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Air quality is a global concern, with particulate matter receiving considerable attention due to its impact on human health and climate change. Recent advances in low-cost sensors allow their deployment in large number to measure spatio-temporal and real-time air quality data. Low-cost sensors need careful evaluation with both regulatory approved methods and other data sets to understand their efficacy. In this work, PM concentrations measured by deploying low-cost sensors at four regional sites are evaluated through comparison with satellite-based model MERRA-2 and the SASS reference instrument. Daily PM2.5 mass concentration variation was analyzed at four regional sites of India from January 2020 to July 2020, including pre-lockdown and six different lockdown periods. Higher PM2.5 concentration was observed at Rohtak (119 mu g m-3) compared to Mahabaleshwar (33 mu g m-3), Bhopal (45 mu g m-3) and Kashmir sites during the pre-lock down period. During the lockdown period, the PM2.5 mass concentration was reduced significantly compared to the pre-lockdown period at every location, although the PM2.5 concentration was different at each location. The air quality trend was quite similar in both the measurements, however, MERRA-2 reconstructed PM2.5 was significantly lower in the pre-lockdown period compared to the lockdown periods. Significant differences were observed between low-cost sensor measurements and MERRA-2 reanalysis data. These are attributed to the MERRA-2 modelling analysis that measures less PM2.5 concentration as compared to ground-based measurements, whereas low-cost sensor are and biases.
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Background: People experiencing homelessness (PEH) are vulnerable to COVID-19 transmission due to substance use, congregate living conditions, and underlying medical conditions. Yet, little is known about factors impacting drug use disorder among PEH during COVID-19 pandemic. The purpose of this study was to identify correlates associated with substance use disorder among PEH, both those who were diagnosed with COVID-19 and those who tested negative or never tested. Methods: A cross-sectional, structured survey was administered to PEH (N = 102) who were recruited from sheltered and unsheltered settings. Descriptive analysis, t-tests, Fisher's exact test or chi-squared test, and bivariate and multiple linear regression were conducted. Results: PEH with a COVID-19 diagnosis included male gender, and Latino race/ethnicity (p <.05). Moreover, substance use disorder scores (p -.037) and days on the street were negatively associated with COVID-19 (p <.001). Multivariable analyses revealed a significant positive relationship between days slept on the street and substance use disorder (p <.001), and a significant negative relationship with alcohol use (p <.05);COVID-19 remained negatively associated with substance use disorder, but it was not significant. Conclusions: This study provides evidence about correlates of drug use disorder among PEH. More studies are needed to understand successful individual and system-level strategies for reducing drug-related problems during COVID-19. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
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Data innovation has made the social improvement encountered another significant change, nearly covering and infiltrating into varying backgrounds. Structural designing plan, with its primary qualities: enormous designing plan;large measure of estimation;Long plan cycle;the power of work is high. With the persistent advancement of data innovation in structural designing plan, different sorts of assistant plan innovations addressed by PCs have step by step appear, which have effectively settled the above troubles and extraordinarily decreased the trouble of structural designing plan. In this way it very well may be seen that the improvement of data innovation has carried limitless advantages to the general public. Consequently, how to additionally apply data innovation to structural designing and carry immense advantages to structural designing has turned into the focal point of ebb and flow research. Copyright © 2022, Anka Publishers. All rights reserved.
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The COVID-19 emerged as a pandemic and affected many nations. World Health Organization [WHO] declared it as a worldwide pandemic alert on March 11, 2020, people had to stay indoors with lockdowns imposed, turning all daily activities to a halt. The lockdown was lifted in phase by manner from June 2020 as the cases were in control. A rise in pandemic was observed in many countries again in 2021, it was termed as the second wave of COVID-19. In India daily cases reached the mark of 4 lakhs in April 2021. This resulted in increased demand for oxygen supplies and other medical equipment’s to tackle the situation. With this, social media or the microblogging platforms like Twitter became a popular means of expressing emotions, making request for help and a daily information channel. The present study analyses the Twitter data extracted using Twitter API. It analyses and classifies people's sentiments related to the supply of oxygen during the second wave of the pandemic in India. The paper analyses the sentiment of the tweets for Indian users from June 20th, 2021, to June 26th, 2021, using Natural language processing (NLP) and Machine Learning (ML) techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background: The Severe Acute Respiratory Coronavirus (SARS-CoV-2) has emerged in a variety of forms since its first appearance in early December 2019. The Omicron variation (B.1.1.529) was recently confirmed as a relatively new Variant of Concern (VOC). There are several mutations in this S-protein, making it an exclusively lethal version of the protein. Omicron variants feature multiple mutations clustered in a region of S protein that is the principal target of antibodies, and these mutations may have an impact on the binding affinities of antibodies to the S protein, as demonstrated by structural analysis. Materials and Methods: Google, Sciencedirect, Web of science, and ResearchGate databases have been explored for potentially existing research to obtain the most emerging trends and up-to-date metadata on various perspectives of Omicron variants. Conclusion: There is evidence that the Omicron variant's mutations may interfere with antibody binding in people who have been exposed to the SARS-CoV-2 virus in the past. At the moment, there is very little information on the Omicron version. Therefore, mutation dispersion evaluations, evolutionary links to previous variants, and putative structural effects on antibody binding effects are all explored in this work. Results: In the current state of pandemic crises, the comprehension of Omicron will pave a path for healthcare professionals to treat infectious conditions very well.
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Tracheostomy is one of the oldest surgical procedures known with mentions from Egyptian, Indian and Greco - Roman era. However, the major advances can be considered to have happened in the last few centuries and it has become a state of art in the last few decades only. Almost 250000 tracheostomies are done worldwide, over 100000 in the USA only. Its commonest indications being acute respiratory failure, strokes and neurotrauma with need for prolonged mechanical ventilation. Other important indications are upper airway obstruction due to local tumours, abscesses and inability to intubate such critical patients. Complications include early ones like stomal infections, bleeding, subcutaneous emphysema, tracheal tube obstruction and dislodgement. The common late complications include tracheal stenosis, tracheoesophageal, tracheocutaneous fistulae and tracheomalacia. Tracheostomies are mostly (80-90%) performed in ICU settings, almost 13% of them for respiratory failures and 75% of them in the first two weeks. Studies have shown an increased survival in patients managed with tracheostomies, however long-time survival remains a challenge. Controversies related to tracheostomies persists and are mostly related to optimal timings, domain of ENT specialist or Intensivist, ideal location of carrying out the procedure being ICUs or operation theatres, preferred technique being conventional or percutaneous, ideal weaning off time and latest being Covid-19 related controversies. It remains thus, an important procedure that has saved millions of lives. Therefore a working knowledge of its evolution, techniques, complications, controversiesand recent advances is a must for all practicing clinicians. © 2022 Nova Science Publishers, Inc.
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Introduction: Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2) infection, also known as Coronavirus Disease-2019 (COVID-19) is the global pandemic, first described in Wuhan city of China in December of 2019. Its diagnosis depends upon real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR). On chest Computerised Tomography (CT), it is almost similar to other viral pneumonia with extensive parenchymal involvement. Semiquantitative scores depicting this extensiveness of involvement could correlate with disease severity, laboratory parameters, mortality, like Intensive Care Unit (ICU) admission, requirements of ventilatory support and longer hospital stay. Aim: To define role of chest CT score in determining disease severity, predicting poor prognosis and mortality of COVID-19 pneumonia in short-term follow-up. Materials and Methods: This prospective study enrolled all admitted real-time RT-PCR positive patients for COVID-19 at All India Institute of Medical Sciences, Rishikesh, India between 15th April and 31st May 2021. All patients were assigned semiquantitative CT scores based on the extent of lung parenchymal involvement of 20 lung regions in chest CT. Clinical severity was matched with chest CT scoring and laboratory findings. Survival curves along with univariate and multivariate analysis were applied to define the role of CT scoring in predicting short term prognosis. Results: Total 547 subjects were included in the study, of which the chest CT score showed a significant association with clinical seventies (p-value <0.001). CT score were correlating significantly with increased serum C-Reactive Protein (CRP) (p-value=0.001) and D-dimer (p-value=0.01), and decreased lymphocyte count (p-value=0.003). A CT score >= 31 was found to be associated with an increased risk of mortality in both univariate and multivariate analysis {Odd Ratio (OR)=276.8;95% Confidence Interval (CI). 45.21-1695.43;p-value <0.001}. Conclusion: Chest CT score can be imaging measure of disease severity and predict a higher probability of mortality in score >= 31. It can also predict other defined variables of short-term prognosis. So, it has an advantage in speedy diagnostic workflow of symptomatic cases, timely referral of patients to higher centre, and better management of critical care resources.
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The novel Coronavirus Disease 2019 (COVID-19) is an illness caused by Severe Acute Respiratory Syndrome 2 (SARS-CoV-2), which affects children as well as adults. This case series pertains an observation on six patients, aged 10-17 years, who were admitted to the hospital and found to be COVID-19 positive on testing. All patients had history of contact with COVID-19 positive confirmed family members. Most common symptoms were fever (n=4), cough (n=2) and breathlessness (n=2). No patient had any pre existing co-morbidity. Raised levels of C-Reactive Protein (CRP) and D-dimer were present in four patients (66.6%) each and elevated serum ferritin levels were seen in 3 (50%) patients. Peribronchial cuffing was seen in chest X-ray of one patient. Supportive therapy along with antibiotics (Azithromycin and Doxycycline) was given to all children. Mean duration of hospital stay was 7.5 days. No patient required intensive care support. All patients recovered at discharge.
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The emergence and pandemic spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have led to significant morbidity and mortality on a global scale. With over four (dated July 2021) million deaths and rising, COVID-19 is on par with mortality associated with the pandemic emergence of variant influenza strains such as in 1957 (H2N2;1.1 million deaths), 1968 (H3N2;one million), and 1918 (H1N1;50 million deaths). A massive scientific quest to understand and combat the pathogen has produced significant gains in our knowledge regarding the virus. SARS-CoV-2 infects humans and susceptible animals through interaction with its spike protein and the host protein angiotensin-converting enzyme 2 (ACE-2), transmembrane serine protease 2 (TMPRSS2), and neuropilin-1. Infection of a host leads to differing clinical manifestations including severe lung disease with bilateral diffuse alveolar damage, and pulmonary edema, indicative of acute respiratory distress syndrome (ARDS) in a significant proportion of patients. The complex nature of the disease and interplay of the host immune system in disease severity and clearance necessitates the use of animal models to fully recapitulate disease pathogenesis. Animal models have played a critical role in vaccine and therapeutic development and testing. Further development and optimization to recapitulate human disease are needed to continue assessing the efficacy of treatment options for COVID-19 and other emerging coronavirus diseases. This article serves to review our current knowledge of animal models that are presently in use to study disease pathology associated with COVID-19 and explore other potential model organisms.
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Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel Coronavirus is the causative agent of Coronavirus Disease-2019 (COVID-19) pandemic has grasped the whole world. Healthcare Workers (HCWs) are at an increased risk. The usage and awareness of entire Personal Protective Equipment (PPE) kit in hospitals on such wide scale has not been seen for some time in healthcare setting. Improper use of these equipment may result in the spread of infection. Aim: To assess the knowledge and attitude of HCWs regarding the correct use of PPE at the beginning of COVID-19 pandemic in order to find the gap in knowledge and to address the perceived barriers in compliance and further to assess the same after training and reinforcement to ensure the HCWs safety. Materials and Methods: A cross-sectional hospital based study was carried out in a designated COVID-19 hospital of Shaheed Hasan Khan Mewati Government Medical College from April 2020 to October 2020 on frontline HCWs posted in various areas of hospital. Sample size was calculated as a minimum of 500 HCWs using appropriate statistical formula. A predesigned, pretested structured questionnaire both online and offline mode was used. The data that was obtained was analysed using SPSS version 20. Results: Seven hundred frontline HCWs were included in the study. Mean age of study population was 30.5 years. A total of 52% of the participants were males and 48% were females. Knowledge level of PPE kit and its use varied across doctors, nursing staff and housekeeping staff. Knowledge about donning and doffing was largely lacking with only 9% doctors and none of other staff were aware which improved to more than 80% post-training. Attitude regarding PPE kit usage was largely positive. Conclusion: The study concludes that there is a constant need of training and re-training of HCWs in order to keep them safe from not only COVID-19 but future infections. An active infection prevention training program is crucial to ensure HCWs safety.
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The medical education system in the United States has gone through a rapid transition to emergency remote teaching as a consequence of the COVID 19 pandemic. For the Engineering Medicine (EnMed) track of the College of Medicine at Texas A&M University, the most challenging aspects are the transition from in-class team-based learning (TBL) to online sessions and virtual facilitation with an interdisciplinary group of faculties. This article outlines the TBL format used in the EnMed curriculum, along with challenges in delivery, student perspective, and strategies for transitioning existing TBL online. © The Author(s) 2021.
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Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcareworkers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient's data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage. © 2022 Tech Science Press. All rights reserved.
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Background: The disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and later called Covid-19 has resulted in significant morbidity worldwide. The virus can cause various complications and affect many organ systems. Preliminary reports have shown that Acute Kidney Injury (AKI) is common in patients with Covid-19, however, outcomes of kidney injury in hospitalized patients, especially at the communitybased hospitals are not well described. The aim of this study was to describe the incidence, severity, and outcomes of Covid-19 patients with AKI at the community-based hospital. Methods: This was a single-center, retrospective observational cohort study. All patients (age ≥18) with positive by polymerase chain reaction testing for Covid-19 who required hospitalization were included in the study. Patients with End-Stage Kidney Disease and kidney transplants were excluded. We compared outcomes of patients with and without AKI. We used univariable and multivariable Cox regression model to evaluate the relationship between AKI and in-hospital mortality. Results: 220 patients were included in the study. 89 (40%) patients developed AKI, of whom 6 (7%) required Kidney replacement therapy (KRT) and 131 (60%) did not develop AKI. In-hospital mortality of patients with AKI was markedly higher than patients without AKI. Among the patients with AKI, 39 (43.8%) experienced in-hospital death while in patients without AKI, 23 (17.5%) died (P<0.001). Unadjusted HR was 2.01 (CI 1.23-3.14;P<0.001). The risk of in-hospital death remained significantly high following adjustment for baseline demographics and comorbidities with adjusted HR 1.8 (CI 1.50-2.74, P=0.015). The median hospital length of stay of patients who were discharged alive differed based upon AKI status. Patients with AKI-KRT had the longest median length of stay (15.5 days IQR 8.5-23.7), followed by patients with AKI non-KRT (7 days, IRQ 5-14) and patients without AKI (6 days, IQR 4-10). Conclusions: AKI is a common condition among patients hospitalized with Covid-19 and is associated with an increased risk of in-hospital mortality. It is important to consider this complication in the management of Covid-19 patients.
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Social media data are used to enhance crisis management, as people widely adopt social media to share and acquire information to cope with uncertainties in crises. Identification and extraction of informative communications out of large volumes of data is critical for accurate situational awareness and timely response. Existing studies use conditions of geolocations, keywords, and topics separately or jointly to retrieve data that can be crisis related, but are not enough to filter subsets of data for different crisis management tasks. We propose that the crisis communication purposes of users can be detected to enhance data selection and prioritization for different crisis management tasks. A classification framework was built to identify three facets of a message: content type, audience type, and information source. The definitions of these categories are not dependent on a specific type of crises. So the classification framework can be potentially applied to different crisis scenarios. Machine learning models were created for the automatic classification of messages. Results showed the CNN-based model achieved the best accuracy (88.5%) for the classification of content type. The proposed Naive Bayes and logistic repression with predetermined features can best differentiate audience types and information source with an accuracy of 72.7% and 72.2%, respectively. © 2021 IEEE.
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As the Indian economy is slowly opening up after the COVID-19 lockdown, it seems like a number of states are overriding even the most basic human rights of their workers in the name of labour reforms.These moves have been criticised in a number of national and international spheres, as along with the Constitution of India, they are inconsistent with various international instruments. Under these circumstances, this article provides a comprehensive view of the changes that have been made and why they are inhumane and derogatory towards the worker communities, and suggests possible ways forward to remedy the atrocious situation. © Policy Press 2021
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Since 2015, there has been a huge increase in laws that ostensibly seek to counter misinformation. Since the pandemic began, this trend has only accelerated. Both authoritarian and democratic governments have introduced more new policies to fight misinformation in 2019 and in 2020. In authoritarian states pandemic-related misinformation provided a new justification for repressive policies. Questions of political motivations aside, as the continuing problem of pandemic misinformation illustrates, it’s unclear how effective these laws are. © 2021 Bulletin of the Atomic Scientists.
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Chronic low grade inflammation and oxidative stress is major pathological process that takes part in obesity and it restrict ventilation, impairs immune responses. Oxidative stress may be liable for the alveolar harm, thrombosis and RBC dysregulation and leptin might be the connection high pervasiveness as a comorbidity of the SARS-CoV-2 contamination. In current situation, obesity with hyper leptin, is a perceived hazard factor for clinical results of SARS-CoV-2. Conventional spices from assorted topographical areas and different territories are considered as likely wellsprings of new medications for treatment of viral contaminations. Spices like Curcuma longa, Shilajeet, Commiphora mukul and Plumbago zeylanica independently and alongside its mix is useful in decrease of oxidative stress as well as leptin concentration. Thus, we can assume that might be useful in avoidance of corpulence and seriousness of SARS-CoV-2 contamination