Background Growing evidences indicate that the alterations in gut microbiota are associated with the efficacy of glucocorticoids (GCs) in the treatment of systemic lupus erythematosus (SLE). However, there is no evidence to prove whether gut microbiota directly mediates the effects of GCs. Methods Using the MRL/lpr mice, this study firstly addressed the effects of three doses of prednisone on gut microbiota. Then, this study used fecal microbiota transplantation (FMT) to transfer the gut microbiota of prednisone-treated MRL/lpr mice into the blank MRL/lpr mice to reveal whether the gut microbiota regulated by prednisone had similar therapeutic efficiency and side effects as prednisone. Results The effects of prednisone on gut microbiota were dose-dependent in the treatment of MRL/lpr mice. After transplantation into MRL/lpr mice, prednisone-regulated gut microbiota could alleviate lupus, which might be due to decreasing Ruminococcus and Alistipes and retaining the abundance of Lactobacillus. However, prednisone-regulated gut microbiota did not exhibit side effects as prednisone. The reason might be that the pathogens upregulated by prednisone could not survive in the MRL/lpr mice as exogenous microbiota, such as Parasutterella, Parabacteroides, and Escherichia-Shigella. Conclusions These data demonstrated that the transplantation of gut microbiota may be an effective method to obtain the therapeutic effects of GCs and avoid the side effects of GCs.

This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas.

Glioma is the most common malignant intracranial tumor and exhibits diffuse metastasis and a high recurrence rate. The invasive property of glioma results from cell detachment. Anoikis is a special form of apoptosis that is activated upon cell detachment. Resistance to anoikis has proven to be a protumor factor. Therefore, it is suggested that anoikis resistance commonly occurs in glioma and promotes diffuse invasion. Several factors, such as integrin, E-cadherin, EGFR, IGFR, Trk, TGF-β, the Hippo pathway, NF-κB, eEF-2 kinase, MOB2, hypoxia, acidosis, ROS, Hsp and protective autophagy, have been shown to induce anoikis resistance in glioma. In our present review, we aim to summarize the underlying mechanism of resistance and the therapeutic potential of these molecules.

Post-stroke depression (PSD) is the most frequent and important neuropsychiatric consequence of stroke. It is strongly associated with exacerbated deterioration of functional recovery, physical and cognitive recoveries, and quality of life. However, its mechanism is remarkably complicated, including the neurotransmitters hypothesis (which consists of a monoaminergic hypothesis and glutamate-mediated excitotoxicity hypothesis), inflammation hypothesis, dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis, and neurotrophic hypothesis and neuroplasticity. So far, the underlying pathogenesis of PSD has not been clearly defined yet. At present, selective serotonin reuptake inhibitors (SSRIs) have been used as the first-line drugs to treat patients with PSD. Additionally, more than SSRIs, a majority of the current antidepressants complied with multiple side effects, which limits their clinical application. Currently, a wide variety of studies revealed the therapeutic potential of natural products in the management of several diseases, especially PSD, with minor side effects. Accordingly, in our present review, we aim to summarize the therapeutic targets of these compounds and their potential role in-clinic therapy for patients with PSD.