The prophylaxis functions of Lactobacillus fermentum GLF-217 and Lactobacillus plantarum FLP-215 on ulcerative colitis via modulating gut microbiota of mice.

BACKGROUND: The strong connection between gut microbes and human health has been confirmed by an increasing number of studies. Although probiotics have been found to relieve ulcerative colitis, the mechanism varies by the species involved. In this study, the physiological, immune and pathological factors of mice were measured and shotgun metagenomic sequencing was conducted to investigate the potential mechanisms in preventing ulcerative colitis. RESULTS: The results demonstrated that ingestion of Lactobacillus fermentum GLF-217 and Lactobacillus plantarum FLP-215 significantly alleviated ulcerative colitis induced by dextran sulfate sodium (DSS), as evidenced by the increase in body weight, food intake, water intake and colon length as well as the decrease in disease activity index, histopathological score and inflammatory factor. Both strains not only improved intestinal mucosa by increasing mucin-2 and zonula occludens-1, but also improved the immune system response by elevating interleukin-10 levels and decreasing the levels of interleukin-1ß, interleukin-6, tumor necrosis factor-α and interferon-γ. Moreover, L. fermentum GLF-217 and L. plantarum FLP-215 play a role in preventing DSS-induced colitis by regulating the structure of gut microbiota and promoting the formation of short-chain fatty acids. CONCLUSIONS: This study may provide a reference for the prevention strategy of ulcerative colitis. © 2024 Society of Chemical Industry.

Amelioration of Type 2 Diabetes Using Four Strains of Lactobacillus Probiotics: Effects on Gut Microbiota Reconstitution-Mediated Regulation of Glucose Homeostasis, Inflammation, and Oxidative Stress in Mice.

This study aims to explore the preventive effects and underlying mechanisms of Lactobacillus fermentum CKCC1858 (CKCC1), L. fermentum CKCC1369 (CKCC2), Lactobacillus plantarum CKCC1312 (CKCC3), and Lactobacillus gasseri CKCC1913 (CKCC4) on high-fat diet combined with streptozotocin (HFD/STZ)-stimulated type 2 diabetes (T2D) in mice. Generally, the results indicated that most of the four probiotics reduced weight loss and liver and pancreas damage, significantly (p < 0.05) improved glucose metabolism by regulating glucagon-like peptide-1 (GLP-1), fasting glucose and insulin levels, and increasing expression of glucose transporters. Probiotics improved hyperlipemia, inflammation, and oxidative stress by reducing the secretion of blood lipids and proinflammatory cytokines, increasing antioxidant enzymes. Metagenomic results revealed that probiotics restored gut microbiota via enhancing (reducing) the relative abundance of beneficial bacteria (harmful bacteria) and altered specific metabolic pathways in T2D mice. CKCC1, CKCC3, and CKCC4 showed excellent effects compared to CKCC2. These results indicated that probiotics potentially prevented T2D, which is strain-specific.

Lactobacillus fermentum CKCC1858 alleviates hyperlipidemia in golden hamsters on a high-fat diet via modulating gut microbiota.

To investigate the effect of probiotic Lactobacillus fermentum CKCC1858, LF on the prevention of hyperlipidemia and its correlation with gut microbiota, golden hamsters were fed a high-fat diet alone or in combination with the probiotic for 6 weeks. The results showed that the LF intervention alleviated HFD-induced hyperlipidemia and liver damage, as evidenced by the reduced serum lipid profile levels and liver function markers. More importantly, the LF intervention attenuated HFD-induced microbiota dysbiosis by enhancing the abundance of SCFA-producing bacteria and reshaping the metabolic functions of the gut microbiota, likely contributing to its pronounced preventive effects on hyperlipidemia. This study elucidated the mechanism of the preventive effect of probiotics on hyperlipidemia in terms of regulating gut microbiota, and provided suggestions for regulating gut microbiota through probiotic interventions to improve lipid metabolism.

Intelligent injury prediction for traumatic airway obstruction.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a great deal of casualties with airway obstruction in hospitals. The diagnosis of airway obstruction in an emergency mostly relies on the medical experience of physicians. In this paper, we propose the feature selection approach genetic algorithm-mean decrease impurity (GA-MDI) to effectively minimize the number of features as well as ensure the accuracy of prediction. Furthermore, we design a multi-modal neural network, called fully convolutional network with squeeze-and-excitation and multilayer perceptron (FCN-SE + MLP), to help physicians to predict the severity of airway obstruction. We validate the effectiveness of the proposed feature selection approach and multi-modal model on the emergency medical database from the Chinese General Hospital of the PLA. The experimental results show that GA-MDI outperforms the existing feature selection algorithms, while it is also validated that the model FCN-SE + MLP can effectively and accurately achieve the prediction of the severity of airway obstruction, which can assist clinicians in making treatment decisions for airway obstruction casualties.