ABSTRACT
The virus SARS-CoV2 was identified in late 2019. Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety. Deep Learning (DL) is anticipated to be the most excellent strategy for reliably predicting COVID-19. Convolutional Neural Networks(CNNs) have achieved successful outcomes particularly in categorization and analyzing of medical image data. This work proposes a Deep CNN(DCNN) method for the classification of CX-R(Chest X-Ray) images in prediction of COVID-19. The dataset is preprocessed under many phases with different techniques for creating effective training dataset for the DCNN model to achieve best performance. This is done to deal various complexities like availability of very small sized imbalanced dataset with quality issues. In the first instance, model is trained using the train dataset. Then the model is tested for a separate validate X-ray image dataset and Confusion matrix is displayed. Up to 98.3% Accuracy is obtained, when proposed model was tested using the validate dataset. The Accuracy and Loss graph is plotted for the same. Later, random image prediction is made from prediction dataset which include both COVID and Normal X-rays. Other important performance metrics like F1 score, Recall, Precision for the model is displayed. © 2023 IEEE.
ABSTRACT
Introduction: There is a diversity in population regarding the number and doses of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) vaccines and past infection status and the antibody titres may be different across various groups. The antibody titres determined in the same time-frame after the immune evoking event may give clues regarding the prioritisation for boosters and factors causing variability in titres. Aim: To compare and assess the Immunoglobulin G (IgG) anti-spike (S) antibody titres among the Healthcare Workers (HCWs) with history of Adenovirus vector based vaccine AZD1222 (Covishield) and infections, in different orders. Materials and Methods: An observational cross-sectional cohort study was conducted in a tertiary care centre during November 2021 to December 2021. The antibody titres of a healthy cohort of HCWs (n=178) who were either double-vaccinated with no history of SARS-CoV-2 infection or vaccinated but along with a history of SARS-CoV-2 infection were determined six weeks after the last event (infection/vaccination). They were grouped based on the order of vaccination (V) and infection (I). Results: The major groups were group 1 (V+V), group 2 (I+V+V), group 3 (V+ V+ I) and group 4 (V+I+V). The highest titres of Anti-S IgG antibody observed in vaccinated with breakthrough infection group 3-V+V+I (n=71) {20662(10853-34744)}. The group with double vaccination but with no history of infection {group 1-V+V (N=49)} had the lowest titres - {2395(844.4-7443)}. The hybrid immunity group (those who had infection which was followed by vaccination) group 2 (I+V+V) had titres 4241 (2220-7373) and group 4 (V+I+V) had titres 6542 (3772-11700) which were lower than those with breakthrough infection. Conclusion: Anti-S antibody titres are highest among vaccinated with breakthrough infections and lowest in those with two doses of vaccines but no history of previous confirmed infections and booster doses may be prioritised for the second group. The timing of previous infection can also be a criterion for further booster doses.
ABSTRACT
Background: Favipiravir, an RNA-dependent RNA polymerase inhibitor (RdRp), is a broad-spectrum oral antiviral agent approved in India under emergency use authorization, for the treatment of mild-to-moderate coronavirus disease (COVID-19). The present study was planned to evaluate the effectiveness and safety of favipiravir in real-world clinical practice. Materials and Methods: This was a multicentric, retrospective, single-arm study conducted across four centres in India, after obtaining permission from the independent ethics committee. Medical records were analysed to evaluate effectiveness and safety of patients who were prescribed favipiravir. Results: The medical records of a total of 360 patients met the inclusion criteria, with 358 of them available for the final analysis. Males made up 58.46% of the study population. The average age of enrolled patients was 51.80 ± 16.45 years. The most common symptoms were fever, cough, and myalgia-fatigue. The median time to clinical cure and fever relief was five and four days, respectively. The average length of stay in the hospital was six days. In total, 8% of the patients experienced adverse events. Hepatic enzyme elevation, diarrhoea, decreased appetite, headache, fatigue, and giddiness were the common symptoms. Conclusion: In our real-world study, favipiravir was found to have a clinical cure rate of more than 90% in mild-to-moderate COVID-19 patients. This supports the use of favipiravir in the treatment of COVID-19. Favipiravir was well tolerated, with only minimal side effects, which were transient in nature.
ABSTRACT
The upstream oil and gas industry's digital transformation over the last few years has accelerated because of the COVID-19 pandemic. Data analytics and machine learning are key components of this digital transformation and have become essential skills for experienced petrotechnical professionals (PTPs) and aspiring entrants into the field. The objective of our work was to design and deliver a practical, engaging, and online microcredential certification program in upstream energy data analytics for PTPs. The program was conceived as a collaboration between academia (University of Houston's UH Energy) and industry (NExT, a Schlumberger company). It was designed as three belt levels (Bronze, Silver, and Gold), each containing three stackable badges of 12 to 15 hours duration per badge. Key design points included 1. Identifying an online platform for administration 2. Delivering convenient, interactive, live online sessions 3. Delivering hybrid classes blending lectures and hands-on laboratories 4. Designing laboratories using upstream datasets across various stages of oilfield expertise 5. Administering test and quizzes, Kaggle competitions, and team projects. The program contents were designed incorporating appropriate instructional design practices for effective online class delivery. The design and delivery of the laboratories using a code-free approach by leveraging visual programming offers PTPs and new entrants a unique opportunity to learn data analytics concepts without the traditional concern of learning to code. Additionally, the collaboration between academia and industry enables delivering a program that combines academic rigor with application of the skills and knowledge to solve problems facing the industry using the real-world datasets. As a pilot program, all three badges of the Bronze belt were scheduled and successfully delivered during July and August 2020, as six 2-hour sessions per badge. From a total of 26 students registered in badge 1, 24 completed it, resulting in a completion rate of 92%. Out of these students, 19 registered and completed badge 2 and badge 3, resulting in the completion rates of 100%. Based on the success of the pilot program, a second delivery of the Bronze belt with 18 participants was offered from October 2020 through January 2021. All 18 participants completed all three badges. Feedback from participants attests to the success of the pilot program as seen in the following excerpts: • "A very good course and instructors. I have already recommended the course to a friend and I will continue to be an advocate for the course." • "Teachers are very receptive to questions and it is a joy to hear their lectures." • "I found the University of Houston course to be both highly engaging and incredibly informative. The course teaches basic principles of data science without being bogged down by the specific coding language". © 2021, Society of Petroleum Engineers
ABSTRACT
The unprecedented and rapidly spreading Coronavirus Disease-19 (COVID-19) pandemic has challenged public health care systems globally. Based on worldwide experience, India has initiated a nationwide lockdown to prevent the exponential surge of cases. During COVID-19, management of cardiovascular emergencies like acute Myocardial Infarction (MI) may be compromised. Cardiological Society of India (CSI) has ventured in this moment of crisis to evolve a consensus document for care of acute MI. However, this care should be individualized, based on local expertise and governmental advisories.