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Remdesivir, a viral RNA-dependent RNA polymerase inhibitor, found extensive use in coronavirus disease 2019-infected patients because it curbs the viral load expansion. Among patients hospitalized as a result of lower respiratory tract infection, remdesivir proved to improve recovery time; however, remdesivir also can induce significant cytotoxic effects on cardiac myocytes. In this narrative review, we discuss the pathophysiological mechanism of remdesivir-induced bradycardia and diagnostic and management strategies for these patients. We conclude that further research is necessary to understand better the mechanism of bradycardia in coronavirus disease 2019 patients with or without cardiovascular disorder treated with remdesivir.

Background: Although asthma does not appear to be a risk factor for severe coronavirus disease 2019 (COVID-19), outcomes could vary for patients with different asthma subtypes. The objective of this analysis was to compare COVID-19 outcomes in real-world cohorts in the United States among patients with asthma, with or without evidence of allergy., Methods: In a retrospective analysis of the COVID-19 Optum electronic health record dataset (February 20, 2020-January 28, 2021), patients diagnosed with COVID-19 with a history of moderate-to-severe asthma were divided into 2 cohorts: those with evidence of allergic asthma and those without (nonallergic asthma). After 1:1 propensity score matching, in which covariates were balanced and potential bias was removed, COVID-19 outcomes were compared between cohorts., Results: From a COVID-19 population of 591,198 patients, 1595 patients with allergic asthma and 8204 patients with nonallergic asthma were identified. After propensity score matching (n = 1578 per cohort), risk of death from any cause after COVID-19 diagnosis was significantly lower for patients with allergic vs nonallergic asthma (hazard ratio, 0.48; 95% CI 0.28-0.83; P = 0.0087), and a smaller proportion of patients with allergic vs nonallergic asthma was hospitalized within - 7 to + 30 days of COVID-19 diagnosis (13.8% [n = 217] vs 18.3% [n = 289]; P = 0.0005). Among hospitalized patients, there were no significant differences between patients with allergic or nonallergic asthma in need for intensive care unit admission, respiratory support, or COVID-19 treatment., Conclusions: Asthma subtype may influence outcomes after COVID-19; patients with allergic asthma are at lower risk for hospitalization/death than those with nonallergic asthma., (© 2022. The Author(s).)

Background and Objective: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques., Methods: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded., Results: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy., Conclusion: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20-50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner's advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Alok Aggarwal et al.)

Background & Objectives: Serology testing is essential for immunological surveillance in the population. This serosurvey was conducted to ascertain the cumulative population immunity against SARS-CoV-2 among adults in Jammu district and to understand the association of seropositivity with sociodemographic and clinical correlates., Methods: On September 30 and October 1, 2020, a household survey was done in 20 villages/wards chosen from 10 health blocks in district Jammu, India. Demographic, clinical and exposure information was collected from 2000 adults. Serum samples were screened for IgG antibodies using COVID Kavach MERILISA kit. Tests of association were used to identify risk factors associated with IgG positivity. Crude odds ratio with 95 per cent confidence intervals (CIs) was calculated during univariate analysis followed by logistic regression., Results: Overall adjusted seroprevalence for SARS-CoV-2 was 8.8 per cent (95% CI: 8.78-8.82); it varied from 4.1 per cent in Chauki choura to 16.7 per cent Pallanwalla across 10 blocks in the district. Seropositivity was observed to be comparatively higher in 41-50 and 61-70 yr age groups, among males and in rural areas. Fever, sore throat, cough, dyspnoea, myalgias, anosmia, ageusia, fatigue, seizures, history of exposure, medical consultation, hospitalization and missing work showed significant association with seropositivity on univariate analysis. On logistic regression, only sore throat, myalgia and missing work showed significant adjusted odds of IgG positivity. Extrapolation to adult population suggested that exposure to SARS-CoV-2 was 14.4 times higher than reported cases, translating into Infection fatality rate of 0.08 per cent., Interpretation & Conclusions: Since a major part of population was immunologically naive, all efforts to contain COVID-19 need to be vigorously followed while these baseline results provide an important yardstick to monitor the trends of COVID-19 and guide locally appropriate control strategies in the region.

Background: Coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented pressure on healthcare system globally. Lack of high-quality evidence on the respiratory management of COVID-19-related acute respiratory failure (C-ARF) has resulted in wide variation in clinical practice., Methods: Using a Delphi process, an international panel of 39 experts developed clinical practice statements on the respiratory management of C-ARF in areas where evidence is absent or limited. Agreement was defined as achieved when > 70% experts voted for a given option on the Likert scale statement or > 80% voted for a particular option in multiple-choice questions. Stability was assessed between the two concluding rounds for each statement, using the non-parametric Chi-square (χ 2 ) test (p < 0·05 was considered as unstable)., Results: Agreement was achieved for 27 (73%) management strategies which were then used to develop expert clinical practice statements. Experts agreed that COVID-19-related acute respiratory distress syndrome (ARDS) is clinically similar to other forms of ARDS. The Delphi process yielded strong suggestions for use of systemic corticosteroids for critical COVID-19; awake self-proning to improve oxygenation and high flow nasal oxygen to potentially reduce tracheal intubation; non-invasive ventilation for patients with mixed hypoxemic-hypercapnic respiratory failure; tracheal intubation for poor mentation, hemodynamic instability or severe hypoxemia; closed suction systems; lung protective ventilation; prone ventilation (for 16-24 h per day) to improve oxygenation; neuromuscular blocking agents for patient-ventilator dyssynchrony; avoiding delay in extubation for the risk of reintubation; and similar timing of tracheostomy as in non-COVID-19 patients. There was no agreement on positive end expiratory pressure titration or the choice of personal protective equipment., Conclusion: Using a Delphi method, an agreement among experts was reached for 27 statements from which 20 expert clinical practice statements were derived on the respiratory management of C-ARF, addressing important decisions for patient management in areas where evidence is either absent or limited., Trial Registration: The study was registered with Clinical trials.gov Identifier: NCT04534569.

AbstractObjectivesMethodsResultsConclusionTo determine the Effect of pulmonary rehabilitation on functional status dyspnea and quality of life among post-COVID-19 patients.The study utilized an experimental study design with a total of 120 participants to examine the effects of pulmonary rehabilitation. The participants were divided into two groups: Group A (experimental group) and Group B (control group). Demographic variables such as age, weight, and height were collected. Health-related quality of life (HRQL), post-COVID-19 functional status, and perceived exertion were assessed before and after the intervention.Group A demonstrated a higher mean age than Group B, indicating a significant age difference between the two groups, with no significant difference observed in weight and height. Following the intervention, Group A exhibited significant improvement in HRQL, post-COVID-19 functional status, and perceived exertion compared to Group B.Pulmonary rehabilitation had positive effects on health-related quality of life, post-COVID-19 functional status, and perceived exertion. The experimental group benefited from improved HRQL, suggesting an overall enhancement in their well-being. The study provides preliminary evidence supporting the effectiveness of pulmonary rehabilitation as an intervention for improving outcomes in individuals post-COVID-19. [ABSTRACT FROM AUTHOR]

The use of direct-acting oral anticoagulants (DOACs) has increased rapidly in the last decade; becoming the mainstay for both the prophylaxis and the treatment of venous thromboembolism in various situations including non-valvular atrial fibrillation, joint replacement surgeries and acute DVT/PE, etc. In the present times, DOACs are possibly one of the most widely prescribed medications in the developed world. The worldwide epidemic caused by COVID-19 caused significant changes in the practice of medicine worldwide. Patients who developed severe respiratory illness caused by COVID-19 were noted to develop a wide range of complications, including both arterial and venous thromboembolic complications including deep vein thrombosis and pulmonary embolism, etc. This review is an attempt to identify the role of DOACs in the treatment and prevention of these complications as well as the safety of continuing therapy with DOACs in the patients who were receiving them before contracting the infection. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease-2019 (COVID-19) infection-related neurological events are not uncommon but presenting as posterior reversible encephalopathy syndrome (PRES) without hypertension is a very rare presentation and requires a high index of suspicion. Case summary: We report a case of a middle-aged female who presented with severe COVID-19 disease with no neurological symptoms. She complained of diminished vision on day 7 of the illness and underwent an MRI brain to rule out an ischemic stroke but the findings were suggestive of PRES. She had no episode of hypertension during the hospital stay. Probably severe COVID-related inflammation was the reason for such a presentation. Conservative management resolved the issue and her symptoms weaned off. Conclusion: Severe COVID disease can lead to PRES-like symptoms and requires neuroimaging to validate it. Conservative management is the best treatment for such patients. [ABSTRACT FROM AUTHOR]

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%. [Display omitted] • A novel multimodal framework is proposed for the early diagnosis and accurate classification of COVID-19. • The extracted features are fused using the weighted sum-rule fusion technique for early diagnosis and accurate prediction. • The framework provided an accuracy of 98.67% (X-ray based) and 86.53% (cough based diagnosis) for early diagnosis of patients. [ABSTRACT FROM AUTHOR]

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method. [ABSTRACT FROM AUTHOR]