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1.
International Journal of Electronics and Telecommunications ; 68(4):731-739, 2022.
Article in English | Scopus | ID: covidwho-2205288

ABSTRACT

The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class. © The Author(s).

2.
Egyptian Journal of Hospital Medicine ; 88(1):3959-3968, 2022.
Article in English | Scopus | ID: covidwho-2026194

ABSTRACT

Background: Myocardial injury is neither limited to the acute Coronavirus disease 2019 nor moderate-to-severe cases. Objectives: This study aimed to evaluate the relationship between right ventricular diastolic dysfunction and post-Coronavirus disease 2019 cardiovascular sequelae in young adults with mild disease. Patients and Methods: This study recruited 150 young adults (between 18 and 30 years) who were classified into three equal groups: Group A included 50 patients who sustained cardiac symptoms 12 to 14 weeks following mild Coronavirus disease 2019. Group B included 50 patients who did not show cardiac symptoms 12 to 14 weeks following mild Coronavirus disease 2019. Group C included 50 gender-matched healthy subjects of similar ages without previous Coronavirus disease 2019. Each subject underwent a detailed transthoracic echocardiographic study to detect right ventricular diastolic dysfunction by measuring the tricuspid valve E/A ratio, tricuspid deceleration time, tricuspid E/e' ratio and tricuspid e'/a' ratio. Results: Right ventricular diastolic dysfunction was higher in group A (80% versus 30% versus 0%, p < 0.001). Tricuspid valve e’/a’ was lower in group A (0.86 ± 0.2 versus 1.08 ± 0.2 versus 1.44 ± 0.28, p < 0.001) while tricuspid valve E/ e’ was higher (6.7 ± 1.1 versus 3.25 ± 3 versus 3.04 ± 0.36, p < 0.001). Post-Coronavirus disease 2019 patients with right ventricular diastolic dysfunction had a higher right ventricular basal diameter, higher right ventricular systolic pressure, lower right ventricular tricuspid annular plane systolic excursion, and lower fractional area change. Conclusions: After recovery from mild Coronavirus disease 2019, some of young adults had right ventricular diastolic dysfunction, which was more prevalent in those with post-Coronavirus disease 2019 cardiac symptoms. © 2022, Ain Shams University Faculty of Medicine. All rights reserved.

3.
Computers, Materials, & Continua ; 72(2):3985-3997, 2022.
Article in English | ProQuest Central | ID: covidwho-1786604

ABSTRACT

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’ performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees’ performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by “KF” with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

4.
Stroke ; 53(SUPPL 1), 2022.
Article in English | EMBASE | ID: covidwho-1723997

ABSTRACT

Introduction: Coronavirus Disease 2019 (COVID-19) is associated with an increased risk of stroke and worse stroke outcomes. A clinical score that can identify high-risk patients could enable closer monitoring and targeted preventative strategies. Methods: We used data from the AHA's COVID-19 CVD Registry to create a clinical score to predict the risk of stroke among patients hospitalized with COVID-19. We included patients aged >18 years who were hospitalized with COVID-19 at 122 centers from March 2020-March 2021. To build our score, we used demographics, preexisting comorbidities, home medications, and vital sign and lab values at admission. The outcome was a cerebrovascular event, defined as any ischemic or hemorrhagic stroke, TIA, or cerebral vein thrombosis. We used two separate analytical approaches to build the score. First, we used Cox regression with cross validation techniques to identify factors associated with the outcome in both univariable (p<0.10) and multivariable analyses (p<0.05), then assigned points for each variable based on corresponding coefficients. Second, we used regularized Cox regression, XGBoost, and Random Forest machine learning techniques to create an estimator using all available covariates. We used Harrel's C-statistic to measure discriminatory performance. Results: Among 21,420 patients hospitalized with COVID-19 (mean age 61 years, 54% men), 312 (1.5%) had a cerebrovascular event. Using traditional Cox regression, we created and internally validated a risk stratification score (CANDLE) (Fig) with a C-statistic of 0.66 (95% CI, 0.60-0.72). The machine learning estimator had similar discriminatory performance, with a C-statistic of 0.69 (95% CI, 0.65-0.72). For ischemic stroke or TIA, CANDLE's C-statistic was 0.67 (95% 0.59-0.76). Conclusion: We developed an easy-to-use clinical score, with similar performance to a machine learning estimator, to help stratify stroke risk among patients hospitalized with COVID-19.

5.
Br J Biomed Sci ; 79: 10238, 2022.
Article in English | MEDLINE | ID: covidwho-1714986

ABSTRACT

Background: Genetic risk factors may be related to the infectivity and severity of SARS-CoV-2 infection. Angiotensin-converting enzyme 2 (ACE2) and host transmembrane serine protease (TMPRSS2) have key role in viral cell entrance and priming. Methods: This case-control study on 147 healthy controls and 299 COVID-19 patients identified potential determinants and risk factors, including gene polymorphism involved in the severity (mild, moderate, severe) of COVID-19 disease defined by CORAD radiological criteria. Results: The ACE2 s2285666 and TMPRSS2 rs12329760 SNPs were significantly linked with COVID-19 disease severity, as were certain co-morbidities (hypertension, heart disease) and laboratory parameters. Both SNPs were amongst the highest predictors of disease severity: TMPRSS2 rs12329760 CT + TT [odds ratio (95% CI) 17.6 (5.1-61.10), ACE2 rs2285666 CT + TT 9.9 (3.2-30.9), both p < 0.001]. There was an increase in the expression of genotype frequencies of ACE2 rs2285666 and TMPRSS2 rs1232976 (TT), (CT + TT), and (T) allele in severe COVID-19 group compared to control and mild groups. Disease severity was also linked to elevated CRP, ferritin and D-dimer, and lower lymphocytes and platelet count (all p < 0.001). Conclusion: ACE2 rs2285666 and TMPRSS2 rs12329760 SNPs, in addition to lymphocyte count, CRP, D-dimers, ferritin, and hypertension, are predictors of COVID-19 disease severity.


Subject(s)
Angiotensin-Converting Enzyme 2 , COVID-19 , Serine Endopeptidases , Angiotensin-Converting Enzyme 2/genetics , COVID-19/genetics , Case-Control Studies , Ferritins , Humans , Hypertension , Polymorphism, Single Nucleotide , SARS-CoV-2 , Serine Endopeptidases/genetics
7.
Transfusion ; 61:167A-168A, 2021.
Article in English | Web of Science | ID: covidwho-1441754
8.
Alexandria Engineering Journal ; 2021.
Article in English | ScienceDirect | ID: covidwho-1372848

ABSTRACT

Since the outbreak of COVID-19, many efforts have been made to utilize the respiratory sounds and coughs collected by smartphones for training Machine Learning models to classify and distinguish COVID-19 sounds from healthy ones. Embedding those models into mobile applications or Internet of things devices can make effective COVID-19 pre-screening tools afforded by anyone anywhere. Most of the previous researchers trained their classifiers with respiratory sounds such as breathing or coughs, and they achieved promising results. We claim that using special voice patterns besides other respiratory sounds can achieve better performance. In this study, we used the Coswara dataset where each user has recorded 9 different types of sounds as cough, breathing, and speech labeled with COVID-19 status. A combination of models trained on different sounds can diagnose COVID-19 more accurately than a single model trained on cough or breathing only. Our results show that using simple binary classifiers can achieve an AUC of 96.4% and an accuracy of 96% by averaging the predictions of multiple models trained and evaluated separately on different sound types. Finally, this study aims to draw attention to the importance of the human voice alongside other respiratory sounds for the sound-based COVID-19 diagnosis.

10.
Stroke ; 52(SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1234348

ABSTRACT

Background: Emerging data indicates an increased risk for cerebrovascular events with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and highlights the potential impact of coronavirus disease (COVID-19) on the management and outcomes of acute stroke. We conducted a systematic review and meta-analysis to evaluate the aforementioned considerations. Methods: We performed a meta-analysis of observational cohort studies reporting on the occurrence and/or outcomes of patients with cerebrovascular events in association with their SARSCoV- 2 infection status. We used a random-effects model. Summary estimates were reported as odds ratios (ORs) and corresponding 95% confidence intervals (95%CI). Results: We identified 16 cohort studies including 44,004 patients. Among patients with SARS-CoV- 2, 1.3% (95%CI: 0.9-1.8%;I =88%) were hospitalized for cerebrovascular events, 1.2% (95%CI: 0.8-1.5%;I =85%) for ischemic stroke, and 0.2% (95%CI: 0.1-0.4%;I =69%) for hemorrhagic stroke. Compared to non-infected contemporary or historical controls, patients with SARS-CoV-2 infection had increased odds of ischemic stroke (OR=3.58, 95%CI: 1.43-8.92;I =43%) and cryptogenic stroke (OR=3.98, 95%CI: 1.62-9.77;I =0%). Odds for in-hospital mortality were higher among SARS-CoV-2 stroke patients compared to non-infected contemporary or historical stroke patients (OR=5.60, 95%CI: 3.19-9.80;I =45%). SARS-CoV-2 infection status was not associated to the likelihood of receiving intravenous thrombolysis (OR=1.42, 95%CI: 0.65-3.10;I =0%) or endovascular thrombectomy (OR=0.78, 95%CI: 0.35-1.74;I =0%) among hospitalized ischemic stroke patients during the COVID-19 pandemic. Diabetes mellitus was found to be more prevalent among SARS-CoV-2 stroke patients compared to non-infected contemporary or historical controls(OR=1.39, 95%CI: 1.04-1.86;I =0%). Conclusion: SARS-CoV-2 appears to be associated with an increased risk of ischemic stroke,particularly the cryptogenic subtype. SARS-CoV-2 infection in stroke substantially increases themortality risk.

11.
J Taibah Univ Med Sci ; 16(4): 637-642, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1174404

ABSTRACT

The coronavirus disease 2019 (COVID-19) is a highly contagious novel infection that predominantly presents with fever and respiratory symptoms. However, COVID-19 can masquerade as an acute coronary syndrome, leg pain or swelling with venous thrombosis, loss of consciousness with cerebral venous thrombosis, confusion, limb weakness with brain infarction, facial neuralgia, acute conjunctivitis, acute appendicitis, and testicular pain. We report on a 42-year-old man who presented with mild symptoms of COVID-19. The patient's electrocardiogram showed an ST-segment elevation myocardial infarction (STEMI) due to a left coronary thrombosis. The patient was managed conservatively with medicines and had an uneventful recovery. Emergency physicians should have a high index of suspicion for the unusual presentations of COVID-19.

12.
AJNR Am J Neuroradiol ; 41(11): 2001-2008, 2020 11.
Article in English | MEDLINE | ID: covidwho-724936

ABSTRACT

BACKGROUND AND PURPOSE: A large spectrum of neurologic disease has been reported in patients with coronavirus disease 2019 (COVID-19) infection. Our aim was to investigate the yield of neuroimaging in patients with COVID-19 undergoing CT or MR imaging of the brain and to describe associated imaging findings. MATERIALS AND METHODS: We performed a retrospective cohort study involving 2054 patients with laboratory-confirmed COVID-19 presenting to 2 hospitals in New York City between March 4 and May 9, 2020, of whom 278 (14%) underwent either CT or MR imaging of the brain. All images initially received a formal interpretation from a neuroradiologist within the institution and were subsequently reviewed by 2 neuroradiologists in consensus, with disputes resolved by a third neuroradiologist. RESULTS: The median age of these patients was 64 years (interquartile range, 50-75 years), and 43% were women. Among imaged patients, 58 (21%) demonstrated acute or subacute neuroimaging findings, the most common including cerebral infarctions (11%), parenchymal hematomas (3.6%), and posterior reversible encephalopathy syndrome (1.1%). Among the 51 patients with MR imaging examinations, 26 (51%) demonstrated acute or subacute findings; notable findings included 6 cases of cranial nerve abnormalities (including 4 patients with olfactory bulb abnormalities) and 3 patients with a microhemorrhage pattern compatible with critical illness-associated microbleeds. CONCLUSIONS: Our experience confirms the wide range of neurologic imaging findings in patients with COVID-19 and suggests the need for further studies to optimize management for these patients.


Subject(s)
Brain Diseases/diagnostic imaging , Brain Diseases/virology , Coronavirus Infections/complications , Pneumonia, Viral/complications , Aged , Betacoronavirus , COVID-19 , Cohort Studies , Female , Humans , Magnetic Resonance Imaging , Male , Middle Aged , New York City , Pandemics , Retrospective Studies , SARS-CoV-2
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