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1.
Wiad Lek ; 75(4 pt 1): 781-786, 2022.
Article in English | MEDLINE | ID: covidwho-1876550

ABSTRACT

OBJECTIVE: The aim: The purpose of the study is to evaluate the clinical and laboratory features of COVID-19 pneumonia course, the diagnostic significance of laboratory methods for detecting the SARS-CoV-2 virus based on a retrospective analysis. PATIENTS AND METHODS: Materials and methods: We studied the case histories of 96 patients who were treated at the Municipal Non-Profit Enterprise "Lviv Clinical Emergency Care Hospital" for the period from 01/07/2020 to 31/07/2020 with a diagnosis of pneumonia, which corresponded to 5 points on the CO -RADS scale. We analyzed the clinical and laboratory signs of COVID-19 pneumonia depending on the results of the Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR) tests to the SARS-CoV-2 infection (positive result of RT-qPCR was observed in the first group and negative - in the second group). RESULTS: Results: In both groups, no clinical differences in the course of the disease were found. The most common symptoms of coronavirus pneumonia were found with the same frequency in both patients with a laboratory-confirmed diagnosis and without it. A positive PCR test in nasopharyngeal and oropharyngeal swabs was more often detected during testing up to 10 days, in patients over 60 years of age and in severe COVID-19. CONCLUSION: Conclusions: The COVID-19 pneumonia diagnosis should be based on a combination of clinical, laboratory, and radiological signs of this disease. A negative PCR test result does not exclude the diagnosis of coronavirus disease. The test results are influenced by the timing of the sampling, the severity of the disease and the age of the patients.


Subject(s)
COVID-19 , Aged , COVID-19/diagnosis , Humans , Middle Aged , Polymerase Chain Reaction , Retrospective Studies , SARS-CoV-2
2.
J Res Med Sci ; 27: 26, 2022.
Article in English | MEDLINE | ID: covidwho-1856023

ABSTRACT

Background: COVID 19 may affect organs other than lungs, including liver, leading to parenchymal changes. These changes are best assessed by unenhanced computed tomography (CT). We aim to investigate the effect of COVID 19 on liver parenchyma by measuring the attenuation in CT scan Hounsfield unit (HU). Materials and Methods: A cohort of patients, who tested COVID 19 polymerase chain reaction positive, were enrolled and divided into two groups: fatty liver (FL) group (HU ≤ 40) and nonfatty liver (NFL) group (HU > 40) according to liver parenchyma attenuation measurements by high resolution noncontrast CT scan. The CT scan was performed on admission and on follow up (10-14 days later). Liver enzyme tests were submitted on admission and follow up. Results: Three hundred and two patients were enrolled. Liver HU increased significantly from 48.9 on admission to 53.4 on follow up CT scan (P<0.001) in all patients. This increase was more significant in the FL group (increased from 31.9 to 42.9 [P =0.018]) Liver enzymes were abnormal in 22.6% of the full cohort. However, there was no significant change in liver enzymes between the admission and follow up in both groups. Conclusion: The use of unenhanced CT scan for assessment of liver parenchymal represents an objective and noninvasive method. The significant changes in parenchymal HU are not always accompanied by significant changes in liver enzymes. Increased HU values caused by COVID 19 may be due to either a decrease in the fat or an increase in the fibrosis in the liver.

3.
2022 International Mobile and Embedded Technology Conference, MECON 2022 ; : 617-620, 2022.
Article in English | Scopus | ID: covidwho-1840276

ABSTRACT

Coronavirus diseases is a contagious transmissible infectious malady rooted by the SARS-CoV-2 virus and it mostly affects the lungs thereby causing a global health care problem. Coronavirus triggers respiratory tract infection by infecting upper respiratory tract consisting of sinuses, nose, and throat or lower tract of respiratory system that includes windpipe and lungs. WHO proclaimed the COVID-19 outbreak a global epidemic. To control the spreading of novel Coronavirus, early detection and cure is mandatory. Although RT-PCR test is used to detect the infected humans but owing to colossal demand RT-PCR kits are now limited, and its low reliability made way for implementation of radiographic procedures such as X-Rays and Computed Tomography-Scan for symptomatic purposes. These come with a great specificity for diagnosing and detecting Covid-19 instances. This study suggests adopting a Deep Learning technique to automate the diagnosis of COVID19 infection using CT scans of patients for explicit identification of Covid-19. CNN namely Densenet, Inception and Xception networks or architectures are used in a deep learning architecture to distinguish human beings based on whether confirmed positive or not for COVID-19 infection. These networks are then collated on the ground of their accuracy and the outcomes procured from various CNN models are analysed to obtain a robust system. © 2022 IEEE.

4.
2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1840243

ABSTRACT

Battling the progressing Covid sickness 2019 (COVID-19) pandemic requests precise, quick, and point-of-care testing with quick outcomes to anticipate stages for isolation and therapy. The preliminary test to detect COVID-19 is a Swab test and also a Blood test, but these tests will take more than 2 days to receive the results and there is also a risk of transmission of the virus while collecting the samples. To predict the stages of COVID-19's effects on the human lungs accurately for further treatment for further diagnosis on a radiological image, medical experts need a high level of precision. We utilize image processing techniques and convolutional networks to analyze CT images of COVID-19 affected human lungs in this paper for the detection of pulmonary abnormalities in the early stage, Chest X-Ray is not exact. So, we are using Computed Tomography (CT) imaging especially for identifying the stages of lung anomalies. We present and discuss the scoring systems which cause the severity in lungs of COVID-19 patients every day. This will be accurate for predicting the stages of COVID-19 for early treatment and also to protect the uninfected population. © 2022 IEEE.

5.
7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021 ; : 52-56, 2021.
Article in English | Scopus | ID: covidwho-1840233

ABSTRACT

An effective and accurate method of detecting COVID-19 infection is to analyze medical diagnostic images (e.g. CT scans). However, patients' information is privacy, and it is illegal to share diagnostic images among medical institutions. In this case, a critical issue faced by the model that detects the CT images is lacking enough training images dataset, then the features of COVID-19 cannot be accurately obtained. The data privacy attracts extensive attentions recently and is particularly important for the fast-developing medical institution database and. Considering this point, this paper presents a blockchain federated learning model, which overcomes the burden of centralized collection of large amounts of sensitive data. The model uses a trained model to recognize CT scans, and shares data between hospitals with privacy protection mechanism. This model is able to learn from shared resources or data between different hospital repositories to discover patients with new coronary pneumonia by detecting the computed tomography (CT) images. Finally, we conduct extensive experiments to verify the performance of the model. © 2021 IEEE.

6.
2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021 ; : 292-297, 2021.
Article in English | Scopus | ID: covidwho-1831727

ABSTRACT

COVID-19 is breaking out and spreading globally, posing a severe threat to public health and economies worldwide due to its highly transmissible and pathogenic nature. Early, accurate and rapid diagnosis of COVID-19 can effectively stop the spread of the COVID-19 virus. Automatic diagnostic models based on deep learning can detect COVID-19 quickly and accurately. This paper uses a three-dimensional Convolutional Neural Network (3D CNN) to build a COVID-19 diagnostic prediction model for COVID-19 detection. All 192 sets of chest Computed Tomography(CT) data collected are used for this study, including 96 sets of confirmed COVID-19 patients and 96 sets of CT scans of normal human lungs. 5-fold cross-validation is used to train and validate the model. 154 data sets are used to train the model, and 38 sets are used for testing. All experimental data are segmented using a pre-trained SP-V-Net to obtain 3D lung masks fed into 3D CNN for training and validation of the prediction model. In addition, to verify the accuracy of the model predictions and provide interpretability for medical diagnosis, we visualize the experimental results using Class Activation Maps(CAM) to localize the predicted disease regions. The results from several experiments show that the accuracy of our prediction model is 0.911, the Area Under Curve (AUC) 0.976, for no-COVID-19(Precision, 0.902, Recall 0.911, F1-Score 0.900), COVID-19 (Precision, 0.932, Recall 0.911, F1-Score 0.902). The experimental results show that our established diagnostic model can help physicians make a rapid and accurate diagnosis of COVID-19 in response to the spread of COVID-19. © 2021 IEEE.

7.
13th Biomedical Engineering International Conference, BMEiCON 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1806884

ABSTRACT

Since its discovery in late 2019, COVID-19 has become a major worldwide concern due to its incredibly high degree of contagion, and early diagnosis is crucial to limit this global progression. Computed Tomography (CT) scans of the chest offer a low-cost alternative diagnosis modality to the standard reverse polymerase chain reaction (RT-PCR) test for COVID-19. In this paper, we analyze texture features extracted from chest CT scans using Gray Level Run Length Matrix (GLRLM) techniques for their ability to distinguish between COVID-19 and non-COVID-19 patients. Quantitative texture analysis of CT scans provides a measure of the biological heterogeneity in tissue microenvironment which can be useful in the diagnosis of a wide range of diseases, and we hypothesize that GLRLM texture features may hold significance for diagnosis of COVID-19. 13 GLRLM features were extracted from CT scans of 349 positive COVID-19 cases and 397 negative COVID-19 cases. Holdout validation was used to randomly split 70% of the images for training, and the remaining 30% for testing. A GentleBoost classifier was used to evaluate performance. A significant AUROC of 0.92 along with a high classification accuracy of 85.7% was obtained on the independent test set, indicating that GLRLM texture features extracted from chest CT scans have the potential to be a significant tool in the rapid and accurate diagnosis of COVID-19. © 2021 IEEE.

8.
Respir Med Res ; 81: 100892, 2022 May.
Article in English | MEDLINE | ID: covidwho-1805072

ABSTRACT

BACKGROUND: Chest computed tomography (CT) was reported to improve the diagnosis of community-acquired pneumonia (CAP) as compared to chest X-ray (CXR). The aim of this study is to describe the CT-patterns of CAP in a large population visiting the emergency department and to see if some of them are more frequently missed on CXR. MATERIALS AND METHODS: This is an ancillary analysis of the prospective multicenter ESCAPED study including 319 patients. We selected the 163 definite or probable CAP based on adjudication committee classification; 147 available chest CT scans were reinterpreted by 3 chest radiologists to identify CAP patterns. These CT-patterns were correlated to epidemiological, biological and microbiological data, and compared between false negative and true positive CXR CAP. RESULTS: Six patterns were identified: lobar pneumonia (51/147, 35%), including 35 with plurifocal involvement; lobular pneumonia (43/147, 29%); unilobar infra-segmental consolidation (24/147, 16%); bronchiolitis (16/147, 11%), including 4 unilobar bronchiolitis; atelectasis and bronchial abnormalities (8/147, 5.5%); interstitial pneumonia (5/147, 3.5%). Bacteria were isolated in 41% of patients with lobar pneumonia-pattern (mostly Streptococcus pneumoniae and Mycoplasma pneumonia) versus 19% in other patients (p = 0.01). Respiratory viruses were equally distributed within all patterns. CXR was falsely negative in 46/147 (31%) patients. Lobar pneumonia was significantly less missed on CXR than other patterns (p = 0.003), especially lobular pneumonia and unilobar infra-segmental consolidation, missed in 35% and 58% of cases, respectively. CONCLUSION: Lobar and lobular pneumonias are the most frequent CT-patterns. Lobar pneumonia is appropriately detected on CXR and mainly due to Streptococcus pneumoniae or Mycoplasma pneumoniae. Chest CT is very useful to identify CAP in other CT-patterns. Prior the COVID pandemic, CAP was rarely responsible for interstitial opacities on CT.


Subject(s)
Bronchiolitis , COVID-19 , Community-Acquired Infections , Pneumonia, Mycoplasma , Pneumonia, Pneumococcal , Community-Acquired Infections/diagnostic imaging , Community-Acquired Infections/epidemiology , Emergency Service, Hospital , Humans , Pneumonia, Mycoplasma/diagnostic imaging , Pneumonia, Mycoplasma/epidemiology , Pneumonia, Pneumococcal/diagnostic imaging , Pneumonia, Pneumococcal/epidemiology , Prospective Studies , Streptococcus pneumoniae , Tomography, X-Ray Computed/methods
9.
2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022 ; : 96-103, 2022.
Article in English | Scopus | ID: covidwho-1788620

ABSTRACT

As the COVID19 pandemic evolves and coronavirus mutates to different variants, a high workload falls on the shoulders of doctors and radiologists. Identifying COVID19 through X-ray and Computed Tomography (CT) scanning in a short amount of time is vital because it helps doctors start the COVID19 treatment in the early stages. Deep Learning algorithms showed tremendous results in automating COVID19 detection using X-ray and CT scans. As there are not many survey papers on COVID19 detection using deep learning techniques, the goal of this paper is (1) to give a thorough discussion of COVID19 prediction considering Computer Vision problems like COVID19/pneumonia classification, detection, and segmentation, (2) to address new advances in deep learning like Transformers, GANs, and LSTMs, and (3) to cover technical issues like data security and data scarcity of X-ray and CT scans in COVID19. © 2022 IEEE.

10.
2021 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2021 ; : 286-289, 2021.
Article in English | Scopus | ID: covidwho-1784493

ABSTRACT

Coronavirus disease 2019 broke out in early 2020 and quickly spread to over 200 countries, leading to a severe health crisis for people all over the world. In high-risk areas of the epidemic, the shortage of testing reagents and medical facilities have become essential factors restricting the treatment of COVID-19 patients. Computed tomography (CT) has helped doctors make medical diagnoses in many areas as a vital technology in medical field. At present, due to personal privacy issues, it isn't easy to compare different networks because they are all conducted on different data sets, using other metrics, and can not make good use of high-resolution CT images. Based on iCTCF's public data set, 4000 photos from 61 patients are used to propose a network of high-resolution inputs for diagnosing disease using lung CT images of COVID-19 patients. Our work makes better results than traditional image classification methods in limited data sets, contributing to the advancement of deep neural networks in the field of COVID-19CT image recognition. © 2021 IEEE.

11.
1st International Conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1774660

ABSTRACT

Due to the high incident rate of the novel corona virus popularly known as COVID-19, the number of suspected patients needing diagnosis presents overwhelming pressure on hospital and health management systems. This has led to global pandemic and eventual lockdown in many countries. More so, the infected patients present a higher risk of infecting the healthcare workers. This is because once a patient is positive of the virus, the recovery progress or deterioration needs to be monitored by medical experts and other health workers, which eventually exposes them to the infection. In this paper, we present an automatic prognosis of COVID-19 from a computed tomography (CT) scan using deep convolution neural networks (CNN). The models were trained using a super-convergence discriminative fine-tuning algorithm, which uses a layer-specific learning rate to fine-tune a deep CNN model;this learning rate is increased or decreased per iteration to avoid the saddle-point problem and achieve the best performance within few training epochs. The best performance results of our model were obtained as 98.57% accuracy, 98.59% precision and 98.55% recall rate. This work is therefore, presented to aid radiologist to safely and conveniently monitor the recovery of infected patients. © 2021 IEEE.

12.
8th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2021 ; : 7-12, 2021.
Article in English | Scopus | ID: covidwho-1770002

ABSTRACT

The early detection of COVID-19 is one of the current challenges in developing effective diagnosis and treatment mechanisms for patients who are at a high risk for community contagion. Computed Tomography (CT) is an essential support for detecting the infection pattern that causes this disease. CT scans provide relevant information on the morphological appearance of the infected parenchymal tissue, known as ground-glass opacities. Artificial Intelligence (AI) can assist in the quick evaluation of CT scans to differentiate COVID-19 findings in suggestive clinical cases. In this context, AI in the form of, Convolutional Neural Networks (CNN), has achieved successful results in the analysis and classification of medical images. A deep CNN architecture is proposed in this study to diagnose COVID-19 based on the classification of Chest Computed Tomography (CCT) images. In this study 8,624 CCTs of Ecuadorian patients affected by COVID-19 in the first quarter of 2021, were examined. The initial review of CCTs was performed by medical experts to discriminate the CCTs against other chronic lung diseases not associated with COVID-19. The CCTs were pre-processed by techniques such as morphological segmentation, erosion, dilation, and adjustment. After training the model reached an overall F1-score of 97%. © 2021 ACM.

13.
4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021 ; : 173-177, 2021.
Article in English | Scopus | ID: covidwho-1769650

ABSTRACT

Detection of normal and abnormal lung images from the human chest through media scans in the form of computed tomography (CT) scans or radiographs has received attention for early diagnosis of patients. However, even in the study, the diagnosis obtained better accuracy from CT scan results to detect lungs infected with coronavirus disease 2019 (COVID-19) than a swab test with the polymerase chain reaction (PCR) method. This paper aims to detect and classify normal lungs, lung opacities, lungs infected with COVID-19, and viral pneumonia (in this paper is more commonly written as pneumonia) from human chest radiography images. This paper uses 5000 image data consisting of 2461 typical lung images, 1347 lung opacity images, 295 pneumonia images, and 897 COVID-19 images. The method used to detect and classify the labeled images uses the convolutional neural networks (CNN) method. Several image detection and classification studies often implement this method. Comparing the image data for training and testing data uses a ratio of 80 and 20, respectively. Accuracy results for data training got 99.825%, while data testing got 82.6%. © 2021 IEEE.

14.
5th International Conference on Information Systems and Computer Networks, ISCON 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1759104

ABSTRACT

The COVID-19 has the potential to cause serious pneumonia and is predicted to cost the healthcare sector a lot of money. Early detection is essential for proper treatment and, as a result, for lowering healthcare system tension. The most popular imaging methods for checking pneumonia are chest X-rays (CXR) and Computed Tomography (CT) scans. CXRs are still important despite the fact that CT scans are the gold standard since they are less expensive, faster, and more readily available. The use of Artificial Intelligence (AI) to detect early coronavirus infections and track the health of infected patients is a promising new strategy. The development of effective algorithms will vastly enhance treatment continuity and decision-making. Not only in the safe keeping of COVID-19 patients, as well as in the continuous monitoring of patient wellbeing, AI is effective. It can monitor the COVID-19 spread on such a large scale, inclusion of biochemical, medical and epidemiological application. By analyzing data, it is also advantageous to encourage virus analysis. AI can assist in the development of successful effective treatment therapies, protection strategies, as well as the development of drugs and vaccines. This paper will examine the efficacy and diagnostic results of CXR and CT scan imaging to test for pneumonia caused due to COVID-19, and the ability of AI to determine doctors' ability to discern COVID-19 patients from healthy people. © 2021 IEEE.

15.
8th International Conference on Signal Processing and Integrated Networks, SPIN 2021 ; : 390-395, 2021.
Article in English | Scopus | ID: covidwho-1752440

ABSTRACT

The coronavirus pandemic brought the world to a standstill of historic significance. Countries over the world have imposed lockdowns, quarantines and travel bans in an effort to stop the further spread of the disease. Healthcare systems worldwide are under extreme pressure due to the influx of a large amount of patients suffering from COVID-19. Moreover, there is a dearth of doctors, nurses, and support staff in hospitals of many countries. In such a predicament, it is imperative to leverage the advances made in computer vision and deep learning technologies to create a system that attempts to ease the burden on worldwide healthcare. In this research, ten state-of-the-art pre-trained convolutional neural networks were used to identify COVID-19 in chest Computed Tomography (CT) scan images. After extensive experimental testing and tuning, comprehensive comparative analysis was done and very promising results were obtained in this classification task. © 2021 IEEE

16.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752375

ABSTRACT

Using deep learning approaches, this work presents a fully automated system for diagnosing COVID-19 from volumetric chest computed tomography (CT) scans. Transfer learning technique has been used to detect and classify CT scan data into three categories: COVID-19, CAP (Community-acquired pneumonia), and normal cases. The proposed model was built on top of the pre-trained AlexNet model's architecture and was capable of performing multi-classification tasks with a promising accuracy of 98.03%. The results demonstrate that the proposed model outperforms other current models and may thus be utilized as a potential tool for COVID-19 patient diagnosis. © 2021 IEEE.

17.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752369

ABSTRACT

Every human being is discussing a highly addressed topic in the current days which is about the COrona VIrus Disease (COVID) in 2019-2020. The outbreak of corona has affected the human race all over the world, the patient count is increasing day by day, and doctors are in a critically need of computer-aided diagnosis with machine learning (ML) algorithms that will discover and diagnose the coronavirus for a large number of patients. Also, it is more complicated to estimate the discharge time and the criticalness of the patient during treatment. Chest computed tomography (CT) scan was the best tool for the corona diagnosis. Also survival analysis methods in ML outperform better in predicting discharge time. In this, we survey on the COVID 19 diagnosis with a chain of CT scan pictures mined from the COVID-19 data set by using ML algorithms like marine predator, simplified suspected infected recovered (SIR), image acquisition, and some more techniques and also survival analysis techniques of ML. The survey clearly explains the models used up to now which are highly defined for the diagnosis of COVID-19 Virus. © 2021 IEEE.

18.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4387-4395, 2021.
Article in English | Scopus | ID: covidwho-1730874

ABSTRACT

COVID-19 is an air-borne viral infection, which infects the respiratory system in the human body, and it became a global pandemic in early March 2020. The damage caused by the COVID-19 disease in a human lung region can be identified using Computed Tomography (CT) scans. We present a novel approach in classifying COVID-19 infection and normal patients using a Random Forest (RF) model to train on a combination of Deep Learning (DL) features and Radiomic texture features extracted from CT scans of patient's lungs. We developed and trained DL models using CNN architectures for extracting DL features. The Radiomic texture features are calculated using CT scans and its associated infection masks. In this work, we claim that the RFs classification using the DL features in conjunction with Radiomic texture features enhances prediction performance. The experiment results show that our proposed models achieve a higher True Positive rate with the average Area Under the Receiver Curve (AUC) of 0.9768, 95% Confidence Interval (CI) [0.9757, 0.9780]. © 2021 IEEE.

19.
3rd IEEE Bombay Section Signature Conference, IBSSC 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1714000

ABSTRACT

Covid-19 has quickly emerged as a global threat, tipping the world into a new phase. The delay in medical care because of the quickly rising Covid-19 cases makes it necessary to overcome the manual and time taking technique such as RTPCR. This paper implements different pre-trained CNN feature extraction models using various Machine Learning (ML) classifiers on chest CT scans to analyze Covid-19 infected patients. It may be observed from the obtained results that accuracy of 96.4% was obtained using the VGG16 model and neural network classifier. The implementation of pre-trained models and classifiers reduce the time taken for manual detection of disease and helps doctors to prevent life of a patient. © 2021 IEEE.

20.
1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 ; 2161, 2022.
Article in English | Scopus | ID: covidwho-1707274

ABSTRACT

COVID-19 is an emerging infectious disease that has been rampant worldwide since its onset causing Lung irregularity and severe respiratory failure due to pneumonia. The Community-Acquired Pneumonia (CAP), Normal, and COVID-19 Computed Tomography (CT) scan images are classified using Involution Receptive Field Network from Large COVID-19 CT scan slice dataset. The proposed lightweight Involution Receptive Field Network (InRFNet) is spatial specific and channel-agnostic with Receptive Field structure to enhance the feature map extraction. The InRFNet model evaluation results show high training (99%) and validation (96%) accuracy. The performance metrics of the InRFNet model are Sensitivity (94.48%), Specificity (97.87%), Recall (96.34%), F1-score (96.33%), kappa score (94.10%), ROC-AUC (99.41%), mean square error (0.04), and the total number of parameters (33100). © 2022 Institute of Physics Publishing. All rights reserved.

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