Background: Clinical trials have shown that the use of electronic Patient-Reported Outcomes (ePRO) reduces symptom burden and morbidity after thoracic surgery. This study identified barriers to implementing ePRO symptom monitoring-alerting-responding system (ePRO-SMARS) in routine clinical practice.Methods: An implementation science study of ePRO-SMARS was conducted at Guangdong Provincial People's Hospital. All patients reported symptoms on Perioperative Symptom Assessment for Lung Surgery or Esophageal Surgery, baseline, twice daily postoperatively, daily within post-discharge 7 days, and weekly until 3 months. Reported scores that reached the pre-set threshold would alert the monitoring clinician team, who would respond within 24 hours. The primary outcome was the inclusion rate of the ePRO-SMARS. Other outcomes included the accuracy of enrolment rates, provider and patient burdens, patient compliance, and clinical outcomes.Findings: In this study, we transferred and compared a randomised control trial-approved version of ePRO-SMARS (T-ver) to a real-world study version (RWS-ver). Among the 595 patients enrolled in this study, 338 used the T-ver and 257 used the RWS-ver. The inclusion rate was significantly higher in the RWS-ver group (98·83% versus 3·79%, P=0·0019). RWS-ver users’ burdens were recorded by spending medially 2 minutes 16 s for patient inclusion, and 57·06% (574/1,006) of alerts were responded to in about 103 minutes.Interpretation: To transfer a randomised clinical trial-approved ePRO-SMARS to routine thoracic surgical practice, modifications are needed to improve the inclusion and compliance rates and reduce the system burden on patients. The current study demonstrated the feasibility and acceptance of this modified real-world system in a thoracic surgical unit.Funding: 1. National Natural Science Foundation of China (No. 81872506). 2. Natural Science Foundation of Guangdong Province (2022A1515012469). 3. The 2020-2021 Popularization of Science and Technology Innovation Special Project of Guangdong Province of China (2020A1414070007). 4. The Science and Technology Program of Guangzhou, China (202206010103).Declaration of Interest: XW and QG are co-founders and employees of EPRO Vision (Beijing) Health Technology Co., Ltd. Other authors have no competing interests to declare.Ethical Approval: This prospective observational cohort design was approved by the hospital ethics committee (KY-Q-2021-170). All participants provided informed consent.
Objective
To explore a new platform for pre-hospital and in-hospital emergency medical services based on a new generation of 5G communication technology, providing a basis for further improving the level of emergency medical services.
Methods
This study was conducted at the Second Affiliated Hospital of Zhejiang University School of Medicine from October 2017 to April 2019. Based on the latest requirements of emergency medical services at home and abroad, the cross-enterprise and multi-disciplinary technical forces were organized to build platform. Firstly, to determine the process of pre-hospital and in-hospital emergency medical services, various modules and technical routes were constructed under 5G conditions and individual technologies were tested one by one. Then they were gradually integrated into two platforms of ambulance and hospital emergency. Finally, the simulation test is carried out under the support of the whole 5G network.
Results
The pre-hospital and in-hospital emergency medical service platform based on 5G technology comprises of 5G ambulance, 5G panoramic VR real-time display system, 5G remote ultrasonic examination system, medical drone system, and 5G emergency command platform. 5G ambulance contains medical equipments such as multi-function monitor, ventilator, defibrillation monitor, portable B-ultrasound, high-definition remote video interactive system based on 5G network, VR immersive real-time panoramic experience system, and GPS positioning system. 5G panoramic VR real-time display system includes VR panoramic camera and VR glasses. The wearer with VR glasses can view the real-time situation on the ambulance, which makes a preliminary judgment on the patient's condition and provides rescue guidance. 5G remote ultrasonic examination system integrates robot technology, real-time remote control technology, and ultrasonic imaging technology. The specialist can control the movement of the ultrasonic probe set on the 5G ambulance by manipulating the mechanical arm. The patient's image and color super-picture can also be simultaneously returned to the specialist. The medical drone system enables the medical resources to be allocated in the shortest possible time through the 5G networked drones, so as to eliminate the delay caused by traffic congestion. 5G emergency command platform can integrate, transmit, and display data from multiple sources and forms through web pages in assistance of AI and internet of things.
Conclusions
The new platform for pre-hospital and in-hospital emergency medical services based on 5G technology can realize more fluent information exchange in pre-hospital and in-hospital, and realize the functions that are difficult to achieve under the previous network conditions.
Key words:
5G communication technology; Emergency medical service system; Pre-hospital and in-hospital communication
Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.
Neospora caninum is an obligate intracellular apicomplexan parasite, the etiologic agent of neosporosis, and a major cause of reproductive loss in cattle. There is still a lack of effective prevention and treatment measures. The 14-3-3 protein is a widely expressed acidic protein that spontaneously forms dimers within apicomplexan parasites. This protein has been isolated and sequenced in many parasites; however, there are few reports about the N. caninum 14-3-3 protein. Here, we successfully expressed and purified a recombinant fusion protein of Nc14-3-3 (rNc14-3-3) and prepared a polyclonal antibody. Immunofluorescence and immunogold electron microscopy studies of tachyzoites or N. caninum-infected cells suggested that 14-3-3 was localized in the cytosol and the membrane. Western blotting analysis indicated that rNc14-3-3 could be recognized by N. caninum-infected mouse sera, suggesting that 14-3-3 may be an infection-associated antigen that is involved in the host immune response. We demonstrated that rNc14-3-3 induced cytokine expression by activating the MAPK and AKT signaling pathways, and inhibitors of p38, ERK, JNK and AKT could significantly decrease the production of IL-6, IL-12p40 and TNF-α. In addition, phosphorylated nuclear factor-κB (NF-κB/p65) was observed in wild-type peritoneal macrophages treated with rNc14-3-3, and the protein level of NF-κB/p65 was reduced in the cytoplasm but increased correspondingly in the nucleus after 2 h of treatment. These results were also observed in deficient in TLR2-/- peritoneal macrophages. Taken together, our results indicated that the N. caninum 14-3-3 protein can induce effective immune responses and stimulate cytokine expression by activating the MAPK, AKT and NF-κB signaling pathways but did not dependent TLR2, suggesting that Nc14-3-3 is a novel vaccine candidate against neosporosis.
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities. Although effective, most of these models solely rely on fixed relation representations to obtain answers for different question-related KB subgraphs. Hence, the rich structured information of these subgraphs may be overlooked by the relation representation vectors. Meanwhile, the direction information of reasoning, which has been proven effective for the answer prediction on graphs, has not been fully explored in existing work. To address these challenges, we propose a novel neural model, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information. Additionally, we utilize direction information to enhance the reasoning ability. Experimental results show that our model yields substantial improvements on two widely used datasets.
GFFC (global feedback based flow control) is proposed to be used in NoC design for many-core processor. GFFC is designed based on two fundamental principles: (a) when network congestion occurs, the packet sender that causes the congestion needs to know this and needs to be proactively involved in the alleviation of this network congestion; (b) the congestion-causing packets shall not block the traffic of other part of the OCN. Following these principles, we propose the GFFC FIFO in the OCN router. GFFC FIFO is a place to buffer the waiting packets in the OCN to avoid blocking the traffic of other bypassing packets. Besides, we propose a mechanism to relay the congestion in formation from the packet receiver to the packet sender via the GFFC FIFO in the routers. In this paper, we propose the design of GFFC in a mesh based OCN. We also study the performance characteristics of GFFC and report the experimental results. The experimental results show that GFFC can decrease the average non-memory packet transfer latency by 9%.
Cloud is one of common noises in MODIS remote sensing image. Because of cloud interference, much important information covered with cloud can't be obtained. In this paper, an effective method is proposed to detect and remove thin clouds with single MODIS image. The proposed method involves two processing-thin cloud detection and thin cloud removal. As for thin cloud detection, through analyzing the cloud spectral characters in MODIS thirty-six bands, we can draw the conclusion that the spectral reflections of ground and cloud are different in various MODIS band. Hence, the cloud and ground area can be separately identified based on MODIS multispectral analysis. Then, the region label algorithm is used to label thin clouds from many candidate objects. After cloud detection processing, thin cloud removal method is used to process each cloud region. Comparing with traditional methods, the proposed method can realize thin cloud detection and removal with single remote sensing image. Additionally, the cloud removal processing mainly aims to the cloud label region rather than the whole image, so it can improve the processing efficiency. Experiment results show the method can effectively remove thin cloud from MODIS image.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)