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Objective To find out differences in the presentation, management and outcomes of COVID-19 infected STEMI patients compared to age and sex-matched non-infected STEMI patients treated during the same period. Methods This was a retrospective multicentre observational registry in which we collected data of COVID-19 positive STEMI patients from selected tertiary care hospitals across India. For every COVID-19 positive STEMI patient, two age and sex-matched COVID-19 negative STEMI patients were enrolled as control. The primary endpoint was a composite of in-hospital mortality, re-infarction, heart failure, and stroke. Results 410 COVID-19 positive STEMI cases were compared with 799 COVID-19 negative STEMI cases. The composite of death/reinfarction/stroke/heart failure was significantly higher among the COVID-19 positive STEMI patients compared with COVID-19 negative STEMI cases (27.1% vs 20.7% p value=0.01);though mortality rate did not differ significantly (8.0% vs 5.8% p value= 0.13). Significantly lower proportion of COVID-19 positive STEMI patients received reperfusion treatment and primary PCI (60.7% vs 71.1% p value=< 0.001 and 15.4% vs 23.4% p value= 0.001 respectively). Rate of systematic early PCI (pharmaco-invasive treatment) was significantly lower in the COVID-19 positive group compared with COVID-19 negative group. There was no difference in the prevalence of high thrombus burden (14.5% and 12.0% p value=0.55 among COVID-19 positive and negative patients respectively) Conclusions In this large registry of STEMI patients, we did not find significant excess in in-hospital mortality among COVID-19 co-infected patients compared with non-infected patients despite lower rate of primary PCI and reperfusion treatment, though composite of in-hospital mortality, re-infarction, stroke and heart failure was higher.
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BACKGROUND: Myocardial infarction Type II (T2MI) is a prevalent cause of troponin elevation secondary to a variety of conditions causing stress/demand mismatch. The impact of T2MI on outcomes in patients hospitalized with COVID-19 is not well studied. METHODS: The Nationwide Inpatient Sample database from the year 2020 was queried to identify COVID-19 patients with T2MI during the index hospitalization. Clinical Modification (ICD-10-CM) codes 'U07.1' and 'I21.A1' were used as disease identifiers for COVID-19 and T2MI respectively. Multivariate adjusted Odds ratio (aOR) and propensity score matching (PSM) was done to compare outcomes among COVID patients with and without T2MI. The primary outcome was in-hospital mortality. RESULTS: A total of 1,678,995 COVID-19-weighted hospitalizations were identified in the year 2020, of which 41,755 (2.48%) patients had T2MI compared to 1,637,165 (97.5%) without T2MI. Patients with T2MI had higher adjusted odds of in-hospital mortality (aOR 1.44, PSM 32.27%, 95% CI 1.34-1.54) sudden cardiac arrest (aOR 1.29, PSM 6.6%, 95% CI 1.17-1.43) and CS (aOR 2.16, PSM 2.73%, 95% CI 1.85-2.53) compared to patients without T2MI. The rate of coronary angiography (CA) in T2MI with COVID was 1.19%, with significant use of CA among patients with T2MI complicated by CS compared to those without CS (4% vs 1.1%, p < 0.001). Additionally, COVID-19 patients with T2MI had an increased prevalence of sepsis compared to COVID-19 without T2MI (48% vs 24.1%, p < 0.001). CONCLUSION: COVID-19 patients with T2MI had worse cardiovascular outcomes with significantly higher in-hospital mortality, SCA, and CS compared to those without T2MI. Long-term mortality and morbidity among COVID-19 patients who had T2MI will need to be clarified in future studies. [Figure: see text].
Subject(s)
COVID-19 , Myocardial Infarction , Humans , COVID-19/complications , COVID-19/therapy , Heart , Myocardial Infarction/epidemiology , Coronary Angiography , TroponinABSTRACT
The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries' governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus's spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data: normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.
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The clinical manifestation of the recent pandemic COVID-19, caused by the novel SARS-CoV-2 virus, varies from mild to severe respiratory illness. Although environmental, demographic and co-morbidity factors have an impact on the severity of the disease, contribution of the mutations in each of the viral genes towards the degree of severity needs a deeper understanding for designing a better therapeutic approach against COVID-19. Open Reading Frame-3a (ORF3a) protein has been found to be mutated at several positions. In this work, we have studied the effect of one of the most frequently occurring mutants, D155Y of ORF3a protein, found in Indian COVID-19 patients. Using computational simulations we demonstrated that the substitution at 155th changed the amino acids involved in salt bridge formation, hydrogen-bond occupancy, interactome clusters, and the stability of the protein compared with the other substitutions found in Indian patients. Protein-protein docking using HADDOCK analysis revealed that substitution D155Y weakened the binding affinity of ORF3a with caveolin-1 compared with the other substitutions, suggesting its importance in the overall stability of ORF3a-caveolin-1 complex, which may modulate the virulence property of SARS-CoV-2.
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In this work, our target point is to focus on rough approximation operators generated from infra-topology spaces and examine their features. First, we show how infra-topology spaces are constructed from N j -neighborhood systems under an arbitrary relation. Then, we exploit these infra-topology spaces to form new rough set models and scrutinize their master characterizations. The main advantages of these models are to preserve all properties of Pawlak approximation operators and produce accuracy values higher than those given in several methods published in the literature. One of the unique characterizations of the current approach is that all the approximation operators and accuracy measures produced by the current approach are identical under a symmetric relation. Finally, we present two medical applications of the current methods regarding Dengue fever and COVID-19 pandemic. Some debates regarding the pros and cons of the followed technique are given as well as some upcoming work are proposed.
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OBJECTIVE: Lung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks. METHODS: This paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch. After the actual analysis of the imbalanced medical image data, the key target domain images are selected, the key migration branch is established, and a single generator is used to complete the medical image multi-domain translation. The conversion between domains ensures the attention performance of the medical image multi-domain translation model and the quality of the synthesized images. At the same time, a lung image classification model based on synthetic image data augmentation is proposed. The synthetic lung CT medical images and the original real medical images are used as the training set together to study the performance of the auxiliary diagnosis model in the classification of normal healthy subjects, and also of the mild and severe COVID-19 patients. RESULTS: Based on the chest CT image dataset, MI-GAN has completed the mutual conversion and generation of normal lung images without disease, viral pneumonia and Mild COVID-19 images. The synthetic images GAN-test and GAN-train indicators reached, respectively 92.188% and 85.069%, compared with other generative models in terms of authenticity and diversity, there is a considerable improvement. The accuracy rate of pneumonia diagnosis of the lung image classification model is 93.85%, which is 3.1% higher than that of the diagnosis model trained only with real images; the sensitivity of disease diagnosis is 96.69%, a relative improvement of 7.1%. 1%, the specificity was 89.70%; the area under the ROC curve (AUC) increased from 94.00% to 96.17%. CONCLUSION: In this paper, a multi-domain translation model of medical images based on the key transfer branch is proposed, which enables the translation network to have key transfer and attention performance. It is verified on lung CT images and achieved good results. The required medical images are synthesized by the above medical image translation model, and the effectiveness of the synthesized images on the lung image classification network is verified experimentally.
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COVID-19 , Pneumonia, Viral , Humans , COVID-19/diagnostic imaging , Algorithms , Area Under Curve , Lung/diagnostic imaging , Image Processing, Computer-AssistedABSTRACT
To determine if people infected with SARS-CoV-2 were at higher risk of developing selected medical conditions post-recovery, data were extracted from the database of a large health maintenance organization (HMO) in Israel between March 2020 and May 2021. For each condition, a condition-naïve group prior to COVID-19 (PCR-positive) infection were compared to a condition-naïve, non-COVID-19 infected group, matched by gender, age, socioeconomic status, minority group status and number of months visited primary care physician (PCP) in previous year. Diagnosis and recuperation dates for each COVID-19 infected participant were applied to their matched comparison participant (1:1 ratio). Incidence of each condition was measured between date of recuperation and end of study period for each group and Cox regression models developed to determine hazard ratios by group status, controlling for demographic and health variables. Crude and adjusted incidence rates were higher for the COVID-19 infected group than those not infected with COVID-19 for treatment for depression/anxiety, sleep disturbance, diagnosis of deep venous thrombosis, lung disease and fibromyalgia. Differences in incidence were no longer observed between the two groups for treatment of sleep disturbance, and diagnosis of lung disease when those hospitalized during the acute-phase of illness (any reason) were excluded. No difference was found by COVID-19 infection status for post-acute incidence of diabetes, cerebrovascular accident, myocardial infarction, acute kidney disease, hypertension and ischemic heart disease. Patients post-COVID-19 infection should be evaluated for depression, anxiety, sleep disturbance, DVT, lung disease and fibromyalgia.
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Overdispersed count data arise commonly in disease mapping and infectious disease studies. Typically, the level of overdispersion is assumed to be constant over time and space. In some applications, however, this assumption is violated, and in such cases, it is necessary to model the dispersion as a function of time and space in order to obtain valid inferences. Motivated by a study examining spatiotemporal patterns in COVID-19 incidence, we develop a Bayesian negative binomial model that accounts for heterogeneity in both the incidence rate and degree of overdispersion. To fully capture the heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects in both the mean and dispersion components of the model. The random effects are assigned bivariate intrinsic conditionally autoregressive priors that promote spatial smoothing and permit the model components to borrow information, which is appealing when the mean and dispersion are spatially correlated. Through simulation studies, we show that ignoring heterogeneity in the dispersion can lead to biased and imprecise estimates. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We apply the model to a study of COVID-19 incidence in the state of Georgia, USA from March 15 to December 31, 2020.
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Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.
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With severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as an emergent human virus since December 2019, the world population is susceptible to coronavirus disease 2019 (COVID-19). SARS-CoV-2 has higher transmissibility than the previous coronaviruses, associated by the ribonucleic acid (RNA) virus nature with high mutation rate, caused SARS-CoV-2 variants to arise while circulating worldwide. Neutralizing antibodies are identified as immediate and direct-acting therapeutic against COVID-19. Single-domain antibodies (sdAbs), as small biomolecules with non-complex structure and intrinsic stability, can acquire antigen-binding capabilities comparable to conventional antibodies, which serve as an attractive neutralizing solution. SARS-CoV-2 spike protein attaches to human angiotensin-converting enzyme 2 (ACE2) receptor on lung epithelial cells to initiate viral infection, serves as potential therapeutic target. sdAbs have shown broad neutralization towards SARS-CoV-2 with various mutations, effectively stop and prevent infection while efficiently block mutational escape. In addition, sdAbs can be developed into multivalent antibodies or inhaled biotherapeutics against COVID-19.
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COVID-19 pandemic has given a sudden shock to economy indices worldwide and especially to the tourism sector, which is already very sensitive to such crises as natural calamities, terrorist activities, virus outbreaks and unwanted conditions. The economic implications for a reduction in tourism demand, and the need to analyse post-COVID-19 tourism motivates our research. This study aims to forecast the future trends for foreign tourist arrivals and foreign exchange earnings for India and to formulate a model to predict the future trends based on the COVID-19 parameters, vaccinations and stringency index (Government travelling guidelines). In the study, we have developed artificial intelligence models (random forest, linear regression) using the stacked based ensemble learning method for the development of base models and meta models for the study of COVID-19 and its effect on the tourism industry. The architecture of a stacking model consists of two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, and is referred to as a level-1 model (Smyth & Wolpert, 1999). The results show that the projected losses require quick action on developing new practices to sustain and complement the resilience of tourism per se.