Chronic liver diseases are a group of diseases in which the liver is subjected to a variety of injuries over a long period of time， resulting in irreversible pathological changes that last longer than 6 months. Emodin （EMO） is a natural anthraquinone derivative derived from Rheum officinale， and its pharmacological effect has been extensively studied， exhibiting a variety of biological properties and involving multiple signaling molecules and pathways. Western medicine or surgical treatment is currently the main treatment regimen for chronic liver diseases， and the advance in treatment is limited by various reasons such as side effects and high costs. Due to its natural origin and efficacy， EMO has unique advantages in the treatment of chronic liver diseases and has now become a research hotspot. This article summarizes the therapeutic effect of EMO on chronic liver diseases and its mechanism， in order to provide a certain scientific basis for the traditional Chinese medicine treatment of chronic liver diseases and the development of drugs in clinical practice.

Diabetic cardiomyopathy （DCM） is one of the most common complications of diabetes mellitus and is a major threat to global health. As a key mechanism in the occurrence and progression of DCM， the inflammatory response persists throughout the entire course of the DCM. The Gaozhuo theory suggests that the basic pathogenesis of inflammatory injury in DCM is the Qi deficiency of spleen and kidney and Gaozhuo invasion， and divides the pathological process into three phases： Gaozhuo invasion， turbid heat damage to the channels， and turbid blood stasis and heat junction. Among them， the Qi deficiency of spleen and kidney and the endogenous formation of Gaozhuo represent the process of inflammatory factor formation induced by glucose metabolism disorders. Turbid heat damage to the channels refers to the process of myocardial inflammatory injury mediated by inflammatory factors， and turbid blood stasis and heat junction are the process of myocardial injury developing toward myocardial fibrosis and ventricular remodeling. As the disease continues to progress， it eventually develops into a depletion of the heart Yang， leading to the ultimate regression of heart failure. According to the theory of Gaozhuo， traditional Chinese medicine （TCM） should regulate inflammatory injury in DCM by strengthening the spleen and tonifying the kidney to address the root cause， and resolving dampness and lowering turbidity to treat the symptoms. If the turbidity has been stored for a long time and turns into heat， strengthening the spleen and tonifying the kidney， and clearing heat and resolving turbidity should be the therapy. If the turbidity， stasis， and heat are knotted in the heart and collaterals， strengthening the spleen and tonifying the kidney， and resolving stasis and lowering turbidity should be the therapy. TCM compounds and monomers can regulate the inflammatory response in DCM. TCM compounds can be divided into the categories for benefiting Qi to resolve turbidity， benefiting Qi and clearing heat to resolve turbidity， and benefiting Qi and activating blood to reduce turbidity. The compounds can inhibit upstream signals of inflammation and expression of inflammatory factors， improve the inflammatory damage to myocardium and blood vessels， myocardial fibrosis， and cardiac systole and diastole， and thus slow down the onset and progression of DCM.

Non-alcoholic fatty liver disease （NAFLD） is one of the most common complications of type 2 diabetes mellitus （T2DM）， and hepatic stellate cell （HSC） activation is the key link in the progression of NAFLD to liver fibrosis. According to the theory of "Gaozhuo"， spleen deficiency and Qi stagnation， along with Gaozhuo invasion， are the causes of NAFLD progression to liver fibrosis， which reveals the pathogenesis essence of HSC activation in traditional Chinese medicine （TCM）. Among them， spleen deficiency and Qi stagnation are the root causes of the endogenous formation of Gaozhuo. Spleen deficiency indicates the insulin sensitivity decrease and glucose metabolism disorders， and Qi stagnation means the dysregulation of hepatic glucose and lipid metabolism， which creates the preconditions for HSC activation. Gaozhuo invasion is the direct cause of HSC activation， including three stages： Internal retention of Gaozhuo， turbidity and stasis stagnation， and toxic stasis and consolidation. Internal retention of Gaozhuo refers to the abnormal metabolism and deposition of hepatic lipids， as well as the microcirculatory disorders. Turbidity and stasis stagnation is the process by which lipotoxicity stimulates the transformation of HSC into myofibroblast （MFB）， and toxic stasis and consolidation represent the secretion of a large amount of extracellular matrix （ECM） by MFB to promote the fibrosis. According to the theory of Gaozhuo and the activation process of HSC， syndromes for T2DM combined with NAFLD can be classified into spleen deficiency and Qi stagnation with internal retention of Gaozhuo， spleen Qi deficiency with turbidity and stasis stagnation， and spleen Qi deficiency with toxic stasis and consolidation. Clinically， the treatment principle is to strengthen the spleen and promote Qi， resolve turbidity， and eliminate blood stasis. Both TCM compounds and monomers can effectively inhibit the HSC activation. TCM compounds can be classified into categories for regulating spleen and harmonizing liver， resolving turbidity and removing stasis， and detoxifying and removing stasis. They mainly work by improving lipid metabolism， reducing lipid accumulation in the liver， alleviating inflammatory and oxidative stress responses， inhibiting the activation and proliferation of HSC， and reducing ECM deposition， thereby delaying the progression of liver fibrosis.

BACKGROUND:Studies have shown that rheumatoid arthritis and osteoporosis are positively correlated,but the causal relationship and related mechanisms have not yet been confirmed.With the cross-fertilization of computer science and life sciences,Mendelian randomization and bioinformatics analyses based on genome-wide association study(GWAS)and transcriptome sequencing data can assess the causal relationship between two diseases,explore the related mechanisms,and mine the therapeutic targets,which will be beneficial to the precision treatment of rheumatoid arthritis combined with osteoporosis.OBJECTIVE:To explore the causal relationship between rheumatoid arthritis and osteoporosis using two-sample Mendelian randomization and to mine potential co-morbid targets and potential targeted drugs through summary-data-based Mendelian randomization and bioinformatics analyses,aiming to provide theoretical basis for mechanism exploration and precision treatment in the field of rheumatoid arthritis combined with osteoporosis.METHODS:(1)Firstly,GWAS data of rheumatoid arthritis,osteoporosis,and cis-expression quantitative trait locus(cis-eQTL)in Asian and European populations were downloaded from the GWAS Catalog,IEU Open GWAS,FinnGen,and eQTLGen databases,and were used for two-sample Mendelian randomization analysis and summary-data-based Mendelian randomization analysis.(2)Transcriptome sequencing data of rheumatoid arthritis(GSE93272 and GSE15573)were downloaded from the GEO database for bioinformatics analysis.(3)Subsequently,forward and inverse Mendelian randomization analyses between rheumatoid arthritis and osteoporosis were performed,and inverse variance weighted was used as the main metric for the analyses,and the results were corroborated with MR Egger,simple mode,weighted median and weighted mode.(4)Then,the genes closely related to rheumatoid arthritis and osteoporosis were identified based on the summary-data-based Mendelian randomization analysis,and the co-disease targets of rheumatoid arthritis and osteoporosis were mined based on cross-analysis.Meanwhile,the biological functions of the co-morbid targets were verified based on bioinformatics analysis and cellular experiments.(5)In addition,a rheumatoid arthritis risk prediction nomogram was constructed based on DYRK2,and its prediction performance was verified by receiver operating characteristic curve,correction curve and decision curve.Finally,the target potential drugs were mined based on Enrichr database and molecular docking was performed.RESULTS AND CONCLUSION:(1)Forward Mendelian randomization analysis of rheumatoid arthritis and osteoporosis showed statistically significant results except for GCST90044540 and GCST90086118,and all other results indicated a significant causal relationship and positive correlation between rheumatoid arthritis and osteoporosis.(2)Inverse Mendelian randomization analysis suggested that no significant causal relationship was seen between osteoporosis and rheumatoid arthritis.(3)Summary-data-based Mendelian randomization analysis identified a total of 412 and 344 genes positively associated with rheumatoid arthritis and osteoporosis,and 421 and 347 genes negatively associated.Based on the cross-analysis,26 co-morbid genes were subsequently obtained.Among them,DYRK2 was a potential therapeutic target,and subsequent bioinformatics analysis and cellular experiments confirmed its important role in the progression of rheumatoid arthritis and osteoporosis.(4)Furthermore,the constructed nomogram has excellent predictive performance.Finally,four potential DYRK2-targeting drugs(undecanoic acid,metyrapone,JNJ-38877605,and ACA)were discovered and molecular docking also demonstrated reliable targeting ability.(5)In conclusion,based on GWAS data from Asian and European populations,we successfully demonstrated that rheumatoid arthritis and osteoporosis are causally related at the genetic level,DYRK2 is a potential therapeutic target,and four small molecules are potential target drugs.

BACKGROUND:Maxillofacial bone three-dimensional(3D)printing technology has been widely used in clinical diagnosis and treatment,but the data source before performing maxillofacial bone 3D printing mainly comes from the CT scanning data.The lens,thyroid and other parts of the human body are extremely sensitive to X-rays;therefore,it is particularly important to effectively reduce the dose of CT radiation when acquiring the data source.OBJECTIVE:To explore the feasibility of low-dose CT technology in optimizing radiation dose for maxillofacial bone 3D printing.METHODS:The medical records of 65 patients who underwent maxillofacial bone 3D printing in the Department of Stomatology at the General Hospital of Northern Theater Command from March 2021 to December 2023 were retrospectively collected and categorized into a conventional CT-dose 3D printing group(conventional CT-dose,120 kVp,automated tube current modulation,n=32)and a low-CT-dose 3D printing group(low-CT-dose group,80 kVp,automated tube current modulation,n=33).The effective dose of radiation was calculated and compared between the two groups.A Likert scale was used to evaluate the quality of 3D printing in the two groups,and the measurement bias and consistency between evaluators were measured using the Bland-Altman method.RESULTS AND CONCLUSION:(1)There was no significant difference in the general demographic characteristics(age,height,weight,body mass,sex,and body mass index)between the two groups(all P＞0.05).(2)The effective dose value of the low CT-dose 3D printing group was(0.3±0.1)mSv,which was about 62.5％lower than that in the conventional CT-dose 3D printing group[(0.8±0.1)mSv].(3)There was no significant difference in the subjective scoring of 3D printing quality between the two groups(all P＞0.05).The subjective consistency among evaluators was good,with Kappa values of 0.85,0.80,and 0.76.The scatter points in the Bland-Altman for both protocols were uniformly distributed within the standard deviation line,indicating good consistency between the two groups.To conclude,low-dose CT technology can be effectively applied in maxillofacial bone 3D printing,reducing radiation dose without affecting the quality of 3D printing.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

ObjectiveThe accuracy of Y-chromosome haplogroup assignment is crucial for tracing paternal lineage in male samples. With the advancement of high-throughput sequencing technologies, high-density Y-SNP genotyping from whole-genome or array-based data has become a standard method for determiningY-chromosome haplogroups. This study systematically evaluated the performance of 4 commonly used high-density SNP genotyping systems—namely, the Global Screening Array (GSA), Chinese Genotyping Array (CGA), Affymetrix array, and the 1240K capture panel—for haplogroup assignment. This work provides a reference for data comparison across different systems. MethodsWe extracted genotype data for the 4 Y-SNP panels from 30× whole-genome sequencing (WGS) data of 1 590 male samples from the 1000 Genomes Project. Additionally, GSA array genotype data from 384 relative pairs (spanning 1st- to 12th-degree relationships) from 109 Chinese Han families were collected. Haplogroup assignment was performed using Y-LineageTracker v1.3.0 software. We assessed the concordance and resolution of haplogroup assignments between the four Y-SNP panels and the WGS data. The consistency and resolution of haplogroup assignments were also evaluated for both the 1000 Genomes Project samples and the 109 family samples collected in this study. Furthermore, the impact of varying numbers of Y-SNPs on haplogroup assignment was examined. ResultsThe GSA and CGA panels demonstrated superior resolution and discrimination of haplogroup subclades compared with the other two panels. The haplogroup assignments from the GSA, CGA, and 1240K panels showed high concordance with WGS data, with consistency rates exceeding 88.70%, whereas the Affymetrix platform exhibited a significantly lower consistency rate of 61.89%. Specifically, the GSA and CGA panels consistently demonstrated superior performance compared with the other two panels in the assignment of haplogroups O-M175 and H-L901, achieving complete concordance (100%) for both haplogroups. In contrast, the Affymetrix panel erroneously assigned all individuals belonging to haplogroup O-M175 to haplogroup K2-M526. Furthermore, its accuracy for haplogroup H-L901 was exceedingly low, at merely 1.41%. This poor performance was characterized by the misassignment of 98.59% of H-L901 samples—specifically, 1.41% to J-M304 and a predominant 97.18% to F-M89. For haplogroup R-M207, all four panels exhibited uniformly high levels of consistency, with concordance values exceeding 94.00%. Notably, for haplogroup E-M96, the 1240K and Affymetrix panels outperformed the GSA and CGA panels in terms of concordance, representing the first instance in which these two panels surpassed the latter. Conversely, for haplogroups J-M304, Q-M242, and I-M170, all 4 panels showed relatively elevated misclassification rates, with the Affymetrix array demonstrating the poorest overall performance. None of the four panels showed any discordant haplogroup assignments among the familial relative pairs analyzed. A positive correlation was observed between the number of Y-SNPs (ranging from 1 000 to 10 000) and classification consistency; however, classification consistency plateaued when the number of Y-SNPs exceeded 10 000. Furthermore, a random sampling analysis conducted on the GSA and CGA panels demonstrated that the haplogroup misclassification rate exhibited negligible fluctuation across the Y-SNP range of 500 to 1 000. Conversely, a marked enhancement in classification consistency was observed as the number of markers increased from 1 000 to 5 000, ultimately reaching a plateau within the interval of 5 000 to 8 000 markers. ConclusionThese findings indicate that the GSA and CGA panels provide high resolution and concordance, delivering reliable Y-haplogroup assignment for forensic investigations.

ObjectiveThe accuracy of Y-chromosome haplogroup assignment is crucial for tracing paternal lineage in male samples. With the advancement of high-throughput sequencing technologies, high-density Y-SNP genotyping from whole-genome or array-based data has become a standard method for determiningY-chromosome haplogroups. This study systematically evaluated the performance of 4 commonly used high-density SNP genotyping systems—namely, the Global Screening Array (GSA), Chinese Genotyping Array (CGA), Affymetrix array, and the 1240K capture panel—for haplogroup assignment. This work provides a reference for data comparison across different systems. MethodsWe extracted genotype data for the 4 Y-SNP panels from 30× whole-genome sequencing (WGS) data of 1 590 male samples from the 1000 Genomes Project. Additionally, GSA array genotype data from 384 relative pairs (spanning 1st- to 12th-degree relationships) from 109 Chinese Han families were collected. Haplogroup assignment was performed using Y-LineageTracker v1.3.0 software. We assessed the concordance and resolution of haplogroup assignments between the four Y-SNP panels and the WGS data. The consistency and resolution of haplogroup assignments were also evaluated for both the 1000 Genomes Project samples and the 109 family samples collected in this study. Furthermore, the impact of varying numbers of Y-SNPs on haplogroup assignment was examined. ResultsThe GSA and CGA panels demonstrated superior resolution and discrimination of haplogroup subclades compared with the other two panels. The haplogroup assignments from the GSA, CGA, and 1240K panels showed high concordance with WGS data, with consistency rates exceeding 88.70%, whereas the Affymetrix platform exhibited a significantly lower consistency rate of 61.89%. Specifically, the GSA and CGA panels consistently demonstrated superior performance compared with the other two panels in the assignment of haplogroups O-M175 and H-L901, achieving complete concordance (100%) for both haplogroups. In contrast, the Affymetrix panel erroneously assigned all individuals belonging to haplogroup O-M175 to haplogroup K2-M526. Furthermore, its accuracy for haplogroup H-L901 was exceedingly low, at merely 1.41%. This poor performance was characterized by the misassignment of 98.59% of H-L901 samples—specifically, 1.41% to J-M304 and a predominant 97.18% to F-M89. For haplogroup R-M207, all four panels exhibited uniformly high levels of consistency, with concordance values exceeding 94.00%. Notably, for haplogroup E-M96, the 1240K and Affymetrix panels outperformed the GSA and CGA panels in terms of concordance, representing the first instance in which these two panels surpassed the latter. Conversely, for haplogroups J-M304, Q-M242, and I-M170, all 4 panels showed relatively elevated misclassification rates, with the Affymetrix array demonstrating the poorest overall performance. None of the four panels showed any discordant haplogroup assignments among the familial relative pairs analyzed. A positive correlation was observed between the number of Y-SNPs (ranging from 1 000 to 10 000) and classification consistency; however, classification consistency plateaued when the number of Y-SNPs exceeded 10 000. Furthermore, a random sampling analysis conducted on the GSA and CGA panels demonstrated that the haplogroup misclassification rate exhibited negligible fluctuation across the Y-SNP range of 500 to 1 000. Conversely, a marked enhancement in classification consistency was observed as the number of markers increased from 1 000 to 5 000, ultimately reaching a plateau within the interval of 5 000 to 8 000 markers. ConclusionThese findings indicate that the GSA and CGA panels provide high resolution and concordance, delivering reliable Y-haplogroup assignment for forensic investigations.

Objective To construct machine learning models based on preoperative inflammatory and radiological features for the prediction of glioma grading and isocitrate dehydrogenase (IDH) mutation status, and to analyze application values of these models and identify the optimal predictive models. Methods A retrospective analysis was conducted on the data of pathologically confirmed glioma patients admitted to Tongji Hospital Affiliated to Tongji University from March 2019 to March 2023. LASSO regression was used to screen feature variables, and predictive models were constructed based on logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting decision tree (XGBoost) and K-nearest neighbor (KNN) algorithms. The model performance was comprehensively evaluated using metrics including discrimination ability, area under the precision-recall curve (AUC), accuracy, F1 score and Brier score. The DeLong test was adopted to compare the AUC values among different models; Friedman rank-sum test was used to determine the overall performance differences of the models, with the Nemenyi test applied for multiple comparison correction. Results In the task of glioma grading prediction, the LR model achieved the highest comprehensive score (0.726), and no significant difference was observed between the LR model and the other four models; age was positively correlated with glioma grading (P=0.003). In the task of IDH mutation status prediction, the XGBoost model obtained the highest comprehensive score (0.832), which was superior to the LR (0.762, P=0.035) and KNN models (0.754, P=0.025), while no statistical differences were found between the XGBoost model and the RF or SVM models. Conclusions The LR model for glioma grading prediction and XGBoost model for IDH mutation prediction constructed based on a task-oriented strategy achieve a favorable interpretability while ensuring optimized performance, thereby providing reliable decision support for the individualized diagnosis and treatment of glioma.

ObjectiveTo investigate the molecular mechanism by which the Bushen Kaixuan Tongluo prescription exerts a renal protective effect in mice with diabetic kidney disease （DKD） by regulating the cyclic adenosine monophosphate （cAMP） signaling pathway. MethodsThirty specific pathogen-free （SPF） male db/db mice were adaptively fed for three weeks. Mice with a random tail vein blood glucose level ≥ 11.1 mmol·L^-1 and urinary albumin-creatinine ratio （ACR） ≥ 30 mg·g^-1 were considered successfully modeled. The successfully modeled mice were randomly divided into five groups with six mice in each group： the model group， the low-， medium-， and high-dose Bushen Kaixuan Tongluo prescription groups （administered at doses of 7， 14， 28 g·kg^-1·d^-1 respectively）， and the positive drug irbesartan group （administered at a dose of 20 mg·kg^-1·d^-1）. Additionally， six db/m mice were selected as the blank group. Mice in each group were given intragastric administration of the Bushen Kaixuan Tongluo prescription at the corresponding concentrations， irbesartan， or an equal volume of pure water， and the intervention lasted for 12 weeks. During the experiment， the general conditions， body weight changes， and renal function indicators of the mice were dynamically monitored. After the intervention， a blood glucose meter was used to measure the fasting blood glucose （FBG） of the mice. An automatic biochemical analyzer was employed to detect the levels of serum creatinine （SCr）， blood urea nitrogen （BUN）， urinary microalbumin （uALB）， ACR， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， total cholesterol （TC）， triglycerides （TG）， leptin （LEP）， glycosylated serum protein （GSP）， and insulin （INS） in the mice. Renal tissues were collected for hematoxylin-eosin （HE） staining， periodic acid-Schiff （PAS） staining， and Masson's trichrome staining to observe the histopathological changes. Immunohistochemistry （IHC） was used to detect the expressions of protein kinase A （PKA） and cAMP response element-binding protein （CREB） in the mice. Western blot analysis was performed to determine the expression levels of PKA， phosphorylated protein kinase A （p-PKA）， CREB， phosphorylated cAMP response element-binding protein （p-CREB）， and B-cell lymphoma-2 （Bcl-2） proteins in the renal tissues of the mice. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression levels of PKA， CREB， and Bcl-2 in the renal tissues of the mice. ResultsCompared with the blank group， the mice in the model group showed listlessness， decreased activity， and a significant increase in body weight （P<0.01）. Biochemical indicators revealed that the levels of BUN， uALB， ACR， AST， ALT， TC， TG， FBG， LEP， GSP， and INS were significantly increased （P<0.01）， while SCr showed an increasing trend with no statistically significant difference. Compared with the model group， the mice in the Bushen Kaixuan Tongluo prescription intervention groups had improved general conditions and a decreasing trend in body weight. Biochemical indicators showed that the levels of BUN， uALB， ACR， TC， GSP， and INS were significantly decreased （P<0.05）， while SCr， AST， ALT， TG， and LEP showed a decreasing trend with no statistically significant difference. Renal histopathological analysis showed that the model group exhibited typical DKD pathological features such as thickening of the glomerular basement membrane， expansion of the mesangial matrix， and deposition of collagen fibers in the renal tubulointerstitium， and all treatment groups could alleviate the above pathological damages. The IHC results showed that compared with the blank group， the expression levels of p-PKA and p-CREB in the renal tissues of the model group were significantly decreased （P<0.01）. Compared with the model group， the expression level of p-PKA in the medium-dose Bushen Kaixuan Tongluo prescription group was significantly increased （P<0.01）， while the expression level of p-CREB showed an increasing trend with no statistically significant difference. Western blot results showed that compared with the blank group， the expression levels of p-PKA/PKA， p-CREB/CREB， and Bcl-2 in the model group were significantly decreased （P<0.05）. Compared with the model group， the expression levels of these proteins in the medium-dose Bushen Kaixuan Tongluo prescription group were significantly increased （P<0.01）. Real-time PCR results showed that compared with the blank group， the mRNA expressions of PKA， CREB， and Bcl-2 in the model group were significantly down-regulated （P<0.05）. Compared with the model group， the mRNA expressions of these genes in the medium-dose Bushen Kaixuan Tongluo prescription group were significantly up-regulated （P<0.05）. ConclusionThe Bushen Kaixuan Tongluo prescription can improve the liver and kidney functions of db/db mice， correct lipid metabolism disorders and glucose metabolism imbalance. Its renal protective effect is associated with up-regulating the cAMP signaling pathway to improve renal fibrosis and reduce the level of oxidative stress， thereby protecting renal function.