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Background Few large-sample studies in China have focused on the early survival of dental implants. The present study aimed to report the early survival rates of implants and determine the related influencing factors. Methods All patients receiving dental implants at our institution between 2006 and 2017 were included. The endpoint of the study was early survival rates of implants, according to gender, age, maxilla/mandible, dental position, bone augmentation, bone augmentation category, immediate implant, submerged implant category, implant diameter, implant length, implant torque, and other related factors. Initially, SPSS22.0 was used for statistical analysis. The Chi-square test was used to screen all factors, and those with p < 0.05 were further introduced into a multiple logistic regression model to illustrate the risk factors for early survival rates of implants. Results In this study, we included 1078 cases (601 males and 477 females) with 2053 implants. After implantation, 1974 implants were retained, and the early survival rate was 96.15%. Patients aged 30–60 years (OR  2.392), with Class I bone quality (OR  3.689), bone augmentation (OR  1.742), immediate implantation (OR  3.509), and implant length < 10 mm (OR  2.972), were said to possess risk factors conducive to early survival rates. Conclusions The early survival rate of implants in our cohort exceeded 96%, with risk factors including age, tooth position, bone quality, implant length, bone augmentation surgery, and immediate implantation. When the above factors coexist, implant placement should be treated carefully.

BackgroundPeriodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets.MethodsTwo periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis.ResultsWe produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways.ConclusionThis study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis.