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The purpose of this study was to reveal the changes in total phenolic content and antioxidant capacity of broccoli, and an untargeted metabolomics approach was developed to investigate the effect of lactic acid bacteria fermentation on the metabolome of broccoli florets. The results showed that the total phenolic content and antioxidant capacity significantly increased after fermentation. The untargeted metabolite profile showed that the main chemical components of fermented and unfermented broccoli are lipids and lipid-like molecules, organic acids and derivatives and organoheterocyclic compounds. Univariate and multivariate statistical analyses of the identified metabolites showed some metabolites such as sorbitol are upregulated after fermentation, and that other metabolites such as l-malic acid are downregulated after fermentation. Moreover, metabolite pathway analyses were used to study the identification of subtle but significant changes among groups of related metabolites that cannot be observed with conventional approaches. KEGG pathway analysis showed that metabolites are mainly enriched in the glucagon signaling pathway, pyruvate metabolism, glycolysis/gluconeogenesis and fructose and mannose metabolism after fermentation, compared with raw broccoli. The results of this study can help to further our understanding of the impact of LAB fermentation on bioactivity changes in and the metabolites profile of fermented broccoli, and the application of fermented broccoli in health foods and special dietary foods.

Background: To propose a new method for real-time monitoring of human blood pressure under blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters in order to establish a model for the estimation of BPBL.Methods: Effective parameters such as PTT, R-R internal (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were established: the PTT model, the RRI model, and the HRV model, and they were divided into experimental group and control group. Finally, the effects of different estimation models on the accuracy of BPBL estimation were evaluated based on the experimental results.Results: The Pearson correlation coefficients R were 0.7731, 0.8943 and 0.9169 for the PTT model, the RRI model, and the HRV model, respectively. The root means square error of the estimation set (RMSEP) were 16.83 mmHg, 11.87 mmHg and 10.59 mmHg, respectively.Conclusion: The results suggest that the accuracy of the BPBL estimated by the RRI and HRV models is better than that of the PTT model, which means that both RRI and HRV can enhance the accuracy of BPBL estimation, and HRV seems to be more effective in improving the accuracy of BP prediction compared to RRI. These results provide a new idea for other scholars in the field of BPBL estimation research.

Background: To propose a new method for real-time monitoring of human blood pressure under blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters in order to establish a model for the estimation of BPBL.Methods: Effective parameters such as PTT, R-R internal (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were established: the PTT model, the RRI model, and the HRV model, and they were divided into experimental group and control group. Finally, the effects of different estimation models on the accuracy of BPBL estimation were evaluated based on the experimental results. Results:The Pearson correlation coefficients R were 0.7731, 0.8943 and 0.9169 for the PTT model, the RRI model, and the HRV model, respectively. The root means square error of the estimation set (RMSEP) were 16.83 mmHg, 11.87 mmHg and 10.59 mmHg, respectively. Conclusion:The results suggest that the accuracy of the BPBL estimated by the RRI and HRV models is better than that of the PTT model, which means that both RRI and HRV can enhance the accuracy of BPBL estimation, and HRV seems to be more effective in improving the accuracy of BP prediction compared to RRI. These results provide a new idea for other scholars in the field of BPBL estimation research.