Diabetic nephropathy （DN） is one of the most common and serious microvascular complications of diabetes mellitus and represents a major cause of chronic kidney disease progression and end-stage kidney disease， thereby constituting a major public health problem that adversely affects the prognosis and quality of life of patients with diabetes mellitus. The pathogenesis and progression of DN are complex and involve multiple pathological processes， including insulin resistance， inflammation， oxidative stress， lipid deposition， podocyte injury， and renal fibrosis. Although current clinical therapies can delay the progression of DN to some extent， their effectiveness remains limited by marked interindividual variability in therapeutic response and the difficulty of fully reversing renal injury. Therefore， further elucidation of the key molecular mechanisms underlying DN and the exploration of effective therapeutic strategies are of considerable importance. In recent years， with advances in the understanding of DN pathogenesis， peroxisome proliferator-activated receptor γ （PPARγ）， an important nuclear receptor transcription factor involved in the regulation of insulin resistance， inflammation， oxidative stress， and fibrotic processes， has attracted increasing attention for its role in the development and progression of DN. Traditional Chinese medicine （TCM）， characterized by multi-component， multi-target， and multi-pathway synergistic regulation， may ameliorate pathological processes including metabolic abnormalities， inflammation， oxidative stress， lipid deposition， and renal fibrosis in DN by targeting PPARγ and its associated signaling networks. This review systematically summarizes the mechanisms by which PPARγ and its related signaling pathways regulate DN and reviews recent advances in TCM interventions targeting the PPARγ signaling pathway in DN. Such TCM interventions encompass single herbs such as Coptidis Rhizoma， active constituents such as quercetin， astragaloside Ⅳ， and trigonelline， extracts of herbs such as Plantaginis Herba and Dendrobii Nobilis Caulis， and formulas such as Huangkui capsules， Shenshuaining granules， and Mudan granules. This review aims to provide a theoretical basis for the prevention and treatment of DN with TCM.

Objective:To develop a machine learning algorithm based on habitat imaging(HI),which can be used in the grading of gliomas by using multimodal magnetic resonance imaging(MRI),so as to construct the model of support vector machine(SVM)and the visualized heterogeneous regions of gliomas.Methods:A total of 335 glioma patients were collected from the 2019 brain tumor segmentation(BraTS)challenge competition of World Health Organization(WHO),which included 259 cases with high-grade gliomas(HGG)and 76 cases with low-grade gliomas(LGG).Subregions were divided based on HI technology.The PyRadiomics open-source package was used to extract the image features of region of interest(ROI),and to screen the features that stronger correlated with the high and low-grade gliomas.An SVM model was used to classify and predict the screened feature data.The heterogeneity of gliomas in images was analyzed through visualized characterization.The efficacy of glioma grading was assessed by using the area under curve(AUC)of the receiver operating characteristic(ROC)curve.Results:The AUC of test set exceeded 90%.The average accuracy of the performance indicators of test set was(92.74±2.88)%,and the average sensitivity was(93.90±2.10)%,and the average specificity was(90.36±4.59)%,and the average F1 score was(95.24±0.66)%when the tumors were divided into six habitat regions.The SVM model could showed important sub-regions in glioma grading in three-dimensional space.Conclusion:The study method based on HI has significant advantages in glioma grading,which can effectively realize visualized heterogeneity of tumor and construct model of the heterogeneity of tumor.

Objective:To propose a model that could improve image quality of cone-beam computed tomography(CBCT),which based on region-discriminative generative adversarial networks(GAN),in radiotherapy for cervical cancer,so as to meet the requirements of self-adaptive radiotherapy for image quality.Methods:We employed a region-discriminative strategy and a generative adversarial networks idea to construct a model of improving CBCT image quality that could focus on local details of the images of radiotherapy for cervical cancer,which discriminator could improve the quality of generating local details of images.This model of image quality was applied to CBCT images of radiotherapy for cervical cancer.And then,the effects of processing image were evaluated through quantitative indicators and visualization.Results:Both texture clarity and contrast were significantly enhanced after CBCT image quality was improved.The signal to noise ratio of peak value of images was increased by 47.2%,and the indicator of similarity of structure was enhanced to>0.838.Compared with other model,both visualization and indicators can appear better efficiency of model.Compared with Unet network and CycleGAN network,the similarities of structure were respectively increased by 11.88% and 19.54%,and the signal to noise ratios were respectively increased by 19.75% and 25.99%.Conclusion:The GAN bases on region-discrimination can significantly improve the quality of generating integral and detailed CBCT image of radiotherapy for cervical cancer,which can provide new technical pathway for image quality of CBCT with low dose,and can play an important role for improving safety and effectiveness of radiotherapy.It has importantly clinical value for formulating and executing radiotherapy plan.

OBJECTIVE： To investigate the mechanism of Drynariae rhizoma in the treatment of osteoporosis （OP）. METHODS： The active compounds and targets of D. rhizoma were obtained by using Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine （BATMAN-TCM database）. The targets of relevant compounds were also obtained by GeneCards database， and targets of D. rhizoma were obtained by the combination of the two. The disease targets corresponding to OP were obtained by using TTD， DrugBank， OMIM， GAD， PharmGKB and CTD database. The D. rhizoma-OP disease intersection targets were obtained after intersecting with the target of D. rhizoma. PPI network was constructed by STRING online database， analyzed by using Cytoscape 3.6.1 software to obtain key targets and showed by network visualization. Gene ontology（GO） analysis of drug-disease intersection target were conducted by DAVID online tools. KEGG pathway enrichment analysis was conducted by KOBAS online tools to screen the significant enrichment pathway （P＜0.05）. The key genes were screened by MCC algorithm. RESULTS： There were 7 active compounds of D. rhizoma 136 intersection targets of D. rhizoma-OP disease. GO analysis results showed that the biological function of intersection target mainly included chemical reaction， steroid metabolic process as well as cellular response to chemical stimulus and so on； cell composition mainly included extracellular space， extracellular area and cytoplasm；molecular functions included heme binding， tetrapyrrole binding and monooxygenase activity， etc. KEGG pathway enrichment showed that above targets were mainly related to bone metabolism， endocrinology， inflammation， tumor， apoptosis， etc. Thirty key genes （such as ALB， AKT1， JUN， etc.， P≤1.96×10－9） were screened by MCC algorithm. CONCLUSIONS： The mechanism of action of D. rhizoma in the treatment of OP is in multi-target and multi-system manner. In addition to influencing the related pathways of bone metabolism， it can also affect various metabolic pathways in vivo.

Objective To evaluate the efficacy and safety of sirolimus and tacrolimus after renal transplantation.Method PubMed,Web of knowledge,Medline and the Cochrane controlled trials register,Chinese Biomedicaldatabase,and Vip database were searched with the terms and Boolean operators as (kidney transplantation OR renal transplantation) AND (sirolimus OR rapamycin OR rapamune) AND (tacrolimus OR FK506 OR prograf).Results retrieved were updated on November,2015.Data were extracted for patient and graft mortality,acute rejection (AR),wound complications,infection,GFR,withdrawl.Professional meta analysis software RevMan 5.3 was employed.Result Altogether,1810 patients from 10 randomized controlled trials (RCTs) were included.Patients in the sirolimus group showed a decreased rate of graft mortality and infection (RR =0.63,95％ CI,0.45-0.89,P=0.009;RR=4.42,95％ CI,1.73-11.31,P=0.002).Patients in the sirolimus group showed an increased rate of AR,wound complications,GFR,withdrawl (SMD=-0.52,95％ CI,-0.73-0.31,P＜0.000 01;RR=0.54,95％ CI,0.40-0.73,P＜0.000 1;RR=0.17,95％ CI,0.11-0.25,P＜0.000 01;RR =0.44,95％ CI,0.37-0.51,P＜0.000 01).The patient mortality was insignificantly different between two groups.Conclusion This meta-analysis concluded that sirolimus showed advantage over tacrolimus about safety when used early after renal transplantation.The options of immunosuppressive regimens after kidney transplantation should be based on the specific condition.To obtain more reliable and accurate clinical data,the RCTs with more rational design,higher methodological quality,larger sample size,including domestic patients,longer follow-up are still needed.