Search

In recent years, the prevalence of non-alcoholic fatty liver disease (NAFLD) has risen annually, yet due to the intricacies of its pathogenesis and therapeutic challenges, there remains no definitive medication for this condition. This review explores the intricate relationship between the intestinal microbiome and the pathogenesis of NAFLD, emphasizing the substantial roles played by Lactobacillus plantarum and Bifidobacterium bifidum. These probiotics manipulate lipid synthesis genes and phosphorylated proteins through pathways such as the AMPK/Nrf2, LPS-TLR4-NF-κB, AMPKα/PGC-1α, SREBP-1/FAS, and SREBP-1/ACC signaling pathways to reduce hepatic lipid accumulation and oxidative stress, key components of NAFLD progression. By modifying the intestinal microbial composition and abundance, they combat the overgrowth of harmful bacteria, alleviating the inflammatory response precipitated by dysbiosis and bolstering the intestinal mucosal barrier. Furthermore, they participate in cellular immune regulation, including CD4+ T cells and Treg cells, to suppress systemic inflammation. L. plantarum and B. bifidum also modulate lipid metabolism and immune reactions by adjusting gut metabolites, including propionic and butyric acids, which inhibit liver inflammation and fat deposition. The capacity of probiotics to modulate lipid metabolism, immune responses, and gut microbiota presents an innovative therapeutic strategy. With a global increase in NAFLD prevalence, these insights propose a promising natural method to decelerate disease progression, avert liver damage, and tackle associated metabolic issues, significantly advancing microbiome-focused treatments for NAFLD. [ABSTRACT FROM AUTHOR]

Objective: To compare the effectiveness of radiomic features based on 18 F-FDG PET/CT images within (intranodular) and around (perinodular) lung nodules/masses in distinguishing between lung adenocarcinoma and pulmonary granulomas., Methods: For this retrospective study, 18 F-FDG PET/CT images were collected for 228 patients. Patients diagnosed with lung adenocarcinoma ( n = 156) or granulomas ( n = 72) were randomly assigned to a training ( n = 159) and validation ( n = 69) groups. The volume of interest (VOI) of intranodular, perinodular (1-5 voxels, termed Lesion&#95;margin1 to Lesion&#95;margin5) and total area (intra- plus perinodular region, termed Lesion&#95;total1 to Lesion&#95;total5) on PET/CT images were delineated using PETtumor and Marge tool of segmentation editor. A total of 1,037 radiomic features were extracted separately from PET and CT images, and the optimal features were selected to develop radiomic models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC)., Results: Good and acceptable performance was, respectively, observed in the training (AUC = 0.868, p < 0.001) and validation (AUC = 0.715, p = 0.004) sets for the intranodular radiomic model. Among the perinodular models, the Lesion&#95;margin2 model demonstrated the highest AUC in both sets (0.883 and 0.616, p < 0.001 and p = 0.122). Similarly, in terms of total models, Lesion&#95;total2 model was found to outperform others in the training (AUC = 0.879, p < 0.001) and validation (AUC = 0.742, p = 0.001) sets, slightly surpassing the intranodular model., Conclusion: When intra- and perinodular radiomic features extracted from the immediate vicinity of the nodule/mass up to 2 voxels distance on 18 F-FDG PET/CT imaging are combined, improved differential diagnostic performance in distinguishing between lung adenocarcinomas and granulomas is achieved compared to the intra- and perinodular radiomic features alone., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tian, Hu, Li, Zhang, Wei, Li, Chen, Zheng, Yang, Qin and Bian.)