Association of Long COVID with Seeing Difficulties Among Adults in the United States

Objectives: Many patients with long COVID experience at least one vision problem. This study determines the association of long COVID with seeing difficulties. Method(s): We conducted a cross-sectional analysis with the Census Household Pulse Survey data (N = 51,288). We excluded adults who reported contracting COVID within the past four weeks, those with missing data on seeing difficulty when infected with COVID, and long COVID. Long COVID was defined as having symptoms lasting three months or longer that the adults did not have prior to having COVID. Adults self-reporting to a question on seeing with "some difficulty," "a lot of difficulty," or "unable to do" were classified as having "seeing difficulties." We conducted Chi-square tests and logistic regressions with replicate weights. Logistic regressions adjusted for long COVID, sex, age, race and ethnicity, marital status, income, education, food sufficiency, health insurance, remote work, vaccine doses, region, depression, and anxiety. Result(s): During the survey period (November 2 - November 14, 2022), 37.3% reported seeing difficulties, and 14.4% reported long COVID. A higher percentage of adults with long COVID reported seeing difficulties than those without long COVID (47.6% vs. 31.9%). In the fully adjusted logistic regression model, compared to adults with no COVID or without long COVID, those with long COVID had greater odds of seeing difficulties (AOR = 1.50, 95%CI = 1.32, 1.70). We did not observe a statistically significant difference between adults without long COVID and no COVID (AOR = 1.01, 95%CI = 0.93, 1.10 p = 0.7888). Conclusion(s): One in eight adults had long COVID. Adults with long COVID had significantly higher odds of seeing difficulties than those without long COVID. Therefore, a follow-up of patients with long COVID needs to include screening for seeing difficulties. More research is needed on the links between long Covid and vision care.Copyright © 2023

Rationalizing the TPACK Framework in Online Education: Perception of College Faculties Towards Aakash BYJU'S App in the ‘New Normal'

The past couple of years have witnessed an inexorable upsurge in the usage of internet activities, especially, after the emergence of the COVID-19 pandemic. Keeping in pace with the fast track mercurial changes in the aura of technology, a handful of electronic gadgets and head turning mobile applications have also emerged, further propelling the ambit of technological development. Ever since the emergence of the COVID-19 pandemic, education has been largely supported by via online mode. There has also been large scale acceptance of Online Learning Apps. One of the latest grown Online Learning app is the latest version of BYJU'S known as "Aakash BYJU'S”. Teachers that of late, especially the college faculties have shown huge penchant towards the online classes delivered by Aakash BYJU'S. In this light, it is vital to throw light upon the perception of such college teachers towards Aakash BYJU'S online classes. The present research undertaking aims at probing into the attitudes and behaviour of such college teachers towards Aakash BYJU'S online classes by the application of Technology Pedagogical and Content Knowledge (TPACK) model. For this purpose, a survey has been conducted among 343 college faculties in selected districts of West Bengal and their responses were recorded. "Structural Equation Modeling” (SEM) has been used to unravel the model fits and hypothesis testing done at the ultimate stage for validation. The findings reveal positive perception among the surveyed consumers towards the online classes of Aakash BYJU's. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Impact of COVID-19 on fish consumption and nutrition intake—An empirical study

This study investigates the effects of COVID-19 on fish consumption and nutrition intake based on a random survey of 247 fish consumers in Bangladesh. The Propensity Score Matching technique is used to compare fish consumption and fish-sourced nutrition intake between two groups of consumers before and during COVID-19. The result shows that 38% overall reduction in fish consumption for the low-income group compared to lower-middle, upper-middle- and high-income groups. Furthermore, per capita consumption of culture and capture fish species decreased significantly for low-income, lower-middle-income, and upper-middle-income groups of consumers. It indicates that nutrition and mineral intake have reduced sharply as well Higher energy and K reduction are observed for Pangasius hypophthalmus among different culture fish species while energy and Ca reduction were higher for Wallago attu and Mystus vittatus respectively. Therefore, the government might place a greater emphasis on excluding the food supply chain from lockdown restrictions during a COVID-19-like pandemic. © 2023 The Authors. Journal of the World Aquaculture Society published by Wiley Periodicals LLC on behalf of World Aquaculture Society.

Probiotics and immunity: An overview

Recent outbreak of novel coronavirus COVID 19 as pandemic has threatened mankind and revealed the drawbacks in medical science. Finding innovative ways to treat a disease without generating resistance in pathogens is the need of the hour. Probiotics are commensal microorganisms that reside in the body and confer health benefits to the host. Till date, they have been used merely as food supplement that would enhance digestion. But research shows that they also have effects on the immune system of their host and can be used effectively as an immunomodulator in several diseases like bacterial, viral, cancerous, and autoimmune disorders. This chapter gives a brief insight into the role played by probiotics in enhancing immune response against the cited diseases. © 2023 Elsevier Inc. All rights reserved.

A survey and classification of publicly available COVID-19 datasets

The current study curates a list of authentic and open-access sources of alphanumeric COVID-19 pandemic data. We have gathered 74 datasets from 42 sources, including sources from 18 countries. The datasets are searched through the Kaggle and GitHub repositories besides Google, providing a representation of varieties of pandemic-related datasets. The datasets are categorized according to their sources- primary and secondary, and according to their geographical distribution. While analyzing the dataset, we came across some classes in which the datasets can be categorized. We present the categorization in the form of taxonomy and highlight the present COVID-19 data collection and use challenges. The study will help researchers and data curators in the identification and classification of pandemic data.

Catastrophe bond pricing in the primary market: The issuer effect and pricing factors

COVID pandemic has highlighted the importance of hedging against catastrophic events, for which the catastrophe bond market plays a critical role. Our paper develops a two-level modelling and uses a unique, hand-collected dataset, which is one of the largest and most detailed datasets to date containing: 101 different issuers, 794 different bonds, spanning 1997–2020. We identify issuer effects robustly, isolating them from bond specific pricing effects, therefore providing more credible pricing factor results. We find that bond pricing and volatility are heavily impacted by the issuer, causing 26% of total price variation. We also identify specific issuer characteristics that significantly impact bond pricing and volatility, such as the issuer's line of business accounting for up to 36% of total price variation. We further find that issuer effects are significant over different market cycles and time periods, causing substantial price variation. The size and content of our data also enables us to identify the counter-intuitive relation between bond premiums and maturity, and bond premiums and hybrid bond triggers. © 2022 Elsevier Inc.

Assessing the Attitudes and Behavioural responses of society towards COVID-19 Precautionary Measures: The Mediating effect of Risk Perception

The pandemic of Covid-19 Novel Coronavirus is making bold headlines every day, claiming deaths and reeling the economy of each and every nation, ever since its emergence and potential outbreak. The effects of the pandemic has been such that it has stunningly brought a seismic shift in the attitudes and behaviour of the people in a short span of time. It has been observed as of late that people are trying their level best to create mass awareness amongst themselves, involving in practices to curtail the disease and evading any such risks that exposes them to the blood-thirsty disease. The objective of the current research paper is to examine and analyze the attitudes and behaviour of the people of West Bengal towards Covid-19 precautionary measures. For this purpose a framework has been developed by incorporating the construct of Risk Perception as a mediator. To this end, 453 respondents in different districts of West Bengal has been surveyed and their responses has been analyzed. The findings show that risk perception has a positive impact in influencing such attitudes and behaviour among the people of West Bengal.

A Hybrid Approach to Find COVID-19 Related Lung Infection Utilizing 2-Bit Image Processing

This study describes the deployment of an image processing approach for finding COVID-19 affected lungs. Medical scans are useful in diagnosing illnesses and determining if organs are working normally. Medical image processing is an ongoing research subject in where numerous ways are used to help diagnosis, as well as different image processing techniques that may be used. Picture processing was used in this work, which includes image pretreatment, histogram leveling, smothering, eroding, and dilation. The usage of 2-bit picture is selected since this characteristic is well-known and there are several resources accessible. The Open CV library, which includes a plethora of image processing functions, is likewise free to use. Our experiment has shown how COVID-19 affected lung disorders can easily be identified with the help of a 2-bit image segmentation technique. The plan comprises (1) using a deep robust acquisition access to portion proper regions of interest from bleak medical examination image sizes of 903 total, (2) using a propagative neural network to improve contrast, sharpness, and illuminance of image contents, and (3) from the beginning to the conclusion, a regression strategy plan was used to accomplish medical picture categorization by material design in deep neural networks.

A Critical Review on Sudarshan Churna

Sudarshan Churna is one of the valuable polyherbal Ayurvedic formulations used to treat all kinds of acute and chronic fevers, fever of unknown aetiology, malaria, typhoid, loss of appetite, jaundice, respiratory infections, cough, debility and breathing difficulties linked with fever. Sudarshan Churna helps to ameliorate digestion and appetite;eliminates harmful toxins from the blood;improves immunity and preserves the body against various infections;promotes diaphoresis and urination. In Sudarshan Churna, Swertia chirayita (Roxb. ex Flem.) Karst. is 50% of the total quantity of the remaining other ingredients. It is an esteemed Ayurvedic preparation well known for its antipyretic, antimalarial, antiviral and antidiabetic activity. Various renowned Acharyas have mentioned it in their different classical texts. Acharyas have mentioned its other therapeutic uses, but mainly it has been used in curing different types of Jwara Vikara (fever). Various pharmacological studies have proved antipyretic activity, antimicrobial activity, antioxidant activity, antihistamine activity, analgesic activity and docking study against the SARS CoV-2 of Sudarshan Churna. This article compile review of Ayurvedic literature, an analytical study and pharmacological study of Sudarshan Churna. Copyright © 2022 International Journal of Research in Ayurveda and Pharmacy. All rights reserved.

A survey and classification of publicly available COVID-19 datasets

The current study curates a list of authentic and open-access sources of alphanumeric COVID-19 pandemic data. We have gathered 74 datasets from 42 sources, including sources from 18 countries. The datasets are searched through the Kaggle and GitHub repositories besides Google, providing a representation of varieties of pandemic-related datasets. The datasets are categorized according to their sources-primary and secondary, and according to their geographical distribution. While analyzing the dataset, we came across some classes in which the datasets can be categorized. We present the categorization in the form of taxonomy and highlight the present COVID-19 data collection and use challenges. The study will help researchers and data curators in the identification and classification of pandemic data. © 2022, National Institute of Science Communication and Policy Research. All rights reserved.

Reflecting on the impact of COVID-19 on healthcare and IT sector with special emphasis on India: A collection of multifarious cases with few empirical evidences

The unprecedented crisis of COVID-19 pandemic has wreaked havoc across the whole world. The menacing disease has not only claimed lives of innumerable people but also rattled all the countries pushing them at the face of an inevitable and inimitable global meltdown. Ever since its outbreak, all major sectors of each and every nation have been stymied, and India is no exception. In this context, it becomes highly relevant to deliberate on the impact of COVID-19 on different sectors of India. The current case study is attempted in discussing the impact of COVID-19 on healthcare and technology sector of India, in which both the merits and demerits of the impact of COVID-19 will be highlighted. To serve this purpose, an anonymous choice of various relevant subsectors has been made that has been delved into which will ultimately help to have an intimate understanding of the effects triggered by COVID-19 on the sectors of healthcare and technology. To strengthen the worth of the present case-based study, few surveys have also been conducted among the people of India especially from selected districts in the states of West Bengal, Jharkhand, and Tamil Nadu, through which robust empirical insights will be reflected. © 2022 Elsevier Inc. All rights reserved.

Tweet Classification and Sentiment Analysis of Covid 19 Epidemic by Applying Hybrid Based Techniques

World wide spread of COVID-19 pandemic, is throttling the normal life nearly for two years and claiming millions of life all over the globe. Starting from Wuhan of China it crosses more than 200 countries, thereby imposing a overwhelming challenge to health care system. On the other hand, there has been unprecedented advancement of the social media, namely, Twitter, Facebook, WhatsApp and Instagram etc. in an exponential manner. The essence of this paper is to extract and elucidate the opinion or sentiments of the people all around the globe regarding Coronavirus pandemic based on Twitter data. The analysis are based on both lexicon-based approach followed by machine learning algorithms and aims to express the state-of-the-art of the sentiment analysis on the current Coronavirus epidemic prevailing in the entire world and the awareness of the people regarding the disease, its symptoms and impact followed by the preventive measures that need to be undertaken. © 2022 IEEE.

A COMPARATIVE STUDY OF PATIENT MORTALITY DURING FIRST AND SECOND WAVES OF COVID-19 PANDEMIC IN DIALYSIS PATIENTS AND KIDNEY TRANSPLANT RECIPIENTS

BACKGROUND AND AIMS: Patients on kidney replacement therapy (KRT) are at a particularly high risk of mortality from COVID-19. In this study, we investigated COVID-19 mortality in KRT patients in the first and second waves of the pandemic and potential reasons for any difference in mortality between the two waves. METHOD: Data from the European Renal Association COVID-19 Database (ERACODA) of KRT patients who presented between 1 March 2020 and 28 February 2021 with COVID-19 were analyzed. The cut-off for dividing the first and second waves was set for 1 August 2020. The primary study outcome was 28-day mortality. Multivariable Cox proportional-hazards regression analysis was used to examine the relationship between the pandemic waves and mortality with follow-up time starting at the date of presentation. Dialysis patients and kidney transplant recipients were analyzed separately. RESULTS: Among 3004 dialysis patients (1253 in the first and 1751 in the second wave), the 28-day mortality was 24.3% in the first wave and 19.6% in the second wave (P = .002). Compared with the first wave, in the second wave, identification of patients with limited to no symptoms was higher (14.3% versus 24.8%;P < .001), hospitalization was lower (71.3% versus 44.3%;P < .001), but in-hospital mortality was similar (30.4% versus 30.7%;P = .92) (Fig. 1). Crude hazard ratio (HR) for 28-day mortality in the second wave was 0.77 (95% CI: 0.66, 0.89). However, in a fully adjusted model, when correcting for differences in patient and disease characteristics, including the reason for COVID-19 screening and disease severity, the HR for mortality in the second wave was 0.93 [95% confidence interval (95% CI): 0.79-1.10]. When follow-up was chosen to start at the date of first symptoms to account for possible lead-time bias, crude HR for 28-day mortality in the second wave was 0.90 (95% CI: 0.75-1.07) and the fully adjusted HR was 0.98 (95% CI: 0.81-1.18). Among 1035 kidney transplant recipients (475 in the first and 560 in the second wave), results were essentially similar except that patients in the second wave were younger (55.6 years versus 58.2 years;P = .002), and crude HR for 28-day mortality from the date of first symptoms was 0.66 (95% CI: 0.47-0.93), whereas the fully adjusted HR was 1.02 (95% CI: 0.70-1.49). CONCLUSION: Among patients on KRT with COVID-19, 28-day mortality rates were lower in the second wave compared with the first wave. However, a greater proportion of patients with minimal symptoms, lead-time bias in dialysis patients, and younger age in kidney transplant recipients possibly explain the lower mortality during the second wave. Any improvement in patient management during the second wave may not be the main reason for lower mortality. (Table Presented).

COVID-19: In the direction of monitoring the pandemic in India

The coronavirus disease 2019 (COVID-19) pandemic has witnessed a total of 2, 631, 338 infected cases as on April 22, 2020. The first case being recorded in January 2020, followed by more than 20, 000 case in a matter of 3 months creating a tremor among the population of India. The United States tops the total number of infected cases in the world with 8, 45, 822 cases, followed by Spain summing up to 2, 08, 389 cases. India having 21, 370 total infected cases, along with 4370 recoveries and a total death toll of 681, ranks 17th among the other countries. In this study, a detailed summary has been conducted on how India is working together to fight this pandemic. The countrymen, along with following the strict instructions provided by the WHO, have set up new rules to fight this disease. This study includes the various medical procedures adopted by the government of India along with the economic guidelines for its fellow citizens during these hard times;a geospatial approach has also been used to identify the infected regions of the country and their pandemic control methods in the 1-month period (22 March-22 April). To date, being the second most populated developing country in the world, India has managed to control the spread of the virus to a large extent. The WHO has praised India’s efforts in monitoring the spread of the virus and has alerted India that a simple lockdown would not stop the spread of the virus. They have recommended India to increase the number of tests to segregate the people showing early/mild symptoms. The outcomes of the precautions taken are also discussed in this study. © 2022 Elsevier Inc.

Wireless Battery-Free Body Temperature Sensing Device for Key Workers

We propose a battery-free temperature monitoring device that can be fitted inside the ear for an accurate body temperature measurement of a subject. The proposed application consists of two primary systems: 1) a battery-free temperature sensing ultra-high-frequency radio frequency identification sensory tag and 2) an auxiliary energy harvesting system, which enhances the sensing device's measurement accuracy and precision. The system can record changes in the localized body temperature of authenticated users with an average latency of 501 ms. The assembly demonstrated a temperature average accuracy of +/- 0.14 degrees C operating at 866 MHz. The system performance demonstrated high stability and repeatability of reported temperature measurements. The device's dimension is a form factor that can easily fit in a front shirt pocket, with a wire tethered earbud temperature sensor. The system is developed to make sensor measurements without requiring a battery for the device. Measurements are made remotely as users pass by checkpoints installed throughout a building. The device is a cost-effective solution for monitoring body temperature in work environments.

Multidisciplinary approaches to coping with neurodegenerative disorders amid COVID-19 pandemic

Neurodegenerative disorders, including Alzheimer’s and Parkinson’s, are the leading causes of dementia in the elderly. In the coming days, an alarming upsurge of dementia patients is expected with increasing life expectancy. This is the scenario not only in the developed world but also in the developing world, where older people live in vulnerable situations. Even in the COVID-19 (coronavirus disease-19) pandemic, the situation has worsened. Due to the limitations of conventional therapeutic strategies, it is necessary to explore integrated approaches consisting of both pharmacological and non-pharmaceutical interventions. As existing anti-dementia drugs pose many adverse effects on patients, pharmacological intervention through naturally occurring agents should be employed to explore targeted therapy. Alongside, non-pharmacological interventions such as cognitive and motor rehabilitation, occupational therapy, and psychological therapy need to be explored. From this perspective, multidisciplinary approaches need to be employed in order to develop a sustainable patient-friendly treatment strategy for the management of these emerging health issues with tremendous social burdens. © 2022, Bangladesh Society for Microbiology, Immunology and Advanced Biotechnology. All rights reserved.