ABSTRACT
During the second wave of the viral pandemic, hospitals were overcrowded by the escalation of Coronavirus Disease-2019 (COVID-19) cases. To effectively address the drastic escalation of the COVID-19 pandemic, innovative solutions are warranted. The rising demand for critical-care services burdens hospitals;hence, to alleviate the burden on the healthcare system, asymptomatic patients or those with mild symptoms can be treated at home through continuous monitoring and care. Affected patients are at risk of hypoxia, which urgently requires oxygen therapy. Depending on the extent of oxygen demand, patients can boost their oxygen levels by making use of a nasal cannula, face mask, oxygen cylinder, and/or oxygen concentrator. Several risk factors are associated with the augmented probability of COVID-19 progression to severe status due to increased oxygen requirement, and they include advanced age, obesity, glucose intolerance, hypertension, and cardiovascular disease. A close monitoring of oxygen saturation (SpO2) along with other clinical investigations like complete and differential blood counts, serum electrolytes, random blood sugar, liver function tests, coagulation profile (Prothrombin Time (PT), activated Partial Thromboplastin Time (aPTT) and International Normalized Ratio (INR)), renal function test, C-reactive protein (CRP), D-dimer and ferritin level are mandatory for patients receiving home-based oxygen therapy. An awareness of safety considerations such as perfectly fitting, proper sized mask, availability of ventilation, knowledge of caregiver about danger signs and good functioning of fire alarm system at home are of prime importance before setting up oxygenation devices at home, and this further mandates a comprehensive evaluation of home-based management and treatment of mildly symptomatic patients with COVID-19. Copyright © 2023 Journal of Clinical and Diagnostic Research. All rights reserved.
ABSTRACT
Chest CT has a potential role in the diagnosis, detection of complications, and prognostication of corona virus disease 2019 (COVID-19). Implementation of appropriate precautionary safety measures, chest CT protocol optimization, and a standardized reporting system based on the pulmonary findings in this disease will enhance the clinical utility of chest CT However, chest CT examinations may lead to both false-negative and false-positive results. Furthermore, the added value of chest CT in diagnostic decision making is dependent on several dynamic variables, most notably available resources (real-time reverse transcription-polymerase chain reaction [RT-PCR] tests, personal protective equipment, CT scanners, hospital and radiology personnel availability, and isolation room capacity) and the prevalence of both COVID-19 and other diseases with overlapping manifestations at chest CT.
ABSTRACT
We propose a network based framework to model spread of disease. We study the evolution and control of spread of virus using the standard SIR-like rules while incorporating the various available models for social interaction. The dynamics of the framework has been compared with the real-world data of COVID-19 spread in India. This framework is further used to compare vaccination strategies. © 2021, Springer Nature Switzerland AG.
ABSTRACT
Introduction: Patient with chronic liver disease (CLD) can have adverse outcomes in setting of COVID-19 infection. Our goal is to determine the prevalence of liver disease in COVID-19 infection and outcomes as compared to individuals without CLD. Methods: We conducted a retrospective review of the patients admitted for COVID-19 infection from March 1st, 2020 till May 31st, 2020. The patients who had chronic liver disease were identified based on imaging interpretation and chronically elevated liver enzymes. Chart review was done for 332 patients, the one with missing data were excluded (n=16). We included 316 patients in the analysis. Of them 12.0% patients had underlying chronic liver disease. Results: Of total 43.7% were female and 48.4% were Caucasians. The patients with liver disease were older (64.6 ± 15.3 vs 57.6 ± 17.4, p=0.02) as compared to non-CLD. The CLD patients had higher number of coronary artery disease (47.4% vs 18.9%, p<0.001). The other comorbid conditions including chronic obstructive pulmonary disease, asthma, cancer, chronic kidney disease, diabetes mellitus, hypertension, obesity, obstructive sleep apnea and smoking were similar in both groups. The CLD patients had higher mortality (aOR: 3.3, 95% CI: 1.37-8.05), thromboembolism (aOR: 3.77, 95% CI: 1.33-10.71), acute respiratory distress syndrome (aOR:2.25, 95% CI: 1.04-4.85) and trend of severe COVID-19 infection (aOR:1.90, 95% CI:0.91-3.98) whereas the 3 month readmission was similar in both groups. The Kaplan Meier survival curve suggest that COVID-19 patients with CLD died early during the study period. Conclusion: The presence of chronic liver disease in inpatient COVID-19 infections is associated with three fold higher mortality. The CLD patients had higher incidence of severe infection. $Φgure
ABSTRACT
In the current times, the fear and danger of COVID-19 virus still stands large. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification using object detection, clustering and Convolution Neural Network (CNN) based binary classifier. For this, YOLOv3, Density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and MobileNetV2 based binary classifier have been employed on surveillance video datasets. This paper also provides a comparative study of different face detection and face mask classification models. Finally, a video dataset labelling method is proposed along with the labelled video dataset to compensate for the lack of dataset in the community and is used for evaluation of the system. The system performance is evaluated in terms of accuracy, F1 score as well as the prediction time, which has to be low for practical applicability. The system performs with an accuracy of 91.2% and F1 score of 90.79% on the labelled video dataset and has an average prediction time of 7.12 seconds for 78 frames of a video.
ABSTRACT
Purpose - Corona Virus Disease 2019 (COVID-19) is a deadly virus named after severe acute respiratory syndrome coronavirus 2;it affects the respiratory system of the human and sometimes leads to death. The COVID-19 mainly attacks the person with previous lung diseases;the major cause of lung diseases is the exposure to nitrogen dioxide (NO2) for a longer duration. NO2 is a gaseous air pollutant caused as an outcome of the vehicles, industrial smoke and other combustion processes. Exposure of NO2 for long-term leads to the risk of respiratory and cardiovascular diseases and sometimes leads to fatality. This paper aims to analyze the NO2 level impact in India during pre- and post-COVID-19 lockdown. The study also examines the relationship between the fatality rate of humans because of exposure to NO2 and COVID-19. Design/methodology/approach - Spatial analysis has been conducted in India based on the mortality rate caused by the COVID-19 using the data obtained through Internet of Medical things. Meanwhile, the mortality rate because of the exposure of NO2 has been conducted in India to analyze the relationship. Further, NO2 level assessment is carried out using Copernicus Sentinel-5P satellite data. Moreover, aerosol optical depth analysis has been carried out based on NASA's Earth Observing System data. Findings - The results indicate that NO2 level has dropped 20-year low because of the COVID-19 lockdown. The results also determine that the mortality rate because of long-time exposure to NO2 is higher than COVID-19 and the mortality rate because of COVID-19 may be a circumlocutory effect owing to the inhalation of NO2. Originality/value - Using the proposed approach, the COVID-19 spread can be identified by knowing the air pollution in major cities. The research also identifies that COVID-19 may have an effect because of the inhalation of NO2, which can severe the COVID-19 in the human body.