The Effects of Crataegus pinnatifida and Wolfiporia extensa Combination on Diet-Induced Obesity and Gut Microbiota.

Obesity is a multifactorial chronic metabolic disease with multiple complications. Crataegus pinnatifida (CP) and Wolfiporia extensa (WE) are traditional functional foods with improving metabolic health properties. This study demonstrated the effect of CP and WE combination on ameliorating obesity induced by a high-fat diet (HFD). Moreover, the CP-WE food pair ameliorated HFD-induced metabolic disorders, including glucose intolerance, insulin resistance, hyperlipidemia, and hepatic steatosis. 16S rRNA gene amplicon sequencing and analysis revealed that CP combined with WE reshaped the composition of gut microbiota in HFD-fed mice. Furthermore, correlation analysis revealed a substantial association between the obesity-related parameters and the shifts in predominant bacterial genera influenced by the food pair intervention. In conclusion, this study demonstrated that the CP-WE food pair ameliorated HFD-induced obesity and reshaped gut microbiota composition, providing a promising approach to combat obesity through specific food combinations.

Is there a causal association between gestational diabetes mellitus and immune mediators? A bidirectional Mendelian randomization analysis.

Background: Diabetes that only appears or is diagnosed during pregnancy is referred to as gestational diabetes mellitus (GDM). The maternal physiological immune profile is essential for a positive pregnancy outcome. However, the causal relationship between GDM and immunophenotypes is not fully defined. Methods: Based on the high-density genetic variation data at the genome-wide level, we evaluated the logical associations between 731 specific immune mediators and GDM using bidirectional Mendelian randomization (MR). The inverse variance weighted (IVW) was the main method employed for MR analysis. We performed multiple methods to verify the robustness and dependability of the MR results, and sensitivity measures were applied to rule out potential heterogeneity and horizontal pleiotropy. Results: A substantial causal association between several immune mediators and GDM was detected. After FDR testing, HLA DR++ monocyte %leukocyte and HLA DR on plasmacytoid DC were shown to increase the risk of GDM; in contrast, CD127 on CD28+ CD45RA+ CD8br and CD19 on PB/PC were shown to attenuate the effect of GDM. Moreover, the progression of GDM has been shown to decrease the maternal levels of CD39+ activated Treg AC, CD39+ activated Treg %CD4 Treg, CD39+ resting Treg AC, CD39+ resting Treg %CD4 Treg, and CD39+ CD8BR %T cell. Conclusions: Our findings support a possible causal association between GDM and various immunophenotypes, thus facilitating the provision of multiple options for preventive recognition as well as for the diagnostic and therapeutic management of GDM in clinical practice.

Predicting the immunity landscape and prognosis with an NCLs signature in liver hepatocellular carcinoma.

BACKGROUND: Activated neutrophils release depolymerized chromatin and protein particles into the extracellular space, forming reticular Neutrophil Extracellular Traps (NETs). This process is accompanied by programmed inflammatory cell death of neutrophils, known as NETosis. Previous reports have demonstrated that NETosis plays a significant role in immune resistance and microenvironmental regulation in cancer. This study sought to characterize the function and molecular mechanism of NETosis-correlated long non-coding RNAs (NCLs) in the prognostic treatment of liver hepatocellular carcinoma (LIHC). METHODS: We obtained the transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and evaluated the expression of NCLs in LIHC. A prognostic signature of NCLs was constructed using Cox and Last Absolute Shrinkage and Selection Operator (Lasso) regression, while the accuracy of model was validated by the ROC curves and nomogram, etc. In addition, we analyzed the associations between NCLs and oncogenic mutation, immune infiltration and evasion. Finally, LIHC patients were classified into four subgroups based on consensus cluster analysis, and drug sensitivity was predicted. RESULTS: After screening, we established a risk model combining 5 hub-NCLs and demonstrated its reliability. Independence checks suggest that the model may serve as an independent predictor of LIHC prognosis. Enrichment analysis revealed a concentration of immune-related pathways in the high-risk group. Immune infiltration indicates that immunotherapy could be more effective in the low-risk group. Upon consistent cluster analysis, cluster subgroup 4 presented a better prognosis. Sensitivity tests showed the distinctions in therapeutic effectiveness among various drugs in different subgroups. CONCLUSION: Overall, we have developed a prognostic signature that can discriminate different LIHC subgroups through the 5 selected NCLs, with the objective of providing LIHC patients a more precise, personalized treatment regimen.