ABSTRACT
The global spread of COVID-19 has triggered a range of public health responses. Schools and Universities closures are some of the highest-profile social (physical) distancing measures used to break the chain of this infectious disease. Many countries in Asia and Europe have instituted a nationwide school closure, while some US school districts and states have also closed schools. Extensively the pandemic has changed social interaction and education is not an exception to it. In snowballing pandemic, the need for academic continuation educational institutions have shifted rapidly to distance and online learning. A quantitative research approach with survey research design was adopted for 100 nursing students who were enrolled by convenient sampling technique. The primary objective of the study was to assess the benefits and challenges faced by nursing students during virtual teaching and learning in corona lockdown. A self structured 5 - point likert scale was established for data collection. The result showed that many students did not benefit from virtual teaching and learning and majority experienced challenges with technical aspect of this. The conclusion of the study was students were forced to attend online virtual classes due to pandemic, that greatly reduced their critical learning skills. Traditional methods of teaching and learning can never be replaced by online teaching learning platform.
ABSTRACT
Introduction: COVID-19 has spread to the world since the end of December 2019 from Wuhan City, Hubei Province in China and till the today date many has lost their loved and dearest one. The first case was detected in Wuhan, China in December, 2019. Since that the disease has been spread worldwide causing as worldwide. Since, COVID-19 has become pandemic it has affected the daily living of people and as the result it have been changing a lots during this lockdown which can be easily visual through people as well as government. There we people have following different protective measure like wearing masks, washing the hands, maintaining social distancing etc. in order to minimize and control the rate of infection of COVID-19.
ABSTRACT
Introduction: The Coronavirus Disease 2019 (COVID-19) is associated with damage of cells of both innate and adaptive immunity, which results in immune system's impairment leading to secondary infections. Microbiological evaluation helps in diagnostic as well as antimicrobial stewardship leading to accurate treatment of COVID-19 infected patients. Aim: To evaluate superadded bacterial and fungal infections in COVID-19 infected patients and to evaluate bacterial and fungal infections in COVID-19 non infected patients admitted with Acute Respiratory Illness (ARI). Materials and Methods: This retrospective study was carried out in a tertiary care hospital in Delhi, India, over a period of eight months (May to December, 2020). Respiratory samples, received from indoor patients with history of ARI, were processed for COVID-19 (TrueNat Real Time Polymerase chain reaction) as well as for bacterial and fungal cultures following Standard Operating Procedures (SOP). Identification and susceptibility pattern was evaluated by Vitek2 compact system (bioMérieux, Inc. Durham, North Carolina/USA). Quality control strains used were American Type Culture Collection (ATCC) Staphylococcus aureus 29213, Escherichia coli 25922 and Candida parapsilosis ATCC 22019. Minimum Inhibitory Concentration (MIC) levels were standardised as per Clinical and Laboratory Standards Institute (CLSI) guideline, 2020. All statistical analysis was done by Chi-square test using Software Statistical Package for the Social Sciences (SPSS) version 22.0. Results: Total patients admitted with the history of ARI were 542;COVID-19 Positive Group (CPG) included 115 (21.22%) while COVID-19 Negative Group (CNG) included 427 (78.78%). Growth in bacterial and fungal cultures in CPG was 59.13% (68/115) while in CNG;it was 47.78% (204/427). Among the bacterial isolates, most common isolate was Klebsiella pneumoniae {CPG: 41.93% (26/62);CNG: 36.72% (76/207)}, followed by Pseudomonas aeruginosa {CPG: 33.87% (21/62);CNG: 31.88% (66/207)}. Fungal isolates in CPG was 19.48% (15/77) (p-value 0.0445). On comparing Antimicrobial Susceptibility (AST) pattern of Enterobacterales in both CPG (n=36) and CNG (n=102), no statistically significant difference was observed. Co-morbid conditions were found mostly in CNG 89% (140/158) with ARI while only 11% (18/158) was found in CPG. Conclusion: Secondary respiratory infections are quite common amongst COVID-19 positive patients. However, growth in culture, type of isolates, Antimicrobial Resistance (AMR) was almost similar with COVID-19 non infected patients admitted with ARI. Co-morbidity had the similar impact as COVID-19 infection with respect to co-infections.
ABSTRACT
Coronavirus disease 2019 (COVID-19) is an infectious disease with acute intense respiratory syndrome which spread around the world for the very first time impacting the way of life with drastic uncertainty. It rapidly reached almost every nook and corner of the world and the World Health Organization (WHO) has announced COVID-19 as a pandemic. The health care institutions around the globe are looking for viable and real-time technological solutions to handle the virus for evading its spread and circumvent probable demises. Importantly, the artificial intelligence tools and techniques are playing a major role in fighting the effect of virus on the economic jolt by mimicking human intelligence by screening, analyzing, predicting and tracking the existing and likely future patients. Since the first reported case, all the government organizations in the world jumped into action to prevent it and many studies reported the role of AI in taking decisions analyzing big data available in public sphere. Thereby, this review focuses on identifying the significant implication of AI techniques used for the COVID-19 disease management in the public sphere by agglomerating the latest available information. It also discusses the pitfalls and future directions in handling sensitive big data required for advanced neural networks.
ABSTRACT
Nowadays we are facing a pandemic, there is a situation where people are not ready to wear face masks, or they do not wear them properly, so, in this research, we are introducing an automatic mask detection system using image processing and soft computing techniques to tackle this problem. In the midst of the pandemic, covering our faces with a mask has become a new normal, as face masks are active in preventing the spread of the virus. Other precautionary measures are also advocated by the government apart from covering faces, to ensure protection and hygiene. In addition, because of the limited supply of masks in the industry, millions of people are learning to make their face masks. On the opposite, identifying faces with masks on any surveillance devices would be demanding while ensuring less access control in buildings. Face coverage with masks is a problem for algorithms and success in face detection. Currently, the authorities have to manually ask people to wear masks even then they tend to fool the authorities, to avoid that we are proposing a face Machine learning-based model of recognition. In the field of computer vision, this is a common research direction by extracting features directly from the detection region and then using machine detection learning algorithms to identify and recognize. In this Face mask detection-based attendance System, people will be only able to mark their attendance only if they wear a mask, besides if they do not wear a mask, they are given an alert and they would have to wear a mask. © 2021 IEEE.
ABSTRACT
Coronavirus disease 2019 (COVID-19) has been a major health concern globally ever since it was declared as Pandemic by the World Health Organization in March 2020. Due to the evolving and contagious nature of coronavirus, it continues to remain a threat for dental health-care personnel. As the virus travels from person-to-person via direct contact through droplet inhalation, cough, and sneeze or through contact transmission, it remains infectious even through inanimate surfaces. A seemingly healthy asymptomatic person may have the potential to trigger the spread of this disease. Coronavirus has the capability of spreading through community transmission. There is no specific treatment or vaccine as of now for stopping the spread of COVID-19, hence universal precautions and awareness with mass involvement is required to ward off this pandemic. Dental health-care personnel are at immense risk due to the near proximity with patients and continual exposure to saliva, blood, and other body fluids. Management protocol regarding awareness and preventive measures should be laid down for dental clinic/hospital to contain the outspread of this infectious disease.