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The dreaded coronavirus (COVID-19) disease traceable to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) has killed thousands of people worldwide, and the World Health Organization (WHO) has proclaimed the viral respiratory disease a human pandemic. The adverse flare of COVID-19 and its variants has triggered collaborative research interests across all disciplines, especially in medicine and healthcare delivery. Complex healthcare data collected from patients via sensors and devices are transmitted to the cloud for analysis and sharing. However, it is pretty difficult to achieve rapid and intelligent decisions on the processed information due to the heterogeneity and complexity of the data. Artificial intelligence (AI) has recently appeared as a promising paradigm to address this issue. The introduction of AI to the Internet of Medical Things (IoMT) births the era of AI of Medical Things (AIoMT). The AIoMT enables the autonomous operation of sensors and devices to provide a favourable and secure environmental landscape to healthcare personnel and patients. AIoMT finds successful applications in natural language processing (NLP), speech recognition, and computer vision. In the current emergency, medical-related records comprising blood pressure, heart rate, oxygen level, temperature, and more are collected to examine the medical conditions of patients. However, the power usage of the low-power sensor nodes employed for data transmission to the remote data centres poses significant limitations. Currently, sensitive medical information is transmitted over open wireless channels, which are highly susceptible to malicious attacks, posing a significant security risk. An insightful privacy-aware energy-efficient architecture using AIoMT for COVID-19 pandemic data handling is presented in this chapter. The goal is to secure sensitive medical records of patients and other stakeholders in the healthcare domain. Additionally, this chapter presents an elaborate discussion on improving energy efficiency and minimizing the communication cost to improve healthcare information security. Finally, the chapter highlights the open research issues and possible lines of future research in AIoMT.
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With the expanding progressions in computing devices, mechanical devices, and software to Liaoning and interchange information with diverse electronic gadgets and skeletons over cyberspace, there is a high chance of headway in another region. Well-being is a fundamental viewpoint for everybody in this world regardless of old enough. Everybody needs to prompt a better well-being life cycle by following cleanliness propensities. During this Corona virus pandemic circumstance, IoT has a significant effect on the medical care industry by refining gadget and human connections which drove numerous medical care applications and offers part of advantages to people who are under medical care, households, practitioners, critical care facility centers, and life insurance agencies. In this review, we did a complete review of the Internet of Things progressions, techniques, insights, and achievement cases applied to medical care.
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Centella asiatica encompass be utilize towards healing a set of affliction of humans. Legendary mechanism exemplify to its existence of plenteous genetic activities. In this explore revise be projected to make out the phytoderived antiviral moieties from Centella asiatica against Covid-19 Mpro protein as well as comprehend the Insilico study foundation of molecular activity and during in current examine five isolate molecules in Centella asiatica retrieve as of the PubMed database and be subjected towards docking investigation. Dock analysis were done by using Auto dock vina and PyRx software and followed by admet SAR in addition to pkCSM servers, were used for analyse the drug-likeness prediction. Among 5 Phyto-Molecules, 4 moieties of Centella asiatica are very probable aligned with the Mpro protein of Sars Co V 2. Further, the selected Phyto-molecules on the natural source strength launch consistent prescription and bear frontage discovery. Acknowledged beat molecules could be further in use for in vitro, in vivo evaluation and to investigate their efficiency opposed to COVID-19. Copyright ©2009-2021, JGPT. All Rights Reserved.
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The year 2019 is a outbreak year during which the whole globe has suffered from Covid19 pandemic which has been spotted initially in China and later spread to the whole world;as a result of this viral disease, the whole world had shut down affecting billions of people but till today the Covid battle is on and people are suffering not only from this disease but also in terms of economy, starving being jobless etc. This paper briefs about Corona virus, its types, and structure;the replication and spreading of this virus, Covid19 detection methods, research on vaccination developed across the world to curb this virus;virus impact on various sectors, precautions to be taken to stay away from this virus and Ayurvedic remedy for it. The waves of corona had taken many lives on the globe & have its effect on life style of people. To curb this virus, prevention vaccination has to be found and we people must change in a way so that we could avoid future consequences for the upcoming generation. Keywords.
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Introduction: The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) as a pandemic has put the global population at risk for its infection. It has also led to an accelerated effort to develop vaccines that can mitigate progression to severe infections at a minimum. The ambiguity about existence of antibodies in the human serum poses problem in formulating public health policies like suitable interval between doses of vaccines, appropriate time for vaccinating population, post natural infection, necessity of booster doses along with single dose. Aim: To estimate neutralising antibody level following vaccination of Healthcare Workers (HCWs) after three months and six months respectively. Materials and Methods: This was a prospective observational study performed in Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru, Karnataka, India after Institutional Ethics Committee (IEC) approval from January 2021 to February 2022. The study was conducted in 304 HCWs in the institute who had received two doses of Recombinant ChAdOx1 nCoV- 19 Corona Virus Vaccine (Covishield). 41 HCWs who were naturally infected with SARS-CoV-2 either before or after vaccination were also included. These participants were then subjected to IgG neutralising antibody titer estimation at three months and six months, postvaccination. Results: The study included 304 eligible HCWs. Majority of the participants belonged to the age group of 31-40 years (35.9%). Majority of the study participants were females (51%). Of the 304 participants, 263 were uninfected and 41 participants had been infected before and after vaccination. At the six month follow-up, it was observed that all but one HCW had seroconverted with majority of the participants showing more than 60% antibody level. Participants in the age group of 31-40 years showed the highest level and this observation was found to be statistically significant. Conclusion: Neutralising antibody response in HCWs is a key indicator of the efficacy of the vaccination program for Coronavirus Disease-2019 (COVID-19) in India.
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In a retrospective, cohort study at 4 medical centers with high coronavirus disease 2019 vaccination rates, we evaluated breakthrough severe acute respiratory syndrome coronavirus 2 Delta variant infections in vaccinated healthcare workers. Few work-related secondary cases were identified. Breakthrough cases were largely due to unmasked social activities outside of work.
Subject(s)
COVID-19 , COVID-19/prevention & control , Cohort Studies , Health Personnel , Humans , Retrospective Studies , SARS-CoV-2 , VaccinationABSTRACT
BACKGROUND: Candida auris is an emerging fungal pathogen causing outbreaks in healthcare facilities. Five distinctive genomic clades exhibit clade-unique characteristics, highlighting the importance of real-time genomic surveillance and incorporating genotypic information to inform infection prevention practices and treatment algorithms. METHODS: Both active and passive surveillance were used to screen hospitalized patients. C. auris polymerase chain reaction (PCR) assay on inguinal-axillary swabs was performed on high-risk patients upon admission. All clinical yeast isolates were identified to the species level. C. auris isolates were characterized by both phenotypic antifungal susceptibility tests and whole-genome sequencing. RESULTS: From late 2019 to early 2022, we identified 45 patients with C. auris. Most had a tracheostomy or were from a facility with a known outbreak. Moreover, 7 patients (15%) were only identified through passive surveillance. Also, 8 (18%) of the patients had a history of severe COVID-19. The overall mortality was 18%. Invasive C. auris infections were identified in 13 patients (29%), 9 (69%) of whom had bloodstream infections. Patients with invasive infection were more likely to have a central line. All C. auris isolates were resistant to fluconazole but susceptible to echinocandins. Genomic analysis showed that 1 dominant clade-III lineage is circulating in Los Angeles, with very limited intrahost and interhost genetic diversity. CONCLUSIONS: We have demonstrated that a robust C. auris surveillance program can be established using both active and passive surveillance, with multidisciplinary efforts involving the microbiology laboratory and the hospital epidemiology team. In Los Angeles County, C. auris strains are highly related and echinocandins should be used for empiric therapy.
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Background Mask-related dermatoses among health-care workers can impact their quality of life, work and the safety afforded by the mask. Hence their prompt recognition and remedial measures assume importance during the pandemic. To collect data about-types of masks used, facial skin problems encountered and factors involved, skin care practices followed and attitude to mask-wearing among medical students and healthcare workers of South India, a cross-sectional survey was conducted using an online structured questionnaire filled by the respondents after informed consent.Data was analysed using appropriate statistical tests. Results and Conclusion Of the 576 respondents, majority used unscientific combinations of masks. Most common mask-related dermatosis was new-onset acne. Female gender, younger age, oily skin and longer hours of mask-wearing were predisposing factors. Improper care of skin and mask and reluctance to seek medical advice was observed. Majority had a positive attitude to wearing mask during the pandemic and found several other benefits to mask-wearing. Information about scientific mask-wearing practices and common mask-related skin problems must be disseminated among the medical fraternity and remedial measures offered. A general positive attitude to mask-wearing gives assurance of adherence to mask wearing even during the trough phase of pandemic.
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Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus2. COVID-19 has created the worldwide pandemic situation and it is causing a greater health crisis and deaths of the millions of humans all over the world. All the socio-economic activities are very much affected and there is a huge loss over the world in many aspects. If safety measures are not followed strictly in the public places, then there is a rapid spread of the disese at a very faster rate. Hence, this paper provides a thorough survey of the existing computer vision and machine learning-based technological solutions for controlling the spread of the disease. It also discusses some challenges and future perspectives in developing systems for monitoring the COVID-19 safety violations.
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In the time of the Covid-19 pandemic there is a need to maintain social distancing and prioritize personal hygiene by the use of face masks and proper sanitary precautions. This although is hard to be monitored and controlled accurately and efficiently, can be done through the use of object detection using convolutional neural networks. This can be done in a way using Tiny-YOLOv4 which is an object detection algorithm that provides lightning-fast detection for many classes of objects without the use of such hardware resources. This project aims to train and test a custom data set using this algorithm to create a highly efficient and accurate face mask detection system that can be easily customized to add additional features such as warning systems, etc. It aims to be a system that can prove to be useful once the pandemic is over as it provides crucial data for the prevention and control of any other possible pandemics that may occur in the future. © 2021 IEEE.