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.

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 animal models and 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.

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.

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.

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.

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.

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.

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.