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In recent years, the extraction of hypoglycemic peptides from food proteins has gained increasing attention. Neuropeptides, hormone peptides, antimicrobial peptides, immune peptides, antioxidant peptides, hypoglycemic peptides and antihypertensive peptides have become research hotspots. In this study, bioinformatic methods were used to screen and predict the properties of pig collagen-derived hypoglycemic peptides, and their inhibitory effects on α-glucosidase were determined in vitro. Two peptides (RL and NWYR) were found to exhibit good water solubility, adequate ADMET (absorption, distribution, metabolism, elimination, and toxicity) properties, potentially high biological activity, and non-toxic. After synthesizing these peptides, NWYR showed the best inhibitory effect on α-glucosidase with IC50 = 0.200±0.040 mg/mL, and it can regulate a variety of biological processes, play a variety of molecular functions in different cellular components, and play a hypoglycemic role by participating in diabetic cardiomyopathy and IL-17 signaling pathway. Molecular docking results showed that NWYR had the best binding effect with the core target DPP4 (4n8d), with binding energy of -8.8 kcal/mol. NWYR mainly bonded with the target protein through hydrogen bonding, and bound with various amino acid residues such as Asp-729, Gln-731, Leu-765, etc., thus affecting the role of the target in each pathway. It is the best core target for adjuvant treatment of T2DM. In short, NWYR has the potential to reduce type 2 diabetes, providing a basis for further research or food applications as well as improved utilization of pig by-products. However, in subsequent studies, it is necessary to further verify the hypoglycemic ability of porcine collagen active peptide (NWYR), and explore the hypoglycemic mechanism of NWYR from multiple perspectives such as key target genes, protein expression levels and differences in metabolites in animal models of hyperglycemia, which will provide further theoretical support for its improvement in the treatment of T2DM. [ABSTRACT FROM AUTHOR]

Objective: To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated. Method: According to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system. Results: Through the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study. Conclusion: The research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest remaining fire, the results are applicable to the forests of various countries, with a wide range of applications. [ABSTRACT FROM AUTHOR]