological cancer pain. Spinal glial cells play a critical role in maintenance of cancer pain by releasing proinflammatory cytokines and chemokines. In this study, we verified the role of levo-corydalmine (l-CDL) on TCIP. Methods: Spontaneous pain, paw withdrawal threshold and latency were assessed using TCIP mouse model. Immunofluorescence was used to identify the reactions of glia. RT-PCR and western blot or ELISA were used to determine mRNA or protein expression of tumor necrosis factor-α (TNF-α), interlukin-1β (IL-1β), CC chemokine ligand 2 (CCL2) and chemotactic cytokine receptor 2 (CCR2) in vivo and in vitro. Results: l-CDL significantly attenuated TCIP hypersensitivity, accompanying with downregulation of TNF-α and IL-1β expression levels and declined astrocytes and microglial activation. It also significantly decreased the expression of the mRNA and protein level for CCL2 and CCR2. Further, l-CDL could suppress TNF-α-induced astrocytes activation and IL-1β expression through downregulating the CCL2/CCR2. Besides, CCL2-induced BV-microglia activation and inflammatory factors secretion were suppressed by l-CDL via CCR2. Conclusions: Suppression of CCL2/CCR2 by l-CDL may contribute to alleviate TCIP, offering an alternative medication for TCIP.

Background. Pyroptosis is closely related to the programmed death of cancer cells as well as the tumor immune microenvironment (TIME) via the host-tumor crosstalk. However, the role of pyroptosis-related genes as prognosis and TIME-related biomarkers in skin cutaneous melanoma (SKCM) patients remains unknown. Methods. We evaluated the expression profiles, copy number variations, and somatic mutations (CNVs) of 27 genes obtained from MSigDB database regulating pyroptosis among TCGA-SKCM patients. Thereafter, we conducted single-sample gene set enrichment analysis (ssGSEA) for evaluating pyroptosis-associated expression patterns among cases and for exploring the associations with clinicopathological factors and prognostic outcome. In addition, a prognostic pyroptosis-related signature (PPRS) model was constructed by performing Cox regression, weighted gene coexpression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis to score SKCM patients. On the other hand, we plotted the ROC and survival curves for model evaluation and verified the robustness of the model through external test sets (GSE22153, GSE54467, and GSE65904). Meanwhile, we examined the relations of clinical characteristics, oncogene mutations, biological processes (BPs), tumor stemness, immune infiltration degrees, immune checkpoints (ICs), and treatment response with PPRS via multiple methods, including immunophenoscore (IPS) analysis, gene set variation analysis (GSVA), ESTIMATE, and CIBERSORT. Finally, we constructed a nomogram incorporating PPRS and clinical characteristics to improve risk evaluation of SKCM. Results. Many pyroptosis-regulated genes showed abnormal expression within SKCM. TP53, TP63, IL1B, IL18, IRF2, CASP5, CHMP4C, CHMP7, CASP1, and GSDME were detected with somatic mutations, among which, a majority displayed CNVs at high frequencies. Pyroptosis-associated profiles established based on pyroptosis-regulated genes showed markedly negative relation to low stage and superior prognostic outcome. Blue module was found to be highly positively correlated with pyroptosis. Later, this study established PPRS based on the expression of 8 PAGs (namely, GBP2, HPDL, FCGR2A, IFITM1, HAPLN3, CCL8, TRIM34, and GRIPAP1), which was highly associated with OS, oncogene mutations, tumor stemness, immune infiltration degrees, IC levels, treatment responses, and multiple biological processes (including cell cycle and immunoinflammatory response) in training and test set samples. Conclusions. Based on our observations, analyzing modification patterns associated with pyroptosis among diverse cancer samples via PPRS is important, which can provide more insights into TIME infiltration features and facilitate immunotherapeutic development as well as prognosis prediction.

B7-H3 is one of the most important members of the B7/CD28 family, and its expression level is abnormally high in a variety of tumors. B7-H3 inhibits T cell activation via binding to the corresponding receptors on T cells, thereby mediating tumor immune escape. Glycosylation is a common post-translational modification of proteins, which plays an essential role in protein expression patterns and biological functions. Current evidence has shown that the abnormal glycosylation of B7-H3 in tumors is of great significance for protein expression and ligand-receptor binding. Therefore, in-depth exploration of the underlying mechanism of glycosylation modification of B7-H3 is expected to provide new insights for tumor immunotherapy. To investigate the underlying mechanism of glycosyltransferase-mediated glycosylation of B7-H3. Firstly, the CHIPBase database was used to screen glycosyltransferases with a high correlation with the protein expression of B7-H3. Then their siRNAs were designed and synthesized to transfect into cells, and the western blotting assay was performed for further screening. Secondly, the siRNA and overexpression plasmid of the screened glycosyltransferase were respectively transfected into cells to verify the effect on the expression level of B7-H3. Thirdly, the effect of glycosyltransferase on the expression level of B7-H3 was explored by changing its substrate level. Finally, the co-immunoprecipitation experiment was conducted to verify whether protein binding existed between B7-H3 and the glycosyltransferase. The glycosyltransferase called A4GALT had the highest correlation with the protein expression of B7-H3. After knocking down or overexpressing A4GALT, the protein expression level of B7-H3 both changed significantly. When knocked down GALT to reduce the galactosyl donors, B7-H3 was significantly down-regulated. And B7-H3 was up-regulated while increasing the galactose in the medium. In addition, the Co-immunoprecipitation experiment proved that there was protein binding between B7-H3 and A4GALT. A4GALT can positively regulate the expression of B7-H3, and changing the level of galactosyl donors can also positively regulate the expression of B7-H3. There is a protein interaction between A4GALT and B7-H3.

Background: Body temperature is a readily available physiological signal that may reflect dynamic host responses in sepsis, yet most studies rely on single or early measurements and do not capture the dynamic nature of thermoregulation during critical illness. Whether the association between body temperature and mortality varies over the course of sepsis remains unclear. Methods: In this multicenter retrospective cohort study, patients with sepsis were identified from the MIMIC-IV database as the derivation cohort and externally validated using the eICU Collaborative Research Database and a single-center intensive care unit (ICU) cohort from Nanjing Drum Tower Hospital. Body temperature was summarized into 6-hour intervals during the ICU stay. The primary outcome was 28-day in-hospital mortality. Joint latent class mixed models were used to identify temperature trajectory classes and generate landmark-based dynamic survival predictions. Piece-wise exponential additive mixed models were applied to estimate the nonlinear and time-varying association between infection-related temperature responses and in-hospital mortality. Results: A total of 8681 patients were included in the derivation cohort and 13717 in the validation cohort. Five distinct temperature trajectory classes were identified, demonstrating heterogeneous thermoregulatory patterns during the ICU stay. Mortality risk differed significantly across trajectory classes. The low-increase-moderate hyperthermia trajectory was associated with the highest mortality risk in both early and later periods of hospitalization. Dynamic prediction models incorporating longitudinal temperature trajectories showed better overall discrimination than models based on single temperature measurements. Time-varying analysis revealed a nonlinear U-shaped relationship between temperature and mortality, with the lowest risk near the upper range of normothermia. Both hypothermia and hyperthermia were associated with increased mortality, and the prognostic impact of infection-related temperature responses varied over the ICU course, peaking at approximately 4 days after ICU admission. Conclusion: Body temperature may serve as a dynamic host-response marker in sepsis, with prognostic significance determined by both temporal trajectory patterns and disease stage. Longitudinal temperature monitoring could provide a clinically accessible strategy for dynamic risk stratification and future temperature-guided precision management in sepsis.