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Various health agencies, such as the European Medical Agency (EMA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO), timely cited the upsurge of antibiotic resistance as a severe threat to the public health and global economy. Importantly, there is a rise in nosocomial infections among covid-19 patients and in-hospitalized patients with the delineating disorder. Most of nosocomial infections are related to the bacteria residing in biofilm, which are commonly formed on material surfaces. In biofilms, microcolonies of various bacteria live in syntropy;therefore, their infections require a higher antibiotic dosage or cocktail of broad-spectrum antibiotics, aggravating the severity of antibiotic resistance. Notably, the lack of intrinsic antibacterial properties in commercial-grade materials desires to develop newer functionalized materials to prevent biofilm formation on their surfaces. To devise newer strategies, materials prepared at the nanoscale demonstrated reasonable antibacterial properties or enhanced the activity of antimicrobial agents (that are encapsulated/chemically functionalized onto the material surface). In this manuscript, we compiled such nanosized materials, specifying their role in targeting specific strains of bacteria. We also enlisted the examples of nanomaterials, nanodevice, nanomachines, nano-camouflaging, and nano-antibiotics for bactericidal activity and their possible clinical implications.Copyright © 2023 The Author(s)
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The COVID-19 pandemic has changed the way we go about our everyday lives, and we will continue to see its impact for a long time. These changes especially apply to the business world, where the market is very volatile as a result. Requirements of the people are changing rapidly, as are the restrictions on transport and trade of goods. Due to the intense competition and struggles brought about due to the pandemic, acting first on profit opportunities is crucial to businesses doing well in the current climate. Thus, getting the relevant news in time, out of the huge number of COVID-19 related articles published daily is of utmost importance. The same applies to other industries, like the medical industry, where innovations and solutions to managing COVID-19 can save lives, and money in other parts of the world. Manually combing through the massive number of articles posted every day is both impractical and laborious. This task has the potential to be automated using Natural Language Processing (NLP) with Deep Learning based approaches. In this paper, we conduct exhaustive experiments to find the best combination of word-embedding, feature selection, and classification techniques;and find the best structure for the Deep Learning model for article classification in the COVID-19 context. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Background: Alarming increase of hepatitis C infection among the general population has put a significant risk among health-care workers (HCWs). This study aims to find the prevalence of hepatitis C infection among HCWs and its importance for surveillance. Objective(s): The objective of this study was to assess the prevalence of hepatitis C infection among HCWs in a tertiary care hospital. Material(s) and Method(s): A retrospective study was conducted in the Department of Microbiology, RIMS, Imphal, among healthy volunteer HCWs in October 2020 regardless of their COVID-19 status where blood samples were collected and tested for anti-hepatitis C virus (HCV) antibodies using enzyme-linked immunosorbent assay (Merilisa HCV). Result(s): A total of 378 nonconsecutive blood samples were obtained from volunteers aged between 21 and 65 years. The majority of samples were from Imphal West district with a female preponderance of 66.93%. Two positive cases were detected with a prevalence of 0.53% belonging to occupational Category II and III. Maximum prevalence was seen in the age group of 30-39 years, with both positive cases falling in this group. One case was newly detected, while the other was known as a case of hepatitis infection. Conclusion(s): Hepatitis C infection among HCWs is common. Infection control should be prioritized when dealing with patients directly or indirectly. Since no vaccine is available, HCWs require periodic screening. Thus, routine surveillance will help combat such infections among HCWs as a result of occupational exposure. Copyright © 2022 Journal of Medical Society Published by Wolters Kluwer-Medknow.
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An online booking system works all the time. This gives freedom to potential visitors to book a room, ticket at anytime they want. It also maximises your sales because you are not limited to your working hours. An online booking system is a piece of a software used for reservation management. In fact, studies shows that a 24/7 online reservation system greatly increases the number of bookings. With the development in science and technology the usage of online ticket booking has been increased tremendously. Increase in online literacy encourages online ticket booking and customer buying behaviour. Highly demanded lifestyle, convenience, information wide, scarcity of time induces the customers to move from traditional ticket booking to online ticket booking. During Covid the need for online ticket booking increased among the general public since people were not ready to face the crowd and they were insisted about safety. That too during pandemic people were not ready to lose their safety and they were very much conscious about their hygiene. Hence the researchers made an attempt to study on customer attitude towards online ticket booking during COVID-19 with special reference to Coimbatore city. It was found that customer preferred online ticket booking for their convenience and they are satisfied with the online ticket booking in various factors.
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Background: The global wide adoption of telemedicine provided a considerable and long-lasting influence in healthcare but was not firmly established in a catastrophic situation like the COVID-19 pandemic. Hence, the purpose of this study was to enlighten the efficacy of telemedicine and its competence in treating mild COVID-19 patients in a home isolation setup.
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Background and Objectives: Globally, suicide is a pertinent public health crisis that affects almost all nations cross-culturally. Suicide is one of the leading causes of death in many countries, even before the COVID-19 pandemic hit worldwide. India, a nation developing rapidly, is also not free from the leashes of suicide deaths. COVID-19 augmented the rate of suicide due to multifaceted determinants. Adequate empirical evidence about data on suicide is also scarce. Materials and Methods: This review synthesizes determinants, available demographic correlates, and reported rates of suicide published in the Indian context. The authors conducted a thorough literature search to find published English free full-text scientific articles related to suicide during the COVID-19 pandemic in the Indian context. Databases relied on for literature were PubMed, Google scholar, and PLOS one databases using comprehensive search strategies to avail the maximum number of studies. Results: Ten out of 76 studies available in the initial search were analyzed thoroughly for ruling out determinants, rates, and sociodemographic correlates of suicide. Fear of COVID-19 infection, financial crisis, mental breakdown, and job loss are cardinal reasons attributed for suicide, and male suicides are more prevalent in this arena. Interpretation and Conclusions: Findings portray factors such as fear of COVID-19 diagnosis, apprehension to become infected, financial crisis, loss of job, and isolation are some of the significant determinants quoted out. The study points out the need for multifaceted policies in preventing this public health crisis.
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Air pollution causes several diseases like suffocation, chronic obstructive pulmonary disease (COPD), lung cancer, throat infection, and so forth. So, there is a need to monitor indoor air quality for the safety of human life. Indoor air pollution is even more dangerous than outdoor air pollution. Even, after the COVID-19 pandemic, humans are spending most of their time in indoor houses. In addition to this, air pollution is increasing day by day due to varying climate changes. In view of this fact, this research wor has designed and developed a novel system based on the latest IoT technology that monitors indoor air quality and provides a web portal for data visualization. The proposed system consists of several gas sensors integrated on a single PCB that helps in reading seven pollutants like CO2, CO, O3, NO2, VOC, and Particulate Matter along with humidity and temperature. In our work, Raspberry Pi acts as a processor as well as the communicating node to the cloud. The experimental setup is deployed in several indoor places like closed labs, classrooms, homes, etc., where humans spend more time. Raspberry Pi is having an inbuilt wi-fi functionality and the real-time data is sent to Google Firebase with help of a Jio Fi router. After visualizing the data, Indoor Air Quality Index (IAQI) is measured and generates an alarm for the safety of humans when air standard crosses a marginal value. © 2022 IEEE.
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In this pandemic corona virus put a huge impact on our daily life and It also put a huge impact on global trade. Protect face has become a new trend and It has become normal this day. Now these day or in future many people will required to were mask in order to protect themselves and also to protect nearby people and also to the surrounding. Face detection has become important in protecting worldwide population. So in this project we are making AI machine to recognize a people who is wearing a mask or not. It will helps us to protect the environment from spreading the virus. To build this project we need the help machine learning, deep learning and neural network which will help us to achieve the aim of this project. The tolls we required to achieve this project is jupyter notebook and we have to install the numpy, opencv, tensorflow and numpy and learning tool. This approach will help us to detect the person is wear mask in the picture as well as in the picture. It will also recognize and identify a moving face and mask. © 2022 IEEE.
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Background: Regional variations in the impact of the coronavirus disease-2019 (COVID-19) pandemic on out-of-hospital cardiac arrest (OHCA) have been reported. We aimed to examine differences in the community response, emergency medical services (EMS) interventions, and outcomes of OHCA, in Singapore (population 5.7 million) and Atlanta (population 4.16 million), before and during the pandemic. Methods: Using prospectively collected Singapore Pan-Asian Resuscitation Outcomes Study (PAROS) and Atlanta Cardiac Arrest Registry to Enhance Survival (CARES) data, we compared EMS-treated adult OHCAs (≥18 years) during the pandemic period (17weeks from the date of first confirmed COVID-19 case) and pre-pandemic period (corresponding weeks in 2019). The primary outcome was pre-hospital return of spontaneous circulation (ROSC). We reported adjusted odds ratios (aOR) for OHCA characteristics, pre-hospital interventions, and outcomes using binary logistic regression. Results: Of the 3987 EMS-treated OHCAs (overall median age 69 years, 60.1% males) in Singapore and Atlanta, 2084 occurred during the pandemic and 1903 during the pre-pandemic period. Compared with Atlanta, OHCA cases in Singapore were older (median age 72 vs 66 years), received more bystander interventions (65.1% vs 41.4% received cardiopulmonary resuscitation (CPR) and 28.4% vs 10.1% had automated external defibrillator application), yet observed less pre-hospital ROSC (11.3% vs 27.1%). When compared with the pre-pandemic period, the likelihood of residential OHCAs doubled in both cities during the pandemic;in Singapore, OHCAs were more likely to be witnessed (aOR 1.95, 95% confidence interval (CI), 1.59-2.39) yet less likely to receive CPR (aOR 0.81, 95% CI, 0.65-0.99) during the pandemic. OHCAs occurring during the pandemic, compared with pre-pandemic, were less likely to be transported in Singapore and Atlanta (aOR 0.50, 95% CI, 0.42%-0.85%, and 0.36, 95% CI, 0.26-0.50, respectively), without significant differences in overall pre-hospital ROSC. Conclusion: Changes in OHCA characteristics and pre-hospital interventions during the pandemic were likely collateral consequences, with regional variations partly reflecting differences in systems of care and other sociocultural factors. These highlight opportunities for public education and the need for further study into lower transport rates during the pandemic.
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Introduction: Consequent upon continued nationwide lockdown to check the spread of COVID-19 pandemic, online teaching and learning has emerged as a new mode to continue the regular educational programs in India. It is vital to assess the perception of this new method by various stakeholders of educational sector. Objectives: The objectives included identification of the problems and benefits felt by medical students and college teachers about online classes and to assess its effectiveness on attendance and academic performance of students. Methods: The cross-sectional study was conducted during March-October 2021 among 150 first MBBS students admitted in 2020 in a medical college at Udaipur, Rajasthan. The participants who gave informed consent and attended three online and offline monthly tests were included and the rest were excluded. The quantitative techniques included frequency tables, mean, standard deviation and ‘t-test’. Results: Among 100 respondents, 48% were male and 52% were female. The major problems faced by students included lack of space at home to attend class (71%), interrupted internet connectivity (42%), missing interaction with stakeholders (>70%) and mismatch in theory and practicals (69%). The benefits included homemade food, family care, risk minimization of COVID-19 and regular parental monitoring. The major advantages felt by teachers included continued teaching (90.90%) and saving time (100%) in taking attendance in online mode. The average attendance for online classes was significantly higher over offline classes (p<0.0001), whereas the average retention of knowledge level as evidenced by average marks was significantly lower for online class tests compared to offline class tests (p<0.0001). Conclusion: The higher attendance does not reveal higher knowledge retention during online mode of classes.
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Background: Globally Covid 19 has leased almost all domains of human life in and around;business, education, tourism and so on are not an exception. Medical professionals are group which actively got involved in this disaster management arena. Training of medical and nursing professionals has also got into stake or it got narrowed into online mode of education. Methods: A phenomenological qualitative study was conducted to identify perceived benefits and challenges confronted by nursing students of a selected nursing college in Northern India. Data was collected from thirty nursing students with the help of a validated focus group discussion guide. Five FGD were conducted to collect data until availing data saturation. Data was transcribed, coded and developed themes, sub-themes based on codes keeping participant verbatim in support. Results: The thematic content analysis of Data revealed four core themes 1) academic usefulness, 2) Time effectiveness, 3) Challenges faced, 4) attitude and perception of students and couple of sub themes along with it. Overall impression of the study shows nursing students were less favorable to online mode of learning. Conclusion:This study portrays out all aspects pertaining to online education mode considering ill effects and benefits as perceived by nursing students from their nearly one year of experience. © 2022 Author(s).
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The impact of the COVID-19 pandemic on the socially networked world cannot be understated. Entire industries need the latest information from across the globe at the earliest possible. The business world needs to cope with a very volatile market due to the pandemic. Businesses need to be swift in sensing potential profit opportunities and be updated on the changing consumer demands. Technological advances and medical procedures that successfully deal with COVID-19 can help save lives on the other side of the world. This seamless passage of crucial information, now more than ever, is only possible through the networked world. There are on average 821 articles published online on COVID-19 a day. Manually going through around 800 articles in a day is not feasible and highly time-consuming. This can prevent the industries and businesses from getting to the relevant information in time. We can optimize this task by applying machine learning techniques. In this work, six different word embedding techniques have been applied to the title and content of the articles to get an n-dimensional vector. These vectors are inputs for article classification models that employ Extreme Learning Machine (ELM) with linear, sigmoid, polynomial, and radial basis function kernels to train these models. We have also used feature selection techniques like the Analysis of Variance (ANOVA) test and Principal Component Analysis (PCA) to optimize the models. These models help to filter out relevant articles and speed up the process of getting crucial information to stay ahead of the competition and be the first to exploit new market opportunities. The experimental results highlight that the usage of word embedding techniques, feature selection techniques, and different ELM kernels help improve the accuracy of article classification. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Introduction: During the initial peak of the COVID-19 pandemic, many centers globally reported a significant decrease in volumes of emergencies including acute stroke (AS) and acute myocardial infarction (AMI). While the reason for this remains unknown, pandemic-driven anxiety among patients may have resulted in unwarranted refusals to transport when deemed necessary by EMS (Emergency Medical Services) providers. We sought to study the impact of COVID-19 pandemic on the patterns of Emergency Medical transport (EMTr) and patient refusals to transport when serious medical conditions were suspected by EMS personnel. Methods: In this retrospective, observational study of Grady Health System's EMS, we compared the rates of EMTr and refusals for adult patients with suspected diagnoses of AS, AMI, and other medical conditions in the first year of the pandemic (Y1, Mar 2020-Feb 2021) with the corresponding period in the year prior (Y0). We also compared the temporal trends for these variables across the different pandemic waves (1st , Mar-May 2020;2nd , Jun-Aug 2020;3rd, Sep 2020-Feb 2021) with the corresponding periods in the year before. Results: Grady EMS responded to 207,888 calls in Y1 compared to 201,968 in Y0. The overall rate of refusals for all diagnoses was 15.5% in Y1 vs 14.1% in Y0, that for AS was 2.25% in Y1 vs 1.77% in Y0 and 7.5% in Y1 vs 5.67% in Y0 for AMI (Figure). Conclusion: There were more refusals in the first two waves of the pandemic. While refusals were higher for AS and AMI, this was not statistically significant. Our study provides valuable insight into the behavioral patterns of patients seeking emergency care during the pandemic and emphasizes a need for public education and more research. (Figure Presented).
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Aim: This scoping review paper aimed to overview the published research related to nursing students and online learning during the COVID-19 pandemic over the last year 2020-2021. Methodology: Online Google Scholar Database was searched for articles related to nursing students and online learning during Covid 19 pandemic published between1st June 2020 to 1st June 2021. Results: Initial search with key words "nursing students" found 20300 results, finally 39 articles were selected which meet the eligibility criteria. Majority of the authors (69.23 %) have an academic affiliation, only one single author (11.2 %) with clinical affiliation and those who had both academic and clinical were (28.19 %). Only (5.12 %) study applied any theory or conceptual frame work. The focuses of the studies selected were mainly (33.33 %) perception or attitude, (28.20 %) impact and satisfaction were as (20.52 %) focused on experiences and challenges faced. We could only find (5.12 %) studies those where funded. In the selection of research designs majority (46.15 %) adopted quantitative approach followed by (20.51 %) qualitative approaches and (10.25 %) mixed methods;others (23.07 %) included reports, editorials, reflective articles, opinions. About (94.8 %) studies were done without any collaboration with other disciplines only (5.12 %) studies were multidisciplinary. Conclusion: Nursing teaching faculties swiftly responded to COVID-19 by conducting researches mainly adopting quantitative approaches. Nursing researches need more collaboration and funding.
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The virus SARS-COV2 also known as COVID-19 is a pandemic that affecting the entire world. During this tough time, all the industries, manufacturing units, and the related entities are either closed or partially open. During this pandemic, the fabrication industry is also got affected. During the lockdown, industry people are going through various internet platforms and technology to accomplish their work. The fabrication and welding inspection are also part of it. Conventionally it was done physically by inspectors. inspecting the weld. But, during this tough time, some new methodologies of inspection such as the use of the highresolution camera, use of Industry 4.0, and smart glasses are suggested in this paper for various fabrication inspection activities without going to the site. The cost of the inspection also can be reduced by adopting these new approaches along with the demand for maintenance of social distancing needed during COVID-19. © 2021 Institute of Physics Publishing. All rights reserved.
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An unprecedented year has past with Covid-19 lockdown. It has underscored the importance of reliable and uninterrupted power supply. Microgrid ensures reliability and continuity of power supply in a local region with its own local generation and load despatch system, thereby reducing or eliminating the need of a central generator. A microgrid is capable of autonomous operation or it can be connected to a central ac grid that it separates from during disturbances. In this paper results of a microgrid simulation model is presented. Here microgrid system uses two renewable sources namely, solar PV and wind generator along with a battery feeding an inverter supplying load. The system is modeled and implemented using Matlab/simulink environment. The simulation model consists of mono-crystalline solar PV panel of 2.5?kW and a wind turbine emulator having PMDC as wind generator of 1?kW rating as micro sources. For stabilisation of the system a battery bank of 48?V, 100?Ah is also provided. The system is designed to supply a maximum load of 2.5?kW. The system autonomy is approximately two hours for rated load of 2.5?kW. Stability of the system was tested during load variations. The voltage and frequency were found to be stable during load variations. The performance of the inverter to provide constant output voltage of 400?V is good and the output frequency of the inverter is also maintained at 50?Hz. The output voltage conforms to IEC 60038 Standards. An energy management scheme is also developed and simulation results show effectiveness of the scheme.
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Clinical governance in protecting the health-care worker (HCW) refers to measures taken by the organization in providing a safe environment for the HCW while maintaining excellence in the quality of care for the patients. In the wake of the SARS-CoV-2 virus pandemic, the key regulatory measures are taken by the infection control authority of the hospital. The Donabedian model suggests that this process is considered as structure, process, and outcome review measures. Structural changes include surveillance, screening measures, creation of outpatient clinics for COVID-suspected patients, and separate isolated bay for collection of the nasopharyngeal swab. Structural processes also include the creation of separate intensive care units (ICUs) and theaters for infected patients, negative pressure gradient in the operating room (OR), and sites where aerosol generation could occur. Creation of operational pathways such as intubation in the ICU and in the OR should be included in this. The process involves training of HCWs at various levels on the use of personal protective equipment (PPE). Provision of adequate numbers of PPE and cleaning solutions and establishing the diagnostic pathways such as the antigen test, reverse transcriptase-polymerase chain reaction, or nucleic acid amplification test are part of the processes set up by any organization. Outcome analysis involves rates of HCW infection from COVID care wards and ICU, patients testing positive at screening, and patients who may test positive after they undergo treatment at the facility. Long-term outcome measure may include mortality and length of hospital stay.
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Coronavirus is the latest virus not detected in humans until which the coronavirus disease or COVID-19 is caused by it. The disease was first discovered in December 2019 in China, and by now has spread around the world. The virus will easily move from person to person allowing it rapidly spread. A few of the normal, readily recognizable symptoms of COVID-19 is fever. Since the virus epidemic, in public places thermal screening using infrared thermometers is used to test the body temperature to identify the indicated infection among crowd. This early detection is still obviously missing because it spends too much time checking each person's body temperature as well as the most important thing is that the infectious close contact could result in spreading it to the person doing the screening process or from the somebody in charge of showing to the persons being checked. This research paper goal is device design capable of automatically detecting the corona virus affected peoples from the thermal picture and face detection with fewer human encounters using smart IoT-based Drone with Mounted Thermal and Face Imaging Systems. The thermal imaging camera system is built into the drone and coupled with IoT technologies to monitor the scanning process in order to get the data in real time. Furthermore, this proposed device is Equipped with facial recognition technology, it can also view personal details from the pedestrian that can automatically take temperatures from the pedestrians. This proposed system has high healthcare system specifications and will hopefully help deter broader dissemination of corona virus.
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OBJECTIVES: To evaluate the occurrence, clinical course and outcomes of olfactory and gustatory dysfunction in patients with laboratory confirmed coronavirus disease 2019 infection. METHODS: This is a prospective cross-sectional study of patients diagnosed with coronavirus disease 2019 infection by reverse transcription polymerase chain reaction over two months. The epidemiological and clinical outcomes studied were: age, sex, general symptoms, and olfactory and taste dysfunction. RESULTS: A total of 410 coronavirus disease 2019 infected patients were included in the study, with 262 males (63.9 per cent) and 148 females (36.1 per cent). Ninety-nine patients (24.1 per cent) reported chemosensory dysfunction, of which 85 patients (20.7 per cent) reported both olfactory and taste dysfunction. Olfactory and taste dysfunction were proportionally more common in females. The mean duration of olfactory and taste dysfunction was 4.9 days, with a range of 2-15 days. CONCLUSION: Olfactory and taste dysfunction are prevalent symptoms in coronavirus disease 2019 patients. In this study, they were more common in females than males. The occurrence of such dysfunctions is lower in the Indian population than in the European population.