Antimicrobial peptides from a wide spectrum of insects possess potent microbicidal properties against microbial-related diseases. In this study, seven new gene fragments of three types of antimicrobial peptides were obtained from Hermetia illucens (L), and were named cecropinZ1, sarcotoxin1, sarcotoxin (2a), sarcotoxin (2b), sarcotoxin3, stomoxynZH1, and stomoxynZH1(a). Among these genes, a 189-basepair gene (stomoxynZH1) was cloned into the pET32a expression vector and expressed in the Escherichia coli as a fusion protein with thioredoxin. Results show that Trx-stomoxynZH1 exhibits diverse inhibitory activity on various pathogens, including Gram-positive bacterium Staphylococcus aureus, Gram-negative bacterium Escherichia coli, fungus Rhizoctonia solani Khün (rice)-10, and fungus Sclerotinia sclerotiorum (Lib.) de Bary-14. The minimum inhibitory concentration of Trx-stomoxynZH1 is higher against Gram-positive bacteria than against Gram-negative bacteria but similar between the fungal strains. These results indicate that H. illucens (L.) could provide a rich source for the discovery of novel antimicrobial peptides. Importantly, stomoxynZH1 displays a potential benefit in controlling antibiotic-resistant pathogens.

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Background/Aims: Hepatocellular carcinoma, a highly malignant tumor, is difficult to diagnose, treat, and predict the prognosis. Notch signaling pathway can affect hepatocellular carcinoma. We aimed to predict the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms. Materials and Methods: We downloaded hepatocellular carcinoma data from the Cancer Genome Atlas and Gene Expression Omnibus databases and used machine learning methods to screen the hub Notch signal-related genes. Machine learning classification was used to construct a prediction model for the classification and diagnosis of hepatocellular carcinoma cancer. Bioinformatics methods were applied to explore the expression of these hub genes in the hepatocellular carcinoma tumor immune microenvironment. Results: We identified 4 hub genes, namely, LAMA4, POLA2, RAD51, and TYMS, which were used as the final variables, and found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of hepatocellular carcinoma. The area under curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500, and 0.932; respectively. The area under curves were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489, and 0.926. The area under curve in the external validation set was 0.934. Immune cell infiltration was related to the expression of 4 hub genes. Patients in the low-risk group of hepatocellular carcinoma were more likely to have an immune escape. Conclusion: TheNotch signaling pathway was closely related to the occurrence and development of hepatocellular carcinoma. The hepatocellular carcinoma classification and diagnosis model established based on this had a high degree of reliability and stability.

Background:Hepatocellular carcinoma (HCC) is a highly malignant malignant tumor, and is difficult to diagnose, treat and predict the prognosis. Notch signaling pathway can affect HCC. Therefore, our paper aimed to explore the prediction of the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms.Methods:In our presented study, we downloaded HCC data from TCGA and GEO databases. Firstly, we used machine learning methods to screen the hub Notch signal-related genes. Then, machine learning classification was used to construct a prediction model for the classification and diagnosis of HCC cancer. In our presented study, we also used bioinformatics methods to explore the relationship between the expression of these hub genes in the HCC tumor immune microenvironment to further improve the reliablity of the model.Results:After screening, we identified four hub genes of LAMA4, POLA2, RAD51 and TYMS, which were used as the final variables to construct the model. It was found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of HCC. The area under curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500 and 0.932; respectively. The AUC, accuracy, sensitivity, specificity, PPV, NPV and F1 score in the testing set were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489 and 0.926. The AUC in the external validation set was 0.934. In the immune microenvironment, immune cell infiltration played an important role in HCC and was related to the expression of 4 hub genes. Further researches found that patients in the low-risk group of HCC were more likely to have immune escape.Conclusion: Notch signaling pathway were closely related to the occurrence and development of HCC. The HCC classification and diagnosis model established based on this had a high degree of reliability and stability.