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1.
Asian Journal of Pharmaceutical and Clinical Research ; 15(6):17-18, 2022.
Article in English | EMBASE | ID: covidwho-1918273

ABSTRACT

Mucormycosis started during COVID 19 when patients were treated with number of steroids oxygen, that further lead to increase in diabetes mellitus which was main cause of mucormycosis increase in black fungus further caused rhino-orbito-cerebral mucormycosis and angio invasive behavior of fungal hype that is from Mucoraceae family is main cause of the infection increases rapidly also damages the facial tissues vigorously uncontrolled diabetes, immunosuppressive, steroids poor glycemic control are main causes MRI is a technique that is been used for observing the growth of fungal hype from Epidermiological data its been proven that the mucormycosis is been spreading in countries such as India, Nepal, and Bangladesh rapidly its serious health concern in future.

2.
Mausam ; 73(1):115-128, 2022.
Article in English | English Web of Science | ID: covidwho-1880647

ABSTRACT

This paper presents the comparative results of surface and satellite measurements made during the Phase 1 (25 March to 14 April), Phase 2 (15 April to 3 May) and Phase 3 (3 May to 17 May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O-3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase 1, Phase 2 and Phase 3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O-3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by-8.29% during 2nd Phase of lockdown period.

3.
Journal of Clinical and Diagnostic Research ; 16(4):EE01-EE05, 2022.
Article in English | EMBASE | ID: covidwho-1856271

ABSTRACT

Natural Killer (NK) cells are the key lymphocyte subset of the natural immune system that arbitrates antiviral and anticancer responses. In the human body NK cells inhabit in the bone marrow, lymph nodes, tonsils, skin, liver, gut, and lungs. This bibliographic study covers the origins and evolution of these cells. This review of NK cells includes synopsis of their well-known and evolving themes including their development, functions of cytokine production, anticancer cytotoxicity, clearing of viral infections and exhaustion. Within the liver, NK cells are enhanced in lymphocytes and possess distinctive phenotypic characters and useful properties, which contain tumour cytotoxicity and explicit cytokine profiles. NK cells, while providing innate immunity in the liver, play important roles in providing protection versus pathogens and tumours utilising their cytotoxicity and cytokine production. Accruing substantiation from the last few decades proposes that NK cells perform a vital role in regulating viral hepatitis and liver tumours. In addition, they contribute to the pathogenesis of liver damage including its inflammation. Understanding the description of hepatic NK cell functions has aided us in better understanding the pathogenesis of diseases of the liver and consequently divulging novel therapeutic goals for treating these illnesses.

5.
ACM Journal on Emerging Technologies in Computing Systems ; 18(2), 2022.
Article in English | Scopus | ID: covidwho-1846548

ABSTRACT

Epidemiology models are central to understanding and controlling large-scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19. The statistical inference framework is implemented and compared on Intel's Xeon CPU, NVIDIA's Tesla V100 GPU, Google's V2 Tensor Processing Unit (TPU), and the Graphcore's Mk1 Intelligence Processing Unit (IPU), and the results are discussed in the context of their computational architectures. Results show that TPUs are 3×, GPUs are 4×, and IPUs are 30× faster than Xeon CPUs. Extensive performance analysis indicates that the difference between IPU and GPU can be attributed to higher communication bandwidth, closeness of memory to compute, and higher compute power in the IPU. The proposed framework scales across 16 IPUs, with scaling overhead not exceeding 8% for the experiments performed. We present an example of our framework in practice, performing inference on the epidemiology model across three countries and giving a brief overview of the results. © 2022 Association for Computing Machinery.

6.
2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1831725

ABSTRACT

Finding Semantic similarity in text is a vital concept in the fields of information mining, text-based profiling. There have been many approaches to improve information retrieval by mining the semantics of the text. With the pandemic situation prevailing all over the world, we come across many useful posts about the COVID infection that is being tweeted by medical practitioners and people in the health care sector. While we come across such tweets, we also have tweets related to the vaccines, medical facilities, change in economic conditions due to pandemic, etc. But there is no methodology to efficiently study the tweet data and retrieve useful information out of them. Also, we need to utilize the geographical information that comes with each tweet. Though there have been many studies conducted on sentiment analysis, statistical analysis related to twitter data, there has not been much research on finding out the geographical distribution of COVID related tweets combined with query-based textual similarity of COVID related tweets. In this paper, we try to study the semantics of geo-Tagged twitter data related to COVID and segregate the tweets based on their geographical location and according to the content of tweets. We use an improved version of Density-Based Spatial Clustering for clustering the tweets according to geo-spatial information. Then, we apply cosine similarity techniques to do the textural clustering and evaluate the performance of proposed model. The proposed model is able to cluster tweets using the spatial coordinates and classify the tweets based on the textual similarity measure. © 2022 IEEE.

7.
International Journal of Pharmaceutical and Clinical Research ; 14(4):178-184, 2022.
Article in English | EMBASE | ID: covidwho-1820636

ABSTRACT

Introduction: COVID-19 spread was due to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Even today, COVID cases are being continually recorded. On this basis, it can be said that there is still the danger of COVID-19 cases getting increased at a rapid rate. There is no way of distinguishing the clinical findings and radiological findings of secondary fungal infection from that of COVID-19 pneumonitis and pneumonia. Aim: To Assess the MRI Evaluation of Cases of Mucormycosis after COVID-19”. Material and Methods: In the current study, the researcher conducted an observational study at Pacific Medical College, Udaipur, Rajasthan, India. Data for all the confirmed mucormycosis cases among patients with and without COVID-19 reported from September 2020 to December 2020, for the current study was collected. The researcher used SPSS Statistics 21.0 for performing an analysis of the data obtained from the health care centre. The descriptive statistics were measured and analysed using frequencies, mean, standard deviations, and median. Results: Out of the 286 cases, 65% (N = 185) had CAM (COVID-19 Associated Mucormycosis), with the mean age of 52 (SD = 16) years. Furthermore, 75% (N = 214) of the entire study population was male;and the remaining 25% (N = 72) were female. The prevalence of CAM was 0.28% and the range was 0.04% to 0.60%;on the other hand, CAM prevalence in ICU patients was determined to be 1.9% and the range was o.68% to 2%. Conclusion: From the current results, it can be concluded that Uncontrolled Diabetes Mellitus, found to be among 63% of the participants, was one of the common diseases in both CAM as well as non-CAM groups. In addition, the rhino-orbital area was among the most well-known sites of mucormycosis, with 58% participants, followed by rhino-orbital-cerebral, pneumonic, and other such areas.

8.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333775

ABSTRACT

BACKGROUND: Describing SARS-CoV-2 testing and positivity trends among urgent care users is crucial for understanding the trajectory of the pandemic. OBJECTIVE: To describe demographic and clinical characteristics, positivity rates, and repeat testing patterns among patients tested for SARS-CoV-2 at CityMD, an urgent care provider in the New York City metropolitan area. DESIGN: Retrospective study of all persons testing for SARS-CoV-2 between March 1, 2020 and January 8, 2021 at 115 CityMD locations in the New York metropolitan area. PATIENTS: Individuals receiving a SARS-CoV-2 diagnostic or serologic test. MEASUREMENTS: Test and individual level SARS-CoV-2 positivity by PCR, rapid antigen, or serologic tests. RESULTS: During the study period, 3.4 million COVID tests were performed on 1.8 million individuals. In New York City, CityMD diagnosed 268,298 individuals, including 17% of all reported cases. Testing levels were higher among 20-29 year olds, non-Hispanic Whites, and females compared with other groups. About 24.8% (n=464,902) were repeat testers. Test positivity was higher in non-Hispanic Black (6.4%), Hispanic (8.0%), and Native American (8.0%) patients compared to non-Hispanic White (5.4%) patients. Overall seropositivity was estimated to be 21.7% (95% Confidence Interval [CI]: 21.6-21.8) and was highest among 10-14 year olds (27.3%). Seropositivity was also high among non-Hispanic Black (24.5%) and Hispanic (30.6%) testers, and residents of the Bronx (31.3%) and Queens (30.5%). Using PCR as the gold standard, SARS-CoV-2 rapid tests had a false positive rate of 5.4% (95%CI 5.3-5.5). CONCLUSION: Urgent care centers can provide broad access to critical evaluation, diagnostic testing and treatment of a substantial number of ambulatory patients during pandemics, especially in population-dense, urban epicenters.

9.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333648

ABSTRACT

In order to understand preferences about SARS-CoV-2 testing, we conducted a discrete choice experiment among 4793 participants in the Communities, Households, and SARS-CoV-2 Epidemiology (CHASING COVID) Cohort Study from July 30-September 8, 2020. We used latent class analysis to identify distinct patterns of preferences related to testing and conducted a simulation to predict testing uptake if additional testing scenarios were offered. Five distinct patterns of SARS-CoV-2 testing emerged. "Comprehensive testers" (18.9%) ranked specimen type as most important and favored less invasive specimen types, with saliva most preferred, and also ranked venue and result turnaround time as highly important, with preferences for home testing and fast result turnaround time. "Fast track testers" (26.0%) ranked result turnaround time as most important and favored immediate and same day turnaround time. "Dual testers" (18.5%) ranked test type as most important and preferred both antibody and viral tests. "Non-invasive dual testers" (33.0%) ranked specimen type and test type as similarly most important, preferring cheek swab specimen type and both antibody and viral tests. "Home testers" (3.6%) ranked venue as most important and favored home-based testing. By offering less invasive (saliva specimen type), dual testing (both viral and antibody tests), and at home testing scenarios in addition to standard testing scenarios, simulation models predicted that testing uptake would increase from 81.7% to 98.1%. We identified substantial differences in preferences for SARS-CoV-2 testing and found that offering additional testing options, which consider this heterogeneity, would likely increase testing uptake. SIGNIFICANCE: During the COVID-19 pandemic, diagnostic testing has allowed for early detection of cases and implementation of measures to reduce community transmission of SARS-CoV-2 infection. Understanding individuals' preferences about testing and the service models that deliver tests are relevant in efforts to increase and sustain uptake of SARS-CoV-2 testing, which, despite vaccine availability, will be required for the foreseeable future. We identified substantial differences in preferences for SARS-CoV-2 testing in a discrete choice experiment among a large national cohort of adults in the US. Offering additional testing options that account for or anticipate this heterogeneity in preferences (e.g., both viral and antibody tests, at home testing), would likely increase testing uptake. CLASSIFICATION: Biological Sciences (major);Psychological and Cognitive Sciences (minor).

10.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333626

ABSTRACT

OBJECTIVE: To investigate the role of children in the home and household crowding as risk factors for severe COVID-19 disease. METHODS: We used interview data from 6,831 U.S. adults screened for the Communities, Households and SARS/CoV-2 Epidemiology (CHASING) COVID Cohort Study in April 2020. RESULTS: In logistic regression models, the adjusted odds ratio [aOR] of hospitalization due to COVID-19 for having (versus not having) children in the home was 10.5 (95% CI:5.7-19.1) among study participants living in multi-unit dwellings and 2.2 (95% CI:1.2-6.5) among those living in single unit dwellings. Among participants living in multi-unit dwellings, the aOR for COVID-19 hospitalization among participants with more than 4 persons in their household (versus 1 person) was 2.5 (95% CI:1.0-6.1), and 0.8 (95% CI:0.15-4.1) among those living in single unit dwellings. CONCLUSION: Early in the US SARS-CoV-2 pandemic, certain household exposures likely increased the risk of both SARS-CoV-2 acquisition and the risk of severe COVID-19 disease.

11.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333585

ABSTRACT

OBJECTIVE: To estimate the prevalence of anxiety symptoms and the association between moderate or severe anxiety symptoms and health and potential stressors among adults in the U.S. during the COVID-19 pandemic. METHODS: This analysis includes data from 5,250 adults in the Communities, Households and SARS/CoV-2 Epidemiology (CHASING) COVID Cohort Study surveyed in April 2020. Poisson models were used to estimate the association between moderate or severe anxiety symptoms and health and potential stressors among U.S. adults during the COVID-19 pandemic. RESULTS: Greater than one-third (35%) of participants reported moderate or severe anxiety symptoms. Having lost income due to COVID-19 (adjusted prevalence ratio [aPR] 1.27 (95% CI 1.16, 1.30), having recent COVID-like symptoms (aPR 1.17 (95% CI 1.05, 1,31), and having been previously diagnosed with depression (aPR 1.49, (95% CI 1.35, 1.64) were positively associated with anxiety symptoms. CONCLUSIONS: Anxiety symptoms were common among adults in the U.S. during the COVID-19 pandemic. Strategies to screen and treat individuals at increased risk of anxiety, such as individuals experiencing financial hardship and individuals with prior diagnoses of depression, should be developed and implemented.

12.
Journal of Engineering Education Transformations ; 35(Special Issue 1):249-255, 2022.
Article in English | Scopus | ID: covidwho-1787479

ABSTRACT

Covid-19 pandemic forced academic Institutes to switch from 100% offline to 100% online delivery mode in very short amount of time. Most of academic Institutes were not ready for this change in terms of required faculty training. Also many Institutes lagged essential computing hardware, software, and Internet bandwidth support for effective implementation of ICT based education. Due to pressure from apex bodies like AICTE, peer Institutes, students and parents almost all academic Institutes started implementation of the online academic delivery in hurry. However, this unprepared start caused increased level of frustration among students and faculty community. Very soon a need of effective implementation of online academic delivery was recognized by the different Institutes. The paper presents how our Institute implemented and ensured effective implementation of online academic delivery using Innovative Practice League (IPL) competition. The paper explains methodology adopted by the Institute in details for improving quality of online academic delivery. The Institute undertook various initiatives at faculty-level, department-level, and Institute-level for the same;the paper discusses the same in details. The paper also presents details of IPL competition and discusses how IPL helped to have awareness of quality issues related with online academic delivery. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

13.
Journal of Engineering Education Transformations ; 35(Special Issue 1):194-198, 2022.
Article in English | Scopus | ID: covidwho-1787369

ABSTRACT

The COVID-19 pandemic forced the worldwide and sudden transformations of conventional live teaching strategy to online ICT based format in engineering education. Cognitive engagement of students is the main challenge during online teaching-learning platform. Team-based learning (TBL) is a collaborative teaching-learning approach that allows students to follow a structured procedure to enrich student cognitive engagement in the online platform. This paper is focused on comparative studies of team based learning and non-team based learning for a course “Advance Database System” in computer science and engineering department. The hypothesis is considered that TBL is best learning approach to cognitive engagement of students in the online platform. T-test and Chi-square test is applied to analyse the result. The results exhibited that TBL is the superior learning method and the hypothesis was proved and accepted. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

14.
4th International Conference on Communication, Information and Computing Technology, ICCICT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1709709

ABSTRACT

Medical imaging techniques are often used in treatment and follow-ups for diagnosed diseases. Image scans provide quick acquisition of images and clear and precise information, along with a magnified view of a particular portion of the body. Chest images can demonstrate various lung disorders, such as, COVID-19, Interstitial Lung Diseases (ILD) and Chronic lung disease, Pneumonia, Bronchiectasis, Cystic Fibrosis, etc. However, subtle changes in the volume and character of lung abnormalities can be difficult to assess even by expert radiologists. This is where Artificial Intelligence (AI) comes in. AI can aid traditional medical imaging technology by offering computational prowess that process images with greater speed and precision. This work presents a solution that performs AI-empowered analysis of Chest image scans for diagnosis, tracking and prognosis of various lung diseases. © 2021 IEEE

15.
10th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2021 ; : 393-400, 2021.
Article in English | Scopus | ID: covidwho-1705057

ABSTRACT

Due to the COVID-19 pandemic, there has been an increased focus on contactless biometric systems for authentication. With face masks covering half of the face, it becomes significantly difficult for existing face recognition algorithms to work with the same accuracy (as for non-occluded faces). The main objective of this paper is to successfully build an authentication system that can detect and recognize a user based only on the periocular region of their face. The UBIPr - ubiris dataset has been analyzed and by using transfer learning a CNN model based on VGG-16 has been built for feature extraction. Later, these features have been passed through an ANN for fine-tuned classification. Two such ANN models have been maintained depending on the eye position i.e., separate models for left and right eyes and their outputs have then been combined for classification. A traditional split of 70-30 for training and testing has been followed. The accuracy for the test set is found to be 99.4%. The overall reduction in the loss for the test set is 0.09%. The average confidence for true positives is 0.78 and false positives is 0.11. This provides security to the system from false positives by setting the confidence threshold higher as well as minimizes the chances of physical contact. Currently, the system works only on still images. © 2021 IEEE.

16.
European Respiratory Journal ; 58:2, 2021.
Article in English | Web of Science | ID: covidwho-1703433
18.
MEDLINE;
Preprint in English | MEDLINE | ID: ppcovidwho-327313

ABSTRACT

Background: Kidney transplant recipients (KTRs) with COVID-19 have poor outcomes compared to non-KTRs. To provide insight into management of immunosuppression during acute illness, we studied immune signatures from the peripheral blood during and after COVID-19 infection from a multicenter KTR cohort.. Methods: Clinical data were collected by chart review. PAXgene blood RNA was poly-A selected and RNA sequencing was performed to evaluate transcriptome changes. Results: A total of 64 cases of COVID-19 in KTRs were enrolled, including 31 acute cases (< 4 weeks from diagnosis) and 33 post-acute cases (>4 weeks). In the blood transcriptome of acute cases, we identified differentially expressed genes (DEGs) in positive or negative association COVID-19 severity scores. Functional enrichment analyses showed upregulation of neutrophil and innate immune pathways, but downregulation of T-cell and adaptive immune-activation pathways proportional to severity score. This finding was independent of lymphocyte count and despite reduction in immunosuppression (IS) in most KTRs. Comparison with post-acute cases showed "normalization" of these enriched pathways after >4 weeks, suggesting recovery of adaptive immune system activation despite reinstitution of IS. The latter analysis was adjusted for COVID-19 severity score and lymphocyte count. DEGs associated with worsening disease severity in a non-KTR cohort with COVID-19 (GSE152418) showed significant overlap with KTRs in these identified enriched pathways. Conclusion: Blood transcriptome of KTRs affected by COVID-19 shows decrease in T-cell and adaptive immune activation pathways during acute disease that associate with severity despite IS reduction and show recovery after acute illness. Significance statement: Kidney transplant recipients (KTRs) are reported to have worse outcomes with COVID-19, and empiric reduction of maintenance immunosuppression is pursued. Surprisingly, reported rates of acute rejection have been low despite reduced immunosuppression. We evaluated the peripheral blood transcriptome of 64 KTRs either during or after acute COVID-19. We identified transcriptomic signatures consistent with suppression of adaptive T-cell responses which significantly associated with disease severity and showed evidence of recovery after acute disease, even after adjustment for lymphocyte number. Our transcriptomic findings of immune-insufficiency during acute COVID-19 provide an explanation for the low rates of acute rejection in KTRs despite reduced immunosuppression. Our data support the approach of temporarily reducing T -cell-directed immunosuppression in KTRs with acute COVID-19.

19.
Journal of Content, Community and Communication ; 14(8):210-217, 2021.
Article in English | Scopus | ID: covidwho-1687839

ABSTRACT

India is one of the most populous country in the world. In last few years, it has shown unprecedented development in its digital infrastructure. India has the second largest telecom subscribers and the 3rd largest Internet users in the world. India is known to be young nation with 65% population below the age of 35. In 2020, the average age of an Indian is 29 years, people are more techno savvy and use Internet more. The Covid-19 pandemic has increased the social media usage in India. This research studied the impact of Covid-19 pandemic on Indian consumer behaviour towards social media. The basic objective was to study the change in behaviour of people towards social media pre and post the start of the pandemic. The literature review was done to understand this change. It was evident from the study that people have started using social media more after the pandemic was started. It was used to gather information about the products, compare the products, understand the review of products, and finally purchase of the product. The study also found out about the social media platforms that are more popular in India. It was also observed from the study that people will continue to use social media even after life will come to normal post pandemic © 2021,Journal of Content, Community and Communication.All Rights Reserved

20.
Indian Journal of Community Health ; 33(4):658-662, 2021.
Article in English | Scopus | ID: covidwho-1675708

ABSTRACT

Background: In 2020, a new global pandemic has emerged, caused by a new strain of Corona virus called SARS-CoV-2. A poor understanding of the disease among healthcare students may implicate in delayed treatment and rapid spread of infection and development of complications. Objectives: 1) To estimate the knowledge, attitude, practice and stigma associated with SARS-CoV-2 infection among healthcare students. 2) To estimate the association between socio-demographic determinants with knowledge, attitude, practice and stigma of SARS-CoV-2 infection among healthcare students. Methods: A web-based cross-sectional study was undertaken among 493 healthcare i.e., medical, nursing and allied sciences students, during 1st May to 20th May 2020, using a pre-designed and semi-structured questionnaire. Data was analyzed using Chi square test, t-test, one-way Anova and Bonferroni test was used for assessing the association among the study variables. Results: The study revealed that, there were about 225(45.64%) medical, 165(33.46%) nursing and 103(20.89%) allied sciences students. Majority were females 349(70.79%), majority were Hindus 333(67.54%). Mean knowledge score of medical, nursing and allied sciences students were 15.66 (2.518), 14.16 (2.92) and 14.46 (3.11) respectively. Practice score was good among nursing than allied sciences students at ‘p’ (0.003). Conclusion: Even though the overall knowledge was less in our study participants, majority of them had followed good practices for preventing SARS-CoV-2 infection. © 2021, Indian Association of Preventive and Social Medicine. All rights reserved.

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