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Acquired von Willebrand syndrome (AVWS) contributes to bleeding during extracorporeal membrane oxygenation (ECMO) support. Although it is recognized that AVWS rapidly resolves after ECMO decannulation, this approach may often be clinically unsuitable. In such cases, optimal AVWS management during ECMO support is not well established. We report our approach to managing AVWS in a patient on veno-venous (VV) ECMO for 59 days. A 19-year-old male developed hypoxemic respiratory failure from SARS-CoV-2 pneumonia. Following intubation, he progressed to VV-ECMO support for refractory hypoxemia and was started on bivalirudin for systemic anticoagulation. Two days later, he developed refractory gastrointestinal and oro-nasopharyngeal bleeding despite blood product transfusions and discontinuing bivalirudin. He was started on pantoprazole along with infusions of octreotide and aminocaproic acid. Upper endoscopy on ECMO day 5 revealed an ulcerative bleeding vessel in the duodenum that was clipped. Recurrent mucosal bleeding precluded resumption of systemic anticoagulation. On ECMO day 23, AVWS was diagnosed based on elevated von Willebrand factor (VWF) activity (207%, normal 55-189%) and antigen (234%, normal 50-210%) levels with abnormally low VWF high-molecular-weight multimers. Factor VIII complex was administered twice over the following week. Between doses, the ECMO circuit was exchanged to empirically mitigate suspected shear-related VWF consumption from the fibrin burden, and a repeat endoscopy controlled additional intestinal bleeding with local hemostatic agents. He received 36 units of red blood cells, 2 units of platelets, 2 units of plasma, and 7 pooled units of cryoprecipitate over 31 days leading into these combined interventions. In the 28 days afterwards, he received 3 units of red blood cells, 3.5 pooled units of cryoprecipitate, and no additional platelets or plasma. Our patient was maintained off systemic anticoagulation for 54 of 59 days of VV-ECMO support without any thrombotic complications occurring. With no subsequent clinical evidence of bleeding, repeat VWF testing was done two months post-decannulation and showed near-normal VWF activity (54%) and normal multimer distribution. Our patient rehabilitated well without any neurologic deficits and on discharge was requiring supplemental oxygen with sleep and strenuous activity. Avoiding systemic anticoagulation, repleting VWF, maintaining circuit integrity, and providing local hemostasis, when possible, may be a safe and effective management strategy of AVWS on ECMO support when decannulation is not a viable option.
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This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.
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Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. Our method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC curve of 0.82 with problems associated with nine symptoms.
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With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.
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With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, as well as the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices as well as cloud computing services, as well as basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, as well as generate multivariate data to provide just-in-time healthcare services. In this paper, we present a novel collaborative disease detection system based on IoMT as well as captured image data. The system can be based on intelligent agents, where each and every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared to baseline solutions for disease detection. IEEE
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To solve the problem of inaccurate entity extraction caused by low application efficiency and big data noise in telemedicine sensing data, a deep learning-based method for entity relationship extraction in telemedicine big data is proposed. By analyzing the distribution structure of the medical sensing big data, the fuzzy function of the distribution shape is calculated and the seed relationship set is transformed by the inverse Shearlet transform. Combined with the deep learning technology, the GMM-GAN data enhancement model is built, the interactive medical sensing big data features are obtained, the association rules are matched one by one, the noiseless medical sensing data are extracted in time sequence, the feature items with the highest similarity are obtained and used as the constraint to complete the feature entity relationship extraction of the medical sensing data. The experimental results show that the extracted similarity of entity relations is more than 70%, which can handle overly long and complex sentences in telemedicine information text;the extraction time is the shortest and the volatility is low.
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This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform's user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.
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We tested the capabilities of urban greenhouse gas (GHG) measurement networks to detect abrupt changes in emissions, such as those caused by the roughly 6-week COVID-19 lockdown in March 2020 using hourly in situ GHG mole fraction measurements from six North American cities. We compared observed changes in CO2, CO, and CH4 for different mole fraction metrics (diurnal amplitude, vertical gradients, enhancements, within-hour variances, and multi-gas enhancement ratios) during 2020 relative to previous years for three periods: pre-lockdown, lockdown, and ongoing recovery. The networks showed decreases in CO2 and CO metrics during the lockdown period in all cities for all metrics, while changes in the CH4 metrics were variable across cities and not statistically significant. Traffic decreases in 2020 were correlated with the changes in GHG metrics, whereas changes in meteorology and biology were not, implying that decreases in the CO2 and CO metrics were related to reduced emissions from traffic and demonstrating the sensitivity of these tower networks to rapid changes in urban emissions. The enhancements showed signatures of the lockdowns more consistently than the three micrometeorological methods, possibly because the urban measurements are collected at relatively high altitudes to be sensitive to whole-city emissions. This suggests that urban observatories might benefit from a mixture of measurement altitudes to improve observational network sensitivity to both city-scale and more local fluxes.
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Recent studies have reported a 9% decrease in global carbon emissions during the COVID-19 lockdown period;however, its impact on the variation of atmospheric CO2 level remains under question. Using atmospheric CO2 observed at Anmyeondo station (AMY) in South Korea, downstream of China, this study examines whether the decrease in China's emissions due to COVID-19 can be detected from the enhancement of CO2 mole fraction (Delta CO2) relative to the background value. The Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport model was applied to determine when the observed mole fractions at AMY were affected by air parcels from China. Atmospheric observations at AMY showed up to a -20% (-1.92 ppm) decrease in Delta CO2 between February and March 2020 compared to the same period in 2018 and 2019, particularly with a -34% (-3.61 ppm) decrease in March. Delta CO, which was analyzed to explore the short-term effect of emission reductions, had a decrease of -43% (-80.66 ppb) during the lockdown in China. Particularly in East China, where emissions are more concentrated than in Northeast China, Delta CO2 and Delta CO decreased by -44% and -65%, respectively. The Delta CO/Delta CO2 ratio (24.8 ppb ppm(-1)), which is the indicator of emission characteristics, did not show a significant difference before and after the COVID-19 lockdown period (alpha = 0.05), suggesting that this decrease in Delta CO2 and Delta CO was associated with emission reductions rather than changes in emission sources or combustion efficiency in China. Reduced carbon emissions due to limited human activity resulted in a decrease in the short-term regional enhancement to the observed atmospheric CO2.
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Examining the genome sequences of the novel coronavirus (COVID-19) strains is critical to properly understand this disease and its functionalities. In bioinformatics, alignment-free (AF) sequence analysis methods offer a natural framework to investigate and understand the patterns and inherent properties of biological sequences. Thus, AF methods are used in this paper for the analysis and comparison of COVID-19 genome sequences. First, frequent patterns of nucleotide base(s) in COVID-19 genome sequences are extracted. Second, the similarity/dissimilarity between COVID-19 genome sequences are measured with different AF methods. This allows to compare sequences and evaluate the performance of various distance measures employed in AF methods. Lastly, the phylogeny for the COVID-19 genome sequences are constructed with various AF methods as well as the consensus tree that shows the level of support (agreement) among phylogenetic trees built by various AF methods. Obtained results show that AF methods can be used efficiently for the analysis of COVID-19 genome sequences. © 2021, Springer Nature Switzerland AG.