Development of implantable electrode based on bioresorbable Mg alloy for tissue welding application.

An implantable electrode based on bioresorbable Mg-Nd-Zn-Zr alloy was developed for next-generation radiofrequency (RF) tissue welding application, aiming to reduce thermal damage and enhance anastomotic strength. The Mg alloy electrode was designed with different structural features of cylindrical surface (CS) and continuous long ring (LR) in the welding area, and the electrothermal simulations were studied by finite element analysis (FEA). Meanwhile, the temperature variation during tissue welding was monitored and the anastomotic strength of welded tissue was assessed by measuring the avulsion force and burst pressure. FEA results showed that the mean temperature in the welding area and the proportion of necrotic tissue were significantly reduced when applying an alternating current of 110 V for 10 s to the LR electrode. In the experiment of tissue welding ex vivo, the maximum and mean temperatures of tissues welded by the LR electrode were also significantly reduced and the anastomotic strength of welded tissue could be obviously improved. Overall, an ideal welding temperature and anastomotic strength which meet the clinical requirement can be obtained after applying the LR electrode, suggesting that Mg-Nd-Zn-Zr alloy with optimal structure design shows great potential to develop implantable electrode for next-generation RF tissue welding application.

A novel discrete linkage-type electrode for radiofrequency-induced intestinal anastomosis.

INTRODUCTION: For decades, radiofrequency (RF)-induced tissue fusion has garnered great attention due to its potential to replace sutures and staples for anastomosis of tissue reconstruction. However, the complexities of achieving high bonding strength and reducing excessive thermal damage present substantial limitations of existing fusion devices. MATERIALS AND METHODS: This study proposed a discrete linkage-type electrode to carry out ex vivo RF-induced intestinal anastomosis experiments. The anastomotic strength was examined by burst pressure and shear strength test. The degree of thermal damage was monitored through an infrared thermal imager. And the anastomotic stoma fused by the electrode was further investigated through histopathological and ultrastructural observation. RESULTS: The burst pressure and shear strength of anastomotic tissue can reach 62.2 ± 3.08 mmHg and 8.73 ± 1.11N, respectively, when the pressure, power and duration are 995 kPa, 160 W and 13 s, and the thermal damage can be controlled within limits. Histopathological and ultrastructural observation indicate that an intact and fully fused stomas with collagenic crosslink can be formed. CONCLUSION: The discrete linkage-type electrode presents favorable efficiency and security in RF-induced tissue fusion, and these results are informative to the design of electrosurgical medical devices with controllable pressure and energy delivery.

A novel electrode for reducing tissue thermal damage in radiofrequency-induced intestinal anastomosis.

PURPOSE: This study aimed to design a novel electrode for reducing tissue thermal damage in radiofrequency-induced intestinal anastomosis. MATERIAL AND METHODS: We developed and compared two electrodes (Ring electrode, and Plum electrode with reduced section of the middle fusion area by nearly 80% arising from novel structural design) by performing ex-vivo experiments and finite element analysis. RESULTS: In contrast to the Ring electrode group, slightly higher mean strength is acquired with the tensile force and burst pressure results increasing from 9.7 ± 1.47 N, 84.0 ± 5.99 mmHg to 11.1 ± 1.71 N, 89.4 ± 6.60 mmHg, respectively, as well as a significant reduction in tissue thermal damage for the Plum electrode group, with compression pressure of 20 kPa, RF energy of 120 W and welding duration of 8 s applied to the target regions to achieve anastomosis. Besides, the novel structural design of the Plum electrode can counteract the tension generated by intestinal peristalsis and enhance the biomechanical strength of the anastomotic area. The histological observation showed that the fusion area of the two-layer intestinal tissue is tightly connected with decreased thickness. CONCLUSION: The novel electrode (Plum electrode) could reduce tissue thermal damage in radiofrequency-induced intestinal anastomosis.

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms.

Due to overlapping tremor features, the medical diagnosis of Parkinson's disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and tremor information of the upper limbs. The tremor information including tremor acceleration and surface electromyogram (sEMG) signals were collected from 398 patients (PD = 257, ET = 141) and then were used to train the established models to separate PD and ET. The performance of the models was evaluated by indices of accuracy and area under the curve (AUC), which indicated the ensemble learning models including RF and XGBoost showed the best overall predictive ability with accuracy above 0.84 and AUC above 0.90. Furthermore, the relative importance of sex, age, four postures, and five tremor features was analyzed and ranked showing that the dominant frequency of sEMG of flexors, the average amplitude of sEMG of flexors, resting posture, and winging posture had a greater impact on the diagnosis of PD, whereas sex and age were less important. These results provide a reference for the intelligent diagnosis of PD and show promise for use in wearable tremor suppression devices.

Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields.

To reduce the cost of production and the pollution of the environment that is due to the overapplication of herbicide in paddy fields, the location information of rice seedlings and weeds must be detected in site-specific weed management (SSWM). With the development of deep learning, a semantic segmentation method with the SegNet that is based on fully convolutional network (FCN) was proposed. In this paper, RGB color images of seedling rice were captured in paddy field, and ground truth (GT) images were obtained by manually labeled the pixels in the RGB images with three separate categories, namely, rice seedlings, background, and weeds. The class weight coefficients were calculated to solve the problem of the unbalance of the number of the classification category. GT images and RGB images were used for data training and data testing. Eighty percent of the samples were randomly selected as the training dataset and 20% of samples were used as the test dataset. The proposed method was compared with a classical semantic segmentation model, namely, FCN, and U-Net models. The average accuracy rate of the SegNet method was 92.7%, whereas the average accuracy rates of the FCN and U-Net methods were 89.5% and 70.8%, respectively. The proposed SegNet method realized higher classification accuracy and could effectively classify the pixels of rice seedlings, background, and weeds in the paddy field images and acquire the positions of their regions.