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. 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. 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). 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.

Background: Increasing 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. Methods: Expression 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. Results: A 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). Conclusion: Our 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.

Dental or tooth wear is a physiological process in the life cycle of teeth. Loss of the occlusal surface may cause excessive tooth wear. Several factors may contribute to tooth wear with different intensities and duration in the oral cavity. The oral cavity is generally compared to a tribological system to determine the various types of wear between teeth and restorative materials and assess the amount of dental wear. However, it is challenging to investigate in vitro and in vivo wear owing to the complexity of tooth wear; thus, a clear correlation between in vitro and in vivo data could not be established. This review is aimed at providing an insight into the etiology of tooth wear and tribological investigations in dentistry.

Various kinds of controlled microtopographies can promote osteogenic differentiation of mesenchymal stem cells (MSCs), such as microgrooves, micropillars, and micropits. However, the optimal shape, size, and mechanism remain unclear. In this review, we summarize the relationship between the parameters of different microtopographies and the behavior of MSCs. Then, we try to reveal the potential mechanism between them. The results showed that the microgrooves with a width of 460μm and ridge width <10μm, micropillars with parameters less than 10μm, and square micropits had the full potential to promote osteogenic differentiation of MSCs, while the micromorphology of the same size could induce larger focal adhesions (FAs), well-organized cytoskeleton, and superior cell areas. Therefore, such events are possibly mediated by microtopography-induced mechanotransduction pathways.

Studies indicate locally-delivered statins offer additional benefits to scaling and root planning (SRP), however, it is still hard to say which type of statins is better. This network meta-analysis aimed to assess the effect of locally-delivered statins and rank the most efficacious statin for treating chronic periodontitis (CP) in combination with SRP. We screened four literature databases (Pubmed, Embase, Cochrane Library, and Web of Science) for randomized controlled clinical trials (RCTs) published up to June 2018 that compared different statins in the treatment of chronic periodontitis. The outcomes analyzed were changes in intrabony defect depth (IBD), pocket depth (PD), and clinical attachment level (CAL). We carried out Bayesian network meta-analysis of CP without systemic diseases. Traditional and Bayesian network meta-analyses were conducted using random-effects models. Greater filling of IBD, reduction in PD, and gain in CAL were observed for SRP treated in combination with statins when compared to SRP alone for treating CP without systemic diseases. Specifically, SRP+ Atorvastatin (ATV) (mean difference [MD]: 1.5 mm, 1.4 mm, 1.8 mm, respectively), SRP + Rosuvastatin (RSV) (MD: 1.8 mm, 2.0 mm, 2.1 mm, respectively), and SRP + Simvastatin (SMV) (MD: 1.1 mm, 2.2 mm, 2.1 mm, respectively) were identified. However, no difference was found among the statins tested. In CP patients with type 2 diabetic (T2DM) or in smokers, additional benefits were observed from locally delivered statins. Local statin use adjunctive to SRP confers additional benefits in treating CP by SRP, even in T2DM and smokers. RSV may be the best one to fill in IBD. However, considering the limitations of this study, clinicians must use cautious when applying the results and further studies are required to explore the efficacy of statins in CP with or without the risk factors (T2DM comorbidity or smoking history).