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.

Background: The purpose of this study was to compare the biocompatibility of three bioactive materials, namely ACTIVA bioactive restorative resin composite, iRoot BP plus and Mineral Trioxide Aggregate (MTA) Angelus-HP. Methods: Seventy-five Wistar male rats were subjected to subcutaneous implantation of four polyethylene tubes; one empty tube was used as control (Group 1), and the other tubes were filled with ACTIVA (Group 2), iRoot BP (Group 3) and MTA-HP (Group 4). Then, the rats were subdivided into 3 groups according to the sacrification time into one, two and 4 weeks (n = 25 rats). Tissue specimens were submitted to histopathological and immunohistochemical analysis of α-SMA and caspase 3. Results: The one-way Anova test revealed that ACTIVA group exhibited minimal inflammation in comparison to calcium silicate cements (iRoot BP and MTA-HP groups). iRoot BP group significantly revealed a more severe degree of chronic inflammation in comparison to other groups (P < 0.05). ACTIVA group showed marked regression of inflammation and fibrosis comparable to the control, while iRoot BP group revealed remarkable fibrosis and calcification, with less degrees in MTA-HP group (P < 0.05). Immunostaining of both α-SMA and caspase 3 revealed lower indexes in ACTIVA group consistent with the control (P < 0.05). Conclusions: ACTIVA showed a higher degree of biocompatibility to subcutaneous tissues in comparison to both iRoot BP and MTA-HP cements in regard to decrease the intensity of inflammation, with subsequent fibrous connective tissue remodeling and better healing patterns. Clinical significance: Preliminary data suggests that the application of ACTIVA in retrograde fillings.

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.

Using machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model’s performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors.

Background 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. Methods 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. Results 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. Conclusion 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). Electronic supplementary material The online version of this article (10.1186/s12903-019-0789-2) contains supplementary material, which is available to authorized users.