Change of Femoral Anteversion Angle in Children With Intoeing Gait Measured by Three-Dimensional Computed Tomography Reconstruction: One-Year Follow-Up Study

ObjectiveTo evaluate femoral anteversion angle (FAA) change in children with intoeing gait depending on age, gender, and initial FAA using three-dimensional computed tomography (3D-CT).MethodsThe 3D-CT data acquired between 2006 and 2016 were retrospectively reviewed. Children 4 to 10 years of age with symptomatic intoeing gait with follow-up interval of at least 1 year without active treatment were enrolled. Subjects were divided into three groups based on age: group 1 (≥4 and <6 years), group 2 (≥6 and <8 years), and group 3 (≥8 and <10 years). Initial and follow-up FAAs were measured using 3D-CT. Mean changes in FAAs were calculated and compared.ResultsA total of 200 lower limbs of 100 children (48 males and 52 females, mean age of 6.1±1.6 years) were included. The mean follow-up period was 18.0±5.4 months. Average initial and follow-up FAA in children with intoeing gait was 31.1°±7.8° and 28.9°±8.2°, respectively. The initial FAA of group 1 was largest (33.5°±7.7°). Follow-up FAA of group 1 was significantly reduced to 28.7°±9.2° (p=0.000). FAA changes in groups 1, 2, and 3 were −6.5°±5.8°, −6.4°±5.1°, and −5.3°±4.0°, respectively. These changes of FAA were not significantly (p=0.355) different among the three age groups. However, FAA changes were higher (p=0.012) in females than those in males. In addition, FAA changes showed difference depending on initial FAA. When initial FAA was smaller than 30°, mean FAA change was −5.6°±4.9°. When initial FAA was more than 30°, mean FAA change was −6.8°±5.4° (p=0.019).ConclusionFAA initial in children with intoeing gait was the greatest in age group 1 (4–6 years). This group also showed significant FAA decrease at follow-up. FAA changes were greater when the child was a female, younger, and had greater initial FAA

Cystatin C, a novel indicator of renal function, reflects severity of cerebral microbleeds

Background: Chronic renal insufficiency, diagnosed using creatinine based estimated glomerular filtration rate (GFR) or microalbumiuria, has been associated with the presence of cerebral microbleeds (CMBs). Cystatin C has been shown to be a more sensitive renal indicator than conventional renal markers. Under the assumption that similar pathologic mechanisms of the small vessel exist in the brain and kidney, we hypothesized that the levels of cystatin C may delineate the relationship between CMBs and renal insufficiency by detecting subclinical kidney dysfunction, which may be underestimated by other indicators, and thus reflect the severity of CMBs more accurately. Methods: Data was prospectively collected for 683 patients with ischemic stroke. The severity of CMBs was categorized by the number of lesions. Patients were divided into quartiles of cystatin C, estimated GFR and microalbumin/creatinine ratios. Ordinal logistic regression analysis was used to examine the association of each renal indicator with CMBs. Results: In models including both quartiles of cystatin C and estimated GFR, only cystatin C quartiles were significant (the highest vs. the lowest, adjusted OR, 1.88; 95% CI 1.05-3.38; p = 0.03) in contrast to estimated GFR (the highest vs. the lowest, adjusted OR, 1.28; 95% CI 0.38-4.36; p = 0.70). A model including both quartiles of cystatin C and microalbumin/creatinine ratio also showed that only cystatin C quartiles was associated with CMBs (the highest vs. the lowest, adjusted OR, 2.06; 95% CI 1.07-3.94; p = 0.03). These associations were also observed in the logistic models using log transformed-cystatin C, albumin/creatinine ratio and estimated GFR as continuous variables. Cystatin C was a significant indicator of deep or infratenorial CMBs, but not strictly lobar CMBs. In addition, cystatin C showed the greatest significance in c-statistics for the presence of CMBs (AUC = 0.73 ± 0.03; 95% CI 0.66-0.76; p = 0.02). Conclusion: Cystatin C may be the most sensitive indicator of CMB severity among the renal disease markers.Peer Reviewe

Soil Classification by Machine Learning Using a Tunnel Boring Machine’s Operating Parameters

This study predicted soil classification using data gathered during the operation of an earth-pressure-balance-type tunnel boring machine (TBM). The prediction methodology used machine learning to find relationships between the TBM’s operating parameters which are monitored continuously during excavation, and the engineering characteristics of the ground which are only available from prior geotechnical investigation. Classification criteria were set using the No. 200 sieve pass rate and N-value and employed classification algorithms that used data for six operating parameters (penetration rate, thrust force, cutterhead torque, screw torque, screw revolution speed, and earth pressure). The results of the ensemble model (i.e., AdaBoost, gradient boosting, XG boosting, and Light GBM), decision tree, and SVM model were examined. As a result, the decision tree and AdaBoost models showed accuracy values of 0.759 to 0.879 in the first and second classification steps, but with poor precision and recall values of around 0.6. In contrast, the gradient boosting, XG boosting, Light GBM, and support vector models all showed excellent performance, with accuracy values over 0.90, and strong precision and recall values. Comparing the performance and the speed of learning using the same PC found Light GBM which showed both excellent learning performance and speed to be a suitable model for predicting soil classification using TBM operating data. The classification model developed here is expected to help guide excavation in sections of ground that lack prior geotechnical information

Numerical Analysis of the Contact Behavior of a Polymer-Based Waterproof Membrane for Tunnel Lining

Waterproof membranes have higher initial strength, faster construction, and better waterproofing than conventional sheet membranes. In addition, their polymer constituents have much higher interfacial adhesion and tensile strength than those of conventional materials. However, despite their advantages, waterproof membranes are not widely used in civil construction. This study evaluates the material properties and interface parameters of a waterproof membrane by considering the results of laboratory experiments and numerical analysis. Since the contact behavior of a membrane at its interface with shotcrete is important for understanding the mechanism of the support it offers known as a shotcrete tunnel lining, modeling should adopt appropriate contact conditions. The numerical analysis identifies the suitability and contact conditions of the waterproof membrane in various conditions

A Geometric Model for a Shield TBM Steering Simulator

This study aimed to simulate curved excavation using a tunnel boring machine (TBM) steering system based on the proposed mathematical methodology applied in a TBM simulator. We introduce the concept and mechanism of the TBM steering system and describe the mathematical formulae used for simulating curved excavation. Curved excavation in the top- right direction was simulated using a Python program to verify the mathematical formulae. In addition, Python simulations were undertaken to determine the effects of horizontal and vertical articulation angles on pitching and yawing angles. Finally, the proposed mathematical formulae were applied in the TBM operation simulator, and tested based on the mechanism of the TBM steering system

Prediction of Coronary Artery Calcium Score Using Machine Learning in a Healthy Population

Background: Coronary artery calcium score (CACS) is a reliable predictor for future cardiovascular disease risk. Although deep learning studies using computed tomography (CT) images to predict CACS have been reported, no study has assessed the feasibility of machine learning (ML) algorithms to predict the CACS using clinical variables in a healthy general population. Therefore, we aimed to assess whether ML algorithms other than binary logistic regression (BLR) could predict high CACS in a healthy population with general health examination data. Methods: This retrospective observational study included participants who had regular health screening including coronary CT angiography. High CACS was defined by the Agatston score &ge; 100. Univariable and multivariable BLR was performed to assess predictors for high CACS in the entire dataset. When performing ML prediction for high CACS, the dataset was randomly divided into a training and test dataset with a 7:3 ratio. BLR, catboost, and xgboost algorithms with 5-fold cross-validation and grid search technique were used to find the best performing classifier. Performance comparison of each ML algorithm was evaluated with the area under the receiver operating characteristic (AUROC) curve. Results: A total of 2133 participants were included in the final analysis. Mean age and proportion of male sex were 55.4 &plusmn; 11.3 years and 1483 (69.5%), respectively. In multivariable BLR analysis, age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.10&ndash;1.15, p &lt; 0.001), male sex (OR, 2.91; 95% CI, 1.57&ndash;5.38, p &lt; 0.001), systolic blood pressure (OR, 1.02; 95% CI, 1.00&ndash;1.03, p = 0.019), and low-density lipoprotein cholesterol (OR, 1.00; 95% CI, 0.99&ndash;1.00, p = 0.047) were significant predictors for high CACS. Performance in predicting high CACS of xgboost was AUROC of 0.823, followed by catboost (0.750) and BLR (0.585). The comparison of AUROC between xgboost and BLR was significant (p for AUROC comparison &lt; 0.001). Conclusions: Xgboost ML algorithm was found to be a more reliable predictor of CACS in healthy participants compared to the BLR algorithm. ML algorithms may be useful for predicting CACS with only laboratory data in healthy participants