Background Glycemic control is vital in the care of type 2 diabetes mellitus (T2DM) and is significantly associated with the incidence of clinical complications. This Bayesian network analysis was conducted with an aim of evaluating the efficacy of scaling and root planning (SRP) and SRP + adjuvant treatments in improving glycemic control in chronic periodontitis (CP) and T2DM patients, and to guide clinical practice. Methods We searched the Pubmed, Embase, Cochrane Library and Web of Science databases up to 4 May 2018 for randomized controlled trials (RCTs). This was at least three months of the duration of study that involved patients with periodontitis and T2DM without other systemic diseases given SRP. Patients in the control group did not receive treatment or SRP combination with adjuvant therapy. Outcomes were given as HbA1c% and levels fasting plasma glucose (FPG). Random-effects meta-analysis and Bayesian network meta-analysis were conducted to pool RCT data. Cochrane’s risk of bias tool was used to assess the risk of bias. Results Fourteen RCTs were included. Most were unclear or with high risk of bias. Compared to patients who did not receive treatment, patients who received periodontal treatments showed improved HbA1c% level, including SRP (the mean difference (MD) -0.399 95% CrI 0.088 to 0.79), SRP + antibiotic (MD 0.62, 95% CrI 0.18 to 1.11), SRP + photodynamic therapy (aPDT) + doxycycline (Doxy) (MD 1.082 95% CrI 0.13 to 2.077) and SRP + laser (MD 0.66 95% CrI 0.1037, 1.33). Among the different treatments, SRP + aPDT + Doxy ranked best. Regarding fasting plasma glucose (FPG), SRP did not show advantage over no treatment (MD 4.91 95% CI − 1.95 to 11.78) and SRP with adjuvant treatments were not better than SRP alone (MD -0.28 95% CI -8.66, 8.11). Conclusion The results of this meta-analysis seem to support that periodontal treatment with aPDT + Doxy possesses the best efficacy in lowering HbA1c% of non-smoking CP without severe T2DM complications. However, longer-term well-executed, multi-center trails are required to corroborate the results. Electronic supplementary material The online version of this article (10.1186/s12903-019-0829-y) contains supplementary material, which is available to authorized users.

Pileus and stipe of mushroom Russula griseocarnosa from South China were analyzed separately for chemical composition and antioxidant activities. The wild mushroom species proved to have antioxidant potential, using assays of reducing power, chelating effect on ferrous ions, scavenging effect on hydroxyl free radicals, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. The mushroom contained very useful phytochemicals such as phenolics, flavonoids, ergosterol, and beta-carotene. The phenolic composition of R. griseocarnosa was analyzed by high-performance liquid chromatography (HPLC). The major component in R. griseocarnosa was quercetin (95.82 microg/g). The combination of bioactive substances and rich nutritional composition (high contents in protein and carbohydrates, low content in fat) in the mushroom should be useful to consumers in encouraging them to utilize the nutritive potential of this edible wild mushroom.

The metal (Cu, Ni, Cd, Hg, As, Pb) contents in wild edible mushrooms collected from three different sites in China were determined by atomic absorption spectrometry and atomic fluorescence spectrometry. All element concentrations were determined on a dry weight basis. A total of 11 species was studied, five being from the urban area and six from rural areas in China. The As content ranged from 0.44 to 1.48 mg/kg. The highest As content was seen in Macrolepiota crustosa from the urban area, and the lowest in Russula virescens from rural areas. A high Ni concentration (1.35 mg/kg) was found in Calvatia craniiformis from the urban area. The lowest Ni level was 0.11 mg/kg, for the species R. virescens and Cantharellus cibarius. The Cu content ranged from 39.0 to 181.5 mg/kg. The highest Cu content was seen in Agaricus silvaticus and the lowest in C. cibarius. The Pb content ranged from 1.9 to 10.8 mg/kg. The highest Pb value was found in C. craniiformis. The Cd content ranged from 0.4 to 91.8 mg/kg. The highest Cd value was found in M. crustosa. The Hg content ranged from 0.28 to 3.92 mg/kg. The highest Hg level was found in Agaricus species. The levels of the heavy metals Cd, Pb, and Hg in the studied mushroom species from urban area can be considered high. The metal-to-metal correlation analysis supported they were the same source of contamination. High automobile traffic was identified as the most likely source of the contamination. Based upon the present safety standards, consumption of those mushrooms that grow in the polluted urban area should be avoided.

BackgroundIncreasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the competitive endogenous RNA (ceRNA) networks in that they regulate protein-coding gene expression by sponging microRNAs (miRNAs). However, the understanding of the ceRNA network in tongue squamous cell carcinoma (TSCC) remains limited. MethodsExpression profile data regarding mRNAs, miRNAs and lncRNAs as well as clinical information on 122 TSCC tissues and 15 normal controls from The Cancer Genome Atlas (TCGA) database were collected. We used the edgR package to identify differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs) and miRNAs (DEmiRNAs) between TSCC samples and normal samples. In order to explore the functions of DEmRNAs, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Subsequently, a ceRNA network was established based on the identified DElncRNAs–DEmiRNAs and DEmiRNAs–DEmRNAs interactions. The RNAs within the ceRNA network were analyzed for their correlation with overall disease survival. Finally, lncRNAs were specifically analyzed for their correlation with clinical features in the included TSCC patient samples. ResultsA total of 1867 mRNAs, 828 lncRNAs and 81 miRNAs were identified as differentially expressed in TSCC tissues (—log 2fold change— ≥ 2; adjusted P value <0.01). The resulting ceRNA network included 16 mRNAs, 56 lncRNAs and 6 miRNAs. Ten out of the 56 lncRNAs were found to be associated with the overall survival in TSCC patients (P < 0.05); 10 lncRNAs were correlated with TSCC progression (P < 0.05). ConclusionOur study deepens the understanding of ceRNA network regulatory mechanisms in TSCC. Furthermore, we identified ten lncRNAs (PART1, LINC00261, AL163952.1, C2orf48, FAM87A, LINC00052, LINC00472, STEAP3-AS1, TSPEAR-AS1 and ERVH48-1) as novel, potential prognostic biomarkers and therapeutic targets for TSCC.

Background Oral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA). Methods Expression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual’s overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed. Results Three mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (−0.38602 × expression level of CLEC3B) + (−0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis. Conclusion A 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.