ABSTRACT
Due to the Covid-19 pandemic, hospitality industry witnessed a massive decline in their revenues. In our research we realized that one of the most effective ways to aid customer retention and boost the revenue of this Our research shows that currently the data analysts in this industry only use the traditional tools for predictive analysis, offering from a limited range of offers that lack customization as per user purchase history. Hence, we put forward a proof of concept for a tool where we make a machine learning model that learns from the historic data of each restaurant, including customer segments and coupon parameters, and predicts the probability of a coupon to work on a specific sub-category of customers. This would thereby increase the chances of transaction and thus boost the revenue. We worked with several classification algorithms, like Logistic Regression, AdaBoost, Random Forest, Gradient Boosting, and realized that Random Forest Classifier was producing the best results. Thus we selected it for building our model. As a result, we have built a web-based tool that can be used by Analysts or the business person themselves, to find out what coupon offers would best suit a particular subset of customers. This would help them make better business decisions, gain more customer traction and retention, and consequently boost their revenue. © 2022 IEEE.
ABSTRACT
Coronavirus disease 2019 (COVID-19), a viral respiratory infection, was declared as a pandemic on March 11, 2020. Studies from across the world centered on patient follow-up are adding to the knowledge on late complications observed in COVID-19 convalescents. Literature search was performed using databases with search terms 'COVID-19,' 'SARS-CoV-2,' 'Long COVID,' 'COVID-19 complications,' 'post COVID sequelae,' 'COVID-19 recovery,' and 'persistent symptoms.' Articles in English excluding pediatric (<18 years) and pregnant population were included for literature review. Studies from across the world reported various pulmonary, cardiac, hematologic, renal, neuropsychiatric, endocrine, and gastrointestinal complications and other nonspecific persistent symptoms. Several of these complications are similar to the postinfectious symptoms reported in previous viral respiratory disease outbreaks. In this narrative review, we review current literature on complications that follow recovery from acute episode of COVID-19. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
Background: SARS coronavirus-2 (SARS-CoV-2) infection causes Novel Coronavirus Disease (COVID-19). It is a respiratory tract infection and currently becoming pandemic worldwide affecting more than 50 lakh people. As of now, there is no treatment or vaccine developed for disease management. The main protease, M-pro in SARS-CoV-2 is a druggable target explored by many scientists. We targeted this with the well-known approach of drug repurposing by using computational tools. Methods: Schrodinger software was used for the study. Ligands were prepared from US-FDA drug-bank by importing it to Maestro graphical user interphase, optimised using LigPrep, and molecular geometry minimized using OPLS3e force-field. M-pro crystal structure 6LU7 was downloaded from PDB and optimised. Molecules were docked using CovDock module in Glide docking. Further, molecular dynamics simulations were carried out for 100 ns using Desmond module. Results: In docking and molecular interactions studies, penicillins emerged as hits with consistent binding pattern by forming hydrophilic, hydrophobic, electrostatic interactions. The molecular dynamics simulations confirmed the interactions. Phenoxymethylpenicillin and Carbenicillin were found to interact consistently and appeared to be the most promising. Conclusion: Usually, antibiotics are discouraged from using in the viral pandemic because of the development of resistance. Azithromycin was combined with hydroxychloroquine to treat COVID-19. Penicillins are less potent and first-line antibiotics for most of the bacterial infections. This study suggests Phenoxymethylpenicillin and Carbenicillin can be tried along with hydroxychloroquine. Further, this study shows the possible exploration by drug repurposing using computer-aided docking tools and the potential roles of beta-lactams in COVID-19.
ABSTRACT
Aims and Objectives: The advent of the novel coronavirus disease-2019 (COVID-19) pandemic has sparked a global crisis. Cumulatively, the modifications in patient care and financial restraints are leading to heightened levels of anxiety amongst dentists, making it imperative to comprehend the psychological health implications of the dental professionals. This study aimed to evaluate the psychological impact of the COVID-19 pandemic among Indian Dentists through an online web-based survey.Materials and Methods: The present randomized survey was designed to evaluate the anxiety levels. A total sample size of 405 was calculated. The questionnaire included demographic information and all the variables linked to probable cause of stress during clinical practices and the future prospects of the profession. The questions had to be responded on a scale of 1-10. The responses were statistically analyzed by subjecting the responses to descriptive analysis, Student's t test, and Pearson's chi-square tests.Results: A total of 405 responses were received. The levels of anxiety reported were high. Majority of the dentists were troubled by the thought of being in a high-risk profession and of transmitting the disease to others. Almost all questions were responded with a score of >5 on a scale of 1-10 depicting heightened anxiety levels. The fear levels were noted to be elevated in patients aged more than 35 years.Conclusion: Long-term unrecognized anxiety can predispose to significant psychiatric morbidity and fatigue. Identifying and acknowledging adverse factors in a crisis situation will facilitate early intervention to reduce and mitigate the impact of stress. © 2020 Journal of International Oral Health. Published by Wolters Kluwer . Medknow.