ABSTRACT
Objective: To explore the effect of host regulation (HMT) therapy on periodontal disease and construct prognosis prediction model. Methods: The clinical data of 106 patients who received HMT treatment in our hospital from January 2020 to January 2023 were retrospectively analyzed. The changes of plaque index (PLI), periodontal pocket depth (PD), tooth looseness (TM), attachment loss (AL) and gingival bleeding index (BI) were observed before and after treatment. According to the clinical efficacy of patients after treatment, they were divided into effective group and ineffective group. Multivariate Logistic regression model was used to analyze the relevant factors affecting the prognosis of patients., and a predictive model of the factors affecting the prognosis of periodontal disease was constructed to predict the diagnostic value of the prognostic outcome of periodontal disease using the relative operating characteristic (ROC) curve. Results: After HMT treatment, PLI, PD, AL and BI were significantly reduced in all patients, while TM(grade II and III) patients were decreased (P < .05). After 4 weeks of treatment, a total of 74 patients were included in the effective group. Univariate and multivariate analysis showed that oral hygiene, early use of broad-spectrum antibiotics, periodontal bone loss shape, gingival flap thickness and alveolar bone resorption type were independent factors influencing the prognosis of HMT (P < .001). ROC results showed that the area under the curve (AUC), sensitivity and specificity of the above independent influencing factors combined to predict the clinical efficacy of HMT in patients with periodontal disease were higher. Conclusion: HMT has a good clinical effect in the treatment of periodontal disease, but there are still cases of poor prognostic effect. The combination of independent factors affecting the prognosis of HMT has a good predictive value for the clinical efficacy of patients with periodontal disease after HMT treatment.
ABSTRACT
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.
ABSTRACT
AIM: To study the influence and mechanisms of dexamethasone on mesenteric lymph node of rats with severe acute pancreatitis (SAP). METHODS: The SAP rats were assigned to model, treated or sham-operated groups. The mortality, pathological changes of mesenteric lymph nodes, expression levels of NF-kappa B, P-selectin, Bax, Bcl-2 and caspase-3 protein and changes in apoptotic indexes in lymph nodes were observed at 3, 6 and 12 h after operation. The blood levels of endotoxin, superoxide dismutase (SOD), malondialdehyde (MDA), and endothelin-1 (ET-1) in blood were determined. RESULTS: SOD content, expression of Bax protein and apoptotic index were significantly higher in the treated group than in the model group at different time points (P < 0.05 or P < 0.01). Other blood-detecting indexes and histopathological scores of mesenteric lymph nodes were lower in the treated than in the model group (P < 0.05, P < 0.01 or P < 0.01). NF-kappa B protein expression was negative in all groups. Comparing P-selectin and caspase-3 expression levels among all three groups, there was no marked difference between the model and treated group. CONCLUSION: Dexamethasone can protect mesenteric lymph nodes. The mechanism may be by reducing the content of inflammatory mediators in the blood and inducing lymphocyte apoptosis.
