Curative effect and technical key points of laparoscopic surgery for choledochal cysts in children

ObjectiveThe purpose of this study was to investigate the curative effect of and experience with laparoscopic surgery for congenital choledochal cysts in children.MethodsThis is a retrospective analysis of 33 children diagnosed with congenital choledochal cyst in the pediatric surgery department of the Affiliated Hospital of Southwest Medical University between January 2019 and December 2021. The cohort included 8 males and 25 females aged 0.25–13.7 years (median age, 3.2 years), including 21 cases of type I and 12 cases of type IV choledochal cyst (Todani classification). Laparoscopic choledochal cyst resection and hepaticojejunostomy with Roux-en-Y anastomosis were performed in all the patients.ResultsLaparoscopy without transit opening was successfully performed in the 33 cases. The duration of the procedure was 235–460 min (mean ± SD, 316 ± 61 min), and intraoperative blood loss volume was 15–40 ml (23 ± 7.6 ml). Postoperative hospital stay was 7–14 days (9 ± 1.8 days). Postoperative biliary fistula and pancreatitis occurred in two cases each, and all four patients were successfully treated with conservative treatment. No anastomotic stenosis, delayed bleeding, cholangitis, intestinal obstruction, or other complications occurred. All the children were followed up for 2–36 months (median period, 17.2 months). The clinical symptoms disappeared, and no obvious hepatic dysfunction was found on abdominal color ultrasound and liver function examination.ConclusionLaparoscopic surgery for congenital choledochal cyst in children is safe and effective, as it is a minimally invasive surgery that is associated with a low degree of trauma and bleeding, rapid postoperative recovery, and satisfactory aesthetic appearance

Efficient cloud-based digital-physical testing method for feeder automation system in electrical power distribution network

A feeder automation (FA) system is usually used by electricity utilities to improve power supply reliability. The FA system was realized by the coordinated control of feeder terminal units (FTUs) in the electrical power distribution network. Existing FA testing technologies can only test basic functions of FTUs, while the coordinated control function among several FTUs during the self-healing process cannot be tested and evaluated. In this paper, a novel cloud-based digital-physical testing method is proposed and discussed for coordinated control capacity test of the FTUs in the distribution network. The coordinated control principle of the FTUs in the local-reclosing FA system is introduced firstly and then, the scheme of the proposed cloud-based digital-physical FA testing method is proposed and discussed. The theoretical action sequences of the FTUs consisting of the FTU under test and the FTUs installed in the same feeder are analyzed and illustrated. The theoretical action sequences are compared with the test results obtained by the realized cloud-based simulation platform and the digital-physical hybrid communication interaction. The coordinated control capacity of the FTUs can be evaluated by the comparative result. Experimental verification shows that the FA function can be tested efficiently and accurately based on our proposed method in the power distribution system inspection

Condition assessment of distribution automation remote terminal units based on double-layer improved cloud model

Distribution automation remote terminal units (DRTUs) are the most important devices to sample voltage and current signals, identify the fault position, isolate the fault, and restore the power supply along distribution lines. We cannot adopt time-based maintenance because of the contradiction between the limited manpower and the plenty of DRTUs distributed in broad regions. In this paper, a double-layer improved cloud model (ICM) is proposed for the first time to realize the condition assessment of DRTUs for condition-based maintenance. The first-layer ICM is applied in the main station of whole distribution automation system (DAS) to evaluate the condition of all DRTUs based on the upload limited condition parameters by DRTUs in real time. And the second-layer ICM is applied in field to evaluate one DRTU which belongs to the abnormal DRTUs obtained after the first-layer evaluation. The DRTU test device are used to obtain detailed condition parameters, and these parameters are used in second-layer ICM to obtain accurate condition assessment score. The results in second-layer ICM would be used to optimize the evaluation algorithm in the first-layer evaluation. In the meantime, our proposed ICM combines the normal cloud model and the trapezoidal cloud model to deal with the optimal, negative, and intermediate indicators more effectively. The comparison with traditional evaluation methods shows that our proposed method is more suitable to realize the condition assessment of DRTUs in the distribution power networks

Development of an integrated power distribution system laboratory platform using modular miniature physical elements: A case study of fault location

The main shortcomings of the software-based power engineering education are a lack of physical understanding of phenomena and hands-on experience. Existing scaled-down analogous educational power system platforms cannot be widely used for experiments in universities due to the high cost, complicated operation, and huge size. An integrated power distribution system laboratory platform (PDSLP) using modular miniature physical elements is proposed in this paper. The printed circuit board (PCB) and microelectronic technology are proposed to construct each physical element. Furthermore, the constructed physical elements are used to set up an integrated PDSLP based on modular assembly technology. The size of the proposed cost-efficient PDSLP is significantly reduced, and the reliability of the proposed PDSLP can be improved greatly because the signal transmission path is shortened and a number of welding points are reduced. A PDSLP for fault location in neutral non-effectively grounded distribution systems (NGDSs) is selected as a typical experimental scenario and one scaled-down distribution network with three feeders is subsequently implemented and discussed. The measured zero-sequence currents by our proposed PDSLP when a single-phase earth fault occurred can reveal the true features of the fault-generated signals, including steady-state and transient characteristics of zero-sequence currents. They can be readily observed and used for students to design corresponding fault location algorithms. Modular renewable energy sources and other elements can be designed, implemented and integrated into the proposed platform for the laboratory education of the active distribution networks in the future

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM