Evolution of the COVID-19 Pandemic: An Analysis of the Brunt of the Second and Third Waves on Patients in Western Uttar Pradesh

Background: The recent second wave and the latest third wave of coronavirus disease 2019 (COVID-19) in India caused havoc on health infrastructure. However, there is a scarcity of studies from India and abroad that compare the second and third waves of the COVID-19 pandemic. We aimed to assess the factors like age, sex, and death comparison among diagnostically proven COVID-19 patients of the Meerut district in both waves. Methodology: A total of 297554 samples during the second wave (1st March 2021 to 30th June 2021) and 240655 during the third wave (1st January 2022 to 30th April 2022) were tested for reverse transcription polymerase chain reaction (RT-PCR) in the Department of Microbiology, Lala Lajpat Rai Medical College, using The Indian Council of Medical Research (ICMR) approved RT-PCR testing kits. The data like age, sex, place, follow-ups, etc. were recorded and data were analyzed statistically. Results: The RT-PCR positivity of 8.24% for COVID-19 in the second wave while 5.66% of patients in the third wave have been reported. The proportion of positive cases in children ≤10 years in the second and third wave were quite similar i.e., 3.59% and 3.40% respectively, whereas the proportion of positive cases in adolescents (10-20 years) was significantly higher (12.96%) in the third wave in contrast to the second wave (10.15%), while age group (41-60 years) is significantly less (26.65%) in proportion during the third wave in comparison to the second wave (29.50%). The proportion of positivity in young males has significantly increased in the third wave as compared to the second wave. The mortality also decreased significantly by 1/3rd of the second wave. Conclusion: The third wave showed low overall positivity (5.66%) as compared to the second wave (8.24%), while the brunt on young children was comparable to the second wave which was assumed to be higher. The mortality and hospitalization also decreased significantly in the second wave. Regular surveillance and analysis should continue to combat this pandemic.

A study on impact of coronavirus pandemic on the education system

Objective: Because of the recent COVID-19 outbreak, the government decides to close the schools to avoid the virus chain. However, that made a tremendous impact on the education system. Hence, the research's main aim focuses on the effect of the education system on the corona. Method: Research has implemented a descriptive method. Through the standardized questionnaire using the Likert scale, the data was collected via an online survey, where 300 respondents were found to be used as the sample size and regression analysis to prove the significance of the findings. Findings: The study found that obstacles act as a roadblock in the education system, while the study found resources that were also helpful to students and teachers. Practical Implication: Application of such factors by leveraging the educational sector as an opportunity could work for them if effectively applied. Students will learn something new and develop technological skills they would not have had before. Originality/Value: Researchers have studied challenges and opportunities during a pandemic situation by applying theories and analyzing the impact on the education system. The current research considers such aspects as to what effect on the education sector or how it handles the framework as a burden or as an opportunity.

Versatility of acetalated dextran in nanocarriers targeting respiratory diseases

Acetalated dextran is a chemically modified version of the FDA approved polysaccharide ‘dextran’, which serves as a perspective drug-delivery material for the pulmonary delivery of therapeutics owing to its biodegradability, sensitivity towards acidic pH for stimuli-sensitive drug release, high encapsulation efficacy, chemical conjugation with pharmaceuticals, and potency to cross the mucosal layer. Mainly, the aerosolized dry powder inhalation formulations of drug-loaded acetalated-dextran prove to be the frontrunner candidates for pulmonary delivery for the effective management of chronic respiratory diseases such as lymphangioleiomyomatosis, tularemia, and the contemporary COVID-19 pandemic. The presented communication provides a succinct account of the pulmonary drug delivery applications of acetalated dextran.

Conscientious objections during COVID-19 pandemic. (Special Issue: The epidemiologist and the pandemic.)

Globally evolving COVID-19 pandemic has raised major questions which may have catastrophic implications like absence of universal facemask use, misunderstanding implications of SARS-CoV-2 test results, ventilator related mortality, cytokine reduction technology and anti-viral treatments being in their infancy still, failure to update advanced healthcare directives during pandemic, and overlooked home hospice options for COVID-19 patients when terminally ill. Moreover, there are inquisitive and interesting avenues worth exploring and innovating during COVID19 pandemic like "cold" viruses such as SARS-CoV-2 uniquely choosing airways which normally and naturally have temperatures much lower than core body temperatures, potential therapeutic role (if any) of facemask usage, potential role of natural disinfection by sunlight and its component ultraviolet-C which is used for artificial cleansing, potential bimodal immune response against SARS-CoV-2, and exploration into BCG vaccination based non-specific protection against intracellular pathogens with SARS-CoV-2 itself being an intracellular pathogen. Summarily, I am praying that the natural delays in establishing reproducible evidence during COVID-19 pandemic should not turn the humanity as we know today into a historical evidence.

Automatic diagnosis of COVID-19 from Chest X-ray images using transfer learning-based deep features and machine learning models (preprint)

The novel Coronavirus, declared as a pandemic by WHO, has caused a health crisis and disrupted the daily course of the people globally. The effectiveness of Chest X-ray (CXR) in the differentiation of COVID from non-COVID has exhorted us to propose a diagnostic model based on deep features. This paper proposes a diagnostic framework to diagnose COVID-19 from Chest X-rays (CXR). Further Grad-CAM visualizations are shown to get a visual interpretation for the predicted images. We validated the performance of the proposed diagnostic model using the area under the curve (AUC), accuracy, precision, recall, F1-score and geometric mean (G-mean). Few popular machine learning models such as random forest, dense neural network, support vector machine (SVM), twin SVM (TWSVM), extreme learning machine (ELM), random vector functional link (RVFL) and kernel ridge regression (KRR) have been selected for diagnosing the COVID cases. The deep features are extracted by transfer learning. Grad-CAM visualizations are presented for the predicted images. We have achieved the best AUC score of 0.98 on TWSVM classifier on the feature vector extracted by ResNet50 architecture. The feature vector extracted from ResNet50 outperforms all other CNN architecture rank wise based on AUC. The experimental outcome indicates the efficiency of the proposed diagnostic framework.

Strategic communication in health and development: concepts, applications and programming. (Special Issue: Health system.)

Significance of communication in health and development is well recognised. Strategic communication informs, educates and influences. In addressing varied health and development issues, including the challenges involving diseases control, more targeted communication strategies are designed to make optimum use of available resources to achieve the planned results in a given context. Based on research, that is, the community-based study of risk factors and the operational research, communication theories evolved and so did the strategies and practices for result-driven health and development communication. In this article, some approaches have been examined to better understand the role of strategic communication in development and health, including disease control. Information dissemination through 'extension approach', first for agriculture development and later for family planning, adapted and boosted through advertising and marketing frameworks led to wide awareness about the methods and techniques of family planning but not the adoption at the same levels. Experience and research studies demonstrated that mere 'awareness' was not adequate for fostering adoption of 'new' practices;instead, it required sustained investments in communication for social and behavioural change processes. For this, bottom-up communication design, participatory communication with community involvement, evidence-based advocacy and preparedness for risk communication are required for effective communication and health and development. As HIV/AIDS posed an initial challenge for communication scholars earlier in the 1980s, so is the COVID-19 pandemic throwing a major communication challenge today. The article attempts to analyse the approaches and shed light on the role of communication in health and development, especially in the context of health crisis.

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection (preprint)

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.