Objective To explore the potential mechanism of artemisinin in the treatment of polycystic ovary syndrome (PCOS) by network pharmacology and molecular docking technology. Methods The corresponding targets of natural product artemisinin were obtained from PubChem, Swiss Target Prediction and PharmMapper databases, targets related to PCOS were obtained through GeneCards and DisGeNET databases; the intersection target genes of Artemisinin and PCOS were screened by Draw Venn diagram. Then the protein-protein interaction network (PPI) was constructed according to the intersection target genes through the STRING Database, and the core targets were screened by Cytoscape. Besides, gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis was performed by DAVID Database, and finally the data were analyzed visually by the online platform. Molecular docking of artemisinin and core targets were performed by Chemdraw, Pymol, Auto Dock Tools and RCSB PDB database. Results A total of 229 targets of artemisinin and 1292 targets of PCOS were screened out, 90 overlapping targets were obtained by Draw Venn diagram, and 5 potential core targets, AKT1, ESR1, MMP9, PPARG, MMP2, were mainly act on PI3K Akt, MAPK, RAS, endocrine resistance and other signal pathways. Molecular docking results showed that there were molecular binding sites between artemisinin and core targets. Conclusion It is preliminarily analyzed that artemisinin may play a therapeutic role in PCOS through multiple targets and mechanisms.

ObjectiveTo evaluate the effectiveness and consistency of three commonly used early colorectal cancer screening models for advanced colorectal adenoma as a noninvasive means， and to assess the predictive value of traditional Chinese medicine （TCM） tongue images in the models. MethodsPatients diagnosed with colorectal adenoma who underwent colonoscopy and pathological examination were selected as the study participants. Basic clinical data and tongue image were collected. The prediction models of Asia-Pacific colorectal screening （APCS） model， its revision （M-APCS） and colorectal neoplasia predict （CNP） model were applied to compare the predictive effects of the three models on advanced stage adenomas of the colon， the differences in clinical data and traditional Chinese medicine tongue characteristics among patients with different degrees of adenomas， and the similarities and differences in tongue characteristics among the models. The discriminative ability of the three risk models was evaluated using the area under the curve （AUC） and receiver operating characteristic （ROC） curves. The calibration was assessed using the Kuder-Richardson coefficient and the Hosmer-Lemeshow test for consistency analysis. ResultsA total of 227 patients with adenoma were analyzed， including 104 patients （45.82%） with advanced adenoma. In the detection of advanced adenoma， those with greasy coating （70 cases， 67.3%） were higher than those without greasy coating （34 cases， 32.7%， P＜0.05）. After multivariate analysis， the odds ratio （OR） value of non-greasy coating was 0.371 （0.204~0.673， P＜0.01）， indicating that non-greasy coating was a protective factor for advanced adenomas. Among the three risk models， the detection rate of advanced adenoma in the high-risk group with APCS was the highest （63.3%）， which was 1.49 times and 2.04 times that of the medium-risk group （42.6%） and the low-risk group （31.1%， P＜0.01）. The detection rate of advanced adenomas in high-risk groups of M-APCS and CNP was slightly higher than that in moderate or low risk groups （P＞0.05）. The proportion of yellow and greasy coating in high-risk group was higher than that in the medium-risk or low-risk group （P＜0.05）. For the ability to distinguish advanced and non-advanced adenomas， the AUC of APCS was 0.629 （95% CI： 0.556~0.702） and was higher than that of M-APCS （0.591） and CNP （0.586）. In calibration evaluation， Cronbach's alpha was 0.919 （＞0.7）， which indicated that the three models were consistent. In the correlation matrix， the correlation coefficients between APCS model and M-APCS model， and CNP model were 0.794 and 0.717， respectively， and the correlation coefficients between M-APCS model and CNP model were 0.873， Hosmer-Lemeshow χ² =2.552， P＞0.05， which suggested that the three models had good calibration ability. ConclusionAll three models demonstrate the efficiency to identify advanced colorectal adenoma， and their calibration ability is considered to be good. Among the three models， the APCS exhibits the highest recognition efficiency， however， the recognition accuracy of the APCS model needs improvement. The presence of a greasy coating is identified as one of the potential predictors of advanced adenoma. Consequently， it can be considered for inclusion in the risk model of advanced colorectal adenoma to enhance the accuracy.