Gut microbiota mediated the effects of high relative humidity on lupus in female MRL/lpr mice

Abstract Introduction The relationship between humidity and systemic lupus erythematosus (SLE) has yielded inconsistent results in prior research, while the effects of humidity on lupus in animal experiments and its underlying mechanism remain inadequately explored. Methods The present study aimed to investigate the impact of high humidity (80 ± 5%) on lupus using female and male MRL/lpr mice, with a particular focus on elucidating the role of gut microbiota in this process. To this end, fecal microbiota transplantation (FMT) was employed to transfer the gut microbiota of MRL/lpr mice under high humidity to blank MRL/lpr mice under normal humidity (50 ± 5%), allowing for an assessment of the effect of FMT on lupus. Results The study revealed that high humidity exacerbated lupus indices (serum anti-dsDNA, ANA, IL-6, and IFN- g, and renal pathology) in female MRL/lpr mice but had no significant effect on male MRL/lpr mice. The aggravation of lupus caused by high humidity may be attributed to the increased abundances of the Rikenella, Romboutsia, Turicibacter, and Escherichia-Shigella genera in female MRL/lpr mice. Furthermore, FMT also exacerbated lupus in female MRL/lpr mice but not in male MRL/lpr mice. Conclusion In summary, this study has demonstrated that high humidity exacerbated lupus by modulating gut microbiota in female MRL/lpr mice. The findings underscore the importance of considering environmental factors and gut microbiota in the development and progression of lupus, particularly among female patients.

Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

Abstract Objective To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.