OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

Background: and Purpose In patients with large vessel occlusion (LVO), intravenous thrombolysis (IVT) frequently alters thrombus location; however, the clinical impact of this phenomenon remains unclear. We aimed to compare post-IVT thrombus dynamics between tenecteplase and alteplase and to evaluate the association between thrombus dynamics and 3-month outcomes. Methods: This retrospective study analyzed prospectively collected, multicenter data from consecutive patients with LVO who underwent bridging therapy between January 2022 and December 2024. Thrombus dynamics were classified as resolution, migration, or stability. Analyses incorporated propensity score matching with weighting to balance baseline characteristics. Results: Of the 806 initially included patients, 746 were included after matching (373 treated with tenecteplase and 373 treated with alteplase). The incidence of thrombus migration was significantly higher in the tenecteplase group than in the alteplase group (19.3% vs. 11.3%; odds ratio [OR]: 1.92; 95% confidence interval [CI] 1.27–2.91). The advantage of tenecteplase over alteplase was restricted to patients with an IVT-to-puncture time of <60 minutes (18.6% vs. 6.2%; p=0.001) and was no longer significant when the interval ≥60 minutes (19.7% vs. 15.0%; p=0.204; pinteraction=0.043). Additionally, thrombus migration was associated with a better functional outcome (OR: 1.62; 95% CI 1.04–2.53). Finally, tenecteplase was associated with improved functional independence compared with alteplase (OR: 1.43; 95% CI 1.04–1.95). Conclusions Tenecteplase demonstrated superior efficacy in inducing thrombus migration compared with alteplase, particularly within 60 minutes of IVT administration. Thrombus migration independently predicted improved functional independence. These findings support the preferential use of tenecteplase for bridging therapy in patients with LVO.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo investigate the effect of SU056， an azo-podophyllotoxin derivative， on carbon tetrachloride （CCl₄）-induced liver fibrosis in mice and related mechanisms of action. MethodsA total of 12 mice were randomly divided into control group， model group （CCl₄+normal saline）， and treatment group （CCl₄+SU056）， with 4 mice in each group. Mice were given intraperitoneal injection of CCl₄ to establish a model of liver fibrosis， and during the middle stage of modeling， the mice in the treatment group were given daily intraperitoneal injection of SU056. Liver histopathological injury， collagen deposition， and liver function were assessed based on HE staining， Masson staining， Sirius Red staining， the content of hydroxyproline in liver tissue， and the serum levels of alanine aminotransferase and aspartate aminotransferase， and immunofluorescence assay was used to measure the expression levels of smooth muscle actin α （α-SMA）， collagen type Ⅰ， and Y-box binding protein 1 （YB1）. The human hepatic stellate cell （HSC） line LX-2 and primary mouse HSC were used， and CCK-8 assay was used to measure cell proliferation； Transwell assay was used to observe cell migration； quantitative reverse transcription-polymerase chain reaction and Western Blot were used to measure the expression levels of collagen type Ⅰ， collagen type Ⅲ， YB1， phosphorylated mammalian target of rapamycin （mTOR）， and phosphorylated S6K， so as to validate the function of the YB1/mTOR signaling axis. The one-way or two-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. ResultsIn the mouse model of liver fibrosis induced by CCl₄， compared with the model group， the treatment group had significant alleviation of inflammatory cell infiltration， collagen deposition， and pseudolobule formation in liver tissue and significant reductions in the serum levels of alanine aminotransferase and aspartate aminotransferase and the content of hydroxyproline in liver tissue （all P<0.01）. Immunofluorescence assay showed that SU056 significantly inhibited the abnormal high expression of α-SMA， collagen type I， and YB1 in liver tissue （all P<0.01）. In vitro experiments showed that SU056 inhibited the transforming growth factor-β1-induced proliferation of LX-2 cells （P<0.01）， the migration of LX-2 cells （P<0.05）， and the transcriptional up-regulation of collagen type Ⅰ and collagen type Ⅲ （all P<0.05） in a dose-dependent manner， and SU056 could inhibit the spontaneous activation of primary HSC in vitro. Mechanistic studies revealed that transforming growth factor-β1 simultaneously upregulated the expression levels of YB1， phosphorylated mTOR， and phosphorylated S6K in LX-2 cells， and treatment with SU056 （10 and 20 µmol/L） could downregulate the protein expression levels of collagen type I， YB1， phosphorylated mTOR， and phosphorylated S6K. Specific knockdown of YB1 or administration of the mTOR inhibitor rapamycin exerted a similar effect as SU056. SU056 also inhibited the co-upregulation of α-SMA and phosphorylated mTOR in liver tissue of model mice （P<0.01）. ConclusionSU056 can effectively inhibit HSC activation， proliferation， migration， and extracellular matrix production both in vivo and in vitro and thus delay the progression of liver fibrosis， by disrupting the YB1/mTOR positive feedback signaling axis.

Objective To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county-level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P>0.1) were excluded. Then, a correlation coefficient matrix was constructed, and |r_s| of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm² in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main-effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main-effects model was −285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second-order interaction terms, the AICc value of the optimal interaction model was −345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main-effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

Background Long-term exposure to ambient fine particulate matter (PM_2.5) is a significant risk factor for cardiometabolic disorders. However, the mechanisms of its interaction with the endogenous circadian system remain incompletely understood. Objective To investigate whether chronic PM_2.5 exposure interferes with the rhythmic expression of the cardiac circadian clock, thereby disrupting downstream antioxidant defenses and metabolic homeostasis, and ultimately driving cardiometabolic dysfunction. Methods Seventy-two male C57BL/6 mice were randomly divided into a PM_2.5 exposure group (PM group) and a filtered air control group (FA group). Whole-body exposure was conducted for 8 weeks in a meteorological environmental animal exposure system. Samples were collected at six distinct zeitgeber time (ZT) points post-exposure. The 24 h ambulatory blood pressure and serum lipid profiles were monitored. Rhythm parameters were derived via cosinor analysis to compare differences in Midline statistic of rhythm (Mesor), amplitude, and phase between the two groups. The rhythmic expression of core circadian clock genes and antioxidant genes in the myocardium was detected by quantitative polymerase chain reaction (qPCR). Myocardial reactive oxygen species (ROS) levels and downstream pathway protein expression were analyzed by immunofluorescence and Western blot (WB), respectively. The expression changes of the clock gene retinoic acid receptor-related orphan receptor α (RORα) were assessed at both the mRNA and protein levels. Finally, Spearman correlation analysis was used to explore the relationships among myocardial RORα expression, lipid profiles, and oxidative stress indicators. Results Compared to the FA group, mice in the PM group exhibited a blunted circadian rhythm in blood pressure, characterized by sustained elevation throughout the day. Chronic PM_2.5 exposure showed a significant interaction with ZT on systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) (F-interaction=9.11, 5.70, and 6.02, respectively; P<0.05), as well as on serum triglycerides (TG), total cholesterol (T-CHO), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) (F-interaction=16.32, 11.12, 15.39, and 28.09, respectively; P<0.05). Cosinor analysis further revealed that the Mesor values of T-CHO, TG, and LDL-C were significantly increased (P<0.05), while that of HDL-C was significantly decreased in the PM group (P<0.05). The oscillation amplitudes of SBP, DBP, and MAP showed a decreasing trend, whereas those of TG and LDL-C were significantly increased (P<0.05). Furthermore, SBP, T-CHO, and HDL-C all exhibited a significant phase delay (P<0.05). Mechanistically, PM_2.5 exposure significantly suppressed the expression of the positive circadian regulator RORα in the myocardium, leading to disordered rhythmic expression of core clock genes (Bmal1, Clock, Per1/2, and Cry1/2). This exposure also inhibited the rhythmic expression of antioxidant genes (GPX1, SOD2, and CAT), resulting in increased ROS generation and elevated expression of calcium/calmodulin-dependent protein kinase II (CaMKII) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) proteins. Correlation analysis further revealed that myocardial RORα expression level was negatively correlated with T-CHO, TG, and LDL-C (r=−0.55, −0.63, and −0.51, respectively; P<0.001), and positively correlated with HDL-C (r=0.37, P=0.010), and antioxidant genes GPX1, SOD2, and CAT expression (r=0.34, 0.35, and 0.56, respectively; P < 0.001). Conclusion Chronic PM_2.5 exposure induces cardiometabolic dysfunction by suppressing myocardial RORα expression. This suppression disrupts the cardiac circadian clock and the diurnal balance of oxidative stress, triggering oxidative damage and elevating expression of CaMKII/NADPH pathway proteins. Collectively, these alterations precipitate the loss of cardiac metabolic rhythms and subsequent functional impairment.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

ObjectiveTo investigate the protective effects and mechanisms of Zhenzhu Tiaozhi capsules on the kidneys in the mouse model of diabetic kidney disease. MethodsThirty male C57BL/6J mice were selected as experimental objects. The model of diabetic kidney disease was induced by intraperitoneal injection of streptozotocin （STZ） at 40 mg·kg^-1 for 5 days combined with a high-fat diet （HFD）. Fasting blood glucose （FBG） ≥ 11.1 mmol·L^-1， increased urine volume， and continuous appearance of proteinuria indicated successful modeling. Mice were grouped as follows： Blank， model， low- and high-dose （0.98 and 1.96 g·kg^-1， respectively） Zhenzhu Tiaozhi capsules， and losartan potassium （30 mg·kg^-1）， with six mice in each group. After 12 weeks of continuous gavage， urine and kidney specimens were collected， and the 24-h urinary protein and the urinary albumin-to-creatinine ratio （UACR） in mice were measured. Hematoxylin-eosin （HE） staining， periodic acid-Schiff （PAS） staining， and Masson staining were performed for observation of histopathological changes in kidneys. Immunofluorescence assay was employed to detect the positive expression of the podocyte marker protein nephrin. Oil red O staining was used to detect renal lipid deposition. Enzyme linked immunosorbent assay was employed to measure the levels of interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） in the renal tissue. Western blot was employed to determine the expression levels of sterol regulatory element-binding protein cleavage-activating protein （SCAP）， sterol regulatory element-binding protein-1c （SREBP-1c）， and NOD-like receptor protein 3 （NLRP3） in the renal tissue. ResultsCompared with the blank group， the model group showed increases in 24-h urinary protein and UACR （P<0.05）， glomeruli exhibiting capsule adhesion， collagen fiber deposition， mesangial proliferation， and inflammatory cell infiltration， elevated levels of IL-1β， IL-6， and TNF-α （P<0.05）， reduced positive expression of nephrin （P<0.05）， increased lipid deposition （P<0.05）， and up-regulated expression of SCAP， SREBP-1c， and NLRP3 （P<0.05） in the renal tissue. Compared with the model group， the treatment with losartan potassium or high-dose Zhenzhu Tiaozhi capsules for 12 weeks decreased 24-h urinary protein and UACR （P<0.05）， and the treatment with low-dose Zhenzhu Tiaozhi capsules for 12 weeks reduced the 24-h urinary protein （P<0.05）. Pathological staining results revealed that kidney damage in mice from all treatment groups was alleviated， with reduced inflammatory infiltration， collagen fiber deposition， and mesangial proliferation， and increased positive expression of nephrin in the renal tissue （P<0.05）. In addition， all the treatment groups showed reduced lipid droplets （P<0.05）， lowered levels of IL-1β， IL-6， and TNF-α （P<0.05）， and down-regulated expression of SCAP， SREBP-1c， and NLRP3 （P<0.05） in the renal tissue. ConclusionZhenzhu Tiaozhi capsules can ameliorate kidney damage in the mouse model of diabetic kidney disease by inhibiting the activation of the SCAP-SREBP-1c/NLRP3 signaling pathway， which reduces renal lipid deposition and inflammation.