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Purpose: Management of Blount disease in adolescents and young adults is complex and associated with high risk of morbidities. Gradual correction with external fixator can minimize soft tissue injury and allow subsequent adjustment in degree of correction. This study investigates the surgical outcome and complication rate of gradual correction of neglected Blount disease through single-level extra-articular corticotomy. Methods: Patients treated for Blount disease using external fixator from 2002 to 2016 were recruited for the study. We used Ilizarov and Taylor Spatial Frame (TSF) external fixator to perform simultaneous correction of all the metaphyseal deformities without elevating the tibia plateau. Surgical outcome was evaluated using mechanical axis deviation (MAD), tibial femoral angle (TFA), and femoral condyle tibial shaft angle (FCTSA). Results: A total of 22 patients with 32 tibias have been recruited for the study. The mean MAD improved from 95 ± 51.4 mm to 9.0 ± 37.7 mm (medial to midpoint of the knee), mean TFA improved from 31 ± 15° varus to 2 ± 14° valgus, and mean FCTSA improved from 53 ± 14° to 86 ± 14°. Mean duration of frame application is 9.4 months. Two patients developed pathological fractures over the distracted bones, one developed delayed consolidation and other developed overcorrection. Conclusions: Correction of Blount disease can be achieved by gradual correction using Ilizarov or TSF external fixator with low risk of soft tissue complication. Longer duration of frame application should be considered to reduce the risk of pathological fracture or subsequent deformation of the corrected bone.

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Taylor’s spatial frame (TSF) and Ilizarov external fixators (IEF) are two circular external fixator commonly used to address complex deformity and fractures. There is currently no data available comparing the biomechanical properties of these two external fixators. This study looks into the mechanical characteristics of each system. TSF rings with 6 oblique struts, 4 tube connectors, 4 threaded rods, and 6 threaded rods were compared to a standard IEF rings with 4 threaded rods. Compression and torsional loading was performed to the frame as well as construct with Polyvinylchloride tubes. TSF rings with 4 tube connectors had the highest stiffness (3288 N/mm) while TSF rings with 6 struts was the least stiff. The situation was reversed for torsion where TSF rings with 6 oblique struts had the highest torsional stiffness (82.01 Nm/Degree) and frame Ilizarov rings with 4 threaded rods the least. Standard TSF construct of two ring with 6 oblique struts have better torsional stiffness and lower axial stiffness compared to the standard IEF.Key WordsTaylor’s Spatial Frame, Ilizarov External Fixator, Biomechanical properties

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

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This paper provides a comprehensive review and analysis of the detection of suspicious terrorist electronic mails (emails) using various phases and methods of text classification. We explored, analyzed, and compared different datasets, features, feature extraction techniques, feature representation techniques, feature selection schemes, text classification techniques, and performance measurement metrics used in the detection of suspicious terrorist e-mails. 30 articles were retrieved from 6 well-known academic databases after rigorous selection. From the study, we found that researchers often generate their own e-mails dataset since there is no public dataset is available in the research area of detecting suspicious terrorist e-mails. In most of the studies, researchers used content and context-based features to detect terrorist e-mails. Our findings also show that the most commonly used feature extraction techniques are the bag of words and n-gram, the most typically applied feature representation schemes are binary representation and term frequency, the most usually adopted feature selection method is information gain,, the most common and most accurate text classification algorithms are naïve bayes, decision trees, and support vector machines, and the widely employed performance measurement metrics are accuracy, precision, and recall. Open research challenges and research issues that involve significant research efforts are also summarized in this review for future researchers in the area of suspicious terrorist e-mail detection using text classification techniques where the critical analysis presented in this paper also provides valuable insights to guide these researchers. Finally, the indicated issues and challenges presented in this paper can be used as future research directions in this area.