Background: The association of tooth loss with mortality from all causes, cardiovascular diseases (CVD), and coronary heart disease (CHD) has been studied for many years; however, the results are inconsistent.Method: PubMed, Embase, Web of Knowledge, and Cochrane Oral Health Group’s Trials Register databases were searched for papers published from 1966 to August 2018. We conducted dose–response meta-analysis to quantitatively evaluate the relation between tooth loss and risk of mortality from all causes, CVD, and CHD.Results: In the present study, 18 prospective studies conducted until August 2018 were considered eligible for analysis. In the analysis of linear association, the summarized relative risk (RR) values for each 10-, 20-, and 32-tooth loss for all-cause mortality were 1.15 (1.11–1.19), 1.33 (1.23–1.29), and 1.57 (1.39–1.51), respectively. Subgroup and sensitivity analyses showed consistent results. A linear relationship was found among all-cause mortality, with Pnonlinearity = 0.306. The susceptibility to all-cause mortality increased by almost 1.48 times at very high tooth loss (28–32), and slight flattening of the curve was noted. However, the summarized RR values for increment for 10-, 20-, and 32-tooth loss were not or were marginally related to increased risk of mortality from CVD/CHD. Subgroup and sensitivity analyses revealed inconsistent results. Tooth loss showed linear association with CHD mortality but not with CVD mortality. The susceptibility to all-cause mortality increased by almost 1.48 and 1.70 times for CVD and CHD, respectively, at very high tooth loss (28–32). The curve exhibited slight flattening; however, no statistical significance was detected.Conclusion: In the meta-analysis, our findings confirmed the positive relationship between tooth loss and susceptibility to all-cause mortality, but not for circulatory mortality. However, the finding that tooth loss might play a harmful role in the development of all-cause mortality remains inconclusive. Tooth loss may be a potential risk marker for all-cause mortality: however, their association must be further validated through large prospective studies.

Observational studies showed that tooth loss is associated with gastric cancer, but the findings are inconsistent. In this study, a meta-analysis was conducted to evaluate the relationship between tooth loss and gastric cancer. Relevant studies were screened in PubMed and Embase databases, and nine observational studies were considered eligible for the analysis. The combined relative risks for the highest versus the lowest categories of tooth loss were 1.86 (95% CI: 1.08–3.21) and 1.31 (95% CI: 1.12–1.53) in case control and cohort studies, respectively. However, unstable results were observed in the stratified and sensitivity analysis. The current evidence, based solely on four case-control studies and five cohort studies, suggested that tooth loss is a potential marker of gastric cancer. However, we can not concluded at this time that tooth loss may be a risk factor for gastric cancer due to significant heterogeneity among studies and mixed results between case-control studies and cohort studies. Additional large-scale and high-quality prospective studies are required to evaluate the association between tooth loss and risk of gastric cancer.

Background/Aims: Kidney renal clear cell carcinoma (KIRC) is one of the most fatal malignancies due to late diagnosis and poor treatment. To improve its prognosis, a screening for molecular biomarkers of KIRC is urgently needed. Long non-coding RNAs (lncRNAs) play important roles in tumorigenesis and prognosis of cancers. However, it is not clear whether lncRNAs can be used as molecular biomarkers in predicting the survival of KIRC patients. Methods: In this study, our aim was to identify lncRNAs/mRNAs signatures and their prognostic values in KIRC. The aberrant expression profile of mRNAs and lncRNAs in 529 KIRC tissues and 72 adjacent non-tumor pancreatic tissues were obtained from the Cancer Genome Atlas (TCGA). A weighted gene co-expression network analysis (WGCNA) of two key lncRNAs was constructed. We constructed an aberrant lncRNA-mRNA-miRNA ceRNA network in CESC. In addition, Gene Ontology (GO) and KEGG pathway analysis were performed. Results: Using lncRNA/mRNA expression profiling data, the overall analysis revealed that two novel lncRNA signatures (DNM1P35 and MIR155HG) and several mRNAs were found to be significantly correlated with KIRC patient’s overall analysis. Based on the target gene of the two lncRNA in co-expression network, the GO and KEGG analysis were also performed. A dysregulated lncRNA-related ceRNA network was also observed. Conclusion: These results suggested that the two novel lncRNAs signatures may act as independent prognostic biomarkers for predicting the survival of KIRC patient.

Several observational studies have investigated the relation between cadmium exposure and risk of any fracture. However, the results from epidemiological studies for the association are inconsistent.We conducted a meta-analysis to evaluate the relationship between cadmium exposure and risk of any fracture. The pertinent studies were identified by a search of PubMed and Embase databases from 1966 to June 2015.Seven articles involving 21,941 fracture cases and 504,346 participants were included. The meta-analysis showed that the pooled relative risk of any fracture for the highest versus lowest category of cadmium concentration was 1.30 (95% confidence interval = 1.13–1.49). In subgroup analyses, the significant association remained consistent when stratified by study type, geographical region, method of cadmium exposure assessment, and gender.Our meta-analysis showed that a high cadmium exposure may be a risk factor for any fracture. However, this result should be interpreted cautiously because of the heterogeneity among studies and existence of publication bias. Additional large, high-quality prospective studies are needed to evaluate the association between cadmium exposure and the risk of development of fracture.

Many epidemiological studies have found that tooth loss is associated with susceptibility to oesophageal cancer. However, a definitive answer is yet to be discovered, and the findings are inconclusive. We performed a meta-analysis to assess the relationship between tooth loss and oesophageal cancer risk. We searched PubMed and Embase databases to screen eligible studies up to June 2015. Nine observational studies (eight articles) involving 2604 patients and 113,995 participants were included in the meta-analysis. The combined odds ratio for tooth loss and oesophageal cancer was 1.53 (95 % CI 1.02–2.29) for the high versus lowest teeth loss categories. However, inconsistent results were detected in the stratified and sensitivity analysis. In dose–response analysis, the summary odds ratio for each one tooth loss increment was 1.01 (95 % CI 1.00–1.02). The current evidence, based solely on six case–control studies and three cohort studies, suggests that tooth loss is a potential marker of oesophageal cancer. However, no firm conclusion can be drawn at this time that tooth loss may play a causal role in development of oesophageal cancer. Additional large-scale and high-quality prospective studies are required to evaluate the association between tooth loss and risk of oesophageal cancer.

A growing number of studies provide epidemiological evidence linking obstructive sleep apnea (OSA) with a number of chronic disorders. Transcriptional analyses have been conducted to analyze the gene expression data. However, the weighted gene coexpression network analysis (WGCNA) method has not been applied to determine the transcriptional consequence of continuous positive airway pressure (CPAP) therapy in patients with severe OSA. The aim of this study was to identify key pathways and genes in patients with OSA that are influenced by CPAP treatment and uncover/unveil potential molecular mechanisms using WGCNA. We analyzed the microarray data of OSA (GSE 49800) listed in the Gene Expression Omnibus database. Coexpression modules were constructed using WGCNA. In addition, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were also conducted. After the initial data processing, 5101 expressed gene profiles were identified. Next, a weighted gene coexpression network was established and 16 modules of coexpressed genes were identified. The interaction analysis demonstrated a relative independence of gene expression in these modules. The black module, tan module, midnightblue module, pink module, and greenyellow module were significantly associated with the alterations in circulating leukocyte gene expression at baseline and after exposure to CPAP. The five hub genes were considered to be candidate OSA‐related genes after CPAP treatment. Functional enrichment analysis revealed that steroid biosynthesis, amino sugar and nucleotide sugar metabolism, protein processing in the endoplasmic reticulum, and the insulin signaling pathway play critical roles in the development of OSA in circulating leukocyte gene expression at baseline and after exposure to CPAP. Using this new systems biology approach, we identified several genes and pathways that appear to be critical to OSA after CPAP treatment, and these findings provide a better understanding of OSA pathogenesis.

Background Dexamethasone (Dexa) and potassium canrenoate (Cane) modulate nociceptive behavior via glucocorticoid receptor (GR) and mineralocorticoid receptor (MR) by two mechanisms (genomic and nongenomic pathways). This study was designed to investigate the Dexa‐ or Cane‐mediated nongenomic and genomic effects on mechanical nociception and inflammation‐induced changes in interleukin‐6 (IL‐6) mediated signaling pathway in rats. Methods Freund's complete adjuvant (FCA) was used to trigger an inflammation of the right hind paw in male Sprague–Dawley rats. First, the mechanical nociceptive behavioral changes were examined following intraplantar administration of GR agonist Dexa and/or MR antagonist Cane in vivo. Subsequently, the protein levels of IL‐6, IL‐6Rα, JAK2, pJAK2, STAT3, pSTAT3Ser727, migration inhibitory factor, and cyclooxygenase‐2 were assessed by Western blot following intraplantar injection of Dexa or Cane or the combination. Moreover, the molecular docking studies determined the interaction between Dexa, Cane, and IL‐6. The competition binding assay was carried out using enzyme‐linked immunosorbent assays (ELISA). Results Administration of Dexa and Cane dose‐dependently attenuated FCA‐induced inflammatory pain. The sub‐additive effect of Dexa/Cane combination was elucidated by isobologram analysis, accompanied by decrease in the spinal levels of IL‐6, pJAK2, and pSTAT3Ser727. The molecular docking study demonstrated that both Dexa and Cane displayed a firm interaction with THR138 binding site of IL‐6 via a strong hydrogen bond. ELISA revealed that Dexa has a higher affinity to IL‐6 than Cane. Conclusions There was no additive or negative effect of Dexa and Cane, and they modulate the IL‐6/JAK2/STAT3 signaling pathway through competitive binding with IL‐6 and relieves hypersensitivity during inflammatory pain.

Background Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs. Methods 1129 images were used to develop a deep learning-based segmentation network for automatic sella turcica segmentation. Besides, 50 images were used to test the generalization ability of the model. The performance of the segmented network was evaluated by the dice coefficient. Images in the test datasets were segmented by the trained segmentation network, and the segmentation results were saved in binary images. Then the extremum points and corner points were detected by calling the function in the OpenCV library to obtain the coordinates of the four landmarks of the sella turcica. Finally, the length, diameter, and depth of the sella turcica can be obtained by calculating the distance between the two points and the distance from the point to the straight line. Meanwhile, images were measured manually using Digimizer. Intraclass correlation coefficients (ICCs) and Bland–Altman plots were used to analyze the consistency between automatic and manual measurements to evaluate the reliability of the proposed methodology. Results The dice coefficient of the segmentation network is 92.84%. For the measurement of sella turcica, there is excellent agreement between the automatic measurement and the manual measurement. In Test1, the ICCs of length, diameter and depth are 0.954, 0.953, and 0.912, respectively. In Test2, ICCs of length, diameter and depth are 0.906, 0.921, and 0.915, respectively. In addition, Bland–Altman plots showed the excellent reliability of the automated measurement method, with the majority measurements differences falling within ± 1.96 SDs intervals around the mean difference and no bias was apparent. Conclusions Our experimental results indicated that the proposed methodology could complete the automatic segmentation of the sella turcica efficiently, and reliably predict the length, diameter, and depth of the sella turcica. Moreover, the proposed method has generalization ability according to its excellent performance on Test2.