Objective: To investigate the prognostic significance and biological functions of neutrophil extracellular traps (NETs) related genes in oral squamous cell carcinoma (OSCC). Methods: A total of 333 transcriptome datasets and 6 single-cell sequencing datasets of OSCC were retrieved from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Based on 69 NETs related gene sets, univariate Cox and Lasso-Cox regression were used to construct a prognostic risk model for OSCC. The model's efficacy was evaluated through Kaplan-Meier analysis and receiver operating characteristic (ROC) curves, and risk scoring and nomogram analysis were conducted. Further, the relationship between NETs risk scores and angiogenesis, epithelial-mesenchymal transition (EMT), and cell cycle was explored. Enrichment analysis was performed to annotate the functional characteristics of relevant pathways. Kaplan-Meier analysis was employed to screen for prognostic key genes. Candidate targets were validated through drug prediction and molecular docking assays. Single-cell RNA sequencing was utilized to characterize the expression profile of the key gene cathepsin G (CTSG) within the tumor microenvironment (TME). Using pan-cancer and OSCC related data retrieved from the TCGA database, we analyzed the differences in CTSG expression between tumor tissues and normal tissues. Subsequently, immunohistochemical staining experiments were performed on tissue microarrays to validate its expression at the protein level. Results: A prognostic risk model based on six NETs related genes (F3, AKT1, CTSG, VNN3, MPO, and IL17A) was successfully established. Patients in the high-risk group exhibited significantly shorter overall survival (OS) (P < 0.000 1). The area under the ROC curve (AUC) of the established model for predicting 1-, 3-, and 5-year overall survival (OS) rates was 0.718, 0.820, and 0.805, respectively. The NETs related risk score was identified as an independent prognostic factor (P < 0.001), with the constructed nomogram demonstrating good calibration. The NETs related risk score correlated with angiogenesis (r = ˗0.20,, P < 0.001), EMT (r = 0.17, P < 0.01), G1/S phase transition (r = 0.11, P < 0.05), and G2/M phase transition (r = 0.17, P < 0.01). GSEA（gene set enrichment analysis）revealed that the high-risk group was significantly enriched in pathways including basal cell carcinoma, whereas the low-risk group exhibited significant enrichment in pathways such as alpha-linolenic acid metabolism (P < 0.05). Kaplan-Meier analysis revealed that patients with low expression of CTSG had a poorer prognosis (P < 0.001). Molecular docking assays demonstrated a stable binding interaction between CTSG and glutathione (binding energy: -7.4 kcal/mol). Single-cell RNA sequencing analysis further showed that CTSG was highly expressed in mast cell subsets but weakly expressed in malignant cells (P < 0.001). TCGA pan-cancer analysis revealed that CTSG is underexpressed in multiple cancer tissues, including OSCC (P < 0.05). Immunohistochemical staining confirmed that CTSG protein expression was lower in tumor tissues than in paracancerous tissues (P < 0.01). Conclusion The NETs related prognostic model established in this study exhibits robust predictive performance. CTSG was identified as a key prognostic gene, thereby providing a novel biomarker and potential therapeutic target for prognostic evaluation and targeted therapy of OSCC.

Objective: To analyze the epidemiological characteristics of category C intestinal infectious diseases among children and adolescents in Shenzhen from 2012 to 2024 and the association with meteorological factors, so as to provide a scientific basis for the targeted prevention and control of infectious diseases for children and adolescents. Methods: Using data from the "Infectious Disease Reporting Information Management System" of the "China Disease Prevention and Control Information System" covering the period from January 1, 2012 to December 31, 2024, the study analyzed clinical and confirmed cases of hand, foot, and mouth disease, other infectious diarrhea, and acute hemorrhagic conjunctivitis among individuals aged 6-19 years old to describe demographic and temporal characteristics. It used Joinpoint regression to calculate the average annual percent change (AAPC) and annual percent change (APC) to analyze incidence trends, and Spearman s correlation was combined to generalize linear models so as to assess the association between category C intestinal infectious diseases and meteorological factors. Results: From 2012 to 2024, a cumulative total of 61 019 cases of hand, foot, and mouth disease among children and adolescents, 58 498 cases of other infectious diarrhea, and 6 377 cases of acute hemorrhagic conjunctivitis were reported. The AAPC in the incidence rates of these three diseases was 19.19%, 31.03% and 31.48 %, respectively(all P <0.05). Notably, the incidence of hand, foot, and mouth disease increased significantly after 2022 (APC= 133.66 %, P <0.01). The temporal distribution showed that hand,foot,and mouth disease was most prevalent in May,June and July (seasonal index of 2.39,3.64,1.97), other infectious diarrhea was most prevalent in February,March and December (seasonal index of 1.22,1.25,1.47), and acute hemorrhagic conjunctivitis peaked in September and October (seasonal index of 4.22,2.16). Monthly average temperature could increase the risk of hand,foot,and mouth disease( β = 0.18 ,95% CI =0.11-0.25); as monthly average wind speed increased, the incidence of other infectious diarrhea ( β =-0.86, 95% CI = -1.50 to -0.22) and acute hemorrhagic conjunctivitis ( β =-1.32, 95% CI =-2.60 to -0.05) both decreased (all P < 0.05 ). Conclusions Among children and adolescents in Shenzhen, category C intestinal infectious diseases remain prevalent throughout the year；the number of reported hand, foot, and mouth disease cases has shown an upward trend in recent years.Temperature and wind speed significantly affect the number of reported cases of three types with category C intestinal infectious diseases.

ObjectiveTo compare the differences between the ultrafiltration method and the dextran gel filtration method during the purification of Tfn-modified PCL micelles by using purification efficiency and micelle purity as indicators. MethodsCoumarin-6 （C6） was used as a fluorescent probe and was loaded into HOOC-PEG-PCL to form PCL micelles by the film-dispersion method. Tfn was then conjugated to the surface of PCL micelles via an amidation reaction， resulting in two types of micelles： Tfn/PCL_H and Tfn/PCL_L. The pharmaceutical properties of the two types of micelles were characterized. The micelles were then purified through ultrafiltration method and dextran gel method respectively， and the efficiency of the two methods， along with the purity of the final micelles， was compared. The density of Tfn on the surface of PCL micelles was also calculated. ResultsThe hydrated diameter of PCL micelles was approximately 73 nm， and the C6 loading efficiency was around 0.046%. The size increased to 134 nm and 158 nm for Tfn/PCL_L and Tfn/PCL_H， respectively. The micelle population was monodisperse. The purification results showed that， for the ultrafiltration method， after two and one rounds of purification， the Tfn/C6 ratio stabilized at 23.6 and 3.4 for Tfn/PCL_H and Tfn/PCL_L， respectively. For the dextran gel filtration method， the Tfn/C6 ratio reached 23.7 for the Tfn/PCL_H group after two rounds of purification. However， for the Tfn/PCL_L group， the Tfn/C6 ratio increased during four rounds of dextran gel purification， and a significant difference （P = 0.042 4） was observed between the first and last filtrations. The density of Tfn in the final micelles were calculated. For the ultrafiltration method， the Tfn density of Tfn/PCL_H and Tfn/PCL_L were 94.9% and 13.8%， respectively. For the dextran gel filtration method， the density of the two micelles were 95.6% and 14.4%， respectively. For Tfn/PCL_L group， the density results revealing a statistically significant difference （P=0.000 2）. ConclusionThe purification efficiency of the two methods is comparable. However， the purity of the final micelles shows a significant difference， with the dextran gel filtration method resulting in higher purity， particularly for the Tfn/PCL_L micelles.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Chinese hamster ovary (CHO) cells are the most established and versatile mammalian expression system for the large-scale production of recombinant therapeutic proteins, owing to their genetic stability, adaptability to serum-free suspension culture, and ability to perform human-like post-translational modifications. More than 70% of biologics approved by the U.S. Food and Drug Administration rely on CHO-based production platforms, underscoring their central role in modern biopharmaceutical manufacturing. Despite these advantages, CHO systems continue to face three persistent bottlenecks that limit their potential for high-yield, reproducible, and cost-efficient production: excessive metabolic burden during high-density culture, heterogeneity of glycosylation patterns, and progressive loss of long-term expression stability. This review provides an integrated analysis of recent advances addressing these challenges and proposes a forward-looking framework for constructing intelligent and sustainable CHO cell factories. In terms of metabolic regulation, excessive lactate and ammonia accumulation disrupts energy balance and reduces recombinant protein synthesis efficiency. Optimization of culture parameters such as temperature, pH, dissolved oxygen, osmolarity, and glucose feeding can effectively alleviate metabolic stress, while supplementation with modulators including sodium butyrate, baicalein, and S-adenosylmethionine promotes specific productivity (qP) by modulating apoptosis and chromatin structure. Furthermore, genetic engineering strategies—such as overexpression of MPC1/2, HSP27, and SIRT6 or knockout of Bax, Apaf1, and IGF-1R—have demonstrated significant improvements in cell viability and product yield. The combination of multi-omics metabolic modeling with artificial intelligence (AI)-based prediction offers new opportunities for building self-regulating CHO systems capable of dynamic adaptation to environmental stress. Regarding glycosylation uniformity, which determines therapeutic efficacy and immunogenicity, gene editing-based glycoengineering (e.g., FUT8 knockdown or ST6Gal1 overexpression) has enabled the humanization of CHO glycan profiles, minimizing non-human sugar residues and enhancing drug stability. Process-level strategies such as galactose or manganese co-feeding and fine control of temperature or osmolarity further allow rational regulation of glycosyltransferase activity. Additionally, in vitro chemoenzymatic remodeling provides a complementary route to construct human-type glycans with defined structures, though industrial applications remain constrained by cost and scalability. The integration of model-driven process design and AI feedback control is expected to enable real-time prediction and correction of glycosylation deviations, ensuring batch-to-batch consistency in continuous biomanufacturing. Long-term expression stability, another critical challenge, is often impaired by promoter silencing, chromatin condensation, and random genomic integration. Molecular optimization—such as the use of improved promoters (CMV, EF-1α, or CHO endogenous promoters), Kozak and signal peptide refinement, and incorporation of chromatin-opening elements (UCOE, MAR, STAR)—helps maintain durable transcriptional activity, while site-specific integration systems including Cre/loxP, Flp/FRT, φC31, and CRISPR/Cas9 can enable single-copy, position-independent gene insertion at genomic safe-harbor loci, ensuring stable, predictable expression. Collectively, this review highlights a paradigm shift in CHO system optimization driven by the convergence of genome editing, synthetic biology, and artificial intelligence. The transition from empirical optimization to rational, data-driven design will facilitate the development of programmable CHO platforms capable of autonomous regulation of metabolic flux, glycosylation fidelity, and transcriptional activity. Such intelligent cell factories are expected to accelerate the transformation from laboratory-scale research to industrial-scale, high-consistency, and economically sustainable biopharmaceutical manufacturing, thereby supporting the next generation of efficient and customizable biologics manufacturing.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Chinese hamster ovary (CHO) cells are the most established and versatile mammalian expression system for the large-scale production of recombinant therapeutic proteins, owing to their genetic stability, adaptability to serum-free suspension culture, and ability to perform human-like post-translational modifications. More than 70% of biologics approved by the U.S. Food and Drug Administration rely on CHO-based production platforms, underscoring their central role in modern biopharmaceutical manufacturing. Despite these advantages, CHO systems continue to face three persistent bottlenecks that limit their potential for high-yield, reproducible, and cost-efficient production: excessive metabolic burden during high-density culture, heterogeneity of glycosylation patterns, and progressive loss of long-term expression stability. This review provides an integrated analysis of recent advances addressing these challenges and proposes a forward-looking framework for constructing intelligent and sustainable CHO cell factories. In terms of metabolic regulation, excessive lactate and ammonia accumulation disrupts energy balance and reduces recombinant protein synthesis efficiency. Optimization of culture parameters such as temperature, pH, dissolved oxygen, osmolarity, and glucose feeding can effectively alleviate metabolic stress, while supplementation with modulators including sodium butyrate, baicalein, and S-adenosylmethionine promotes specific productivity (qP) by modulating apoptosis and chromatin structure. Furthermore, genetic engineering strategies—such as overexpression of MPC1/2, HSP27, and SIRT6 or knockout of Bax, Apaf1, and IGF-1R—have demonstrated significant improvements in cell viability and product yield. The combination of multi-omics metabolic modeling with artificial intelligence (AI)-based prediction offers new opportunities for building self-regulating CHO systems capable of dynamic adaptation to environmental stress. Regarding glycosylation uniformity, which determines therapeutic efficacy and immunogenicity, gene editing-based glycoengineering (e.g., FUT8 knockdown or ST6Gal1 overexpression) has enabled the humanization of CHO glycan profiles, minimizing non-human sugar residues and enhancing drug stability. Process-level strategies such as galactose or manganese co-feeding and fine control of temperature or osmolarity further allow rational regulation of glycosyltransferase activity. Additionally, in vitro chemoenzymatic remodeling provides a complementary route to construct human-type glycans with defined structures, though industrial applications remain constrained by cost and scalability. The integration of model-driven process design and AI feedback control is expected to enable real-time prediction and correction of glycosylation deviations, ensuring batch-to-batch consistency in continuous biomanufacturing. Long-term expression stability, another critical challenge, is often impaired by promoter silencing, chromatin condensation, and random genomic integration. Molecular optimization—such as the use of improved promoters (CMV, EF-1α, or CHO endogenous promoters), Kozak and signal peptide refinement, and incorporation of chromatin-opening elements (UCOE, MAR, STAR)—helps maintain durable transcriptional activity, while site-specific integration systems including Cre/loxP, Flp/FRT, φC31, and CRISPR/Cas9 can enable single-copy, position-independent gene insertion at genomic safe-harbor loci, ensuring stable, predictable expression. Collectively, this review highlights a paradigm shift in CHO system optimization driven by the convergence of genome editing, synthetic biology, and artificial intelligence. The transition from empirical optimization to rational, data-driven design will facilitate the development of programmable CHO platforms capable of autonomous regulation of metabolic flux, glycosylation fidelity, and transcriptional activity. Such intelligent cell factories are expected to accelerate the transformation from laboratory-scale research to industrial-scale, high-consistency, and economically sustainable biopharmaceutical manufacturing, thereby supporting the next generation of efficient and customizable biologics manufacturing.

Hepatocellular carcinoma (HCC) is a lethal cancer with high morbidity rates worldwide. It is a major threat to public health in China, due to the combination of known and new risk factors, such as endemic hepatitis B virus (HBV), dietary aflatoxin exposure, and the occurrence of metabolic dysfunction-associated steatotic liver disease (MASLD). Although many methods for surveillance and multimodal therapies, such as surgery, local ablation, transarterial therapy, and new systemic agents, have been available, the survival rates of HCC remains poor. They have very limited durable responses, long post-treatment recurrence rates, and high resistance to treatment. This reflects an imperfect picture of the biological cause of the disease and a need for new mechanistic or targeted techniques. A significant characteristic of HCC, in common with other aggressive cancers, is the presence of reprogrammed, hyperactive cell metabolism. Tumor cells hijack metabolic pathways to promote their uncontrolled growth, stress survival, invasion and metastasis. While classical mechanisms such as the Warburg effect, lipid metabolism and glutamine utilization have been understood, the lysosome, which was once viewed as a static “waste disposal unit” to remove old organelles and proteins, is instead a dynamic signaling and metabolic core. The lysosomes incorporate nutrients, energy and stress signals by master regulators such as mTORC1 (activated on its surface) that balance anabolic growth and catabolic recycling to the cellular demands. In HCC, lysosomes are not passive, but are highly active and dysregulated. HCC cells upregulate lysosomes, which scavenge intracellular components via enhanced autophagy and engulf extracellular proteins via macropinocytosis, crucial for survival in the nutrient-poor, hypoxic tumor microenvironment. In addition to metabolism, lysosomes exhibit pro-invasive functions by secreting hydrolases to remodel the extracellular matrix, promote angiogenesis, and suppress stromal immune cells to foster a pro-tumor microenvironment. In a clinical context, lysosomes play an important role in therapeutic resistance: they sequester and inactivate chemotherapeutics via lysosomal sequestration, and enhanced autophagic flux protects the cell from therapy-induced damage, contributing to relapse, as lysosomal dysfunction is a key cause of treatment failure. This makes lysosomes promising yet challenging therapeutic targets in HCC. Recent preclinical and early clinical studies investigate multiple strategies to exploit the susceptibility of lysosomes: lysosome-specific agents, alkalinizing the lysosome lumen or inducing membrane permeabilization and lysosome-dependent cell death; pharmacological inhibition of key lysosomal enzymes or autophagy to impair nutrient recycling and stress adaptation; smart nanotherapeutic agents or antibody-drug conjugates, specifically activated in the acidic lysosomal environment or utilizing lysosomal pathways for efficient intracellular drug release; and combination strategies of lysosome-targeting agents with tyrosine kinase inhibitors or immunotherapy to overcome resistance and achieve synergistic antitumor effects. In summary, our review systematically presents the role of lysosomes in HCC, from metabolic reprogramming and microenvironmental adaptation to therapeutic resistance. By synthesizing the latest mechanistic insights and preclinical advances, this review highlights the indispensable role of lysosomes in the complex HCC biological network, emphasizing that an in-depth understanding of this dynamic organelle holds great promise for developing innovative, targeted therapies, offering new hope for improving the poor prognosis of global HCC patients.

ObjectiveTo investigate the therapeutic effect of sitravatinib on carbon tetrachloride （CCl₄）-induced liver fibrosis in mice. MethodsA total of 30 male C57BL/6J mice， aged 8 weeks， were randomly divided into control group， CCl₄ model group， and low- （5 mg/kg）， middle- （10 mg/kg）， and high-dose （20 mg/kg） sitravatinib groups. All mice except those in the control group were given intraperitoneal injection of CCl₄ for 4 consecutive weeks to induce liver fibrosis， and since the first day of modeling， the mice in the low-， middle-， and high-dose sitravatinib groups were given sitravatinib at the corresponding dose by gavage every day. The serum levels of total cholesterol （TC）， triglyceride （TG）， and alanine aminotransferase （ALT） were measured for the mice in each group； hepatic hydroxyproline content was measured； HE staining， Masson staining， and Sirius Red staining were used to observe liver histopathological changes； quantitative real-time PCR and Western blot were used to measure the mRNA and protein expression levels of α-smooth muscle actin （α-SMA） and collagen type I alpha 1 （Col1a1） in liver tissue. The therapeutic effect of sitravatinib was assessed based on the above results. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the control group， the model group had significant increases in the levels of TC， TG， and ALT （all P<0.05）， and there were no significant differences in the levels of TC， TG， and ALT between the model group and the low-， middle-， and high-dose sitravatinib groups （all P>0.05）. Hepatic hydroxyproline content decreased after sitravatinib intervention， with a significant difference between the middle-/high-dose sitravatinib groups and the CCl₄ model group （both P<0.05）. Histopathological staining showed that the sitravatinib treatment groups had a reduction in collagen deposition， along with thinning and fragmentation of fibrous septa， and in the high-dose sitravatinib group， 4 mice had a fibrosis stage of S0—S1 and 2 mice had a fibrosis stage of S2—S3， suggesting a certain degree of alleviation of liver fibrosis degree compared with the CCl₄ model group （mainly S3—S4）. The measurement of related molecules showed that sitravatinib downregulated the mRNA and protein expression levels of α-SMA and Col1a1 （all P<0.05）. ConclusionSitravatinib can effectively alleviate CCl₄-induced liver fibrosis in mice， possibly by inhibiting hepatic stellate cell activation and collagen synthesis.

To interpret the evidence-to-decision （EtD） framework and to illustrate its application in traditional Chinese medicine （TCM） guideline development using the example of the Pharmacovigilance Guideline of Chinese Patent Medicine， thereby providing methodological references for TCM guideline standardization. Based on the core three stages of the EtD framework （formulating the question， making an assessment of the evidence， and drawing conclusions）， critical decision points and evaluation evidence within the evidence-translation process were systematically addressed， aligning with the purpose， scope， and key questions of the guideline. Qualitative research methods， such as the nominal group technique， were employed to formulate recommendations. The analysis was conducted based on the EtD framework. During question formulation， the specific characteristics and practical needs of pharmacovigilance for Chinese patent medicines were clarified， focusing on the core objective of safety assurance throughout the product lifecycle. In the evidence assessment， multi-source evidence was integrated， including policy documents， literature research， and expert consensus， completing the evidence evaluation. Finally， in recommendation-forming， dispersed research evidence and expert experience were synthesized into consensus， culminating in the guideline's completion through solicitation of opinions and peer review. The EtD framework provides a structured tool for evidence-to-decision translation in TCM guideline development， effectively enhancing the transparency and scientific rigor of the process. Therefore， it is recommended that TCM guideline development adopt the EtD framework to improve the evidence-to-decision process with TCM characteristics.