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.

Objective:To investigate the value of a multi-parameter MRI nomogram model in evaluating the recurrence risk of hormone receptor (HR)-positive breast cancer.Methods:This study was a retrospective cross-sectional study. A retrospective analysis was conducted on the clinicopathological data (age, menopausal status, axillary lymph node metastasis, etc.) and imaging data of 220 patients with HR-positive breast cancer who underwent breast MRI examination and were pathologically confirmed at the Second Affiliated Hospital of Nantong University from January 2018 to December 2023. All patients underwent preoperative MRI examinations. Their MRI features were analyzed, and the maximum diameter of the lesion and the apparent diffusion coefficient (ADC) value were measured. Finally, the clinical treatment score (CTS5 score) after 5 years was calculated, and all patients were divided into a low recurrence risk (CTS5 score 3.13 points) and a medium to high recurrence risk (CTS5 score≥3.13 points) group. The patients were followed up through the electronic medical record system or by phone until December 31, 2024 to determine recurrence status. The patients were divided into the recurrence group and the non-recurrence group. The differences in clinicopathological data, MRI features and CTS5 scores between the recurrence group and the non-recurrence group were compared using independent sample t-tests, Mann-Whitney U tests or χ2 tests. Indicators with P0.05 in the univariate analysis were included in the multivariate logistic regression to screen the independent risk factors for predicting the recurrence of HR receptor-positive breast cancer, and a nomogram was constructed to establish the nomogram model. The receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate the efficacy of the nomogram model in predicting the postoperative recurrence risk of patients with HR-positive breast cancer. The variance inflation factor (VIF) was used to evaluate the multicollinearity among independent variables. Calibration curves and decision curve analysis (DCA) were used to assess the fit and net clinical benefit of the nomogram model. Results:Among 220 patients with HR-positive breast cancer, 196 cases were in the non-recurrence group and 24 cases were in the recurrence group. There were statistically significant differences in the maximum diameter of the lesion, axillary lymph node metastasis, ADC value, CTS5 grouping, and CTS5 score between the recurrence group and the non-recurrence group ( P0.05). Multivariate logistic regression analysis showed that the maximum diameter of the lesion ( OR=1.110, 95% CI 1.169-1.503, P0.001), ADC value ( OR=0.993, 95% CI 0.993?0.989, P0.001), and axillary lymph node metastasis ( OR=8.842; 95% CI 2.120?36.884, P=0.003) were independent factors influencing postoperative recurrence in patients with HR-positive breast cancer, and a nomogram model was constructed based on this. VIF analysis showed that no significant multicollinearity was detected among the variables (VIF5). The AUC value of the nomogram model for predicting postoperative recurrence in patients with HR-positive breast cancer was 0.868 (95% CI 0.794-0.942), the sensitivity was 0.875, and the specificity was 0.781. The calibration curve showed that the prediction curve of this model for predicting postoperative recurrence in HR-positive breast cancer patients was basically consistent with the ideal curve trend. DCA showed that this model had a relatively high clinical benefit within the threshold probability range of 0.01% to 90.00%. Conclusion:The nomogram constructed based on multi-parameter MRI features can predict the postoperative recurrence risk of HR-positive breast cancer patients, with good consistency and predictive ability.

Objective:To investigate the value of a multi-parameter MRI nomogram model in evaluating the recurrence risk of hormone receptor (HR)-positive breast cancer.Methods:This study was a retrospective cross-sectional study. A retrospective analysis was conducted on the clinicopathological data (age, menopausal status, axillary lymph node metastasis, etc.) and imaging data of 220 patients with HR-positive breast cancer who underwent breast MRI examination and were pathologically confirmed at the Second Affiliated Hospital of Nantong University from January 2018 to December 2023. All patients underwent preoperative MRI examinations. Their MRI features were analyzed, and the maximum diameter of the lesion and the apparent diffusion coefficient (ADC) value were measured. Finally, the clinical treatment score (CTS5 score) after 5 years was calculated, and all patients were divided into a low recurrence risk (CTS5 score 3.13 points) and a medium to high recurrence risk (CTS5 score≥3.13 points) group. The patients were followed up through the electronic medical record system or by phone until December 31, 2024 to determine recurrence status. The patients were divided into the recurrence group and the non-recurrence group. The differences in clinicopathological data, MRI features and CTS5 scores between the recurrence group and the non-recurrence group were compared using independent sample t-tests, Mann-Whitney U tests or χ2 tests. Indicators with P0.05 in the univariate analysis were included in the multivariate logistic regression to screen the independent risk factors for predicting the recurrence of HR receptor-positive breast cancer, and a nomogram was constructed to establish the nomogram model. The receiver operating characteristic curves and the area under the curve (AUC) were used to evaluate the efficacy of the nomogram model in predicting the postoperative recurrence risk of patients with HR-positive breast cancer. The variance inflation factor (VIF) was used to evaluate the multicollinearity among independent variables. Calibration curves and decision curve analysis (DCA) were used to assess the fit and net clinical benefit of the nomogram model. Results:Among 220 patients with HR-positive breast cancer, 196 cases were in the non-recurrence group and 24 cases were in the recurrence group. There were statistically significant differences in the maximum diameter of the lesion, axillary lymph node metastasis, ADC value, CTS5 grouping, and CTS5 score between the recurrence group and the non-recurrence group ( P0.05). Multivariate logistic regression analysis showed that the maximum diameter of the lesion ( OR=1.110, 95% CI 1.169-1.503, P0.001), ADC value ( OR=0.993, 95% CI 0.993?0.989, P0.001), and axillary lymph node metastasis ( OR=8.842; 95% CI 2.120?36.884, P=0.003) were independent factors influencing postoperative recurrence in patients with HR-positive breast cancer, and a nomogram model was constructed based on this. VIF analysis showed that no significant multicollinearity was detected among the variables (VIF5). The AUC value of the nomogram model for predicting postoperative recurrence in patients with HR-positive breast cancer was 0.868 (95% CI 0.794-0.942), the sensitivity was 0.875, and the specificity was 0.781. The calibration curve showed that the prediction curve of this model for predicting postoperative recurrence in HR-positive breast cancer patients was basically consistent with the ideal curve trend. DCA showed that this model had a relatively high clinical benefit within the threshold probability range of 0.01% to 90.00%. Conclusion:The nomogram constructed based on multi-parameter MRI features can predict the postoperative recurrence risk of HR-positive breast cancer patients, with good consistency and predictive ability.

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.