Reconstructing three-dimensional (3D) models from two-dimensional (2D) images is necessary for preoperative planning and the customization of joint prostheses. However, the traditional statistical modeling reconstruction shows a low accuracy due to limited 3D characteristics and information loss. In this study, we proposed a new method to reconstruct the 3D models of femoral images by combining a statistical shape model with Laplacian surface deformation, which greatly improved the accuracy of the reconstruction. In this method, a Laplace operator was introduced to represent the 3D model derived from the statistical shape model. By coordinate transformations in the Laplacian system, novel skeletal features were established and the model was accurately aligned with its 2D image. Finally, 50 femoral models were utilized to verify the effectiveness of this method. The results indicated that the precision of the method was improved by 16.8%–25.9% compared with the traditional statistical shape model reconstruction. Therefore, the method we proposed allows a more accurate 3D bone reconstruction, which facilitates the development of personalized prosthesis design, precise positioning, and quick biomechanical analysis.
Objective To explore the clinical and inflammatory characteristics and risk factors of severe asthma to improve clinicians' awareness of the disease. Methods The general information of patients with asthma who visited the Department of Respiratory Medicine, the First Hospital of Shanxi Medical University from May 2018 to May 2021, as well as the diagnosis and treatment of asthma, personal history, comorbidities, auxiliary examination, asthma control test (ACT) score were collected. A total of 127 patients were included, including 40 in the severe asthma group and 87 in the mild-to-moderate asthma group. Chi-square test, independent sample t test and logistic regression were used to analyze the clinical characteristics, inflammatory markers and risk factors of severe asthma. Results Compared with the patients with mild to moderate asthma, the patients with severe asthma were more older (51.0±12.0 years vs 40.7±12.8 years, P<0.05), had more smokers (32.5% vs. 14.9%, P<0.05), and more males (67.5% vs. 40.2%, P<0.05). The patients with severe asthma got poor FEV1%pred [(56.1±23.8)% vs. (93.2±18.0)%, P<0.05] and FEV1/FVC [(56.7±13.2)% vs. (75.8±9.0)%, P<0.05)], and more exacerbations in the previous year (2.7±3.1 vs. 0.1±0.4, P<0.05), lower ACT score (14.4±3.7 vs. 18.0±5.0, P<0.05), and higher blood and induced sputum eosinophil counts [(0.54±0.44)×109/L vs. (0.27±0.32)×109/L, P<0.05; (25.9±24.2)% vs. (9.8±17.5)%, P<0.05]. There was no significant difference in the proportion of neutrophils in the induced sputum or FeNO between the two groups (P>0.05). Analysis of related risk factors showed that smoking (OR=2.740, 95%CI 1.053 - 7.130), combined with allergic rhinitis (OR=14.388, 95%CI 1.486 - 139.296) and gastroesophageal reflux (OR=2.514, 95%CI 1.105 - 5.724) were risk factors for severe asthma. Conclusions Compared with patients with mild to moderate asthma, patients with severe asthma are characterized by poor lung function, more exacerbations, and a dominant eosinophil inflammatory phenotype, which is still poorly controlled even with higher level of treatment. Risk factors include smoking, allergic rhinitis, and gastroesophageal reflux, etc.