Objectives To investigate the homology and drug resistance gene of Group B Streptococcus ( GBS) Resistance induced by Clindamycin and provide basic data for clinical prevention and treatment of GBS Resistance infection induced by Clindamycin .Methods 921 strains of GBS were isolated at Obstetrics&Gynecology Hospital of Fudan University from January , 2014 to December , 2015.VITEK2-compact automatic bacterial susceptibility instrument was used to test their sensitivity to 7 antibacterial drugs.63 positive strains were chosen through D-inhibition zone trial which were drug resistant to Erythromycin and susceptible or intermediary to Clindamycin .The strain′s sequence type was identified by the method of multilocus sequence typing ( MLST typing ) .The drug resistance genes mefA & ermB to Erythromycin were detected by using PCR method .The analysis was carried out to reveal the relevance to drug resistance , multilocus sequence typing and drug resistance gene .Results Among 921 strains of GBS , the drug resistance rate was respectively 53.4％ ( 492/921 strains ) to Erythromycin , 50.2％ ( 462/921 strains) to Clindamycin, 34.7％ ( 320/921 strains ) to Levofloxacin and 7.5％ ( 69/921 strains ) to Nitrofurantoin.The drug resistance rate of Levofloxacin for 63 GBS strains was 27.0％(17/63 strains) and no drug resistant strain was found to Penicillin , Vancomycin & Nitrofurantoin.12 different ST types were involved in total, including a new ST type:ST1072.The most common ones were ST12 (30.1％) (20/63 strains) &ST19 (25.4％) (16/63 strains).The drug resistance rate of Levofloxacin with ST 19 (75.0％) (12/16 strains) was much higher than that of other ST types .The relevance ratio of mefA and ermB among 63 GBS strains was respectively 27.0％(17/63 strains) and 41.3％(26/63 strains).Conclusions The genetic diversity existed in Group B Streptococcus resistance induced by Clindamycin detected in this study . There was significant difference on drug resistance and relevant drug resistant genes among different ST types.

Objective To explore the value of texture analysis on ADC maps in the preoperative prediction of histological grade of tongue and mouth floor squamous cell carcinoma (SCC). Methods Forty?nine pathologically confirmed tongue and mouth floor SCC with definite grading from May 2015 to June 2018 were retrospectively analyzed, including 21 cases of gradeⅠ, 21 cases of gradeⅡand 7 cases of gradeⅢ. All subjects underwent preoperative MRI examination with DWI included. Two doctors delineated whole tumor region of interest and extracted texture parameters by the 3D Slicer software, including 8 histogram parameters, 11 grey?level co?occurrence matrix (GLCM) parameters and 7 gray?level run?length matrix (GLRLM) parameters. Intraclass correlation coefficient (ICC) was used to evaluate the inter?observer delineation agreement, and the texture parameters with excellent reproducibility (ICC>0.8) were used for analysis only. Mann?Whitney U test was used to compare the differences of ADC texture parameters between grade Ⅰ and grade Ⅱ?Ⅲ SCCs. Stepwise logistic regression was used to determine the independent predictors and to build combined model. ROC analysis was used to explore the performance of texture parameter and model in predicting histological grade of tongue and mouth floor SCCs. Pearson correlation coefficient was used to evaluate the correlation between texture parameters with statistical significance. Results (1) Excellent inter?observer delineation agreement (ICC: 0.81-0.98) was observed in 69.23% (18/26) texture parameters, including 6 histogram parameters, 7 GLCM parameters and 5 GLRLM parameters. (2) Among histogram parameters, significantly higher 10 percentile ADC value (ADC10) and significantly lower energy and entropy were shown in gradeⅠcompared with gradeⅡandⅢSCCs (all P<0.05). Among GLCM parameters, significantly lower joint entropy, difference entropy, sum entropy, difference variance, difference average and contrast were shown in grade Ⅰ SCCs (all P<0.05). Among GLRLM parameters, significantly lower gray?level nonuniformity and run?length nonuniformity were shown in gradeⅠSCCs (all P<0.05). ADC10 and entropy were identified as independent predictors. The ADC10 and entropy were 960(913, 1 178)×10?6mm2/s and 4.32(4.06, 4.76) in gradeⅠSCCs, and 888(816, 987)×10?6mm2/s and 4.88(4.57, 5.29) in gradeⅡ?ⅢSCCs respectively. The area under ROC curve (AUC) of ADC10, entropy and combined model were 0.72, 0.75, 0.81. (3) Significant correlation (|r|≥0.5) was observed among 52.73% (29/55)texture parameters with statistical significance. Conclusion Texture analysis on ADC maps can provide more quantitative information, which can be more accurately in discriminating grade Ⅰfrom gradeⅡ?Ⅲtongue and mouth floor SCCs.

The failure rate of dental implantation in patients with well-controlled type 2 diabetes mellitus (T2DM) is higher than that in non-diabetic patients. This due, in part, to the impaired function of bone marrow mesenchymal stem cells (BMSCs) from the jawbone marrow of T2DM patients (DM-BMSCs), limiting implant osseointegration. RNA N6-methyladenine (m6A) is important for BMSC function and diabetes regulation. However, it remains unclear how to best regulate m6A modifications in DM-BMSCs to enhance function. Based on the "m6A site methylation stoichiometry" of m6A single nucleotide arrays, we identified 834 differential m6A-methylated genes in DM-BMSCs compared with normal-BMSCs (N-BMSCs), including 43 and 790 m6A hypermethylated and hypomethylated genes, respectively, and 1 gene containing hyper- and hypomethylated m6A sites. Differential m6A hypermethylated sites were primarily distributed in the coding sequence, while hypomethylated sites were mainly in the 3'-untranslated region. The largest and smallest proportions of m6A-methylated genes were on chromosome 1 and 21, respectively. MazF-PCR and real-time RT-PCR results for the validation of erythrocyte membrane protein band 4.1 like 3, activity-dependent neuroprotector homeobox (ADNP), growth differentiation factor 11 (GDF11), and regulator of G protein signalling 2 agree with m6A single nucleotide array results; ADNP and GDF11 mRNA expression decreased in DM-BMSCs. Furthermore, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses suggested that most of these genes were enriched in metabolic processes. This study reveals the differential m6A sites of DM-BMSCs compared with N-BMSCs and identifies candidate target genes to enhance BMSC function and improve implantation success in T2DM patients.
Humans ; Bone Marrow/metabolism* ; Bone Morphogenetic Proteins/metabolism* ; Dental Implants/adverse effects* ; Diabetes Mellitus, Type 2/metabolism* ; Growth Differentiation Factors/metabolism* ; Mesenchymal Stem Cells/metabolism* ; RNA/metabolism* ; RNA Processing, Post-Transcriptional