Object To study the botanic characteristics of leaves from three plants of Goniothalamus (Bl.) Hook. f. et Thoms. in order to correctly distinguish them from numerous plants of the genus, which are important resource of anticancer medicine.Methods The maceration method and paraffin method were used to study the epidermis and structures of leaves from G. griffithii Hook. f. et Thoms., G. leiocarpus (W. T. Wang) P. T. Li and G. yunnanensis W. T. Wang. Results Three leaves were morphologically similar in the structure, but there were some anatomical differences among them. For example, the absence of druses in the epidermis and the presence of fibrous sclereids in the lamina mesophyll of leaves from G. griffithii, while the presence of druses in epidermis and the absence of fibrous sclereids in lamina mesophyll of the leaves from G. griffithii and G. yunnanensis were observed. In addition, epidermal hairs of G. griffithii were composed of three cells, stomatas were always normal, there were seven oil cells and 25 mucilage cells per mm leaf width in lamina mesophyll and the vascular tissue of the midrib consisting of ten small bundles. However, epidermal hairs of G. yunnanensis were composed of two cells, many abortive stomatas were present at the distal surface, there were only four oil cells and 16 mucilage cells per mm leaf width and the vascular tissue of the midrib consisted of 12 small bundles.Conclusion Three species were easily identified on the basis of epidermal and structural characters of the leaves of them.

Objective:To construct a novel prognostic risk model using basement membrane-related genes (BMRG) to explore the relationship between breast cancer and basement membrane.Methods:Transcriptome and clinical data were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the TCGA data was used as the training set and the GEO database as the validation set. Then univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to build a BMRG prognostic model. The risk model was further validated and evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) curve. The risk model and clinical characteristics were then combined to construct a nomogram to predict the overall survival of breast cancer. The biological pathways that may be involved were investigated by gene set enrichment analysis (GSEA). In addition, the differences in drug sensitivity between high-risk and low-risk groups of patients by the Wilcoxon rank sum test.Results:A total of 193 differentially expressed genes were identified, and risk models based on eight BMRG was constructed, including COL6A2, CTSA, EVA1B, ITGAX, MMP-1, ROBO3, SDC1, and UNC5A. Kaplan-Meier and ROC analyses showed that the model could well predict the prognosis of breast cancer, with an area under the curve of 0.779, indicating a high degree of accuracy as well. In addition, the nomogram showed good predictive consistency and net clinical benefit. Univariate and multivariate Cox regression analyses validated the BMRG model as an independent risk factor for breast cancer. GSEA analysis showed that the high-risk group was predominantly enriched in the extracellular matrix receptor interaction pathway. In addition, high-risk patients were more sensitive to taxanes chemotherapeutic agents and targeted therapeutic agents, while low-risk patients were more sensitive to gemcitabine and rapamycin. Conclusion:The risk model constructed based on eight BMRG can be used as a valid prognostic indicator for breast cancer and can improve the prediction of patient response to treatment.

Child ; Consensus ; Drugs, Chinese Herbal ; Humans ; Medicine, Chinese Traditional ; Respiratory System