Objective:To develop and verify a nomogram risk prediction model for stroke-associated pneumonia (SAP) based on the classification of the Kubota water swallowing test (WST) combined with multiple clinical parameters.Methods:Patients with acute stroke admitted to the emergency department, Beijing Jingmei Group General Hospital from August 2015 to March 2024 were retrospectively included. According to whether SAP occurred, the patients were divided into the SAP group and the non-SAP group. Multivariate logistic regression analysis was applied to screen the independent predictors of SAP, and a nomogram prediction model was developed accordingly. The predictive performance of the model was evaluated by the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis. Results:A total of 298 patients with acute stroke were included, including 180 males (60.4%), aged (63.8±11.4) years. The time from onset to WST was (81±22) h. The WST classification assessment shows that grade Ⅰ-Ⅱ accounts for 60.4%, and grade Ⅲ-Ⅴ accounts for 39.6%. A total of 78 cases (26.2%) had SAP. Multivariate logistic regression analysis showed that age (odds ratio [ OR] 1.04, 95% confidence interval [ CI] 1.01-1.07; P=0.014), the baseline National Institutes of Health Stroke Scale score ( OR 1.11, 95% CI 1.05-1.18; P=0.002), the WST classification (Grade Ⅲ-Ⅴ vs. grade Ⅰ-Ⅱ: OR 2.05, 95% CI 1.30-3.22; P=0.001), neutrophil/lymphocyte ratio ( OR 1.26, 95% CI 1.11-1.43; P=0.005) and indwelling gastric tube ( OR 1.88, 95% CI 1.20-2.95; P=0.010) are independent predictors of SAP. ROC curve analysis showed that the area under the curve of SAP predicted by the nomogram model based on the above risk factors was 0.801 (95% CI 0.784-0.898; P<0.001). The model showed good calibration, and the decision curve analysis showed that the clinical net benefit was relatively high in the threshold range of 13% to 82%. Conclusion:The nomogram model based on WST has a relatively high predictive value for SAP.

Currently, cell transplantation in combination with scaffold materials are one of the main strategies in periodontal bone tissue engineering. In periodontal bone tissues, the stiffness and spatial structure of tissues such as alveolar bone and cementum differ, and the difference in mechanical properties of scaffolds also has disparate effects on the proliferation and differentiation of stem cells. Accumulating evidence shows that mechanical stimulating factors such as matrix stiffness and scaffold topography modulate biological behaviors of various seeding cells, including adipose-derived stem cells and periodontal ligament stem cells. A hard matrix can promote cytoskeletal stretching of stem cells, leading to nuclear translocation of Yes-associated protein (YAP) and promoting osteogenic differentiation by upregulating alkaline phosphatase (ALP) and osteocalcin (OCN) via the Wnt/β-catenin pathway. The topologic structure of scaffolds can affect cell adhesion and cytoskeletal remodeling, increase the hardness of cells and promote the osteogenic differentiation of stem cells. In this paper, the effects of mechanical stimulation on the differentiation of stem cells in periodontal bone tissue engineering are reviewed.