Nanosponges: As a Dynamic Drug Delivery Approach for Targeted Delivery

Recent advancements in nanotechnology have resulted in improved medicine delivery to the target site. Nanosponges are three-dimensional drug delivery systems that are nanoscale in size and created by cross-linking polymers. The introduction of Nanosponges has been a significant step toward overcoming issues such as drug toxicity, low bioavailability, and predictable medication release. Using a new way of nanotechnology, nanosponges, which are porous with small sponges (below one microm) flowing throughout the body, have demonstrated excellent results in delivering drugs. As a result, they reach the target place, attach to the skin's surface, and slowly release the medicine. Nanosponges can be used to encapsulate a wide range of medicines, including both hydrophilic and lipophilic pharmaceuticals. The medication delivery method using nanosponges is one of the most promising fields in pharmacy. It can be used as a biocatalyst carrier for vaccines, antibodies, enzymes, and proteins to be released. The existing study enlightens on the preparation method, evaluation, and prospective application in a medication delivery system and also focuses on patents filed in the field of nanosponges.Copyright © 2023 The Authors.

Zero-COVID policy and stock market sectoral performance in China

With the outbreak of COVID-19, the Chinese government implemented the "zero-COVID” policy as a measure to curb the spread of the virus. The different measures of the policy include widespread testing, contact tracing, and strict quarantine and isolation protocols. In view of recent changes in COVID-19 trends and other economic indicators, the Chinese government withdrew significant provisions of the zero-COVID policy in China. The present study investigates the sectoral performance of the Chinese stock market after the withdrawal of the zero-COVID policy. The study considers eighteen sectoral indices of the Shenzhen Stock Exchange of China as a sample and applies the event study methodology to study the impact of the policy withdrawal on the stock prices performance. The results of the study indicate that sectors such as hotel, consumer staples, the financial sector, real estate, media, and culture have reported significant positive movement after the withdrawal of the zero-COVID policy, while other sectors such as consumer discretionary, energy, healthcare, information technology, manufacturing, mining, technology, telecom, transportation, utilities, wholesale, and retail have shown insignificant reactions. These results also indicate that when the COVID-19 outbreak happened in China, different sectors of the economy reacted negatively except the retail and wholesale sectors, while with the withdrawal of the zero-COVID policy by the Chinese government, the reaction of investors is optimistic as different sectors are reporting either positive reactions in the stock price movement or no reaction. © Prashant Sharma, Surender Kumar, 2023.

Unlocking the Mysteries of the Human Microbiome to Combat COVID-19

A newly identified coronavirus, SARS-CoV-2, has caused a worldwide pandemic of respiratory illness, called COVID-19, culminating in major global morbidity and mortality. Due to its fundamental role in regulating immune homeostasis, it is critical to understand human microbiota to broaden our understanding of COVID-19. Furthermore, perturbation to the composition and function of the human microbiota has also been linked to a variety of risk factors for respiratory diseases, which has resulted in concomitant interest in manipulating the microbiome for therapeutic effects. Several studies have demonstrated the role of human microbiota in influencing viral infections, however the possible connection between microbiome and COVID-19 is not well understood. Therefore, understanding of the precise role of the human microbiota in the pathogenesis of COVID-19 disease is necessary prior to determine if manipulation of microbiota represents a novel treatment therapy for COVID-19. This article will explore the evidence that implicates the human microbiome as a contributing factor to the pathogenesis, severity, and disease course of COVID-19, and speculate about the potential of using human microbiota as a therapeutic avenue against COVID-19. These insights would also improve our knowledge to develop better personalized therapeutic strategies for COVID-19 and other diseases caused by respiratory viruses. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Real-time assessment of the Ganga river during pandemic COVID-19 and predictive data modeling by machine learning.

In this study, four water quality parameters were reviewed at 14 stations of river Ganga in pre-, during and post-lockdown and these parameters were modeled by using different machine learning algorithms. Various mathematical models were used for the computation of water quality parameters in pre-, during and post- lockdown period by using Central Pollution Control Board real-time data. Lockdown resulted in the reduction of Biochemical Oxygen Demand ranging from 55 to 92% with increased concentration of dissolved oxygen at few stations. pH was in range of 6.5-8.5 of during lockdown. Total coliform count declined during lockdown period at some stations. The modeling of oxygen saturation deficit showed supremacy of Thomas Mueller model (R 2 = 0.75) during lockdown over Streeter Phelps (R 2 = 0.57). Polynomial regression and Newton's Divided Difference model predicted possible values of water quality parameters till 30th June, 2020 and 07th August, 2020, respectively. It was found that predicted and real values were close to each other. Genetic algorithm was used to optimize hyperparameters of algorithms like Support Vector Regression and Radical Basis Function Neural Network, which were then employed for prediction of all examined water quality metrics. Computed values from ANN model were found close to the experimental ones (R 2 = 1). Support Vector Regression-Genetic Algorithm Hybrid proved to be very effective for accurate prediction of pH, Biochemical Oxygen Demand, Dissolved Oxygen and Total coliform count during lockdown. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04423-1.

Healthcare Seeking Behaviour of Hospitalised COVID-19 Patients During Second Wave in Tertiary Care Hospital of Northern India

Background: The Coronavirus Disease (COVID-19) pandemic continues its deadly reign all over the world. Devising effective strategies for detecting and controlling the infection has become ever more critical. Effective prevention and control of the pandemic is entirely dependent on human behavior in terms of practicing preventive and curative measures. During the second wave of COVID-19, people's perceptions of preventive and curative measures changed.Objective: To study healthcare-seeking behavior of hospitalized COVID-19 patients.Methods: Hospitalized patients due to COVID-19 in the month of March, April and May of 2021 were included in the study. Their attendants/close relatives were contacted telephonically to know about the admitted patients' healthcare-seeking behavior. Verbal consent was taken from attendants before the commencement of the interview, followed by informing them about the purpose of the interview.Results: Amongst the subjects, there were more males than females (67.5 vs 32.4%), age ranged between 18 to 88 with a mean value of 56.61 +/- 14.7 years. Self-medication was significantly associated with study subjects' mortality (p=0.03).Conclusion: Elderly people were having higher mortality rate than their younger counterparts. People were hesitant to visit primary care physicians after having symptoms of COVID-19.

Silent Thyroiditis as a Complication of Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine

Introduction: Silent autoimmune thyroiditis, a type of chronic autoimmune thyroiditis, as an adverse effect of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination is infrequently reported in the literature. We hereby describe a case of silent thyroiditis followed by Grave's orbitopathy after vaccination against SARS-CoV2. Case Description: An 84-year-old male presented to clinic with a 10-pound weight loss with no other symptoms of hyperthyroidism, no personal history of thyroid illnesses, or recent viral infections. He had normal thyroid function 3 months prior to presentation. He had received 3 doses of SARS-CoV2 Pfizer-BioNTech vaccine with the last dose 5 months prior to presentation. Thyroid exam was normal. Laboratory testing revealed thyroid stimulating hormone (TSH) level of 0.005 IU/ml (0.45-4.5 IU/ml), total T4 14.4 g/dl (4.5-12.1 g/dl), and total T3 1.22 nmol/l (0.6-1.81 nmol/l). Thyroid Ultrasound revealed a heterogeneous atrophic thyroid gland with no nodules or hypervascularity. He was started on Methimazole by primary care provider. Four months later, he was seen in the Endocrinology clinic and reported no hyperthyroidism symptoms. His TSH level at that time was 65.9 IU/ml, free T4 0.47 ng/dl (normal: 0.82-1.77 ng/dl), total T3 level 75 ng/dl (normal: 71-180 ng/dl), thyroid stimulating immunoglobulin 2.05 IU/l (0-0.55 IU/L), thyrotropin receptor antibody level 2.8 (0-1.75). Methimazole was discontinued. At 6 months after initial presentation laboratory testing showed TSH 5.010 IU/ml, free T4 1.2 ng/dl, thyroid peroxidase antibody of 148 IU/ml (normal 0-34 IU/ml), thyroglobulin antibody 131.6 IU/ml (normal 0.0-0.9 IU/ml). He was diagnosed with silent autoimmune thyroiditis. A few weeks later, the patient presented to an ophthalmologist with bilateral eye bulging and impaired vision. He was diagnosed with acute Graves' orbitopathy and started on pulse-dose of intravenous Methylprednisolone 250 mg twice daily and urgently referred to a tertiary ophthalmology center for teprotumumab infusion. His thyroid function tests were normal at that time on no thyroid medications. Discussion(s): The underlying mechanisms of thyroid impairment following SARS-CoV2 vaccination are not completely understood. There is a role of molecular mimicry between SARS-CoV2 antigens and thyroid antigens that may help to hasten the emergence of autoimmunity in vulnerable individuals. Our patient developed multiple thyroid-related antibodies following vaccination. Silent painless thyroiditis is a self-limiting condition, characterized by temporary thyrotoxicosis, followed by a brief period of hypothyroidism and then a complete return to normal thyroid function. A radioactive iodine uptake scan can help differentiate between the different causes of thyrotoxicosis in the acute thyrotoxic phase. Development of severe Graves orbitopathy following silent autoimmune thyroiditis after SARS COV2 vaccination has not been previously reported.Copyright © 2023

Student engagement and learning during COVID-19: an empirical analysis

COVID-19 pandemic brought along with it a widespread disruption of education system around the world. Schools, colleges and universities were shut all over the world. In order to maintain the continuity of education, educators and students alike adopted the online mode of teaching and learning. While mainstream education was mostly face-to-face;a sudden shift to the online mode of teaching and learning required teachers and students to get acquainted with the platform and tools. This study attempts to test a model to understand the impact of online education on students' engagement levels in the context of higher education and the COVID-19 pandemic. Results indicate that access to digital resources and teacher effectiveness has positive impact on engagement and student engagement in turn has positive impact on learning outcomes. Stress has negative impact on student learning. The paper also discusses implications of the study and future direction for research. Copyright © 2022 Inderscience Enterprises Ltd.

Triple Trouble: Disseminated Penicilliosis in a Cancer patient with COVID-19

Penicilliosis is a fungal infection caused by the fungus Penicillium marneffei or Talaromyces marneffei . Penicillosis is commonly seen in immunocompromised patients such as in HIV(AIDS). Herein, we present a case of penicilliosis in an oral cavity cancer patient who was admitted for the management of SARS-CoV-2 infection at our hospital. A 50-year-old male patient operated on for squamous cell carcinoma of the oral cavity who completed his adjuvant chemoradiation 2 months ago, presented to our hospital with dry cough for more than 3 weeks. His nasopharyngeal swab was positive for the severe acute respiratory distress syndrome (SARS-CoV-2). During his hospital stay for SARS-CoV-2 infection, he was diagnosed with disseminated penicilliosis. The patient was treated with intravenous antifungals caspofungin and voriconazole. However, he succumbed to disseminated fungal sepsis. This case highlights the need to consider penicilliosis as a possible opportunistic pathogen, especially in immunocompromised patients such as cancer.

Student engagement and learning during COVID-19: an empirical analysis

COVID-19 pandemic brought along with it a widespread disruption of education system around the world. Schools, colleges and universities were shut all over the world. In order to maintain the continuity of education, educators and students alike adopted the online mode of teaching and learning. While mainstream education was mostly face-to-face;a sudden shift to the online mode of teaching and learning required teachers and students to get acquainted with the platform and tools. This study attempts to test a model to understand the impact of online education on students' engagement levels in the context of higher education and the COVID-19 pandemic. Results indicate that access to digital resources and teacher effectiveness has positive impact on engagement and student engagement in turn has positive impact on learning outcomes. Stress has negative impact on student learning. The paper also discusses implications of the study and future direction for research.

Computational approaches for drug discovery against COVID-19

Coronavirus disease 2019 (COVID-19) has caused widespread diseases and deaths, along with the most severe social and economic disruption worldwide. Therefore, to discover potential drug candidates against COVID-19, many researchers have found various types of molecular targets and vaccine development and also explored new bioactive compounds. Although multiple vaccines have been investigated and tested, the frequent mutation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains a matter of concern. In response to this devastating pandemic, a massive experimental and computational research effort has emerged to understand the disease and rapidly develop diagnostics, vaccines, and drugs. In this regard, more than 130,000 COVID-19-related research articles have been published in peer-reviewed journals. Much of the research has focused on the in silico identification of novel therapeutic candidates and the repurposing of existing drugs against COVID-19. In terms of time, the computational approaches offer the best chance to speed up the long and costly process of drug and vaccine development for a life-threatening condition such as COVID-19. Hence, researchers have given many novel drug candidates against COVID-19 by using computational techniques. In this chapter, we have described the essential computational methods and their applications that were used for COVID-19 drug discovery. This chapter provides an investigation of fundamentals, the process of the target identification, drug design, optimization, and production of the medicine for COVID-19 based on the in silico aspects of signature matching, genomics analysis, proteomics, pathogenesis, phylogenetic analysis, viral receptor binding analysis, protein-protein interaction, artificial intelligence and machine learning, drug repurposing, and deep learning (DL) methods. © 2023 Elsevier Inc. All rights reserved.

Modeling of dispersion of aerosolized airborne pathogens exhaled in indoor spaces

Since the beginning of the COVID19 pandemic, there has been a lack of data to quantify the role played by breathing-out of pathogens in the spread of SARS-Cov-2 despite sufficient indication of its culpability. This work aims to establish the role of aerosol dispersion of SARS-Cov-2 virus and similar airborne pathogens on the spread of the disease in enclosed spaces. A steady-state fluid solver is used to simulate the air flow field, which is then used to compute the dispersion of SARS-Cov-2 and spatial probability distribution of infection inside two representative classrooms. In particular, the dependence of the turbulent diffusivity of the passive scalar on the air changes per hour and the number of inlet ducts has been given due consideration. By mimicking the presence of several humans in an enclosed space with a time-periodic inhalation-exhalation cycle, this study firmly establishes breathing as a major contributor in the spread of the pathogen, especially by superspreaders. Second, a spatial gradient of pathogen concentration is established inside the domain, which strongly refutes the well-mixed theory. Furthermore, higher ventilation rates and proximity of the infected person to the inlet and exhaust vents play an important role in determining the spread of the pathogen. In the case of classrooms, a ventilation rate equivalent to 9 air changes or more is recommended. The simulations show that the "one-meter distance rule"between the occupants can significantly reduce the risk of spreading infection by a high-emitter. © 2023 Author(s).

COVID Detection Using Cough Sound

Due to the impact of COVID-19 on its emerging strains, there is now a greater need for quick identification and containment to prevent further superfluous cases. We aim to make a machine learning model which can distinguish an audio file/signal and categorize it as COVID likely or unlikely and identify the virus' infection by analyzing the user's cough sounds. By usage of real-time detection and a precisely trained ML model with verified data, the user can further assess their infection in conjunction with other available tools, which would instruct him/her to either seek medical attention or provide reassurance for a negative or false positive diagnosis provided by the other tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Post Pandemic Cyber Attacks Impacts and Countermeasures: A Systematic Review

COVID-19 is one of the deadliest pandemics of this century's that affected the whole world. As the COVID-19 spread the government had to impose lockdown that pushed the people to follow some new lifestyle like social distancing, work from home, hand washing, and the country have to shut down industries, businesses and public transport. At the same time, doctors were occupied in saving life's and on other side cyber criminals were busy taking this situation as advantage, which creates an another silent pandemic i.e. cyber-security pandemic. During this pandemic with overloaded ICT infrastructure, cyber space was gaining attention of more cyber attacker and number of attacks/threats increased exponentially. This is one of the rapidly growing global challenges for industry as well as for human life. In this paper a systematic surveys and review is done on recent trends of cyber security attacks during and post COVID-19 pandemic and their countermeasures. The relevant information has been collected from different trusted sources and impact landscape discussed with importance of cyber security education and future research challenges highlights. © 2023 IEEE.

Assessment of knowledge and attitude among pediatric dentists regarding spread and control of infection before and after COVID-19 crisis

Background: In this time of COVID-19 crisis, need of hour to appropriately perform dental procedures to reduce the spread of this deadly disease, it is importance to assess the knowledge and attitude of pediatric dentists regarding spread and control of infection before and after COVID-19 crisis. Methods and Materials: Questionnaire-based survey composed of 6 questions that assess the knowledge of pediatric dentists regarding COVID-19 infection and 10 questions each designed to gather information about their clinical practice before and after COVID-19 crisis which shows attitude of pediatric dentists. Online survey link was circulated through social media and an e-mail to pediatric dentists from different locations in India and the responses were collected. 346 pediatric dentists willingly responded in the study. Result(s): On grading the knowledge score according to the number of most appropriate responses chosen by the respondents, it was found that 82.4% of the pediatric dentist had good knowledge regarding the COVID-19 infection, 16.4% had fair and 1.2% had poor knowledge about it. The attitude regarding clinical practice of pediatric dentists, before and after COVID-19 crisis, is shown in percentage and was determined and compared using Chi-square test. Conclusion(s): Our study presented data on the depth of knowledge and the attitude among the pediatric dentists' attitude regarding spread and control of infection before and after COVID-19 crisis it was quite acceptable.Copyright © 2020 Ubiquity Press. All rights reserved.

Assimilation of aircraft observations over the Indian monsoon region: Investigation of the effects of COVID-19 on a reanalysis

Since March 2020, the COVID-19 pandemic has significantly reduced the availability of global aircraft-based observations (ABOs), which has been restored later in 2021. This study focuses on the impact of ABOs on a regional reanalysis. Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional reanalysis for a period from 1979 to 2020 (originally up to 2018) over India and surrounding regions produced at the National Centre for Medium Range Weather Forecasting (NCMRWF), India, in collaboration with the UK Met Office. A comparison of the impact of ABOs on other conventional and satellite observations assimilated in the NCMRWF global model and IMDAA during 2019 and 2020 revealed the importance of ABOs, particularly in IMDAA, since it did not assimilate the latest satellite data as the IMDAA system was frozen in October 2016. A data denial experiment that removes all the ABOs from the IMDAA assimilation system for a period from March to November 2019 is designed. The results from the IMDAA reanalysis run, which assimilates ABOs during the same period, are compared with the data denial experiment. Assimilation of ABOs strengthened the upper tropospheric circulation, the Tropical Easterly Jet (TEJ), during the Indian summer monsoon compared to the data denial experiment. Analysis of the features of two cyclones that developed over the North Indian Ocean during the study period revealed that ABO assimilation played a key role in simulating the track and intensity of these cyclones when they were in the ‘severe' category. Since the sample is small, more cyclone cases need to be analysed to consolidate the result. © 2023 Royal Meteorological Society.

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

Face Mask Detection Using MobileNetV2 and VGG16

As the COVID-19 situation is not over yet, a new strain of corona virus is again affecting population. Strain like Omicron and Deltacron still poses thread to the society. It is very necessary to keep our self-safe. To prevent spread of COVID few precautions are suggested by governments in the world like maintaining distance of 1 m, use of hand sanitizer, and always wear a mask. The new variant of COVID is now reported by the WHO on November 28, 2021;it was first designated as B.1.1.529 and then named as omicron and later a hybrid variant of delta and omicron was also reported. As these are affecting large population and seeing continuous straggle, it can conclude that corona virus can affect people for few more years considering the current scenario. Keeping that in mind people made face detection software which can be used to tell that a person wearing a mask not. This project is based on same object by using two different technologies MobileNetV2 and VGG16 so that a detail comparing can be done. By comparing both of them it can be known that which perform better and people can choose according to their necessity. This research paper is based on machine learning algorithm and deep learning using different Python libraries like OpenCV, TensorFlow with Keras, MobileNetV2, and VGG16. In this project, the main aim this to detect and then identify that person is wearing a mask or not then comparing both technologies and analyzes the result. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.