Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease and continues to rise in the worldwide. Limonin is a triterpenoid compound widely found in the fruits of citrus plants with a wide range of pharmacological effects, including anti-cancer, anti-inflammation, anti-viral, anti-oxidation and liver protection properties. However, the potential molecular mechanism of limonin on NAFLD in zebrafish remains unknown. In this study, zebrafish larvae were exposed to thioacetamide to establish an NAFLD model and the larvae were treated with limonin for 72 h simultaneously. The human liver cell line was stimulated with lipid mixture and meanwhile incubated with limonin for 24 h. The results showed that Limonin significantly reduced the accumulation of lipid droplets in the liver and down-regulated the levels of lipogenic transcription factors FASN and SREBP1 in NAFLD. Limonin suppressed macrophages infiltration and the down-regulated the relative expression levels of the pro-inflammatory factors IL-6, IL-1β and TNF-α secreted by macrophages. Besides, limonin could reversed the reduction of glutathione and the accumulation of reactive oxygen species through up-regulating NRF2/HO-1 signaling pathway in the liver. In conclusion, this study revealed that limonin has a protective effect on NAFLD due to its resistance to lipid deposition as well as antioxidant and anti-inflammatory actions.

Background: The 2017 Oxford classification of immunoglobulin A nephropathy (IgAN) recently reported that crescents could predict a worse renal outcome. Early prediction of crescent formation can help physicians determine the appropriate intervention, and thus, improve the outcomes. Therefore, we aimed to establish a nomogram model for the prediction of crescent formation in IgA nephropathy patients. Methods: We retrospectively analyzed 200 cases of biopsy-proven IgAN patients. Least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic regression was applied to screen for influencing factors of crescent formation in IgAN patients. The performance of the proposed nomogram was evaluated based on Harrell’s concordance index (C-index), calibration plot, and decision curve analysis. Results: Multivariate logistic analysis showed that urinary protein ≥ 1 g (OR = 3.129, 95%CI = 1.454–6.732), urinary red blood cell (URBC) counts ≥ 30/ul (OR = 3.190, 95%CI = 1.590–6.402), mALBU ≥ 1500 mg/L(OR = 2.330, 95%CI = 1.008–5.386), eGFR < 60ml/min/1.73m²(OR = 2.295, 95%CI = 1.016–5.187), Serum IgA/C3 ratio ≥ 2.59 (OR = 2.505, 95%CI = 1.241–5.057), were independent risk factors for crescent formation. Incorporating these factors, our model achieved well-fitted calibration curves and a good C-index of 0.776 (95%CI [0.711–0.840]) in predicting crescent formation. Conclusions: Our nomogram showed good calibration and was effective in predicting crescent formation risk in IgAN patients.

Objective The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning technology to build a liver fibrosis diagnosis model to achieve non-invasive staging of liver fibrosis, avoid complications, and reduce costs. Methods This study uses ultrasound examination to obtain pure liver parenchyma image section data. With the consent of the patient, combined with the results of percutaneous liver puncture biopsy, the degree of liver fibrosis indicated by ultrasound examination data is judged. The concept of Fibrosis Contrast Layer (FCL) is creatively introduced in our experimental method, which can help our model more keenly capture the significant differences in the characteristics of liver fibrosis of various grades. Finally, through label fusion (LF), the characteristics of liver specimens of the same fibrosis stage are abstracted and fused to improve the accuracy and stability of the diagnostic model. Results Experimental evaluation demonstrated that our model achieved an accuracy of 85.6%, outperforming baseline models such as ResNet (81.9%), InceptionNet (80.9%), and VGG (80.8%). Even under a small-sample condition (30% data), the model maintained an accuracy of 84.8%, significantly outperforming traditional deep-learning models exhibiting sharp performance declines. Conclusion The training results show that in the whole sample data set and 30% small sample data set training environments, the FCLLF model’s test performance results are better than those of traditional deep learning models such as VGG, ResNet, and InceptionNet. The performance of the FCLLF model is more stable, especially in the small sample data set environment. Our proposed FCLLF model effectively improves the accuracy and stability of liver fibrosis staging using non-invasive ultrasound imaging.

Frailty predicts poor outcomes in chronic kidney disease (CKD) patients. This study compared frailty's predictive power with other factors and aimed to develop a model for predicting overall survival (OS) in CKD patients. The study included 3,714 CKD participants from the National Health and Nutrition Examination Survey 2005-2018. The death data were updated to December 31, 2019. Lasso-Cox regression identified significant predictors among 42 factors, resulting in a prognostic nomogram using 11 key variables. Subsequent evaluation of the nomogram involved the C-index, the Areas Under Time-dependent Receiver Operating Characteristic Curves (AUC) and calibration curves. Over a median follow-up of 5.92 years, 1,234 deaths occurred. The final predictors of OS in CKD patients included age, ethnicity, smoking status, estimated pulse wave velocity, body fat percentage, blood uric acid concentration, blood urea nitrogen concentration, and albumin concentration, neutrophil-to-lymphocyte ratio, urine albumin-to-creatinine ratio level, and frailty index (FI) score. The FI score was the strongest predictor with an HR of 76.54 (95% CI: 42.93, 136.46, p < 0.0001). In the training set, the AUC values were 80.11% for 1-year, 79.90% for 3-year, 79.53% for 5-year, and 81.34% for 10-year follow-ups. In the internal validation set, AUC values were 78.66%, 77.78%, 77.56%, and 79.54%, respectively. The nomogram's corrected C-index was 0.76 (95% CI: 0.75 - 0.78), and calibration curves showed satisfactory accuracy. The FI score is a significant predictor of CKD OS. The developed nomogram based on the FI score is a promising tool for predicting the OS of CKD patients.