Periodontal infrabony defects: Systematic review of healing by defect morphology following regenerative surgery.

AbstractBackgroundIt is thought that infrabony defect morphology affects the outcome of periodontal regenerative surgery. However, this has not been systematically investigated.AimsTo investigate how well defect morphology is described in papers reporting regenerative therapy of periodontal infrabony defects and to investigate its effect on clinical and radiographic outcomes.Materials and MethodsA search was conducted in 3 electronic databases for publications reporting clinical and radiographic outcomes of periodontal intra‐bony defects after regenerative therapy, divided by defect morphology.ResultsThe initial search resulted in 4487 papers, reduced to 143 after first and second screening. Fifteen of these publications were suitable for a fixed‐effects meta‐analysis. Initial defect depth was found to influence radiographic bone gain 12 months post‐surgery, while narrower angles and increased number of walls influenced both radiographic bone gain and clinical attachment level (CAL) gain at 12 months. These associations seemed to occur irrespective of biomaterials used. Risk of bias ranged from low to high.ConclusionDeeper defects with narrower angles and increased number of walls exhibit improved CAL and radiographic bone gain at 12 months post‐regenerative surgery. More data are needed about other aspects of defect morphology such as extension to buccal/lingual surfaces

Similarity and Potential Relation Between Periimplantitis and Rheumatoid Arthritis on Transcriptomic Level: Results of a Bioinformatics Study

Background: This bioinformatics study aimed to reveal potential cross-talk genes, related pathways, and transcription factors between periimplantitis and rheumatoid arthritis (RA). Methods: The datasets GSE33774 (seven periimplantitis and eight control samples) and GSE106090 (six periimplantitis and six control samples) were included from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). A differential expression analysis (p < 0.05 and |logFC (fold change)| ≥ 1) and a functional enrichment analysis (p < 0.05) were performed. Based on this, a protein–protein interaction (PPI) network was constructed by Cytoscape. RA-related genes were extracted from DisGeNET database, and an overlap between periimplantitis-related genes and these RA-related genes was examined to identify potential cross-talk genes. Gene expression was merged between two datasets, and feature selection was performed by Recursive Feature Elimination (RFE) algorithm. For the feature selection cross-talk genes, support vector machine (SVM) models were constructed. The expression of these feature genes was determined from GSE93272 for RA. Finally, a network including cross-talk genes, related pathways, and transcription factors was constructed. Results: Periimplantitis datasets included 138 common differentially expressed genes (DEGs) including 101 up- and 37 downregulated DEGs. The PPI interwork of periimplantitis comprised 1,818 nodes and 2,517 edges. The RFE method selected six features, i.e., MERTK, CD14, MAPT, CCR1, C3AR1, and FCGR2B, which had the highest prediction. Out of these feature genes, CD14 and FCGR2B were most highly expressed in periimplantitis and RA. The final activated pathway–gene network contained 181 nodes and 360 edges. Nuclear factor (NF) kappa B signaling pathway and osteoclast differentiation were identified as potentially relevant pathways. Conclusions: This current study revealed FCGR2B and CD14 as the most relevant potential cross-talk genes between RA and periimplantitis, which suggests a similarity between RA and periimplantitis and can serve as a theoretical basis for future research

Tooth-implant connection with fixed partial dentures in partially edentulous arches. A retrospective cohort study over an 11.8 year observation period

The fixed tooth-implant connection remains a controversial issue. This private practice-based retrospective study aimed to evaluate the clinical outcomes of a contemporary fixed partial denture (FPD) design for connecting natural teeth and implants (TI-F

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis 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. Methods: Two 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. Results: We 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. Conclusion: This 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

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis 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. Methods: Two 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. Results: We 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. Conclusion: This 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

Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCCrelated genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumorinfiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area

Efficacy of Alveolar Ridge Preservation in Periodontally Compromised Molar Extraction Sites: A Systematic Review and Meta-Analysis

Aim: To investigate the efficacy of alveolar ridge preservation (ARP) in periodontally compromised molar extraction sites. Methods: An electronic search was performed on 10th November 2023 across five databases, seeking randomised/non-randomised controlled trials (RCTs/NCTs) that included a minimum follow-up duration of four months. The RoB2 and Robins-I tools assessed the risk of bias for the included studies. Data on alveolar ridge dimensional and volumetric changes, keratinized mucosal width, and need for additional bone augmentation for implant placement were collected. Subsequently, a meta-analysis was carried out to derive the pooled estimates. Results: Six studies were incorporated in the present review, and a total of 135 molar extraction sockets in 130 subjects were included in the meta-analysis. ARP was undertaken in 68 sites, and 67 sites healed spontaneously. The follow-up time ranged from 4 to 6 months. The meta-analysis of both RCTs and NCTs showed significant differences in mid-buccal ridge width changes at 1 mm level below ridge crest with a mean difference (MD) of 3.80 (95% CI: 1.67–5.94), mid-buccal ridge height changes (MD: 2.18; 95% CI: 1.25–3.12) and volumetric changes (MD: 263.59; 95% CI: 138.44–388.74) in favour of ARP, while the certainty of evidence is graded low to very low. Moreover, ARP appeared to reduce the need for additional sinus and bone augmentation procedures at implant placement with low certainty of evidence. Conclusions: Within the limitations of this study, alveolar ridge preservation in periodontally compromised extraction sites may, to some extent, preserve the ridge vertically and horizontally with reference to spontaneous healing. However, it could not eliminate the need for additional augmentation for implant placement. Further, longitudinal studies with large sample sizes and refined protocols are needed

In Vitro and Ex Vivo Kinetic Release Profile of Growth Factors and Cytokines from Leucocyte- and Platelet-Rich Fibrin (L-PRF) Preparations

L-PRF is an autologous blood-derived biomaterial (ABDB) capable of releasing biologically active agents to promote healing. Little is known about its release profile of growth factors (GFs), cytokines, and MMPs. This study reported the in vitro and ex vivo release kinetics of GFs, cytokines, and MMPs from L-PRF at 6, 24, 72, and 168 h. The in vitro release rates of PDGF, TGF-&beta;1, EGF, FGF-2, VEGF, and MMPs decreased over time with different rates, while those of IL-1&beta;, IL-6, TNF-&alpha;, IL-8, and IL-10 were low at 6 h and then increased rapidly for up to 24 h and subsequently decreased. Of note, the release rates of the GFs followed first-order kinetics both in vitro and ex vivo. Higher rates of release were found ex vivo, suggesting that significant amounts of GFs were produced by the local cells within the wound. In addition, the half-life times of GFs locally produced in the wound, including PDGF-AA, PDGF-AB/BB, and VEGF, were significantly extended (p &lt; 0.05). This work demonstrates that L-PRF can sustain the release of GFs and cytokines for up to 7 days, and it shows that the former can activate cells to produce additional mediators and amplify the communication network for optimizing the wound environment, thereby enhancing healing