Antibiotic resistance is a global concern; however, data on antibiotic-resistant Ureaplasma spp. and Mycoplasma hominis are limited in comparison to similar data on other microbes. A total of 492 Ureaplasma spp. and 13 M. hominis strains obtained in Hangzhou, China, in 2018 were subjected to antimicrobial susceptibility testing for levofloxacin, moxifloxacin, erythromycin, clindamycin, and doxycycline using the broth microdilution method. The mechanisms underlying quinolone and macrolide resistance were determined. Meanwhile, a model of the topoisomerase IV complex bound to levofloxacin in wild-type Ureaplasma spp. was built to study the quinolone resistance mutations. For Ureaplasma spp., the levofloxacin, moxifloxacin, and erythromycin resistance rates were 84.69%, 51.44%, and 3.59% in U. parvum and 82.43%, 62.16%, and 5.40% in U. urealyticum, respectively. Of the 13 M. hominis strains, 11 were resistant to both levofloxacin and moxifloxacin, and five strains showed clindamycin resistance. ParC S83L was the most prevalent mutation in levofloxacin-resistant Ureaplasma strains, followed by ParE R448K. The two mutations GyrA S153L and ParC S91I were commonly identified in quinolone-resistant M. hominis. A molecular dynamics-refined structure revealed that quinolone resistance-associated mutations inhibited the interaction and reduced affinity with gyrase or topoisomerase IV and quinolones. The novel mutations S21A in the L4 protein and G2654T and T2245C in 23S rRNA and the ermB gene were identified in erythromycin-resistant Ureaplasma spp. As fluoroquinolone resistance in Ureaplasma spp. and Mycoplasma hominis remains high in China, the rational use of antibiotics needs to be further enhanced.
It would be important to predict type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN). This study was aimed at evaluating the predicting significance of hemostatic parameters for T2DM and DN. Plasma coagulation and hematologic parameters before treatment were measured in 297 T2DM patients. The risk factors and their predicting power were evaluated. T2DM patients without complications exhibited significantly different activated partial thromboplastin time (aPTT), platelet (PLT), and D-dimer (D-D) levels compared with controls (P < 0.01). Fibrinogen (FIB), PLT, and D-D increased in DN patients compared with those without complications (P < 0.001). Both aPTT and PLT were the independent risk factors for T2DM (OR: 1.320 and 1.211, P < 0.01, resp.), and FIB and PLT were the independent risk factors for DN (OR: 1.611 and 1.194, P < 0.01, resp.). The area under ROC curve (AUC) of aPTT and PLT was 0.592 and 0.647, respectively, with low sensitivity in predicting T2DM. AUC of FIB was 0.874 with high sensitivity (85%) and specificity (76%) for DN, and that of PLT was 0.564, with sensitivity (60%) and specificity (89%) based on the cutoff values of 3.15 g/L and 245 × 109/L, respectively. This study suggests that hemostatic parameters have a low predicting value for T2DM, whereas fibrinogen is a powerful predictor for DN.
Background Although many biomarkers have high diagnostic and predictive power for diabetic kidney disease (DKD), less studies were performed for the predictive assessment in DKD and its progression with combined blood and urinary biomarkers. This study aims to explore the predictive significance of joint plasma fibrinogen (FIB) concentration and urinary alpha-1 microglobulin-creatinine (α1-MG/CR) ratio in DKD. Methods A total of 234 patients with type 2 diabetes were enrolled, and their clinical and laboratory data were retrospectively assessed. A ROC curve analysis was performed to evaluate the power of plasma FIB and urinary α1-MG/CR ratio for identifying DKD and advanced DKD, respectively. The predictive power for DKD and advanced DKD was analyzed by regression analysis. Results Plasma FIB and urinary α1-MG/CR levels were higher in patients with DKD than with pure T2D (p<0.001). The multivariate-adjusted odds ratios (ORs) were 5.047 (95%CI: 2.276–10.720) and 2.192 (95%CI: 1.539–3.122) (p<0.001) for FIB and α1-MG/CR as continuous variables for DKD prediction, respectively. The optimal cut-off values were 3.21 g/L and 2.11mg/mmol for identifying DKD, and 5.58 g/L and 11.07 mg/mmol for advanced DKD from ROC curves. At these cut-off values, the sensitivity and specificity of joint FIB and α1-MG/CR were 0.95 and 0.92 for identifying DKD, and 0.62 and 0.67 for identifying advanced DKD, respectively. The area under curve was 0.972 (95%CI: 0.948–0.995) (p<0.001) and 0.611, 95%CI: 0.488–0.734) (p>0.05). The multivariate-adjusted ORs for joint FIB and α1-MG/CR at the cut-off values were 214.500 (95%CI: 58.054–792.536) and 3.252 (95%CI: 1.040–10.175) (p<0.05), respectively. Conclusion The present study suggests that joint plasma FIB concentration and urinary α1-MG/CR ratio can be used as a powerful predictor for general DKD, but it is less predictive for advanced DKD.
Evidence indicates that macrophages play an important role in the immune system. Therefore, research involving inflammatory and oxidative stress responses in macrophages is of great significance. Many factors contribute to inflammation and oxidative stress, including Salmonella. We investigated the effect of the miR-139-5p/TRAF6 axis on the inflammatory and oxidative stress responses of Salmonella -infected macrophages. Our findings revealed that miR-139-5p decreased IL-1β and TNF-α levels to inhibit Salmonella-induced inflammatory responses in the RAW264.7 macrophage cell line. Furthermore, miR-139-5p inhibited Salmonella-induced oxidative stress by strengthening SOD, CAT, and GSH-PX activity, as well as lowering the malondialdehyde level in the RAW264.7 macrophages cell line. Subsequently, it was verified that TRAF6 was a downstream target of miR-139-5p in RAW264.7 cells. Rescue assays indicated that the over-expression of miR-139-5p inhibits the effects of TRAF6 on inflammatory and oxidative stress responses including Salmonella infection in RAW264.7 cells. To our knowledge, this study is the first to verify that miR-139-5p inhibits inflammatory and oxidative stress responses of Salmonella-infected macrophages through regulating TRAF6. This discovery may offer new insights on inflammatory and oxidative stress responses in macrophages.
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