Protective effect and mechanism of Xiaoyu Xiezhuo decoction on ischemia-reperfusion induced acute kidney injury based on gut-kidney crosstalk.

This study aimed to explore the mechanism of Xiaoyu Xiezhuo decoction (XXD) on ischemia-reperfusion-induced acute kidney injury (IRI-AKI) using network pharmacology methods and gut microbiota analysis. A total of 1778 AKI-related targets were obtained, including 140 targets possibly regulated by AKI in XXD, indicating that the core targets were mainly enriched in inflammatory-related pathways, such as the IL-17 signaling pathway and TNF signaling pathway. The unilateral IRI-AKI animal model was established and randomly divided into four groups: the sham group, the AKI group, the sham + XXD group, and the AKI + XXD group. Compared with the rats in the AKI group, XXD improved not only renal function, urinary enzymes, and biomarkers of renal damage such as Kim-1, cystatin C, and serum inflammatory factors such as IL-17, TNF-α, IL-6, and IL 1-ß, but also intestinal metabolites including lipopolysaccharides, d-lactic acid, indoxyl sulfate, p-cresyl sulfate, and short-chain fatty acids. XXD ameliorated renal and colonic pathological injury as well as inflammation and chemokine gene abundance, such as IL-17, TNF-α, IL-6, IL-1ß, ICAM-1, and MCP-1, in AKI rats via the TLR4/NF-κB/NLRP3 pathway, reducing the AKI score, renal pathological damage, and improving the intestinal mucosa's inflammatory infiltration. It also repaired markers of the mucosal barrier, including claudin-1, occludin, and ZO-1. Compared with the rats in the AKI group, the α diversity was significantly increased, and the Chao1 index was significantly enhanced after XXD treatment in both the sham group and the AKI group. The treatment group significantly reversed this change in microbiota.

Sagittal balance parameters measurement on cervical spine MR images based on superpixel segmentation.

Introduction: Magnetic Resonance Imaging (MRI) is essential in diagnosing cervical spondylosis, providing detailed visualization of osseous and soft tissue structures in the cervical spine. However, manual measurements hinder the assessment of cervical spine sagittal balance, leading to time-consuming and error-prone processes. This study presents the Pyramid DBSCAN Simple Linear Iterative Cluster (PDB-SLIC), an automated segmentation algorithm for vertebral bodies in T2-weighted MR images, aiming to streamline sagittal balance assessment for spinal surgeons. Method: PDB-SLIC combines the SLIC superpixel segmentation algorithm with DBSCAN clustering and underwent rigorous testing using an extensive dataset of T2-weighted mid-sagittal MR images from 4,258 patients across ten hospitals in China. The efficacy of PDB-SLIC was compared against other algorithms and networks in terms of superpixel segmentation quality and vertebral body segmentation accuracy. Validation included a comparative analysis of manual and automated measurements of cervical sagittal parameters and scrutiny of PDB-SLIC's measurement stability across diverse hospital settings and MR scanning machines. Result: PDB-SLIC outperforms other algorithms in vertebral body segmentation quality, with high accuracy, recall, and Jaccard index. Minimal error deviation was observed compared to manual measurements, with correlation coefficients exceeding 95%. PDB-SLIC demonstrated commendable performance in processing cervical spine T2-weighted MR images from various hospital settings, MRI machines, and patient demographics. Discussion: The PDB-SLIC algorithm emerges as an accurate, objective, and efficient tool for evaluating cervical spine sagittal balance, providing valuable assistance to spinal surgeons in preoperative assessment, surgical strategy formulation, and prognostic inference. Additionally, it facilitates comprehensive measurement of sagittal balance parameters across diverse patient cohorts, contributing to the establishment of normative standards for cervical spine MR imaging.