Introduction: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible.

Background:Several studies suggested that celecoxib interferes with bone healing while others contradict these findings. This study was conducted to investigate the effects of celecoxib on bone healing in rats femur mold with a dose based on body surface area conversion.Materials and Methods:72 adult female Sprague Dawley rats were randomly divided into three groups after the internal fixation operation of nondisplaced transverse mid diaphyseal fractures of the right femurs. Each group was treated with 1% methylcellulose, celecoxib (21 mg/kg/d) for 1 week, or celecoxib (21 mg/kg/d) for 4 weeks after surgeries respectively. Bone healing scores and callus formation were evaluated by radiographs at 3, 4, 6 weeks after surgeries. Half of these rats were sacrificed for histological analysis at 4 weeks after surgery. The remaining fractured femurs were evaluated by biomechanical tests at 6 weeks after surgery.Results:The mean radiographic scores for fracture healing of both short and long term groups were lower than that of the control group and the differences among the three groups were statistically significant (P < 0.05) at 3, 4, 6 weeks after surgery. The mean bone trabecula density of both groups was smaller than that of the control group and the differences were also statistically significant (P < 0.05) at 4 week. The maximum load, total energy and stiffness in both the short term and long term groups were significantly decreased compared with those in the control group (P < 0.05) at 6 week.Conclusion:Both short term and long term sustained use of celecoxib in rat models has significantly inhibitory effects on rat fracture healing.

Objective The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). Methods A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t -test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. Results A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). Conclusion The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.

Background: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone and the sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS to help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible.Methods: We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into the training and validation cohorts. The training cohort included 258 patients with AS and 247 non-AS patients. The validation cohort included 90 patients with AS and 113 non-AS patients. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model，In addition, we used the prediction model on the validation cohort.Results: Seven factors—ESR, RBC, MPV, ALB, AST, and Cr—were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and AUC value of this nomogram were 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort were 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%.Conclusion: Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.