ABSTRACT
Aim: The aim of this study was to evaluate the need for analgesia in patients undergoing single-visit root canal treatment, within 2 days after COVID-19 immunisation. Method(s): Two days after receiving the COVID-19 vaccination, 50 patients undergoing Single visit root canal treatments for acute pulpits in molar teeth were divided into two groups of 25 each (Group M for males and Group F for females). Each patient was given a prescription for 650 mg of acetaminophen (Dolo 650 mg) tablets to be taken eight hours a day, with instructions to use the same only if needed for pain. They were instructed to keep a record of the number of tablets consumed as per record sheet 1, and if the pain did not subside then a stronger analgesic, Ketorolac DT 10 mg twice a day, had to be taken and recorded. Result(s): In Group M, the mean number of analgesic tablets required was 0.44+/-0.64 and in Group F, the mean number of analgesic tablets required was 0.80+/-1.09. Although the mean analgesic requirement was higher in females as compared to males, the difference of 0.80+/-1.09 between the two groups was not significant statistically (0.360). Conclusion(s): Within the limitations of this study, it is concluded that acetaminophen is effective in relieving post-obturation pain after a single-visit RCT in patients recently vaccinated against the CoVid-19. Copyright © 2022 Ubiquity Press. All rights reserved.
ABSTRACT
The lives of people around the world have been heavily affected due to the unprecedented outbreak of 2019 novel coronavirus, more commonly known as COVID-19 pandemic. The situation is no more just an economic and public health crisis, rather a disruption in all aspects of the society of which electricity grid is no exception. Electrical utilities and operators of many affected countries of the world have found themselves in a precarious position. Due to governmental restrictions to contain the virus within the country, the grid experienced drastic change in electricity generation and demand patterns. In this paper, the change in daily load curve of Bangladesh is analyzed and compared to that of previous years. Comparison is made between system demand pattern before and after country-wide governmental restrictions were enacted. The results from the study provides valuable insights into Bangladesh power system under a natural and global contingency.
ABSTRACT
The lives of people around the world have been heavily affected due to the unprecedented outbreak of 2019 novel coronavirus, more commonly known as COVID-19 pandemic. The situation is no more just an economic and public health crisis, rather a disruption in all aspects of the society of which electricity grid is no exception. Electrical utilities and operators of many affected countries of the world have found themselves in a precarious position. Due to governmental restrictions to contain the virus within the country, the grid experienced drastic change in electricity generation and demand patterns. In this paper, the change in daily load curve of Bangladesh is analyzed and compared to that of previous years. Comparison is made between system demand pattern before and after country-wide governmental restrictions were enacted. The results from the study provides valuable insights into Bangladesh power system under a natural and global contingency. © 2021 IEEE.
ABSTRACT
INTRODUCTION: The outbreak of COVID-19 and the consequential isolation measures have significantly threatened the mental well-being of the public. Previous research suggests that a pandemic may result in the lifelong prevalence of psychological morbidities. EVIDENCEACQUISITION: Studies that reported the prevalence of depression, anxiety, stress, insomnia as a response to the pandemic, across several populations in PubMed and ScienceDirect databases, were included. Of the 136 studies included, 45 studies were on the general population, 45 on healthcare workers, 18 on students and young adults, 9 on psychiatric patients, 3 on COVID-19 patients and 16 on other populations. EVIDENCESYNTHESIS: Though the results across populations were inconsistent, all populations exhibited elevated levels of depression, anxiety and associated psychological symptoms (like posttraumatic stress disorder, stress, insomnia). Acomparison among the populations revealed that healthcare workers (especially frontline workers) were at the highest risk of mental health problems. Other risk factors included: being female, younger, single/divorced/widowed and having a history of mental illness. CONCLUSIONS: COVID-19 is not just a threat to physical health but also the mental health of the public. Further research is needed in this aspect. There also exists a need to identify vulnerable populations and design suitable psycho-logical interventions.
ABSTRACT
The year 2020, has seen the advent of a pandemic that has affected the world as we know it globally. The origin reportedly from Wuhan, China, this pandemic is caused by COVID-19 which belongs to the family of Coronavirus. The increase of infection and mortality has shot up exponentially and has left mankind bewildered amongst the remains of the unseen disaster. During these times of hardship mankind has to face with a series of emotions. Analysis of all these emotions becomes a primary target for the well-being of an individual and mankind as a whole. The main motive of our study is to analyze these emotions correctly. Gathering these big chunks of data about this study from different social platforms like Twitter, Facebook, Instagram, etc. plays a major role. For this study we will be considering only the corona virus related tweets from Twitter. Analysis of all these tweets will give us a proper insight about the real emotions that the people has to face during these COVID-19 times. The main objective is to work with multinomial attributed to assess the sentiments more precisely. The next step is cleaning the data and labelling them for further processing. Hereafter a model is developed which is used to access the data and then predict the actual sentiment behind the tweet. The data is assessed using the binary-class and multi-class property with the cross-data evaluation of various machine learning algorithms to form the model. After tedious training of models, it is seen that the proposed model gives us a 96.58% accuracy with Support Vector Machine algorithm.