BackgroundWe used multimodal compositional magnetic resonance imaging (MRI) techniques, combined with clinical outcomes, to differentiate the alternations of composition in repair cartilage with allogeneic human adipose-derived mesenchymal progenitor cells (haMPCs) in knee osteoarthritis (KOA) patients.MethodsEighteen patients participated a phase I/IIa clinical trial. All patients were divided randomly into three groups with intra-articular injections of haMPCs: the low-dose (1.0 × 107 cells), mid-dose (2.0 × 107), and high-dose (5.0 × 107) groups with six patients each. Compositional MRI examinations and clinical evaluations were performed at different time points.ResultsSignificant differences were observed in quantitative T1rho, T2, T2star, R2star, and ADC measurements in patients of three dose groups, suggesting a possible compositional changes of cartilage with the treatment of allogeneic haMPCs. Also significant reduction in WOMAC and SF-36 scores showed the symptoms might be alleviated to some extent with this new treatment. As regards sensibilities of multi-parametric mappings to detect compositional or structural changes of cartilage, T1rho mapping was most sensitive to differentiate difference between three dose groups.ConclusionsThese results showed that multi-compositional MRI sequences might be an effective tool to evaluate the promotion of the repair of cartilage with allogeneic haMPCs by providing information of compositional alterations of cartilage.Trial registrationClinicaltrials, NCT02641860. Registered 3 December 2015.

Accumulating evidence has suggested the importance of gut microbiota in the development of type 2 diabetes mellitus (T2DM). In the present study, 40 patients with T2DM were treated with liraglutide for 4 months. Feces samples and clinical characteristics were collected from these 40 T2DM patients before and after the liraglutide treatment. The diversity and composition of gut microbiota in the two groups were determined by sequencing the V4 region of bacterial 16S rRNA genes. Meanwhile, blood glucose, insulin, hemoglobin A1c (HbA1c), and lipid metabolism were also measured in the pre- and post-liraglutide-treatment groups. We find that Baseline HbA1c was associated with liraglutide treatment response (R2 = 0.527, β =  − 0.726, p < 0.0001). After adjusted for baseline HbA1c, blood urea nitrogen was associated with liraglutide treatment response. Besides, our results showed reduced gut microbial alpha diversity, different community structure distribution and altered microbial interaction network in patients treated with liraglutide. The liner discriminant analysis (LDA) effect size (LEfSe) analysis showed that 21 species of bacteria were abundant in the pre-liraglutide-treatment group and 15 species were abundant in the post-liraglutide-treatment group. In addition, we also find that Megamonas were significantly correlated with older age, diabetes duration and diabetic retinopathy, Clostridum were significantly correlated with family history of diabetes and Oscillospira were significantly correlated with both diabetic retinopathy and diabetic peripheral neuropathy. Functional analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) and cluster of orthologous groups (COG) annotations enriched three KEGG metabolic pathways and six functional COG categories in the post-liraglutide-treatment group. In conclusion, our research suggests that baseline HbA1c, blood urea nitrogen and gut microbiota are associated with the liraglutide treatment applied on patients with T2DM. These findings may contribute to the beneficial effects of liraglutide against diabetes.

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Background: Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development. Methods: A total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked.