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1.
PLoS One ; 17(2): e0263820, 2022.
Article in English | MEDLINE | ID: covidwho-1793524

ABSTRACT

Many factors play a role in outcomes of an emerging highly contagious disease such as COVID-19. Identification and better understanding of these factors are critical in planning and implementation of effective response strategies during such public health crises. The objective of this study is to examine the impact of factors related to social distancing, human mobility, enforcement strategies, hospital capacity, and testing capacity on COVID-19 outcomes within counties located in District of Columbia as well as the states of Maryland and Virginia. Longitudinal data have been used in the analysis to model county-level COVID-19 infection and mortality rates. These data include big location-based service data, which were collected from anonymized mobile devices and characterize various social distancing and human mobility measures within the study area during the pandemic. The results provide empirical evidence that lower rates of COVID-19 infection and mortality are linked with increased levels of social distancing and reduced levels of travel-particularly by public transit modes. Other preventive strategies and polices also prove to be influential in COVID-19 outcomes. Most notably, lower COVID-19 infection and mortality rates are linked with stricter enforcement policies and more severe penalties for violating stay-at-home orders. Further, policies that allow gradual relaxation of social distancing measures and travel restrictions as well as those requiring usage of a face mask are related to lower rates of COVID-19 infections and deaths. Additionally, increased access to ventilators and Intensive Care Unit (ICU) beds, which represent hospital capacity, are linked with lower COVID-19 mortality rates. On the other hand, gaps in testing capacity are related to higher rates of COVID-19 infection. The results also provide empirical evidence for reports suggesting that certain minority groups such as African Americans and Hispanics are disproportionately affected by the COVID-19 pandemic.


Subject(s)
Big Data , COVID-19/prevention & control , Physical Distancing , Public Health , Travel/statistics & numerical data , COVID-19/epidemiology , COVID-19/virology , District of Columbia/epidemiology , Female , Humans , Male , Maryland/epidemiology , Masks/statistics & numerical data , Middle Aged , Quarantine , SARS-CoV-2/isolation & purification , Virginia/epidemiology
3.
Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi ; 57(3): 282-288, 2022 Mar 07.
Article in Chinese | MEDLINE | ID: covidwho-1760874

ABSTRACT

Objective: To analyze the correlation between loss of smell/taste and the number of real confirmed cases of coronavirus disease 2019 (COVID-19) worldwide based on Google Trends data, and to explore the guiding role of smell/taste loss for the COVID-19 prevention and control. Methods: "Loss of smell" and "loss of taste" related keywords were searched in the Google Trends platform, the data were obtained from Jan. 1 2019 to Jul. 11 2021. The daily and newly confirmed COVID-19 case number were collected from World Health Organization (WHO) since Dec. 30 2019. All data were statistically analyzed by SPSS 23.0 software. The correlation was finally tested by Spearman correlation analysis. Results: A total of data from 80 weeks were collected. The retrospective analysis was performed on the new trend of COVID-19 confirmed cases in a total of 186 292 441 cases worldwide. Since the epidemic of COVID-19 was recorded on the WHO website, the relative searches related to loss of smell/taste in the Google Trends platform had been increasing globally. The global relative search volumes of "loss of smell" and "loss of taste" on Google Trends was 10.23±2.58 and 16.33±2.47 before the record of epidemic while 80.25±39.81 and 80.45±40.04 after (t value was 8.67, 14.43, respectively, both P<0.001). In the United States and India, the relative searches for "loss of smell" and "loss of taste" after the record of epidemic were also much higher than before (all P<0.001). The correlation coefficients between the trend of weekly new COVID-19 cases and the Google Trends of "loss of smell" in the global, United States, and India was 0.53, 0.76, and 0.82 respectively (all P<0.001), the correlation coefficients with Google Trends of "loss of taste" was 0.54, 0.78, and 0.82 respectively (all P<0.001). The lowest and highest point of loss of smell/taste search curves of Google Trends in different periods appeared 7 to 14 days earlier than that of the weekly newly COVID-19 confirmed cases curves, respectively. Conclusions: There is a significant positive correlation between the number of newly confirmed cases of COVID-19 worldwide and the amount of keywords, such as "loss of smell" and "loss of taste", retrieved in Google Trends. The trend of big data based on Google Trends might predict the outbreak trend of COVID-19 in advance.


Subject(s)
Ageusia , COVID-19 , Big Data , Disease Outbreaks , Humans , Internet , Retrospective Studies , Smell , United States
5.
Curr Neurol Neurosci Rep ; 22(3): 151-160, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1739417

ABSTRACT

PURPOSE OF REVIEW: To critically appraise literature on recent advances and methods using "big data" to evaluate stroke outcomes and associated factors. RECENT FINDINGS: Recent big data studies provided new evidence on the incidence of stroke outcomes, and important emerging predictors of these outcomes. Main highlights included the identification of COVID-19 infection and exposure to a low-dose particulate matter as emerging predictors of mortality post-stroke. Demographic (age, sex) and geographical (rural vs. urban) disparities in outcomes were also identified. There was a surge in methodological (e.g., machine learning and validation) studies aimed at maximizing the efficiency of big data for improving the prediction of stroke outcomes. However, considerable delays remain between data generation and publication. Big data are driving rapid innovations in research of stroke outcomes, generating novel evidence for bridging practice gaps. Opportunity exists to harness big data to drive real-time improvements in stroke outcomes.


Subject(s)
COVID-19 , Stroke , Big Data , Humans , Machine Learning , Stroke/epidemiology , Stroke/therapy
6.
Int J Environ Res Public Health ; 17(9)2020 05 02.
Article in English | MEDLINE | ID: covidwho-1725599

ABSTRACT

SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.


Subject(s)
Artificial Intelligence , Big Data , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , COVID-19 , Humans , Randomized Controlled Trials as Topic
7.
J Healthc Eng ; 2022: 9311052, 2022.
Article in English | MEDLINE | ID: covidwho-1723969

ABSTRACT

Big data platforms can effectively analyze the data and maximize the value of the data by mining the text, digital, video, and image data in various industries. The combination of big data and various industries has brought great changes to the development of the industry. Providing data according to demand can save more time and promote the development of the industry. SARS-CoV-2 (COVID-19) is sweeping across the world, and it has spread to several countries and regions. Human infections have been reported all around the world. Due to the unique characteristics of COVID-19, no specific medicine is available yet to cure patients before the successful research and development of vaccines. Hence, it is of important significance to research and develop vaccines. Guided by the biological characteristics of COVID-19 and the philosophy of synthetic biology, this study reviews the developed genetic engineering vaccines.


Subject(s)
COVID-19 , Vaccines , Big Data , COVID-19/prevention & control , COVID-19 Vaccines , Genetic Engineering , Humans , SARS-CoV-2
9.
PLoS One ; 16(12): e0260386, 2021.
Article in English | MEDLINE | ID: covidwho-1634467

ABSTRACT

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has led to radical changes in social distancing awareness and affected social relationships. Owing to large-scale lockdown, home quarantine and social distancing requirements, it was anticipated that sexual activities would be severely impacted. However, retrospective self-report studies showed that pornography use and autoerotism increased during the pandemic. AIM: This study used big-data databases available on the Internet to investigate factors that modulated pornography use during the pandemic. METHODS: Daily relative search volume (RSV) data from Google Trends for the period from 24 February 2020 to 13 July 2020 were extracted. Pornhub traffic data were extracted from the Pornhub Insights website, for the period from 24 February 2020 to 13 July 2020. The parameter was defined as 'percent change in traffic compared to an average day in 2019'. The number of daily new cases of COVID-19 was extracted from the database on Our World in Data. OUTCOME MEASURES: The normality of the data was examined using the Shapiro-Wilk test. All variables included in this study were non-normally distributed. Therefore, non-parametric tests or parametric tests with bootstrapping were adopted where appropriate. RESULTS: According to Google Trends, the RSV for 'pornography' increased after late March 2020, which is close to the date when the World Health Organization declared COVID-19 a global pandemic. The number of daily new cases of COVID-19 was positively correlated with the traffic of Pornhub, a popular pornography website, and the RSV for 'pornography'. Moderation analysis demonstrated a significant main effect of daily new cases of COVID-19 and the RSV for 'social distancing' in predicting Pornhub traffic/RSV for 'pornography'. Furthermore, the RSV for 'social distancing' significantly moderated the relationship between daily new cases and Pornhub traffic/RSV for 'pornography'. A stronger COVID-pornography use association was observed with increased social distancing awareness. CONCLUSION: Increased pornography consumption during the pandemic was observed, and it was associated with the severity of the pandemic. Social distancing awareness could be a key factor influencing interest in and use of pornography. Further studies on the changes in sexual desire and birth-rate control are worthwhile because long-term public health may be affected by the changes in sexual behaviour during the pandemic.


Subject(s)
COVID-19/epidemiology , Erotica , Internet Use/statistics & numerical data , Big Data , COVID-19/psychology , Humans , Physical Distancing , Regression Analysis
10.
Ethics Hum Res ; 44(1): 2-17, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1597364

ABSTRACT

In this article, we analyze legal and ethical issues raised in Big Data health research projects in the Covid-19 era and consider how these issues might be addressed in ways that advance positive values (e.g., furtherance of respect for persons and accordance with relevant legal frameworks) while mitigating or eliminating any negative aspects (e.g., exacerbation of social inequality and injustice). We apply this analysis specifically to UK-REACH (The United Kingdom Research Study into Ethnicity and Covid-19 Outcomes in Healthcare Workers), a project with which we are involved. We argue that Big Data projects like UK-REACH can be conducted in an ethically robust manner and that funders and sponsors ought to encourage similar projects to drive better evidence-based public policy in public health. As part of this, we advocate that a Big Data ethics-by-design approach be undertaken when such projects are constructed. This principle extends the work of those who advocate ethics by design by addressing prominent issues in Big Data health research projects; it holds that ethical values and principles in Big Data health research projects are best adhered to when they are already integrated into the project aims and methods at the design stage. In advocating this principle, we present a unique perspective regarding pressing ethical problems around large-scale, data-driven Covid-19 research, as well as legal issues associated with processing ostensibly anonymized health data.


Subject(s)
Big Data , COVID-19 , Health Personnel , Humans , Public Health , SARS-CoV-2
11.
Nature ; 600(7890): 695-700, 2021 12.
Article in English | MEDLINE | ID: covidwho-1562062

ABSTRACT

Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox1. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi-Facebook2,3 (about 250,000 responses per week) and Census Household Pulse4 (about 75,000 every two weeks). In May 2021, Delphi-Facebook overestimated uptake by 17 percentage points (14-20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11-17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios-Ipsos online panel5 with about 1,000 responses per week following survey research best practices6 provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework1 to explain the inaccuracy in the three surveys. We then analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition.


Subject(s)
COVID-19 Vaccines/administration & dosage , Health Care Surveys , Vaccination/statistics & numerical data , Benchmarking , Bias , Big Data , COVID-19/epidemiology , COVID-19/prevention & control , Centers for Disease Control and Prevention, U.S. , Datasets as Topic/standards , Female , Health Care Surveys/standards , Humans , Male , Research Design , Sample Size , Social Media , United States/epidemiology , /statistics & numerical data
12.
Sci Data ; 8(1): 297, 2021 11 22.
Article in English | MEDLINE | ID: covidwho-1528020

ABSTRACT

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation. The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers. We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipeline that enables reproducible research across an international research group for the Covid Symptom Study.


Subject(s)
COVID-19/epidemiology , Citizen Science , Data Curation , Big Data , Data Science , Datasets as Topic , Epidemiological Monitoring , Humans , Mobile Applications , Smartphone , Software
13.
Clin Ther ; 43(10): 1646-1648, 2021 10.
Article in English | MEDLINE | ID: covidwho-1525739
14.
Scand J Clin Lab Invest ; 81(8): 653-660, 2021 12.
Article in English | MEDLINE | ID: covidwho-1521954

ABSTRACT

Coagulation disturbances are common in severe COVID-19 infection. We examined laboratory markers in COVID-19 patients during the first wave of the pandemic in Finland. We analysed a wide panel of coagulation tests (IL ACL TOP 750/500®) from anonymously collected samples of 78 hospitalized COVID-19 patients in intensive care units (ICUs; n = 34) or medical wards (n = 44) at Helsinki University Hospital in April-May 2020. These coagulation data were supplemented with the laboratory information system results, including complete blood count and C reactive protein (CRP). Coagulation and inflammatory markers were elevated in most: FVIII in 52%, fibrinogen 77%, D-dimer 74%, CRP 94%, platelet count 37%. Anaemia was common, especially in men (73% vs. 44% in women), and overall weakly correlated with FVIII (women R2 = 0.48, men R2 = 0.24). ICU patients had higher fibrinogen and D-dimer levels (p < .01). Men admitted to the ICU also had higher platelet count, leukocytes and FVIII and lower haemoglobin than the non-ICU patients. None of the patients met the disseminated intravascular coagulation (DIC) criteria, but 31% had a D-dimer level of at least 1.5 mg/L. Presence of both anaemia and high D-dimer together with FVIII is independently associated with ICU admission. Antithrombin was reduced in 47% of the patients but did not distinguish severity. Overall, CRP was associated with coagulation activation. Elevated FVIII, fibrinogen and D-dimer reflected a strong inflammatory response and were characteristic of hospitalized COVID-19 patients. The patients were often anaemic, as is typical in severe inflammation, while anaemia was also associated with coagulation activity.


Subject(s)
Anemia/virology , Blood Coagulation Disorders/virology , Blood Coagulation , COVID-19/complications , Adolescent , Adult , Aged , Aged, 80 and over , Antithrombins , Big Data , Blood Coagulation Tests , C-Reactive Protein , Female , Fibrin Fibrinogen Degradation Products , Fibrinogen , Finland/epidemiology , Humans , Intensive Care Units , Male , Middle Aged , Platelet Count , Retrospective Studies , Young Adult
15.
PLoS Comput Biol ; 17(11): e1009594, 2021 11.
Article in English | MEDLINE | ID: covidwho-1518350

ABSTRACT

The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics.


Subject(s)
Big Data , DNA, Mitochondrial/genetics , High-Throughput Nucleotide Sequencing/methods , Machine Learning , Genes, Mitochondrial , Humans
16.
BMC Public Health ; 21(1): 2001, 2021 11 04.
Article in English | MEDLINE | ID: covidwho-1504352

ABSTRACT

BACKGROUND: As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. METHODS: This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management. RESULTS: Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. CONCLUSIONS: This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.


Subject(s)
COVID-19 , Epidemics , Big Data , China/epidemiology , Humans , Local Government , SARS-CoV-2 , Technology
17.
Chaos ; 31(10): 101104, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1493328

ABSTRACT

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55×106 Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 500×500m2 grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google's Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.


Subject(s)
COVID-19 , Pandemics , Big Data , Hong Kong/epidemiology , Humans , SARS-CoV-2
18.
Eur Rev Med Pharmacol Sci ; 25(18): 5865-5870, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1451045

ABSTRACT

OBJECTIVE: Dupilumab (Dupixent®) is a monoclonal antibody that inhibits IL-4 and IL-13 signaling used for the treatment of allergic diseases. Whilst biologic therapy is traditionally regarded as immunosuppressive and capable to increase the infectious risk, Dupilumab does not display these characteristics and may be even protective in certain cases. We investigated the link between Dupilumab therapy and SARS-CoV-2 infection. MATERIALS AND METHODS: We carried out a comprehensive data mining and disproportionality analysis of the WHO global pharmacovigilance database. One asymptomatic COVID-19 case, 106 cases of symptomatic COVID-19, and 2 cases of severe COVID-19 pneumonia were found. RESULTS: Dupilumab treated patients were at higher risk of COVID-19 (with an IC0.25 of 3.05), even though infections were less severe (IC0.25 of -1.71). The risk of developing COVID-19 was significant both among males and females (with an IC0.25 of 0.24 and 0.58, respectively). The risk of developing COVID-19 was significant in the age-group of 45-64 years (with an IC0.25 of 0.17). CONCLUSIONS: Dupilumab use seems to reduce COVID-19 related severity. Further studies are needed to better understand the immunological mechanisms and clinical implications of these findings. Remarkably, the heterogenous nature of the reports and the database structure did not allow to establish a cause-effect link, but only an epidemiologically decreased risk in the patients subset treated with dupilumab.


Subject(s)
Antibodies, Monoclonal, Humanized/adverse effects , Antibodies, Monoclonal, Humanized/therapeutic use , Big Data , COVID-19/epidemiology , COVID-19/immunology , Adolescent , Adult , Aged , COVID-19/drug therapy , Databases, Factual , Female , Humans , Immunosuppressive Agents/therapeutic use , Male , Middle Aged , Risk Factors , SARS-CoV-2/drug effects , SARS-CoV-2/immunology , Severity of Illness Index , World Health Organization , Young Adult
19.
Nat Commun ; 12(1): 5757, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1447304

ABSTRACT

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.


Subject(s)
Data Science/methods , Medical Records Systems, Computerized , Big Data , Computer Security , Data Analysis , Health Information Interoperability , Humans , Information Storage and Retrieval , Software
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