Plausible Impacts of SARS-CoV-2 on Human Reproductive System

SARS-CoV-2, a member of the family Coronaviridae, is a positive-stranded RNA virus with the spike glycoproteins present on its envelope. ACE2 serves as the entry mediator of SARS-CoV-2 as it attacks mainly the organs of the respiratory, cardiovascular, digestive, and urinary system showing high expression of ACE2 or TMPRSS2. ACE2 is found to have significant differential expression in all the reproductive tissues, thus posing the reproductive system vulnerable to the adverse effects of SARS-CoV-2 infection. Previous coronavirus attacks (SARSCoV and MERS) have also been known to impose adverse effects on the reproductive system. Therefore, there is a dire need to safeguard the reproductive system against COVID-19 as it not only bothers the present generation but may also affect the well-being of future progeny. Since the inception of pandemic, several scientific studies have been carried out to assess its impact;yet there are research lacunas to claim reproductive system as a potential target of this deadly virus. To avoid the detrimental effects of the current pandemic on reproductive sustainability, well-planned large-scale and multicentric cohort follow-up studies are mandatory for accurate evaluation of the enduring effects of SARS-CoV-2 infection on human fertility and pregnancy outcomes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.

An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. The World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. The COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. The global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. This paper proposes a deep neural network modification based on the Xception model. The model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. The layer addition is intended to avoid overfitting the proposed model. The proposed as a result, we compare the model's loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. The architecture of this study has been presented for the diagnosis of COVID-19. © 2022 IEEE.

Unveiling heterogenous knowledge-oriented leadership and knowledge acquisition based hybrid work agility of knowledge workers

Purpose: With new hybrid working models in place post COVID-19, it is requisite that knowledge workers (KWs) stay agile. Knowledge-oriented leadership (KOL) can help employees with essential knowledge acquisition (KA) facilitating the journey toward hybrid work agility (HWA). This study, thus, aims to explore the impact of KOL and KA on HWA and reveal whether this effect stems uniformly from a single homogenous population or if there is unobserved heterogeneity leading to identifiable segments of agile KWs. Design/methodology/approach: Data was collected through stratified sampling from 416 employees from 20 information technology enabled services companies involved in knowledge-intensive tasks. Partial least squares (PLS) structural equation modeling approach, using SMART PLS 4.0, has been applied to examine the effect of KOL and KA on HWA. Finite mixture PLS, PLS prediction-oriented segmentation and multigroup analysis have been used to identify segments, test segment-specific path models and analyze the significance of the differences in the path coefficients for unobserved heterogeneity. Predictive relevance of the model has been determined using PLS Predict. Findings: Results indicate that KOL contributes to employees' KA and HWA. A significant positive relationship is also reported between KA and HWA. The model has medium predictive relevance. A two-segment solution has been delineated, wherein independent agile KWs (who value autonomy and personal agency over leadership for KA) and dependent agile KWs (who depend on leaders for relational and structural support for KA) have been identified. Thus, KOL and KA play a differential role in determining HWA. Research limitations/implications: The authors' major contribution to the knowledge body constitutes the determination of antecedents of HWA and a typology of agile KWs. Future researchers may conduct segment-wise qualitative analysis to delineate other variables that contribute to HWA. Practical implications: Technological advances necessitate that knowledge-intensive industries foster agility in employees for strategic agility of the organization. For effecting agile adaption of an organization to the knowledge economy conditions, it is pertinent that the full potential of this human resource be used. By profiling HWA of KWs on the basis of dimensions of KOL and the level of their KA, organizations will be able to help employees adapt better to rapidly changing work conditions. Originality/value: HWA is a novel concept and very germane in a hybrid working environment. To the best of the authors' knowledge, this is the first study to examine the effects of the dimensions of KOL and KA in relation to HWA, along with an empirical examination of unobserved heterogeneity in the aforementioned relationship. © 2023, Emerald Publishing Limited.

Depression Analysis Due to Impact of Working from Home during Covid-19 Pandemic

The corona virus [corona virus disease 2019 (COVID-19)] pandemic had adversely affected the people's lives. Work from home during this pandemic has affected various age group of people which leads to the several mental health issues such as depression. Initial indications imply that depression level is higher during COVID-19 pandemic. This research study reveals the quantitative analysis of affected people from depression by work from home during this pandemic an analytical online survey was conducted during this pandemic which contains a sample of 300 people from the different age groups they may include students, unemployed, IT sector workers, research scholars and had no prior history regarding their mental illness. Their working situation in this pandemic time of COVID-19 described their level of depression. The data of this survey show that the maximum number of depressed adults is belong from the age group of 36-40 mainly it includes working professional, unemployed and IT professionals, during this pandemic COVID-19. The adults who were working in IT sector or working professional due to this situation the interaction with the people decrease and screen time increase which results into the aggression, anxiety, mood swings and pessimistic thinking. As in case of student's depression due to less understanding and interaction with teacher in online classes which increases digital divide, dropouts, learning losses and their increase interest towards social media platforms. In our study age-based differences revealed that older age-groups were more vulnerable to stress, depression and anxiety symptoms.Copyright © 2022, Medico Legal Society. All rights reserved.

AZD1222 (ChAdOx1 nCoV-19) Vaccine: Hurdles and Visions

ChAdOx1 nCoV-19 (AZD1222) is a replication-deficient chimpanzee adenovirus vectored vaccine developed by Oxford and AstraZeneca for a disease we all know as Coronavirus, or COVID-19. Ongoing clinical studies reveal that the ChAdOx1 nCoV-19 vaccine has a tolerable safety profile and is effective against symptomatic COVID-19. This vaccine may prove crucial in boosting herd immunity, averting life threatening illness, and relieving the current pandemic. In this mini review, we performed a thorough literature search through PubMed and Google Scholar and reported various case reports associated with complications of the adenovirus-vectored COVID-19 vaccine. Various adverse effects of the ChAdOx1 nCoV-19 vaccine were reported around the globe, which were often serious but rare and developed into life-threatening pathologies such as GBS, thrombocytopenia, demyelinating neuropathies, progressive dementia, cerebral infarction, IgA vasculitis, hemophagocytic lymphohistiocytosis, herpes zoster, cutaneous reactions, and vein thrombosis. These worldwide reported complications, which are usually rare and severe, will aid clinicians in understanding and managing unforeseen situations. There is a need for more research to find out more about these complications and their etiopathogenesis. However, the benefits of these vaccinations for stopping the spread of the outbreak and lowering the fatality rate outweigh the potential risk of the uncommon complications.Copyright © The Author(s) 2023.

Post Covid Anxiety Among University Students in Odisha India

From the last two years due to emergence of COVID-19, a first pandemic of the century, caused hard time to continuing normal lifestyle in all aspects including the campus lifestyle of students. All the academic activities such as classes, examinations, evaluations and placement are going as usual in online mode like earlier. In this regard, we have conducted a Web-based survey on students about their mental condition concerning corona anxiety, coping with stress, worry, and fear. In our survey, 620 students participated from different discipline and states to rejoin the campus either online or offline mode. 372 (60%) students want to attend offline classes while 248 (40%) students want online classes. Additionally, generating the rules using a rough set approach to identify corona anxiety in students. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Antibiotic point prevalence survey at a tertiary healthcare hospital in India: Identifying strategies to improve the antibiotic stewardship program immediately after a COVID-19 wave.

Background: The COVID-19 pandemic has substantially affected the antibiotic stewardship activities in most hospitals of India. Aims: We conducted an antibiotic point prevalence survey (PPS) immediately after the decline of a major COVID-19 wave at a dedicated COVID-19 hospital. By doing so we aimed to identify the antibiotic prescription patterns, identify factors influencing the choice of antibiotics, and identify/develop strategies to improve the antibiotic stewardship program in such setups. Methods: The PPS was single-centred, cross-sectional, and retrospective in nature. Patients admitted in various wards and intensive care units (ICUs) between September 2021 to October 2021 were included in our PPS. Results: Of the included 460 patients, 192 were prescribed antibiotics. Of these 192 patients, ICU-admitted patients had the highest number of antibiotics prescribed i.e. 2.09 ± 0.92. Only a minor fraction (7.92 %) of antibiotics prescriptions were on the basis of culture reports. Most of the antibiotics were prescribed empirically by the parenteral route. The most common group of antibiotics prescribed were third-generation cephalosporins. Carbapenems were the most common designated antibiotics prescribed. A large number of patients (22.40 %) were prescribed a double anaerobic coverage. Conclusion: The strategies that we identified to improve the antibiotic stewardship program at our institute included reviving the culture of sending culture reports to prescribe antibiotics, improving surgical prophylaxis guidelines, training resident doctors to categorize antibiotic prescriptions appropriately, closely monitoring prescriptions providing double anaerobic coverage, and improving the electronic medical record system for improving prescription auditing.

Role and the importance of green approach in biosynthesis of nanopropolis and effectiveness of propolis in the treatment of COVID-19 pandemic

The most fascinating product of honeybee is propolis. It has an immense role in dentistry, dermatology, and otorhinolaryngology. The increased popularity of propolis as an important remedy is due to its constituents, which have anti-inflammatory, immunomodulatory, antihepatotoxic, anti-cancerous, antifungal, antioxidant, antidiabetic, and antiviral activities. The diverse biological and pharmacological activities of propolis have piqued the interest of many scientists. Many techniques like gas chromatography-mass spectrometry, chromatography, and spectroscopy are being used to identify different propolis constituents. Flavonoids, phenolic acids, and their esters are the most pharmacologically active molecules of propolis and are known to disrupt the replication machinery of the virus corroborating the anti-coronavirus activity of propolis. The main aim of this article is to provide an insight of the increasing theragnostic uses of propolis and its nanoparticles, including their chemical analysis, diverse biological activities, and the necessity for chemical standardization. In this review, we have focused at the promising effects of propolis, its optimization, and its liposomal formulation as a therapeutic intervention for COVID-19 and its accompanying comorbidities. © 2023 the author(s), published by De Gruyter.

Post Covid Anxiety Among University Students in Odisha India

From the last two years due to emergence of COVID-19, a first pandemic of the century, caused hard time to continuing normal lifestyle in all aspects including the campus lifestyle of students. All the academic activities such as classes, examinations, evaluations and placement are going as usual in online mode like earlier. In this regard, we have conducted a Web-based survey on students about their mental condition concerning corona anxiety, coping with stress, worry, and fear. In our survey, 620 students participated from different discipline and states to rejoin the campus either online or offline mode. 372 (60%) students want to attend offline classes while 248 (40%) students want online classes. Additionally, generating the rules using a rough set approach to identify corona anxiety in students. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Characteristic Features of Impact of COVID-19 Pandemics on Mental Health of Population of Different Countries: Results of Cross-Sectional Online Studies in Albania, India, Iran and Nigeria

BACKGROUND: The coronavirus infection of 2019 (COVID-19) produced an incontestable impact on the mental health of people around the world. This impact is conditioned by a complex interrelation of social, cultural, economic and COVID-19-associated factors. However, insufficient data on comparison of parameters of mental health of the population in different countries limits our understanding of these interrelations. AIM: To evaluate and compare the frequency of mental health disorders (general problems and problems related to COVID-19) and their correlations in four countries: Albania, India, Iran and Nigeria. MATERIALS AND METHODS: In this study, the problems of mental health of the population of four countries (Albania, India, Iran and Nigeria) were investigated. The participants were selected in the period from July 07, 2020 to November 13, 2020. The study used a cross-check anonymous online questioning to assess the degree of depression, anxiety and insomnia, which included “Patient Health Questionnaire” (PHQ-9), “Generalized Anxiety Disorder” 7 (GAD-7) questionnaire and Insomnia Severity Index (ISI). To assess the mental health problems associated with COVID-19, the survey included Corona Anxiety Scale (CAS), Obsession with COVID-19 Scale (OCS) and Fear of COVID-19 Scale (FCV-19S). To analyze the data, x2, Kruskal–Wallis tests and multiple linear regression were used. RESULTS: In general, the prevalence of general anxiety, depression, insomnia and COVID-19-associated anxiety, was higher among the Iranian population compared to the other three countries. Risk factors for increased anxiety about the new coronavirus infection were fear, depression, trouble and age;however, these factors were different in the four studied countries. The parameter was highest (47%) in the Albanian population and lowest (20%) in India. CONCLUSIONS: This study shows different prevalence of psychological health disorders during the ongoing pandemics, including problems associated with COVID-19, in different countries. Therefore, healthcare policy and measures adopted in different countries, should be adapted to specific needs of the country rather than be based on the universal global responsive measures. © 2022, Eco-Vector LLC. All rights reserved.

Cross-Sectional Descriptive Online Survey to Assess the Adequacy of Knowledge to Prevent COVID-19 Pandemic

Aim: To determine the level of knowledge towards COVID-19 among people. Material & Methods: A cross-sectional descriptive research design was used for the present study and was conducted among people attending Anugrah Narayan Magadh Medical College, Gaya, Bihar, India, to assess their knowledge regarding COVID-19. A total of 461 people were recruited for this study and sample of 400 eligible people who fulfill the inclusion criteria were enrolled. Results: The association of socio-demographic variables of participants and their knowledge score. It shows that group (p>0.001), gender (p=0.020), education (p=0.001), marital status (p=0.001), age (p=0.020), and inhabitants (p=0.001) were significantly associated with knowledge. Majority of participants 63% having good knowledge while 33% and 1.4% having average and poor knowledge respectively regarding the corona virus pandemic. Conclusion: Study concluded that many people were still had average and poor knowledge on COVID-19. Higher authorities must find the ways for making people more aware on this pandemic to control its impact.

“Modelling the effects of capital adequacy, credit losses, and efficiency ratio on return on assets and return on equity of banks during COVID-19 pandemic”

The study aims to determine the impact of Capital Adequacy Ratio, Credit Losses Ratio and Efficiency Ratio on the two significant profitability ratios, namely Return on Assets (ROA) and Return on Equity (ROE), during the pandemic. Panel Data Regression is used to model the effects of Capital Adequacy, Credit Losses and Efficiency Ratio on Return on Assets and Return on Equity of Indian banks. A suitable model has been developed by analyzing the results of the Hausman test and the p-values. It has been found that Capital Adequacy Ratio (CAR) with coefficient value of –0.664, CET1 with coefficient value of 1.83 and efficiency ratio with coefficient value of 1.825 have significantly affected the return on assets as their p-values are less than 0.05. However, the accepted relationship between CAR and ROA, efficiency ratio and ROA were inverse, but their coefficients were significant. The provision for credit losses (PCL) was not affecting the ROA significantly during the pandemic and hence was not considered while framing the model. Again, the dependent variable is the return on equity, except CAR. Other ratios, i.e., CET1, efficiency ratio, and PCL ratio have unacceptable correlations and are even non-significant as their p-values are less than 0.05. © The author(s) 2022. This publication is an open access article.

Averting Crippling Fungal Super-Infection in Era of Viral Pandemic: A Case-Control Retrospective Evidence-Based Study

The second wave of COVID-19 has affected India substantially, with the highest number of daily cases reaching 0.4 million in a single day. COVID-19 infection, its treatment, resultant immune suppression, and pre-existing co-morbidities have made patients vulnerable to secondary infections including mucormycosis. Nasal irrigation is an ancient and well-practiced treatment for nasal and sinus pathologies. It has been proved that Mucorales live as commensals in nasal epithelium and a drop in immunity leads to its super-infection. This practice of nasal douching reduces its survival and washes out the spores from the nasal cavity. The study aims to prove that practicing Nasal Irrigation with prescribed medications reduces the incidence and severity of Mucormycosis. 110 patients who reported with Post-Covid sinusitis were selected for the study. History of Covid-19 infection was taken. Pre-treatment KOH and culture were advised. Also, pre-treatment radiological analysis was done to assess the severity of involvement. According to the guidelines, antibiotic, antifungal, decongestant, anti-inflammatory, and antacid drugs were prescribed. Along with this, nasal irrigation of buffered saline solution, povidone-iodine, and 2% acetic acid was prescribed to alleviate the symptoms or to prevent them all together. Post-treatment culture and radiological analysis were carried out if symptoms persisted. Collected data underwent Chi-square test. The P-value of the study was kept at less than 0.05 according to the sample size. The study revealed a significant decrease in the incidence and severity of symptoms of mucormycosis. This study concludes that Nasal Irrigation has an important role in the prevention of crippling Mucormycosis in Post-Covid patients with initial symptoms of sinusitis. © 2022. Journal of International Dental and Medical Research.All Rights Reserved

COVID-19: Geospatial Analysis of the Pandemic—A Case Study of Bihar State, India, Using Data Derived from Remote Sensing Satellites and COVID-19 National Geoportal

The state of Bihar has a sizeable population which is spread all over the nation especially the skilled and non-skilled labourers. This populace contributes towards human resource as service providers for nation building in various sectors across the length and breadth of India. The declaration by the World Health Organization (WHO) of the spread of COVID-19 virus as a pandemic brought a nationwide lockdown from 23rd of March 2020 to curb its spread. These daily wage workers were stranded far and wide without any resources. The transport communication was also withdrawn initially against the spread. Now, as soon as the conditions became conducive for the migrant labours to return to the native state of Bihar, there was a fear for disaster due to these returning migrant labour force as they probably could become a vector for the spread of COVID-19 pandemic in the state. The state government in consonance with the Central Government formed strict protocols to be adhered to, for these returning migrants. The present paper statistically analyses the spread of this pandemic once the migrant populace began returning to their home state. It investigates whether the influx of so many humans from various parts of the country would become the hub of the spread of the virus causing infectious hot spots or not. Simultaneously, as many researchers were trying to correlate the presence of atmospheric nitrogen dioxide (NO2) with the spread of COVID-19 virus, the paper tried to relate the amount of NO2 present over the study area on the day the maximum number of cases were reported in the study area. Evaluation of atmospheric nitrogen dioxide (NO2) used for the present paper was derived from satellite data. Time series analysis of this NO2 data was done. This enabled us to identify the peak day and the day when the NO2 levels were minimum. Incidentally, the number of COVID-19 cases reported synchronized with the NO2 levels in the atmosphere. Spatial auto-correlation was performed using Moran’s I test on the above two days. The values so obtained indicated that there were no hot spots identified, and the virus was found to be spread in a dispersed manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Immunity-Boosting Natural Herbs to Combat COVID-19 Pandemic: A Narrative Review

Coronaviruses cause some severe forms of respiratory infections such as Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and Coronavirus disease 2019 (Covid-19). These viruses cause diarrhea in pigs and cows and upper respiratory disease in chickens, while other symptoms may differ. In humans, a total of six coronaviruses have been identified HCoVs-NL63, HCoVs-OC43, HCoVs-229E, HCoVs-HKU1, MERS-CoV, and SARS-CoV. The world health organization (WHO) has done a great deal of hard work regarding combating the monstrous effects of this virus. So far, no specific antiviral drugs have been developed for the treatment of Covid-19. Therefore, the medicinal plants used for the previous epidemic outbreaks are getting attention for their potential treatment against the virus. It has been reported that 70 to 80% of people in developing countries depend on medicinal plants or phytomedicine compared to allopathic drugs for their primary healthcare. The south Asian subcontinents have used almost up to 25,000 formulations and extracts obtained from medicinal plants for treatment in folk medicine. The present review discusses an overview of the coronavirus, its immune responses, and some immunity-boosting herbs to combat Covid-19.

Ensemble learning based classifier to predict depression caused due to pandemic

Pandemic caused due to Corona Virus Disease 2019 (COVID-19) affected each and every person life throughout the world. First wave of COVID-19 followed by second wave made situation more panic. Government declared Lockdown imposed strict prohibition on social gathering, unnecessary outing, travelling, and education. During home quarantine, people shared opinion, expressed views, feelings on social media. Home isolation and quarantine affected mental health of people which may lead to depression. Hence in this research article depression is predicted by implementing Neural Network based model. At first level this model implements text classification of COVID-19 based Tweets. Neural network model accuracy is 86.85%. In next level, using same tweet dataset as input, Ensemble learning based model is constructed. This model uses one of the boosting techniques known as Adaboost. Model is executed by varying Train-test-validation ratio. It is observed that accuracy of the model is improved. The model showed accuracy of 99.33 % successfully in every execution. Obtained results are compared with previous work in same area. © 2021 Institute of Physics Publishing. All rights reserved.

The Omic Insights on Unfolding Saga of COVID-19.

The year 2019 has seen an emergence of the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease of 2019 (COVID-19). Since the onset of the pandemic, biological and interdisciplinary research is being carried out across the world at a rapid pace to beat the pandemic. There is an increased need to comprehensively understand various aspects of the virus from detection to treatment options including drugs and vaccines for effective global management of the disease. In this review, we summarize the salient findings pertaining to SARS-CoV-2 biology, including symptoms, hosts, epidemiology, SARS-CoV-2 genome, and its emerging variants, viral diagnostics, host-pathogen interactions, alternative antiviral strategies and application of machine learning heuristics and artificial intelligence for effective management of COVID-19 and future pandemics.

Real-Time Face Mask Detection Using Raspberry Pi and Camera

We all know that this is the ERA of Coronavirus diseases (COVID-19). It is a kind of pandemic which is growing at a fast rate and causing a health crisis. It caused many deaths last years and still counting. So, the doctors and scientists are working on the vaccine for this virus and also, they have suggested some ways to be safe from this virus, face masks are one of the most common measures to fight such virus. In this paper, we have discussed on the project about face mask detection using Raspberry Pi and a live video streaming camera. The face mask detection model was done with the help of a computer vision, CNN, image classification algorithm based on the MobileNetV2 neural network. The steps involved in the project are: firstly, we have taken the data-set of people wearing the face mask and then those not wearing the face mask, after then we pre-processed it, split the data, trained the model using MobileNetV2 neural network, tested the model, and finally implemented the model. The model has been trained with an accuracy of 95.85%. This system automatically opens the door when people are wearing the face mask and sends an alert if they don’t have the face mask, to the authorities or the owner of the place. It can be used in a variety of places, include educational institutions, hospitals, churches, and retail outlets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.