A regulatory role of Piezo1 in apoptosis of periodontal tissue and periodontal ligament fibroblasts during orthodontic tooth movement.

Investigation on the effect of Piezo1 on periodontal tissue and periodontal ligament fibroblasts (PDLFs) under mechanical stress and the underlying mechanism. The orthodontic tooth movement rat model was established via an orthodontic spiral tension spring. PDLFs were cultured and subjected to 2.0 g/cm2 static compressive loading. Blocked the Piezo1 via Piezo1 inhibitor, GsMTx4. TUNEL staining and flow cytometry determined the apoptosis rate of periodontal tissue and PDLFs in rats. Expression of Piezo1, p-p38 and ERK1/2 was analysed by immunofluorescence assay and western blotting. Piezo1 inhibitor GsMTx4 relieved the increased expression of Piezo1, ERK1/2 and p-p38, and alleviated apoptosis in periodontal tissue and PDLFs under compressive loading. Piezo1 inhibition can alleviate force-induced apoptosis and damage in rats' periodontal tissue and PDLFs, and regulate the p38/ERK1/2 signalling pathway.

Establishment of models to predict factors influencing periodontitis in patients with type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with periodontitis. Currently, there are few studies proposing predictive models for periodontitis in patients with T2DM. AIM: To determine the factors influencing periodontitis in patients with T2DM by constructing logistic regression and random forest models. METHODS: In this a retrospective study, 300 patients with T2DM who were hospitalized at the First People's Hospital of Wenling from January 2022 to June 2022 were selected for inclusion, and their data were collected from hospital records. We used logistic regression to analyze factors associated with periodontitis in patients with T2DM, and random forest and logistic regression prediction models were established. The prediction efficiency of the models was compared using the area under the receiver operating characteristic curve (AUC). RESULTS: Of 300 patients with T2DM, 224 had periodontitis, with an incidence of 74.67%. Logistic regression analysis showed that age [odds ratio (OR) = 1.047, 95% confidence interval (CI): 1.017-1.078], teeth brushing frequency (OR = 4.303, 95%CI: 2.154-8.599), education level (OR = 0.528, 95%CI: 0.348-0.800), glycosylated hemoglobin (HbA1c) (OR = 2.545, 95%CI: 1.770-3.661), total cholesterol (TC) (OR = 2.872, 95%CI: 1.725-4.781), and triglyceride (TG) (OR = 3.306, 95%CI: 1.019-10.723) influenced the occurrence of periodontitis (P < 0.05). The random forest model showed that the most influential variable was HbA1c followed by age, TC, TG, education level, brushing frequency, and sex. Comparison of the prediction effects of the two models showed that in the training dataset, the AUC of the random forest model was higher than that of the logistic regression model (AUC = 1.000 vs AUC = 0.851; P < 0.05). In the validation dataset, there was no significant difference in AUC between the random forest and logistic regression models (AUC = 0.946 vs AUC = 0.915; P > 0.05). CONCLUSION: Both random forest and logistic regression models have good predictive value and can accurately predict the risk of periodontitis in patients with T2DM.

[Supernumerary teeth located in molar region: three cases report].

Supernumerary teeth is one of the teeth dysplasia that the number of teeth exceeded normal. Most of supernumerary teeth reported were located in anterior teeth region, but rare cases were reported in molar region. This paper reported three cases that supernumerary teeth located in molar region.