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COVID-19 transmission rates among vaccinated persons attending large gatherings have not been reported widely. This research was intended to track the potential incidence of COVID-19 among physicians and their families who attended a large in-person gathering in Atlanta in August 2021. After the successful conclusion of a large-scale indoor gathering, we encouraged all attendees to self-report the incidence of COVID-19 illness. In addition, an online questionnaire was disseminated to collect basic information about age, gender, place of residence, vaccination status including the number of doses, type, and date of each dose as well as behavioral and convention factors that would have contributed to the infection rates. Information about current COVID-19 infection status, symptoms, and severity were also collected. We also contacted the attendees through telephone to gather pending information about their COVID-19 status, after attending the meeting. Most attendees were physicians, employees in the healthcare industry or family members of healthcare professionals. Among the 520 participants of the meeting, no COVID-19 illness was reported up to six weeks after attending the convention. As a sub-group analysis, we obtained demographic data from 143 attendees, through an online survey. Among the survey respondents, 43% were over the age of 60 years, 10% over the age of 70 years, 29% and 14% each between 31-45 years and 12-30 years. 53% were women. Almost 99% had received both doses of COVID-19 mRNA vaccine in January and February of 2021. Public health measures including the use of indoor masks, social distancing, and personal hygiene were followed by 76%. None of the convention attendees who responded had any symptoms or tested positive for COVID-19 infection six weeks after leaving the convention. None reported being diagnosed with COVID-19 for at least 30 days before attending the convention. This report confirms the efficacy of COVID-19 mRNA vaccines against protection from COVID-19 illness among participants of large scale indoor gatherings. Our findings support the notion that large-scale events can be successfully conducted among fully vaccinated persons who follow public health guidelines.
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Background and Aim: While India's vaccination drive against COVID-19 continues to progress, the number of Breakthrough Infections are also revealing an uptick due to Community spread of COVID-19. There is a dearth of data quantifying the extent of breakthrough infections, defined as infections following two doses of vaccine. We aimed to understand the occurrence of Breakthrough Infections among the public in the City of Thrissur, Kerala, India, during the recent surge of COVID-19 in Kerala. Methods: Patients visiting the Internal Medicine Outpatient Department (OPD) in a private hospital in the City of Thrissur in Kerala, India were selected for the study. Subjects above the age of 18 years presenting to the OPD between August 01, 2021 and September 30, 2021 were surveyed through a short interview on the COVID-19 infection history, symptoms, severity and vaccination status. Results: Of the 56 participants who tested positive for COVID-19, 38 had received both doses of vaccine and all had received their first dose of vaccine. 4 patients had no symptoms, 37 patients reported mild symptoms and nine patients reported moderate to severe symptoms. Conclusion: Our study demonstrates the occurrence and describes the epidemiology of COVID-19 breakthrough infections in a City from the Indian State of Kerala in a real-world setting. We conclude the occurrence of Symptomatic Breakthrough Infections of COVID-19 in patients who had received two doses of the vaccine were mild in the majority of the patients (87%). Further research is required to understand the mechanisms behind these Breakthrough infections.
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COVID-19 is a deadly disease which has become a global issue in the recent years. At present, the whole world is trying to combat the disease and scientific communities are putting rigorous efforts in developing 100 per cent effective treatment. Though some synthetic drugs are eff icient in controlling the COVID-19, they possess numerous side effects on human body. In these consequences, there is a global need to search for the natural agents that can act against COVID-19 as a precautionary measure which can improve our immunity and also to control the COVID-19 at various stages of infection. Curcuminoids (curcumin, demethoxycurcumin and bisdemethoxycurcumin) are naturally derived plant compounds from turmeric rhizomes and are having the ability to f ight against COVID-19. Earlier studies have focused on docking of curcuminoids against spike protein and main protease of COVID-19. In the present study, docking of curcuminoids against four COVID-19 proteins and one human receptor has been studied. The efficiency of curcuminoids was compared with that of synthetic drugs. Results clearly showed the potentiality of curcuminoids against COVID-19 and can be used as an alternative to the existing synthetic drugs after sufficient clinical trials. Copyright © 2022, Indian journals. All rights reserved.
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BACKGROUND: Vaccine hesitancy leads to an increase in morbidity, mortality, and health-care burden. Reasons for vaccine hesitancy include anti-vax group statements, misinformation about vaccine side effects, speed of vaccine development, and general disbelief in the existence of viruses like COVID-19. Medical students are future physicians and are key influencers in the uptake of vaccines. Hence, investigating vaccine hesitancy in this population can help to overcome any barrier in vaccine acceptance. METHODS: In this paper, we review five articles on COVID-19 vaccine hesitancy in medical students and consider potential future research. All published papers relevant to the topic were obtained through extensive search using major databases. Inclusion criteria included studies that specifically investigated COVID-19 vaccine hesitancy in medical students published between 2020 and 2021. Exclusion criteria included studies that investigated vaccine hesitancy in health-care professionals, allied health, and viruses apart from COVID-19. A total of 10 studies were found from our search. RESULTS: Based on our exclusion criteria, only five studies were included in our review. The sample size ranged from 168 to 2133 medical students. The percentage of vaccine hesitancy in medical students ranged from 10.6 to 65.1%. Reasons for vaccine hesitancy included concern about serious side effects, vaccine efficacy, misinformation and insufficient information, disbelief in public health experts, financial costs, and belief that they had acquired immunity. CONCLUSION: These results suggest that vaccine hesitancy is an important cause of the incidence and prevalence of COVID-19 cases. Identifying the barriers of vaccine hesitancy in prospective physicians can help increase vaccination uptake in the general public. Further research is necessary to identify the root cause of these barriers. © Shahad A. Hafez et al., 2022;Published by Mary Ann Liebert, Inc. 2022.
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In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction mechanism in the basis of deep learning (DL), machine learning (Ml), etc., on detecting the COVID virus and severity in the human body in an earlier stage. However, the complexity of X-ray images has made it difficult to attain the finest prediction accuracy. Hence, the present research work has aimed to develop a novel Vulture Based Adaboost-Feedforward Neural (VbAFN) scheme to forecast the COVID-19 severity early. Here, the chest X-ray images were employed to identify the COVID risk feature in humans. The preprocessing function is done in the initial phase; the error-free data is imported to the classification layer for the feature extraction and segmentation process. This investigation aims to track and segment the affected parts from the trained X-ray images by the vulture fitness and to segment them with a good exactness rate. Subsequently, the designed model has gained a better segmentation accuracy of 99.9% and a lower error rate of 0.0145, which is better than other compared models. Hence, this proposed model in medical applications will offer the finest results.
Subject(s)
COVID-19 , Deep Learning , Humans , Machine Learning , SARS-CoV-2 , ThoraxABSTRACT
Corona Virus Disease 2019 (COVID-19) has been causing a worldwide pandemic since the end of 2019. This study looked at the mental health of COVID-19 patients who had been quarantined. This cross-sectional study included fifty COVID -19 patients who were under quarantine. Online self-reported questionnaires were used in the study. Demographic data and an inventory of pandemic -related stress factors (PRSF) were used to analyse COVID-19-related stress domains. The quarantined COVID-19 patients had pandemic related stress among themselves. Lack of knowledge about infectiveness and virulence (78%), sleep disorders (94%), financial concerns (92 %) and feeling isolated and avoidance by others (90%) were the most common stressors. Eighty two percent of the patients felt that they were being protected by the family and relatives and only 48% felt that the government is taking the responsibility of protecting them. The findings show that COVID-19 patients in quarantine reported severe psychological distress. As a result, this study urge that required measures be taken to alleviate COVID-19 patients stress, with special focus paid to patients perceptions of stigma and coping techniques.
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A recent global crisis associated with COVID-19 has encouraged millions of people to work from home, thus causing a drastic increase in overall network traffic, data-rate requirements and end network capabilities. This has also produced more noise, cross-talk and undesirable optical-fibre nonlinearities, especially a four-wave mixing (FWM) effect that deteriorates performance of dense wavelength-division multiplexing (DWDM) systems. A presence of FWM in the DWDM systems imposes increasing complexity and latency of networks, and decreases their spectral efficiency. In its turn, this degrades efficient utilization of optical bandwidth. To mitigate the above problems, we suggest a supervised regression modelling (SRM). A relevant SRM-DWDM approach performs self-parametric optimization of the DWDM systems with machine-learning techniques and finds real trade-offs among various factors that affect the FWM. Our model reduces complexity of modelling and computational time, resulting in accurate and reliable prediction of parameter values. We also evaluate the performance of our SRM-DWDM technique by comparing its data with the iterative results obtained for different parameters (e.g., output signal-to-noise ratio, Q-factor, signal power and noise power). Finally, we specify the procedures necessary for global optimization of DWDM systems. © 2021, Institute of Physical Optics. All rights reserved.