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1.
Topics in Antiviral Medicine ; 30(1 SUPPL):38-39, 2022.
Article in English | EMBASE | ID: covidwho-1880187

ABSTRACT

Background: Cardiopulmonary symptoms and reduced exercise capacity can persist after SARS-CoV-2 infection. Mechanisms of post-acute sequelae of COVID-19 ("PASC" or "Long COVID") remain poorly understood. We hypothesized that systemic inflammation would be associated with reduced exercise capacity and pericardial/myocardial inflammation. Methods: As part of a COVID recovery cohort (NCT04362150) we assessed symptoms, biomarkers, and echocardiograms in adults >2 months after PCR-confirmed SARS-CoV-2 infection. In a subset, we performed cardiac magnetic resonance imaging (CMR), ambulatory rhythm monitoring (RM), and cardiopulmonary exercise testing (CPET) >12 months after acute infection. Associations between symptoms and oxygen consumption (VO2), cardiopulmonary parameters and biomarkers were evaluated using linear and logistic regression with adjustment for age, sex, BMI, and time since infection. Results: We studied 120 participants (median age 51, 42% female, and 47% had cardiopulmonary symptoms at median 7 months after acute infection). Elevated hsCRP was associated with symptoms (OR 1.32 per doubling, 95%CI 1.01-1.73, p=0.04). No differences in echocardiographic indices were found except for presence of pericardial effusions among those with symptoms (p=0.04). Of the subset (n=33) who underwent CMR at a median 17 months, all had normal cardiac function (LVEF 53-76%), 9 (27%) had pericardial effusions and none had findings suggestive of prior myocarditis. There were no differences on RM by symptoms. On CPET, 33% had reduced exercise capacity (peak VO2 <85% predicted). Individuals with symptoms had lower peak VO2 compared to those reporting recovery (28.4 vs 21.4 ml/kg/min, p=0.04, Figure). Elevated hsCRP was independently associated with lower peak VO2 after adjustment (-9.8 ml/kg/min per doubling, 95%CI-17.0 to-2.5;p=0.01, Figure). The predominant mechanism of reduced peak VO2 was chronotropic incompetence (HR 19% lower than predicted, 95%CI 11-26%;p<0.0001, Figure). Chronotropic incompetence on CPET correlated with lower peak HR during ambulatory RM (p<0.001). Conclusion: Persistent systemic inflammation (hsCRP) is associated with pericardial effusions and reduced exercise capacity > 1 year after acute SARS-CoV-2 infection. This finding appears to be driven mainly by chronotropic incompetence rather than respiratory compromise, cardiac pump dysfunction, or deconditioning. Evaluation of therapeutic strategies to target inflammation and/or chronotropy to alleviate PASC is urgently needed.

2.
PubMed; 2022.
Preprint in English | PubMed | ID: ppcovidwho-338328

ABSTRACT

BACKGROUND: Mechanisms underlying persistent cardiopulmonary symptoms following SARS-CoV-2 infection (post-acute sequelae of COVID-19 "PASC" or "Long COVID") remain unclear. The purpose of this study was to elucidate the pathophysiology of cardiopulmonary PASC using multimodality cardiovascular imaging including cardiopulmonary exercise testing (CPET), cardiac magnetic resonance imaging (CMR) and ambulatory rhythm monitoring. METHODS: We performed CMR, CPET, and ambulatory rhythm monitoring among adults > 1 year after PCR-confirmed SARS-CoV-2 infection in the UCSF Long-Term Impact of Infection with Novel Coronavirus cohort (LIINC;NCT04362150 ) and correlated findings with previously measured biomarkers. We used logistic regression to estimate associations with PASC symptoms (dyspnea, chest pain, palpitations, and fatigue) adjusted for confounders and linear regression to estimate differences between those with and without symptoms adjusted for confounders. RESULTS: Out of 120 participants in the cohort, 46 participants (unselected for symptom status) had at least one advanced cardiac test performed at median 17 months following initial SARS-CoV-2 infection. Median age was 52 (IQR 42-61), 18 (39%) were female, and 6 (13%) were hospitalized for severe acute infection. On CMR (n=39), higher extracellular volume was associated with symptoms, but no evidence of late-gadolinium enhancement or differences in T1 or T2 mapping were demonstrated. We did not find arrhythmias on ambulatory monitoring. In contrast, on CPET (n=39), 13/23 (57%) with cardiopulmonary symptoms or fatigue had reduced exercise capacity (peak VO 2 <85% predicted) compared to 2/16 (13%) without symptoms (p=0.008). The adjusted difference in peak VO 2 was 5.9 ml/kg/min lower (-9.6 to -2.3;p=0.002) or -21% predicted (-35 to -7;p=0.006) among those with symptoms. Chronotropic incompetence was the primary abnormality among 9/15 (60%) with reduced peak VO 2 . Adjusted heart rate reserve <80% was associated with reduced exercise capacity (OR 15.6, 95%CI 1.30-187;p=0.03). Inflammatory markers (hsCRP, IL-6, TNF-alpha) and SARS-CoV-2 antibody levels measured early in PASC were negatively correlated with peak VO 2 more than 1 year later. CONCLUSIONS: Cardiopulmonary symptoms and elevated inflammatory markers present early in PASC are associated with objectively reduced exercise capacity measured on cardiopulmonary exercise testing more than 1 year following COVID-19. Chronotropic incompetence may explain reduced exercise capacity among some individuals with PASC. Clinical Perspective: What is New?Elevated inflammatory markers in early post-acute COVID-19 are associated with reduced exercise capacity more than 1 year later.Impaired chronotropic response to exercise is associated with reduced exercise capacity and cardiopulmonary symptoms more than 1 year after SARS-CoV-2 infection.Findings on ambulatory rhythm monitoring point to perturbed autonomic function, while cardiac MRI findings argue against myocardial dysfunction and myocarditis. Clinical Implications: Cardiopulmonary testing to identify etiologies of persistent symptoms in post-acute sequalae of COVID-19 or "Long COVID" should be performed in a manner that allows for assessment of heart rate response to exercise. Therapeutic trials of anti-inflammatory and exercise strategies in PASC are urgently needed and should include assessment of symptoms and objective testing with cardiopulmonary exercise testing.

5.
Health Inf Sci Syst ; 10(1): 1, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-1648736

ABSTRACT

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

6.
6th International Conference on Information Management and Technology, ICIMTech 2021 ; : 292-297, 2021.
Article in English | Scopus | ID: covidwho-1462637

ABSTRACT

COVID-19 that hit Indonesia quite hard pushed more than 68 million students and around 4 million teachers to move their teaching and learning online. Technology disruptions couldn't be avoided, and they could be in the form of the connection and other things such as class preparation and management. The new form of learning caused quite some hassle that pushed teachers and students to cooperate and adapt to it. Teachers have a hard time motivating their students to keep going and keep their learning spirit. This, however, caused teachers to spend extra time preparing for materials, searching for the best class management method also affecting their teaching motivation and spirit. This research tries to determine how technology disruptions during online classes could affect teachers' teaching motivation and spirit also what could be the possible causes. The qualitative approach with the case study method is conducted by doing an in-depth interview with four experienced teachers using purposive sampling and comparing the result with previous research. This study found that online class is quite a time consuming and it's pretty challenging to do the class management, especially in big classes. Lack of human presence and direct interaction also affect teacher's motivation during teaching online. However, it only affects their motivation on a particular task instead of the basis of being a teacher itself. © 2021 IEEE.

7.
Journal of Theoretical and Applied Information Technology ; 99(9):2068-2078, 2021.
Article in English | Scopus | ID: covidwho-1257794

ABSTRACT

This study aims to understand public tendencies in response to the Covid-19 policy obtained from social media in Indonesia. This research used descriptive quantitative methodology. It involved 580 respondents recruited using quota sampling technique. Research data was collected by using a questionnaire which was shared via social media platforms (Facebook, WhatsApp, and Instagram). Then, the data was analyzed using frequency distribution, where the respondents’ answers were grouped into separated categories to show patterns. Generally, public saw construction on Covid-19 as a virus which spreads rapidly, and a virus that comes from Wuhan-China. Therefore, public cognitively perceived Covid-19 as a scary virus causing acute diseases. There is also an element of social cognition that has opened a potential space for public to disobey government policies. In relation to affection, public looked down on people who disobey the government policies on Covid-19. Thus, this fact made public function as information channel, persuasion agent, and coercion to others especially to their family members. While public showed knowledge, affection, and behavior supporting government policies on Covid 19, many people disobeyed the policies. The disobedience does not necessarily indicate that people are not aware of the government policies on Covid-19. Yet, the main reason for the disobedience is economic factors. If they stay home, they will lose their income. © 2021 Little Lion Scientific.

8.
Health Inf Sci Syst ; 8(1): 29, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-813372

ABSTRACT

COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

9.
Expert Syst Appl ; 164: 114054, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-799363

ABSTRACT

COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.

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