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Objective: To investigate the clinical efficacy of epidermal growth factor combined with nano silver dressing in the treatment of diabetic foot wounds.Methods: A total of 160 patients with diabetic foot ulcers admitted to the Second Affiliated Hospital of Nanchang University from 2015-06 to 2018-06 were selected to participate in the experiment. A randomized table method was used to randomly divide 160 patients into 4 groups: 40 in the epidermal growth factor group, 40 in the nano-silver dressing group, 40 in the combined group, and 40 in the saline control group (normal saline). The healing stage of the wound surface and the growth degree of granulation tissue were graded. Each group was given a dressing change every other day, and the time required for wound repairing to each healing stage was observed. After 2 and 4 weeks of treatment, the wound exudate was collected for bacterial culture.Results: There was no significant difference in the time between the four groups of patients reaching the effective phase of treatment (level 1). Compared with the control group, the epidermal growth factor group and the combined group achieved a shorter time for wound repairing to healing stages 2 and 3, and the difference was significant (p < 0.05). The combined group had a shorter wound repairing time than the epidermal growth factor group (p < 0.05). Compared with the control group, the positive rate of bacteria in the combined group and the silver nanoparticles group was significantly lower after 2 and 4 weeks of treatment.Conclusion: There is no significant difference in wound healing between the four groups during the clinically effective period. After this period, the combined use of recombinant epidermis Growth factors and nano-silver dressings have a significant effect on promoting wound healing and can effectively prevent infection.

Objective To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC). Methods We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared. Results By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis. Conclusion LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.

Objective: To explore the molecular mechanism of Scutellaria baicalensis Georgi in treating gastric cancer by network pharmacological analysis and molecular docking.Methods: Taking Scutellaria baicalensis Georgi as the object, the active components and corresponding potential drug targets in Scutellaria baicalensis Georgi were obtained from the database of TCM Pharmacological System Analysis Platform (TCMSP). GeneCards/OMIM/DrugBank and other databases were used to collect gastric cancer-related genes, and the obtained genes were intersected with drug targets to obtain the target genes of Scutellaria baicalensis Georgi on gastric cancer. Furthermore, the interaction network of Scutellaria baicalensis Georgi-active ingredients-target-gastric cancer-related genes was constructed. Protein–protein interaction analysis and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on target genes. The PubChem website was used to screen the compounds corresponding to the target genes, and the target protein and 3D structure pdb format files were obtained from the PDB database. Finally, the molecular docking calculation was performed by the AutoDock Vina program. The in vivo cell experiments on the effect of Scutellaria baicalensis on proliferation and migration of gastric cancer cells were used to determine the therapeutic effect of Scutellaria baicalensis on gastric cancer, and the two genes ESR1 and FOS are the key targets of Scutellaria baicalensis on gastric cancer.Results: A total of 10 gastric cancer-related target genes were screened out, and Scutellaria baicalensis Georgi contained 10 active compounds targeting 10 gene sites. There are 30 effective compounds in Scutellaria baicalensis Georgi targeted to treat gastric cancer, and there are 91 corresponding targeting gene sites, involving a total of 10 pathways. The results of molecular docking show that ESR1, FOS, and Scutellaria baicalensis Georgi have good binding free energy and docking fraction. The docking fraction of FOS is −4.200 and the binding free energy is −27.893 kcal/mol. The docking fraction of ESR1 is −5.833 and the binding free energy is −30.001 kcal/mol. The effect of Scutellaria baicalensis Georgi on gastric cancer was verified by in vitro cell experiments and Western blotting.Conclusion:Scutellaria baicalensis Georgi can target and regulate multiple signal pathways by acting on ESR1 and FOS gene loci, thus having a potential therapeutic effect on gastric cancer.