Chalcone-based Ferrocenyl-Derivatives as an Antimicrobial Drug

The lack of development of new antibiotics is the major concern at the present scenario. One key factor contributing to the rise of antibiotic-resistant bacteria is the widespread movement of people throughout the world. The world has seen the consequences of the migration in the case of COVID-19 very recently. To tackle or cope with the situation, development of new antibiotics is very essential, which can be inhibited multidrug-resistant bacteria. In this framework, chalcone-based ferrocenyl containing compounds showed a diversity of pharmacological properties and its derivatives possess a high degree of structural diversity and it is helpful for the discovery of new therapeutic agents. Thus, there is a need for new antibacterial drug candidates with increased strength, new targets, low cost, superior pharmacokinetic properties and minimum side effects. The present review concluded and focuses on the recent developments in the area of medicinal chemistry to explore the diverse chemical structures of potent antibacterial agents and also describes its structure-activity relationship studies (SAR). This review will help to the researchers in the medical field to find out the future generation potential drug discovery and development. © 2023 Chemical Publishing Co.. All rights reserved.

Aggravation of CoVID-19 infections due to air pollutant concentrations in Indian cities

The CoVID-19 infections began rising worldwide during the initial weeks of March 2020, reacting to which the Government of India called for nationwide lockdown for ~ 3 weeks. The concentration of pollutants during the lockdown were compared with pollution levels recorded during the preceding year for the same time frame. A direct relationship was established between the high level of air pollutants (PM2.5, PM10, NO2 and SO2) and CoVID-19 infections being reported in the Indian cities. The correlation indicates that the air pollutants like PM2.5, PM10, NO2 and SO2 are aggravating the number of casualties due to the CoVID-19 infections. The transmission of the virus in the air is in the form of aerosols;and hence places which are highly polluted may see a proportionate rise in CoVID-19 cases The high-level exposure of PM2.5 over a long period is found to be significantly correlated with the mortality per unit confirmed CoVID-19 cases as compared to other air pollutant parameters like PM10, NO2 and SO2. © 2023, The Author(s), under exclusive licence to Korea Spatial Information Society.

COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer

According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.

A COVID-19 Infection Rate Detection Technique Using Bayes Probability

The main objective of this paper is to detect the infection rate of the SARS-Cov-2 virus among patients who are suffering from COVID with different symptoms. In this work, some data inputs from the intended patients (like contact with any COVID infected person and any COVID patient within 1 km.) are collected in the form of a questionnaire and then applied Naïve Bayes probabilistic technique to evaluate the probability of how much that patient is affected in this deadly virus. Following this process, we collect sample data of 80 patients and apply the proposed analysis process using the C programming language. This approach also shows the comparison for different test cases with respect to the feedbacks of actual patient data analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Eat-in or eat-out? A joint model to analyze the new landscape of dinner meal preferences

In this paper, we examine the non-home-cooked meal (NHCM) preferences of individuals for their dinner meal by studying the monthly count of NHCM meals by channel type: eat-out, eat-in takeout, and eat-in delivery. Data from a 2022 online survey collected in Texas is employed to estimate a multivariate joint model. Model estimation results indicate that the most frequent customers of the eat-out channel are white individuals, individuals from 3+ motorized vehicle-owning households, those in non-joint families, those in households with no children, full-time employees who never work from home or do so only for a small fraction of their workdays, and those residing in areas with a high density of restaurants. The distinct consumer segments for the eat-in takeout channel include young individuals, those with high household incomes, those working from home all their workdays or a substantial fraction of their workdays, and urban residents;the most enthusiastic consumers of the eat-in delivery channel are white individuals, those with less than three vehicles in the household, individuals with children, urban residents, and those worried about pandemic-related personal health risks. Older individuals, non-white individuals, individuals with a graduate degree, individuals in fewer motorized vehicle-owning households and in joint families, those with children in the household, and rural residents constitute the most committed population segments of the home-cooked meal (HCM) consumption channel. The results suggest the important impact of workplace location on dining channel choice. The results also show clear evidence of complementary and substitution effects at play;the delivery channel complements eating out but substitutes takeout. Similarly, eat-out has a substitution effect on eat-in takeout. These effects have important implications for activity-travel behavior due to emerging technology-based ordering options for dining choices, especially in the aftermath of the COVID pandemic. © 2023 Elsevier Ltd

COVID-19 prophylactic & management studies based on Electro-Homeopathy

Electro-Homeopathy (EH) is a herbal spagyric based safe & scientific medical system. The usual homeostasis balance between blood and lymph in human body is deviated during illness. In EH, disease is cured by restoring homeostasis balance, using minute amount of specially made spagyric complex remedies. Precise dose of complex set of remedies act on vitiated blood or lymph or both to establish a balanced condition. During this process, various variants of T and B lymphocytes get stimulated by absorbing living plant energy stored in EH remedies. Hence, the harmful invaders are identified and destroyed by lymphocytes (WBC) on and around the diseased organ. Total excretory system simultaneously gets stimulated by EH remedy enabling to drive out quickly the toxic cluster of dead harmful invaders and the infected cells. This process results a complete cure. The EH remedy neither kill any virus directly nor allow virus to reside inside the body for lengthy time. The set of remedy is chosen, such that it does not allow Corona virus to cause harm, even at the cell and tissue level of respiratory, gastro intestinal and other related systems. Suitable dose of medicine applied beforehand, acts as the prophylactic remedy and prescribed medicines applied to Covid patient cures them successfully. A study using EH remedies on 2384 Covid and non Covid mixed population, was carried out during March, 2020 to July, 2021. A prolong consistent result proves that the EH remedy is irrespective of virus strains. The treatment summary has been presented here. © 2022, National Institute of Science Communication and Policy Research. All rights reserved.

Multiclass Categorisation of Respiratory Sound Signals Using Neural Network

Respiratory diseases have seriously impacted human life in the last couple of years;as Covid 19 arrived, many lost their beloved ones. Since respiratory diseases directly attack the patient's lungs, it is becoming risky day by day for human life and doctors because a confined number of resources are available in hospitals to detect these respiratory diseases, and detection of these diseases is a difficult job to the doctors. Therefore early-stage diagnosis can help the doctor in saving human lives. Researchers are continuously trying to help doctors by designing efficient and more accurate tools for detecting different types of respiratory diseases. This paper uses a convolution-based deep learning model to classify these respiratory diseases using patient respiratory sound signals with Mel frequency cepstral coefficients (MFFCs) as a feature vector. In this paper, we have tried to keep our neural network model as simple as possible with less trainable parameters and good classification accuracy. The model performance is measured in terms of sensitivity, specificity, average score, and harmonic score. © 2022 IEEE.

A COVID-19 Infection Rate Detection Technique Using Bayes Probability

The main objective of this paper is to detect the infection rate of the SARS-Cov-2 virus among patients who are suffering from COVID with different symptoms. In this work, some data inputs from the intended patients (like contact with any COVID infected person and any COVID patient within 1 km.) are collected in the form of a questionnaire and then applied Naïve Bayes probabilistic technique to evaluate the probability of how much that patient is affected in this deadly virus. Following this process, we collect sample data of 80 patients and apply the proposed analysis process using the C programming language. This approach also shows the comparison for different test cases with respect to the feedbacks of actual patient data analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Black Fungus Mutilating COVID-19 Pandemic in India: Facts and Immunological Perspectives

While the world is still struggling with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, an aggressive and rare fungal infection which is commonly ascribed as the black fungus has emerged as a new medical challenge in India. India had already experienced the devastating consequences of the COVID-19 and, being a rare "opportunistic" fungal infection, black fungus infection has severely complicated the post-COVID-19 recoveries. Together with the uncertain treatment modalities at the beginning of the pandemic, indiscriminate use of a plethora of medications has driven the surging cases of black fungus-associated complications. Moreover, low oxygen, high iron levels, and prolonged hospitalization with mechanical ventilators created a superlative condition for contracting black fungus infection. The disease mainly spreads through the respiratory tract and erodes facial structures. Since mucormycosis specifically attacks immunosuppressed patients, the disease started spreading rapidly, with an average mortality rate of 54 %. Common symptoms include blackening over the nose, blurred or double vision, breathing difficulties, chest pain and hemoptysis. Although not contagious, the outcome of the disease is often very frightful. If the infection disseminates systematically, the risk of affecting the vital organs such as the spleen and heart is substantially high. We have tried to provide an epidemiological overview of black fungus infection in India. We focused on drawing a comprehensive fact check of the current situation through an immunological perspective to better understand the infection as a major co-infection in patients affected by COVID-19 and its impact on India's fight against the COVID-19 pandemic. © 2022 Revista Mexicana de Ciencias Forestales. All rights reserved.

Limitations and implications of doubling time approach in COVID-19 infection spreading study: A gradient smoothing technique

To control the spread of COVID-19, around the world, many countries imposed lockdowns. Numerous studies were reported on COVID-19 in different disciplines with various aspects. The doubling time is a mathematical technique to estimate the current rate of spread of the disease. Researchers used the doubling technique to address the COVID-19 pandemic situation. The larger doubling period represents a low spreading rate, whereas the smaller doubling period represents a high spreading rate. In other words, high infection implies the low doubling period and low infection implies the high doubling period. So, there is an inverse relationship between doubling time and the infection rate. But the real-life data does not follow such a rule properly in various domains. The data shows that after a certain time when the infection is high, the doubling period is also high, which misleads our general concept of doubling time. This chapter addressed this issue by investigating the real-time COVID-19 data. To overcome this limitation, a gradient smoothing technique has been proposed. © 2021, IGI Global. © 2021 by IGI Global.

Nutrient Food Prediction through Deep Learning

The lifespan of a man can be sustained only with adequate nourishment. To lead a productive, healthy life, human needs nutritious food. In this pandemic COVID-19 situation humans need more nutritious food for combating infectious disease along with a strong immune system in our body. Nutritious foods recognition is one of the major tasks for a customer. In large stores plenty of agricultural products are stored, then there needs a classification for separating normal food and nutritious food. The real time decision will alert the consumer by predicting nutritious foods. By the use of deep learning, it may be possible to classify nutritious food along with their nutrient content and give the possible particular rating view image through the deep learning method. Enormous development in deep learning is possible due to the advancement of the Convolutional Neural Network (CNN) algorithms. CNN is a modern technique inspired by biological neurons mainly used for image processing and data analysis, producing encouraging results. The principal objective of our work is to detect and segregate normal food and nutritious food. This is accomplished using the combination of both nutrition and image Classification techniques. Hence, the proposed system achieved average overall accuracy is more than 91%. © 2021 IEEE.

An Automated System to Identify Sentiment from Micro-Blog Texts of Tweets

The recent trends of Internet help to produce the sentiment and emotion-based opinion at the time of conversation between human beings. During the Coronavirus disease (COVID-19) pandemic, we are spending maximum time of the day on Internet especially on social networking Web sites. The Web sites are Twitter, Facebook, and WhatsApp, which are taking a crucial role to communicate with each other in the form of messages. The messages are representing such as short-texts and micro-texts and carrying sentiment. The sentiment identification is challenging due to the length of the message. In the present paper, we are motivated to design a sentiment analysis system for Twitter micro-texts in the topic of Coronavirus disease (COVID-19). Hence, we have scrawled a dataset from Twitter using Twitter API and presented as our experimental dataset. Additionally, we have developed two state-of-the-art techniques, viz. unsupervised and supervised to build this system. The unsupervised technique helps to understand the characteristic of the dataset, whereas supervised technique assists in improving the accuracy of the system. The developed system may help to design various domain-specific applications such as annotation and emotion identification system for micro-texts in future. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Piece-wise linear regression: A new approach to predict COVID-19 spreading

The coronavirus disease 2019 (COVID-19) pandemic is the most rapidly evolving global emergency since March 2020 and one of the most exercised topics in all aspects of the world. So far there are numerous articles that have been published related to COVID-19 in various disciplines of science and social context. Since from the very beginning, researchers have been trying to address some fundamental questions like how long it will sustain when it will reach the peak point of spreading, what will be the population of infections, cure, or death in the future. To address such issues researchers have been used several mathematical models from the very beginning around the world. The goal of such predictions is to take strategic control of the disease. In most of the cases, the predictions have deviated from the real data. In this paper, a mathematical model has been used which is not explored earlier in the COVID-19 predictions. The contribution of the work is to present a variant of the linear regression model is the piecewise linear regression, which performs relatively better compared to the other existing models. In our study, the COVID-19 data set of several states of India has been used. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd

Humans and Robots: A Mutually Inclusive Relationship in a Contagious World

The Coronavirus global pandemic has spread faster and more severely than experts had anticipated. While this has presented itself as a great challenge, researchers worldwide have shown ingenuity and dexterity in adapting technology and devising new strategies to combat this pandemic. However, implementing these strategies alone impedes the nature of everyone’s daily life. Hence, an intersection between these strategies and the technological advantages of robotics, artificial intelligence, and autonomous systems is essential for near-to-normal operation. In this review paper, different applications of robotic systems, various aspects of modern technologies, including medical imaging, telemedicine, and supply chains, have been covered with respect to the COVID-19 pandemic. Further-more, concerns over user’s data privacy, job losses, and legal aspects of the implementation of robotics are also been discussed. © 2021, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.