A hybrid unsupervised clustering method for predicting the risk of dental implant loss.

OBJECTIVES: The aim of this study was to predict the risk of dental implant loss by clustering features associated with implant survival rates. MATERIALS AND METHODS: Multiple clinical features from 8513 patients who underwent single implant placement were retrospectively analysed. A hybrid method integrating unsupervised learning algorithms with survival analysis was employed for data mining. Two-step cluster, univariate Cox regression, and Kaplan‒Meier survival analyses were performed to identify the clustering features associated with implant survival rates. To predict the risk of dental implant loss, nomograms were constructed on the basis of time-stratified multivariate Cox regression. RESULTS: Six clusters with distinct features and prognoses were identified using two-step cluster analysis and Kaplan‒Meier survival analysis. Compared with the other clusters, only one cluster presented significantly lower implant survival rates, and six specific clustering features within this cluster were identified as high-risk factors, including age, smoking history, implant diameter, implant length, implant position, and surgical procedure. Nomograms were created to assess the impact of the six high-risk factors on implant loss for three periods: 1) 0-120 days, 2) 120-310 days, and 3) more than 310 days after implant placement. The concordance indices of the models were 0.642, 0.781, and 0.715, respectively. CONCLUSIONS: The hybrid unsupervised clustering method, which clusters and identifies high-risk clinical features associated with implant loss without relying on predefined labels or target variables, represents an effective approach for developing a visual model for predicting implant prognosis. However, further validation with a multimodal, multicentre, prospective cohort is needed. CLINICAL SIGNIFICANCE: Visual prognosis prediction utilizing this nomogram that predicts the risk of implant loss on the basis of clustering features can assist dentists in preoperative assessments and clinical decision-making, potentially improving dental implant prognosis.

Integrating network pharmacology and experimental evaluation to explore the complementary therapeutic effect and mechanism of melatonin in periodontitis.

Objective: To explore the potential targets for melatonin in the treatment of periodontitis through network pharmacologic analysis and experimental validation via in vivo animal models and in vitro cellular experiments. Materials and methods: In this study, we first screened melatonin targets from Pharm Mapper for putative targets, Drug Bank, and TCMSP databases for known targets. Then, disease database was searched and screened for differential expressed genes associated with periodontitis. The intersection of disease and melatonin-related genes yielded potential target genes of melatonin treatment for periodontitis. These target genes were further investigated by protein-protein interaction network and GO/KEGG enrichment analysis. In addition, the interactions between melatonin and key target genes were interrogated by molecular docking simulations. Then, we performed animal studies to validate the therapeutic effect of melatonin by injecting melatonin into the peritoneal cavity of ligation-induced periodontitis (LIP) mice. The effects of melatonin on the predicted target proteins were also analyzed using Western blot and immunofluorescence techniques. Finally, we constructed an in vitro cellular model and validated the direct effect of melatonin on the predicted targets by using qPCR. Results: We identified 8 potential target genes by network pharmacology analysis. Enrichment analysis suggests that melatonin may treat periodontitis by inhibiting the expression of three potential targets (MPO, MMP8, and MMP9). Molecular docking results showed that melatonin could effectively bind to MMP8 and MMP9. Subsequently, melatonin was further validated in a mouse LIP model to inhibit the expression of MPO, MMP8, and MMP9 in the periodontal tissue. Finally, we verified the direct effect of melatonin on the mRNA expression of MPO, MMP8, and MMP9 in an in vitro cellular model. Conclusions: Through a combination of network pharmacology and experimental validation, this study provides a more comprehensive understanding of the mechanism of melatonin to treat periodontitis. Our study suggests that MPO, MMP8, and MMP9 as key target genes of melatonin to treat periodontitis. These findings present a more comprehensive basis for further investigation into the mechanisms of pharmacological treatment of periodontitis by melatonin.

Tobacco toxins trigger bone marrow mesenchymal stem cells aging by inhibiting mitophagy.

Smoking disrupts bone homeostasis and serves as an independent risk factor for the development and progression of osteoporosis. Tobacco toxins inhibit the proliferation and osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), promote BMSCs aging and exhaustion, but the specific mechanisms are not yet fully understood. Herein, we successfully established a smoking-related osteoporosis (SROP) model in rats and mice through intraperitoneal injection of cigarette smoke extract (CSE), which significantly reduced bone density and induced aging and inhibited osteogenic differentiation of BMSCs both in vivo and in vitro. Bioinformatics analysis and in vitro experiments confirmed that CSE disrupts mitochondrial homeostasis through oxidative stress and inhibition of mitophagy. Furthermore, we discovered that CSE induced BMSCs aging by upregulating phosphorylated AKT, which in turn inhibited the expression of FOXO3a and the Pink1/Parkin pathway, leading to the suppression of mitophagy and the accumulation of damaged mitochondria. MitoQ, a mitochondrial-targeted antioxidant and mitophagy agonist, was effective in reducing CSE-induced mitochondrial oxidative stress, promoting mitophagy, significantly downregulating the expression of aging markers in BMSCs, restoring osteogenic differentiation, and alleviating bone loss and autophagy levels in CSE-exposed mice. In summary, our results suggest that BMSCs aging caused by the inhibition of mitophagy through the AKT/FOXO3a/Pink1/Parkin axis is a key mechanism in smoking-related osteoporosis.

Computational analysis of curcumin-mediated alleviation of inflammation in periodontitis patients with experimental validation in mice.

AIM: Using network pharmacology and experimental validation to explore the therapeutic efficacy and mechanism of curcumin (Cur) in periodontitis treatment. MATERIALS AND METHODS: Network pharmacology was utilized to predict target gene interactions of Cur-Periodontitis. Molecular docking was used to investigate the binding affinity of Cur for the predicted targets. A mouse model with ligature-induced periodontitis (LIP) was used to verify the therapeutic effect of Cur. Microcomputed tomography (micro-CT) was used to evaluate alveolar bone resorption, while western blotting, haematoxylin-eosin staining and immunohistochemistry were used to analyse the change in immunopathology. SYTOX Green staining was used to assess the in vitro effect of Cur in a mouse bone marrow-isolated neutrophil model exposed to lipopolysaccharide. RESULTS: Network pharmacology identified 114 potential target genes. Enrichment analysis showed that Cur can modulate the production of neutrophil extracellular traps (NETs). Molecular docking experiments suggested that Cur effectively binds to neutrophil elastase (ELANE), peptidylarginine deiminase 4 (PAD4) and cathepsin G, three enzymes involved in NETs. In LIP mice, Cur alleviated alveolar bone resorption and reduced the expression of ELANE and PAD4 in a time-dependent but dose-independent manner. Cur can directly inhibit NET formation in the cell model. CONCLUSIONS: Our research suggested that Cur may alleviate experimental periodontitis by inhibiting NET formation.

Quercetin promotes autophagy to alleviate cigarette smoke-related periodontitis.

BACKGROUND AND OBJECTIVES: Cigarette smoking has been reported as an independent risk factor for periodontitis. Tobacco toxins affect periodontal tissue not only locally but also systemically, leading to the deterioration and recurrence of periodontitis. However, the mechanism of cigarette smoke-related periodontitis (CSRP) is unclear and thus lacks targeted treatment strategies. Quercetin, a plant-derived polyphenolic flavonoid, has been reported to have therapeutic effects on periodontitis due to its documented antioxidant activity. This study aimed to evaluate the effects of quercetin on CSRP and elucidated the underlying mechanism. METHODS: The cigarette smoke-related ligature-induced periodontitis mouse model was established by intraperitoneal injection of cigarette smoke extract (CSE) and silk ligation of bilateral maxillary second molars. Quercetin was adopted by gavage as a therapeutic strategy. Micro-computed tomography was used to evaluate the alveolar bone resorption. Immunohistochemistry detected the oxidative stress and autophagy markers in vivo. Cell viability was determined by Cell Counting Kit-8, and oxidative stress levels were tested by 2,7-dichlorodihydrofluorescein diacetate probe and lipid peroxidation malondialdehyde assay kit. Alkaline phosphatase and alizarin red staining were used to determine osteogenic differentiation. Network pharmacology analysis, molecular docking, and western blot were utilized to elucidate the underlying molecular mechanism. RESULTS: Alveolar bone resorption was exacerbated and oxidative stress products were accumulated during CSE exposure in vivo. Oxidative stress damage induced by CSE caused inhibition of osteogenic differentiation in vitro. Quercetin effectively protected the osteogenic differentiation of human periodontal ligament cells (hPDLCs) and periodontal tissue by upregulating the expression of Beclin-1 thus to promote autophagy and reduce oxidative stress damage. CONCLUSION: Our results established a role of oxidative stress damage and autophagy dysfunction in the mechanism of CSE-induced destruction of periodontal tissue and hPDLCs, and provided a potential application value of quercetin to ameliorate CSRP.

Electroacupuncture regulates macrophage, neutrophil, and oral microbiota to alleviate alveolar bone loss and inflammation in experimental ligature-induced periodontitis.

AIM: Electroacupuncture (EA) regulates distant body physiology through somatic sensory autonomic reflexes, balances the microbiome, and can promote the release of immune cells into bloodstream, thereby inhibiting severe systemic inflammation. This makes it possible to use EA as an integrated treatment for periodontitis. MATERIALS AND METHODS: In this study, EA was applied to the ST36 acupoints in a ligature-induced periodontitis (LIP) mouse model. Then the effects of EA on periodontal myeloid cells, cytokines, and the microbiome were comprehensively analysed using flow cytometry, quantitative Polymerase Chain Reaction (PCR), and 16 S sequencing. RESULTS: Results demonstrated that EA could significantly relieve periodontal bone resorption. EA also suppressed the infiltration of macrophages and neutrophils, reduced gene expression of the pro-inflammatory cytokines IL-1ß, IL-6, IL-17 and TNF-α, and increased expression of the anti-inflammatory factors IL-4 and IL-10 in periodontal tissues. Moreover, composition of the periodontal microbiome was regulated by EA, finding that complex of microbiota, including supragingival Veillonella, subgingival Streptococcus, and subgingival Erysipelatoclostridium, were significantly reduced. Meanwhile, nitrate and nitrate-related activities of subgingival microbiota were reversed. Network analysis revealed close relationships among Veillonella, Streptococcus, and Bacteroides. CONCLUSIONS: Our study indicates that EA can effectively alleviate inflammation and bone resorption in LIP mice, potentially via the regulation of myeloid cells, cytokines, and periodontal microbiome.

Clinical significance and prognostic role of an immune-related gene signature in gastric adenocarcinoma.

Limited progress has been made in the treatment of gastric adenocarcinoma (GAC) in recent years, but the potential of immunotherapy in GAC is worthy of consideration. The purpose of this study was to develop a reliable, personalized signature based on immune genes to predict the prognosis of GAC. Here, we identified two groups of patients with significantly different prognoses by performing unsupervised clustering analysis of The Cancer Genome Atlas (TCGA) database based on 881 immune genes. The immune signature was constructed with a training set composed of 350 GAC samples from the TCGA and subsequently validated with 431 samples from GSE84437, 432 samples from GSE26253, and 145 GAC samples from real-time quantitative reverse transcription polymerase chain reaction data. This classification system can also be used to predict prognosis in different clinical subgroups. Further analysis suggested that high-risk patients were characterized by low immune scores, distinctive immune cell proportions, different immune checkpoint profiles, and a low tumor mutational burden. Ultimately, the signature was identified as an independent prognostic factor. In general, the signature can accurately predict recurrence and overall survival in patients with GAC and may serve as a powerful prognostic tool to further optimize cancer immunotherapy.

Morphology-Based Deep Learning Approach for Predicting Osteogenic Differentiation.

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.