BMC Nephrology最新文献

Background: Renal ischemia-reperfusion injury (IRI) is a common clinical condition that triggers a complex cascade of biological responses, including inflammation, oxidative stress, and apoptosis. These responses can lead to impaired renal function and acute kidney injury (AKI). Despite advancements in therapeutic agents, there is still a need for safe and effective drugs. In this study, we investigated the therapeutic effects and mechanisms of Gancao Xiexin Decoction (GCXXD), an herbal compound known for its anti-inflammatory, antioxidant, and immunomodulatory properties, in treating renal IRI.

Methods: We utilized network pharmacology to identify the intersection targets of the active ingredients in GCXXD and AKI. Cytoscape software was used to obtain core targets, and the String database was used to map the interaction network of these targets. GO and KEGG enrichment analyses were conducted to assess the biological functions and potential pathways involved. Molecular docking simulations were performed to examine the binding of the main active components of GCXXD to the key pathways. Validation experiments were conducted using an IRI mouse model and a hypoxia-reoxygenation-mediated HK2 injury model.

Results: A total of 11 core targets of GCXXD for the treatment of AKI were identified. GO and KEGG analyses revealed enrichment in biological functions related to oxidative stress, apoptosis, and the PI3K/AKT signaling pathway. Molecular docking results indicated strong binding affinity between the key active components (baicalein, ginsenoside Rh2, quercetin, and wogonin) of GCXXD and the PI3K/AKT pathway. In both in vivo and ex vivo experiments, GCXXD activated the PI3K/AKT pathway and ameliorated renal IRI.

Conclusion: This study demonstrated that GCXXD effectively improved ischemia-reperfusion-induced renal injury primarily via activating the PI3K/AKT pathway. These findings provided a scientific basis for the clinical application of GCXXD and paved the way for the development of new drugs and therapeutic strategies.

Background: A few models have been developed in recent years to predict all-cause mortality in patients with chronic kidney disease (CKD). However, many have been developed using inappropriate methods and have not been externally validated. This study aims to improve our previously validated tool for predicting 2-year all-cause mortality in stage 4-5 CKD patients by enlarging the training dataset, thereby enhancing its robustness, which is further supported by a second external validation.

Method: The Bayesian network-based 2-year all-cause mortality prediction tool was trained on a comprehensive national dataset, which was further enriched by incorporating data from a previous external validation. Internal performance was assessed using 10-fold cross-validation, while external validity was confirmed through a second validation on the CERRENE cohort. The discriminatory ability of the prediction tool was evaluated both internally and externally using accuracy, c-statistic, sensitivity, and specificity. The calibration of the external validation was visualized with a calibration curve.

Results: The prediction tool was developed using a training dataset of 1,061 patients (median age 72.3 years; 2-year mortality rate 21.2%) and externally validated using data from 409 patients (median age 77.5 years; 2-year mortality rate 17.6%) with CKD stage 4 or 5. The tool demonstrated satisfactory performance in both internal and external validation (accuracy: 77.2% and 77.8%; AUC-ROC: 0.76 and 0.74; sensitivity: 47.1% and 54.2%; specificity: 85.3% and 82.3%, respectively). The calibration curve demonstrated acceptable agreement between predicted and observed outcomes, and Brier score = 0.132.

Conclusion: The updated prediction tool demonstrated satisfactory performance in both internal and external validation processes. Before it can be used in clinical practice, it must undergo national and international external validations, which are currently in progress.