To explore a novel machine learning model to evaluate the vertebral fracture risk using Decision Tree model and train the model by Bone Mineral Density (BMD) of different compartments of vertebral body. We collected a Computed Tomography image dataset, including 10 patients with osteoporotic fracture and 10 patients without osteoporotic fracture. 40 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients with osteoporotic fracture in the CT database and 53 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients without osteoporotic fracture in the CT database. Based on the biomechanical properties, 93 vertebral bodies were further segmented into 11 compartments: eight trabecular bone, cortical shell, top and bottom endplate. BMD of these 11 compartments was calculated based on the HU value in CT images. Decision tree model was used to build fracture prediction model, and Support Vector Machine was built as a compared model. All BMD data was shuffled to a random order. 70% of data was used as training data, and 30% left was used as test data. Then, training prediction accuracy and testing prediction accuracy were calculated separately in the two models. The training accuracy of Decision Tree model is 100% and testing accuracy is 92.14% after trained by BMD data of 11 compartments of the vertebral body. The type I error is 7.14% and type II error is 0%. The training accuracy of Support Vector Machine model is 100% and the testing accuracy is 78.57%. The type I error is 17.86% and type II error is 3.57%. The performance of vertebral body fracture prediction using Decision Tree is significantly higher than using Support Vector Machine. The Decision Tree model is a potential risk assessment method for clinical application. The pilot evidence showed that Decision Tree prediction model overcomes the overfitting drawback of Support Vector Machine Model. However, larger dataset and cohort study should be conducted for further evidence.
This study aimed to explore the role of miR-320a in the pathogenesis of osteoarthritis (OA). Human cartilage cells (C28/I2) were transfected with miR-320a or antisense oligonucleotides (ASO)-miR-320a, and treated with IL-1β. Subsequently the expression of collagen type II alpha 1 (Col2α1) and aggrecan (ACAN), and the concentrations of sulfated glycosaminoglycans (sGAG) and matrix metallopeptidase 13 (MMP-13), were assessed. Luciferase reporter assay, qRT-PCR, and Western blot were performed to explore whether pre-B-cell leukemia Homeobox 3 (PBX3) was a target of miR-320a. Furthermore, cells were co-transfected with miR-320a and PBX3 expressing vector, or cells were transfected with miR-320a and treated with a nuclear factor kappa B (NF-κB) antagonist MG132. The changes in Col2α1 and ACAN expression, and in sGAG and MMP-13 concentrations, were measured again. Statistical comparisons were made between two groups by using the two-tailed paired Objectives
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