Butyrate attenuates sympathetic activation in rats with chronic heart failure by inhibiting microglial inflammation in the paraventricular nucleus.

Sympathetic activation is a hallmark of heart failure and the underlying mechanism remains elusive. Butyrate is generated by gut microbiota and influences numerous physiological and pathological processes in the host. The present study aims to investigate whether the intestinal metabolite butyrate reduces sympathetic activation in rats with heart failure (HF) and the underlying mechanisms involved. Sprague-Dawley rats (220‒250 g) are anaesthetized with isoflurane, and the left anterior descending artery is ligated to model HF. Then, the rats are treated with or without butyrate sodium (NaB, a donor of butyrate, 10 g/L in water) for 8 weeks. Blood pressure and renal sympathetic nerve activity (RSNA) are recorded to assess sympathetic outflow. Cardiac function is improved (mean ejection fraction, 22.6%±4.8% vs 38.3%±5.3%; P<0.05), and sympathetic activation is decreased (RSNA, 36.3%±7.9% vs 23.9%±7.6%; P<0.05) in HF rats treated with NaB compared with untreated HF rats. The plasma and cerebrospinal fluid levels of norepinephrine are decreased in HF rats treated with NaB. The infusion of N-methyl-D-aspartic acid (NMDA) into the paraventricular nucleus (PVN) of the hypothalamus of HF model rats increases sympathetic nervous activity by upregulating the NMDA receptor. Microglia polarized to the M2 phenotype and inflammation are markedly attenuated in the PVN of HF model rats after NaB administration. In addition, HF model rats treated with NaB exhibit enhanced intestinal barrier function and increased levels of GPR109A, zona occludens-1 and occludin, but decreased levels of lipopolysaccharide-binding protein and zonulin. In conclusion, butyrate attenuates sympathetic activation and improves cardiac function in rats with HF. The improvements in intestinal barrier function, reductions in microglia-mediated inflammation and decreases in NMDA receptor 1 expression in the PVN are all due to the protective effects of NaB.

The value of MRI-based radiomics for evaluating early parotid gland injury in primary Sjögren's syndrome.

OBJECTIVE: This study aimed to evaluate the value of machine learning models (ML) based on MRI radiomics in diagnosing early parotid gland injury in primary Sjögren's syndrome (pSS). METHODS: A total of 164 patients (114 in the training cohort and 50 in the testing cohort) with pSS (n=82) or healthy controls (HC) (n=82) were enrolled. Itksnap software was used to perform two-dimensional segmentation of the bilateral parotid glands on T1-weighted (T1WI) and fat-suppressed T2-weighted imaging (fs-T2WI) images. A total of 1548 texture features of the parotid glands were extracted using radiomics software. A radiomics score (Radscore) was constructed and calculated. A t-test was used to compare the Radscore between the two groups. Finally, five machine learning models were trained and tested to identify early pSS parotid injury, and the performance of the machine learning models was evaluated by calculating the acceptance operating curve (ROC) and other parameters. RESULTS: The Radscores between the pSS and HC groups showed significant statistical differences (p<0.001). Among the five machine learning models, the Extra Trees Classifier (ETC) model performed high predictive efficacy in identifying early pSS parotid injury, with an AUC of 0.87 in the testing set. CONCLUSION: MRI radiomics-based machine learning models can effectively diagnose early parotid gland injury in primary Sjögren's syndrome.