Impact of COVID-19-Related Travel Restrictions on the Environment and Travel Time Reliability

This study analyzes the effect of the restrictions in traffic movement enforced in order to combat the spread of coronavirus on air quality and travel time reliability under heterogeneous and laneless traffic conditions. A comparative analysis was conducted to examine quantity of pollutants, average travel time distributions (TTD), and their associated travel time reliability (TTR) metrics during the COVID-19 pandemic, postpandemic, and during partial restrictions. Pollutants data (PM2.5, NO2, and NOX) and travel time data for selected locations from Chennai City in India were collected for a sample period of one week using Wi-Fi sensors and state-run air quality monitoring stations. It was observed that the average quantity of PM2.5, NO2, and NOX were increased by 433.1%, 681.4%, and 99.2%, respectively, during the postlockdown period. Correlation analysis also indicated that all considered air pollutants are moderately correlated to Wi-Fi hits, albeit to varied degrees. From the analysis, it was also found that average TTD mean and interquartile range values were increased by 47.2% and 105.2%. In addition, the buffer time index, planning time index, travel index, and capacity buffer index associated with these TTD metrics were increased by 148.1%, 63.7%, 42.8%, and 202.9%, respectively, soon after relaxing travel restrictions. © 2023 American Society of Civil Engineers.

Primary factors that determine the severity of various infections and effective nutraceutical intervention strategies

Current agricultural practices, food processing, and extensive availability of unhealthy "fast foods” impose a broad spectrum of degenerative disorders including bacterial, viral, parasitic, fungal, and yeast infections, all of which became a leading cause of death. An infection is defined as "The invasion and growth of germs in the body.” Several of these germs are opportunistic anaerobic organisms, while their propagation and proliferation potentiate the anaerobic bio-environment alarmingly. An ideal therapeutic objective is to restore a healthier cellular "aerobic” metabolic environment. This chapter will extensively focus on three important infectious disorders (i) COVID-19 infection, (ii) Herpesvirus infection, and (iii) Candida albicans yeast infections. Disease etiology, mechanisms, and pathology will be extensively discussed. Prevention and the potential of therapeutic interventions by structurally diverse nutraceuticals, phytopharmaceuticals, probiotics, and micronutrients will be extensively reviewed. © 2023 Elsevier Inc. All rights reserved.

Unveiling the prevalence and impact of diabetes on COVID-19

Novel Coronavirus (COVID-19) is an infectious respiratory illness caused by the SARS-CoV-2 virus, which is a novel coronavirus first reported in the Wuhan district of China in December 2019. Its outbreak has caused the second biggest ever pandemic known to humankind with recorded death tallies close to 50 lakhs till date. The primary targets of infection and pathogenicity of the virus are the lung pneumocytes whose gradual destruction leads to acute respiratory distress. Increasing number of evidences have indicated that severe consequences from COVID-19 occur mostly in people predisposed with obesity, hypertension, diabetes, or other related comorbidities. This has initiated a collective quest among researchers and scientists to decipher the crosstalk between glycemic markers, viral proteins, and immune mediators/(modulators). Patients with hyperglycemia have elevated levels of proinflammatory cytokines that immediately suggest their susceptibility to the viral infection. However, concentration of ACE-2, the chief receptor for SARS-COV2 entry into host cells, has been found to be significantly repressed in individuals with impaired insulin sensitivity. Thus, the comorbidity puzzle has continued to intrigue scientists, and subsequently, effective management and therapy of diabetic patients with COVID-19 symptoms and vice versa has thrown a big challenge to the medical science. This chapter is a comprehensive report of the studies carried out so far to understand the disease susceptibility of diabetic patients to COVID-19, corresponding immune dysbiosis and more efficient management strategy for better prognosis of COVID-19-infected diabetic individuals. © 2023 Elsevier Inc. All rights reserved.

Three-step process for the synthesis of favipiravir

A three-step process has been developed for the synthesis of antiviral drug favipiravir (T-705) starting from a readily available ethylenediamine and diethyl 2-oxomalonate. The synthesis involves the formation of ethyl 3-hydroxypyrazine-2-carboxylate by direct condensation of ethylenediamine with diethyl 2-oxomalonate followed by amide formation and electrophilic fluorination. This approach not only consists of three steps but also ensures the usage of inexpensive and easily available starting materials. It is an alternative process that can replace bromination or nitration, POCl3 etc that are used in previous methods. © 2023

Post obturation analgesia in patients recently vaccinated against Covid-19, after Root canal treatment: A short observational study

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.

Impact of COVID-19 Pandemic on Electricity Generation and Demand Patterns of Bangladesh Power System

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.

Impact of COVID-19 pandemic on mental health across different populations

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.

Classification of COVID19 Tweets based on Sentimental Analysis

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.