Analysis of influencing factors of multiple urethrocutaneous fistula after urethroplasty in children with hypospadias

ObjectiveThe objective of this study was to investigate the influencing factors of multiple urethrocutaneous fistula (UF) after urethroplasty in children with hypospadias.MethodsThe clinical data of 195 children with UF after urethroplasty treated surgically in the Third Affiliated Hospital of Zhengzhou University from August 2015 to August 2022 were retrospectively analyzed and divided into the single UF group (n = 134) and the multiple UF group (n = 61) according to whether multiple UF occurred after urethroplasty. The possible correlated factors were collected and compared between the two groups, including hypospadias degree, length of formed urethra, time of urethroplasty, pre-urethroplasty weight, age at urethroplasty, urethroplasty style, season of urethroplasty, the first fistula repair method, season of the first fistula repair, diameter of the largest fistula of the first fistula repair, time of the first fistula repair surgery, and other 13 factors.ResultsBy univariate analysis, statistically significant differences were found between the two groups in age at urethroplasty, length of the formed urethra, method of urinary drainage after urethroplasty, whether or not purulent urethral drainage after first fistula repair was present, the first fistula repair method, and diameter of the largest fistula of the first fistula repair (P < 0.05). After multifactorial analysis, the independent risk factors associated with multiple UF after urethroplasty were determined to be use of a vesicostomy tube as the urinary drainage method after urethroplasty (P < 0.05, OR = 6.574, 95% CI: 2.720–15.891) and the presence of purulent urethral drainage after first fistula repair (P < 0.05, OR = 2.723, 95% CI: 1.214–6.109).ConclusionsA catheter as the drainage method after urethroplasty is an independent protective factor for multiple urethrocutaneous fistula, and the existence of purulent urethral secretions after the first fistula repair is an independent risk factor

An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography

Cardiac diseases have high mortality rates and are a significant threat to human health. Echocardiography is a commonly used imaging technique to diagnose cardiac diseases because of its portability, non-invasiveness and low cost. Precise segmentation of basic cardiac structures is crucial for cardiologists to efficiently diagnose cardiac diseases, but this task is challenging due to several reasons, such as: (1) low image contrast, (2) incomplete structures of cardiac, and (3) unclear border between the ventricle and the atrium in some echocardiographic images. In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively and made use of unlabeled data to achieve a great performance, which could help doctors improve the accuracy of CVD diagnosis and screening. We evaluated this method on a public dataset (CAMUS), achieving mean Dice Similarity Coefficient (DSC) of 0.898, 0.911, 0.916 with 1/4, 1/2 and full labeled data on two-chamber (2CH) echocardiography images, and of 0.903, 0.921, 0.928 with 1/4, 1/2 and full labeled data on four-chamber (4CH) echocardiography images. Compared with other existing methods, the proposed method had fewer parameters and better performance. The code and models are available at https://github.com/gpgzy/CL-Cardiac-segmentation

Ontology-Based Decision Support Tool for Automatic Sleep Staging Using Dual-Channel EEG Data

Sleep staging has attracted significant attention as a critical step in auxiliary diagnosis of sleep disease. To avoid subjectivity of doctor’s manual sleep staging, and to realize scientific management of massive physiological data, an ontology-based decision support tool is proposed. The tool implements an automated procedure for sleep staging using dual-channel electroencephalogram (EEG) signals. First of all, it encodes EEG features, sleep-related concepts and other contextual information to “EEG-Sleep ontology”. Secondly, a rule-set is constructed based on a data mining technique. Finally, the first two steps are processed in a reasoning engine which is automatically assign each 30 s epoch (segment) sleep stage to one of five possible sleep stages: WA, NREM1, NREM2, SWS and REM. The rule set is obtained using EEG data taken from the Sleep-EDF database [EXPANDED] according to the random forest algorithm (RF), we prove that the performance of the proposed method with 89.12% accuracy, and 0.81 Kappa statistics is superior to other algorithms such as Bayesian network, C4.5, support vector machine, and multilayer perceptron. Additionally, our proposed approach improved performance when compared to other studies using a small subset of the Sleep-EDF database [EXPANDED]

Classification of high dimensional biomedical data based on feature selection using redundant removal.

High dimensional biomedical data contain tens of thousands of features, accurate and effective identification of the core features in these data can be used to assist diagnose related diseases. However, there are often a large number of irrelevant or redundant features in biomedical data, which seriously affect subsequent classification accuracy and machine learning efficiency. To solve this problem, a novel filter feature selection algorithm based on redundant removal (FSBRR) is proposed to classify high dimensional biomedical data in this paper. First of all, two redundant criteria are determined by vertical relevance (the relationship between feature and class attribute) and horizontal relevance (the relationship between feature and feature). Secondly, to quantify redundant criteria, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. To evaluate the effectiveness of our proposed algorithm, controlled trials based on typical feature selection algorithm are conducted using three different classifiers, and the experimental results indicate that the FSBRR algorithm can effectively reduce the feature dimension and improve the classification accuracy. In addition, an experiment of small sample dataset is designed and conducted in the section of discussion and analysis to clarify the specific implementation process of FSBRR algorithm more clearly

Advanced Phase Change Materials from Natural Perspectives: Structural Design and Functional Applications

Abstract Phase change materials have garnered extensive interest in heat harvesting and utilization owing to their high energy storage density and isothermal phase transition. Nevertheless, inherent leakage problems and low heat storage efficiencies hinder their widespread utilization. Nature has served as a great source of inspiration for addressing these challenges. Natural strategies are proposed to achieve advanced thermal energy management systems, and breakthroughs are made in recent years. This review focuses on recent advances in the structural design and functions of phase change materials from a natural perspective. By highlighting the structure–function relationship, advanced applications including human motion, medicine, and intelligent thermal management devices are discussed in detail. Finally, the views on the remaining challenges and future prospects are also provided, that is, phase change materials are advancing around the biomimicry design spiral

A nonlinear model predictive control based control method to quadrotor landing on moving platform

Abstract To address the problems that the UAV (Unmanned Aerial Vehicle) is vulnerable to distance limitation and environmental interference when tracking and landing on a moving platform autonomously, the accuracy of position estimation relying only on visual odometry in the point‐featureless environment is insufficient, and the traditional linear path planning solvers and controllers cannot meet the fast and safe requirements under the non‐linear strong coupling characteristics of the cooperative landing system, an nonlinear model predictive control (NMPC)‐based multi‐sensor fusion method for autonomous landing of UAVs on motion platforms is proposed. The UAV combines the position information obtained by the RTK‐GPS and the image information obtained by the camera and uses the special identification codes placed in the landing area of the UAV to carry out cooperative planning and navigation while using UKF (Unscented Kalman Filter) to estimate the position of the moving platform and using the interference‐resistant NMPC algorithm to optimise the UAV tracking trajectory based on the precise positioning of the two platforms to achieve the autonomous landing control of the UAV. The simulation and practical experimental results show the feasibility and effectiveness of the proposed algorithm and the autonomous landing control method and provide an effective solution for the autonomous landing of quadrotors on arbitrarily moving platforms