Search

Purpose: Outbreak of SARS-CoV2 and the COVID-19 pandemic had posed a threat to the healthcare system. The world had witnessed the failure of the healthcare system in the diagnosis, surveillance, and development of therapeutics effective against COVID-19. Hence, research emphasis has been given to technological inventions in developing robust and effective tools to fight against respiratory viral outbreaks, including SARS-CoV2. Methods: The scientific literature was searched and retrieved from PubMed, PubMed Central, the Cochrane Central Register of Controlled Trials (CENTRAL), and Scielo databases. The scientific literature was screened and selected based on recent studies in the area of nanotechnology based inventions and applications in developing diagnostics, surveillance tools, PPE kits, therapeutics, and vaccines. Results: Based on the scientific literature, there are enormous developments in designing robust diagnostics, surveillance tools, therapeutics, and vaccines. These findings show an increasing demand for nanotechnology-based inventions to tackle zoonotic spillover. Nanotechnology remains a growing area as interdisciplinary science and technology- enable a variety of inventions used in the COVID-19 pandemic. PPE design is a classic example where nanotechnology-driven materials are extensively used. Masks and other fabrics were also developed using nanotechnology-based materials. Conclusion: The study provides insights into nanotechnology based inventions in the fight against respiratory viral pathogens, including SARS-CoV2, and associated diseases. The study showed the application of nanotechnology in the development of diagnostics, surveillance tools, therapeutics, and vaccines. [ABSTRACT FROM AUTHOR]

Machine Learning is recent emerging technique in prediction of various health related issues in medical system. It is very essential to predict the COVID-19 virus before it spreads and affects an entire community. Machine Learning is being used to detect the presence of COVID-19 virus as early as possible by analyzing patient's health condition and collecting data such as gender, age, Body Mass Index (BMI), asthma symptoms, wheezing, dyspnea, respiratory failure, cough, blood sugar level etc., with this information used eighteen machine learning algorithms such as ELM, Logistic Regression, SGD, KNN, SVM, QDA, LDA, XGBoost etc., to analyze the data and predict the presence of COVID-19 virus. Table and Charts are plotted with the help of the results acquired from the machine learning algorithm. As a result, early prediction of COVID-19 becomes possible and huge loss in terms of both health and economy can be avoided. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID19) can cause gastrointestinal complications as well as respiratory tract disease. Coagulation abnormalities and thrombosis frequently occur in COVID19, especially in cases with severe clinical outcome. The relationship between gastrointestinal perforation and coagulopathy due to COVID19 remains unclear. Case presentation: A 49-year-old female received Chimeric antigen receptor T (CAR-T) therapy for an early recurrence of diffuse large B-cell lymphoma (DLBCL) that was refractory to chemotherapy. She was diagnosed with cytokine release syndrome (CRS) because of a fever and oxygen desaturation, and administered tocilizumab. Forty days after completing CAR-T therapy, she was infected with COVID19 and transferred to our hospital. Her general condition worsened and she developed COVID19 pneumonia, and then steroid pulse therapy was started. While her respiratory condition improved, she experienced pain in the anal region and computed tomography (CT) revealed a rectal perforation. An emergency surgery was undertaken, and the lower rectum wall was found to be completely necrotic. Removal of the necrotic part of the rectum tissue, and drainage and lavage of necrotic tissue in the pelvic cavity were performed. The remaining rectum was resected with partial sigmoidectomy, but we could not make the anal stump closed. In addition, an end colostomy in the sigmoid colon was performed. Histopathological findings showed thromboses in the rectal mesentery veins. After the first surgery, the pelvic abscess cavity persisted and her high-grade fever continued. Reoperation was laparoscopically performed, and she underwent a resection of anal canal with residual necrotic rectal and mesorectal tissue, and a drainage of the pelvic abscess. After the reoperation, her general condition improved and CT showed that the abscess cavity had significantly improved. Conclusions: Gastrointestinal perforation, especially rectal necrosis due to coagulopathy caused by severe COVID19 infection, is a rare but life-threatening complication. Physicians should have a high degree of clinical suspicion for timely diagnosis and management, and surgical intervention is necessary in cases of rectal necrosis. [ABSTRACT FROM AUTHOR]

This paper aims to identify herding biases and assess the inefficiency of Ethereum using an inefficiency index (MLM). Additionally, it investigates the nonlinear dynamical properties of Ethereum by estimating the MFDFA, aiming to deduce the impact of COVID-19 on Ethereum's performance. The paper also captures abnormal changes resulting from COVID-19-related events and assesses their influence on the Ethereum market response. The empirical results show that Ethereum was multifractal before the pandemic and became less fractal in the period following the outbreak using Generalized Hurst Exponent (GHE) estimation. Based on the MLM measure of efficiency, we found Ethereum to be more efficient in the first phase of the pandemic than before it, and as in the Hausdorff topology, the pandemic reduced herd bias. The event study analysis took into account specific events related to the pandemic and showed that each led to significant abnormal returns in the Ethereum market. The results reported are used to empirically establish differences in the value of Ethereum before and during the COVID-19 pandemic. The results are useful in a general sense for traders, investors, and policy makers because they provide new information about market trading opportunities and social responses. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome caused by a coronavirus is a recent viral infection. There is no scientific evidence or clinical trials to indicate that possible therapies have demonstrated results in suspected or confirmed patients. This work aims to perform a virtual screening of 1430 ligands through molecular docking and to evaluate the possible inhibitory capacity of these drugs about the Mpro protease of Covid-19. The selected drugs were registered with the FDA and available in the virtual drug library, widely used by the population. The simulation was performed using the MolAiCalD algorithm, with a Lamarckian genetic model (GA) combined with energy estimation based on rigid and flexible conformation grids. In addition, molecular dynamics studies were also performed to verify the stability of the receptor-ligand complexes formed through analyses of RMSD, RMSF, H–Bond, SASA, and MMGBSA. Compared to the binding energy of the synthetic redocking coupling (−6.8 kcal/mol/RMSD of 1.34 Å), which was considerably higher, it was then decided to analyze the parameters of only three ligands: ergotamine (−9.9 kcal/mol/RMSD of 2.0 Å), dihydroergotamine (−9.8 kcal/mol/RMSD of 1.46 Å) and olysio (−9.5 kcal/mol/RMSD of 1.5 Å). It can be stated that ergotamine showed the best interactions with the Mpro protease of Covid-19 in the in silico study, showing itself as a promising candidate for treating Covid-19. [ABSTRACT FROM AUTHOR]

Despite the efficacy of COVID-19 vaccines, patients with hematologic malignancy may still be fatal from COVID19. Therefore, we prospectively performed the analysis of administration of tixagevimab/cilgavimab in the real-world. In August 2022, 94 patients under active chemotherapy for lymphoma, multiple myeloma, or acute leukemia received a single dose AZD7442/Evusheld (two consecutive intramuscular injections of tixagevimab and cilgavimab, 300 mg each). Quantitative measurement of anti-SARS-CoV-2 spike protein (anti-S) and viral nucleocapsid (anti-N) titers were conducted before administration of tixagevimab/cilgavimab and at 1, 3, and 6 months after administration. Twenty-five patients (26.6%) had previously confirmed COVID-19 infection. Fifty-eight patients (61.7%) had previously received COVID-19 vaccinations, with a median of two doses (range, 1–5). The median anti-S Ab level increased from baseline (997.05 AU/mL) to 1 month (20,967.25 AU/mL), then decreased at 3 months (13,145.0 AU/mL), and 6 months (7123.0 AU/mL) (p < 0.001). There was no significant safety issue with tixagevimab/cilgavimab. With a median follow-up time of 6 months, thirteen patients (13.8%) had documented SARS-Cov-2 infection. A 20.2% rate of anti-N positivity was observed six months after the administration of tixagevimab/cilgavimab. The results of this study support the potential role of tixagevimab/cilgavimab for the prevention of symptomatic and severe COVID-19. Trial registration: KCT0007617; August 16, 2022. [ABSTRACT FROM AUTHOR]

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google's Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic's behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables. [ABSTRACT FROM AUTHOR]

COVID-19, caused by SARS-CoV-2, affects neuronal cells, causing several symptoms such as memory loss, anosmia and brain inflammation. Curcuminoids (Me08 e Me23) and curcumin (CUR) are derived from Curcuma Longa extract (EXT). Many therapeutic actions have been linked to these compounds, including antiviral action. Given the severe implications of COVID-19, especially within the central nervous system, our study aims to shed light on the therapeutic potential of curcuminoids against SARS-CoV-2 infection, particularly in neuronal cells. Here, we investigated the effects of CUR, EXT, Me08 and Me23 in human neuroblastoma SH-SY5Y. We observed that Me23 significantly decreased the expression of plasma membrane-associated transmembrane protease serine 2 (TMPRSS2) and TMPRSS11D, consequently mitigating the elevated ROS levels induced by SARS-CoV-2. Furthermore, Me23 exhibited antioxidative properties by increasing NRF2 gene expression and restoring NQO1 activity following SARS-CoV-2 infection. Both Me08 and Me23 effectively reduced SARS-CoV-2 replication in SH-SY5Y cells overexpressing ACE2 (SH-ACE2). Additionally, all of these compounds demonstrated the ability to decrease proinflammatory cytokines such as IL-6, TNF-α, and IL-17, while Me08 specifically reduced INF-γ levels. Our findings suggest that curcuminoid Me23 could serve as a potential agent for mitigating the impact of COVID-19, particularly within the context of central nervous system involvement. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has afflicted people from all sectors of life, and families have faced unprecedented challenges. In this study, researchers explore how intergenerational disparities impact the self-efficacy coping and resilience of family members during a crisis. With family members of different ages forced to live together, the pandemic has tested the resilience and self-efficacy coping of each individual. Using a descriptive-correlational research design, the researchers examined the relationships between resilience behavior, sense of coherence, and self-efficacy in family members from different life-stage cohorts. Our findings show that resilience behavior is associated with Sense of Coherence, however, self-efficacy coping shows no variation by generation. Further analysis revealed unique strengths among Generation X and Boomers, who demonstrated greater resilience and a greater sense of coherence compared to other cohorts. In the context of salutogenesis, our work emphasizes the importance of intergenerational differences in fostering family mental health in times of crisis. Our research illuminates family coping mechanisms and attitudes at different life stages, making communities more resilient. This study can help build a more cohesive society where intergenerational support networks thrive and mental health is a priority for all generations. These insights can help future generations overcome challenges together. [ABSTRACT FROM AUTHOR]

COVID-19 is reported as a very infectious which is increasing in rapid speed at present time. In this pandemic World health organization (WHO) is monitoring the situation and providing the preventive measures coordinating the treatment strategies to fight against Covid-19 through out the world. The only way to stop the further spread of this is to detect the disease early. Some works have been started to investigate covid19 using Deep learning algorithm over chestX-ray (CXR) images. In our work we have processed one CNN model which can process CXR images along with the metadata(non imaging data) available with the dataset to classify Covid 19. Resnet 50, Dense net 121, Mobile Net,VGG-16, Inception-V3 and one proposed Convolution Neural Network have been modified to accept the metadata along with CXR image. Some state of the art Deep learning models have been run to classify the covid 19 on the same data set and compared with our best model. Experiments have been done in two phases. In the 1st phase we used CNN models on CXR image only and in the 2nd phase we ran all modified CNN models over the same CXR images with their matadata. The experimental results shows that the output of 2nd phase out performs the output of 1st phase.After that we compared our best model (Proposed CNN) with other state of art models. [ABSTRACT FROM AUTHOR]

The Nobel Prize of Physiology or Medicine 2023 was awarded jointly to two researchers, Katalin Karikó and Drew Weissman, for their discoveries taking part in the development of effective mRNA vaccines against COVID-19. The accelerated development of the novel mRNA-lipid nanoparticle vaccines provided highly effective protection against severe disease or death, and reduction in symptomatic illness, before a full year had passed from the beginning of the pandemic. [ABSTRACT FROM AUTHOR]

The global surge in academic misconduct during the COVID-19 pandemic, exacerbated by remote teaching and online assessment, necessitates a comprehensive understanding of the multidimensional aspects and stakeholders' perspectives associated with this issue. This paper addresses the prevalent use of answer-providing sites and other types of academic misconduct, underscoring the challenge of detecting all or most of the student misconduct. Exploring factors such as faculty inexperience in remote teaching and assessment, the paper advocates for proactive measures to preserve integrity in education. Emphasizing the need for a culture of integrity beyond traditional classrooms, the paper reviews existing models, then details steps to create a framework using the International Labour Organization (ILO)'s TREE training method. It presents the IEPAR framework (Inspiration, Education, Pedagogical considerations, Assessment design, Response and Restorative practice), and assesses its effectiveness. Incorporating faculty feedback, the paper concludes with evidence-based findings, positioning the IEPAR framework as a robust approach for addressing academic misconduct and fostering a culture of academic integrity in higher education through responsible training of all stakeholders. [ABSTRACT FROM AUTHOR]

Background: Cancer is a risk factor for developing severe COVID19. Additionally, SARS-CoV2 has a special tropism for renal cells and complications like thrombosis or cytokine storm could be enhanced by standard treatments in kidney cancer (i.e., antiangiogenics or immunotherapy). Thus, understanding the impact of COVID19 in patients with this tumor is key for their correct management. Methods: We designed a retrospective case–control study comparing the outcome of three groups of advanced kidney cancer patients on systemic treatment: cohort A (developed COVID19 while on antiangiogenics), cohort B (developed COVID19 while on immunotherapy) and cohort C (non-infected). Matching factors were age, gender, and treatment. Results: 95 patients were recruited in 16 centers in Spain from September 2020 to May 2021. Finally, 85 were deemed as eligible (23 cohort A, 21 cohort B, 41 cohort C). Patients with COVID required more dose interruptions (25 vs. six) and hospitalizations (10 vs. none) than those without COVID (both p = 0.001). No difference between cohorts A and B was observed regarding hospitalization or length of stay. No ICU admission was registered and one patient in cohort B died due to COVID19. Regarding cancer evolution, three patients in cohort A presented progressive disease after COVID19 compared to two in cohort B. One case in cohort B, initially deemed as stable disease, achieved a partial response after COVID19. Conclusions: Kidney cancer patients who developed COVID19 while on systemic therapy required more treatment interruptions and hospitalizations than those non-infected. However, no significant impact on cancer outcome was observed. Also, no difference was seen between cases on antiangiogenics or immunotherapy. [ABSTRACT FROM AUTHOR]

Sweden had the most liberal lockdown policies in Europe during the Covid-19 pandemic. Relying on individual responsibility and behavioural nudges, their effectiveness was questioned from the perspective of others who responded with legal restrictions on behaviour. In this study, using mobile phone data, we therefore examine daily spatial mobilities in Stockholm to understand how they changed during the pandemic from their pre-pandemic baseline given this background. The analysis demonstrates: that mobilities did indeed change but with some variations according to (a) the residential social composition of places and (b) their locations within the city; that the changes were long lasting; and that the average fall in spatial mobility across the whole was not caused by everybody moving less but instead by more people joining the group of those who stayed close to home. It showed, furthermore, that there were seasonal differences in spatial behaviour as well as those associated with major religious or national festivals. The analysis indicates the value of mobile phone data for spatially fine-grained mobility research but also shows its weaknesses, namely the lack of personal information on important covariates such as age, gender, and education. [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze temporal changes in social needs (SN), comparing those who received routine annual in-person care to those receiving SN screenings through a combination of tele-social care and in-person care biannually. Our prospective cohort study used a convenience sample of patients from primary care practices. Baseline data were collected from April 2019 to March 2020. The intervention group (n = 336) received SN screening and referral telephone outreach from June 2020 to August 2021. The control group (n = 2890) was screened, in person, during routine visits at baseline and summer 2021. We used a repeated-measures logistic regression with general estimating equations to assess incremental change in individual SN for the intervention group. Food, housing, legal and benefit needs increased and peaked at the beginning of the pandemic and decreased after interventions (P < 0.001). There was a 32% decrease in the odds of food insecurity for those in the intervention group compared to the control group (adjusted OR 0.668, 95% confidence interval 0.444–1.004, P = 0.052), and a 75% decrease in the odds of housing insecurity (adjusted OR 0.247, 95% confidence interval 0.150–0.505, P < 0.001). During COVID-19, there was an increase in SN followed by a decrease after interventions were offered. Those who completed tele-social care showed greater improvements in social needs than those in routine care, with the greatest improvements in food and housing needs. [ABSTRACT FROM AUTHOR]

Young adults and racial/ethnic minorities report the worst mental health outcomes during the COVID19 pandemic, according to the Center for Disease Control (2020). The objectives of this study were (1) to identify common mental health symptoms among Latin American, US Hispanic, and Spanish college students, and (2) to identify clinical features predictive of higher post-traumatic stress symptoms (PTSS) among this population. The study sample included 1,113 college students from the USA, Mexico, Chile, Ecuador, and Spain who completed an online survey containing demographic questions and mental health screeners. Findings revealed higher scores of depression, suicidality, and PTSS compared to pre-pandemic levels and current scores by non-Spanish speaking college students; however, less than 5% of participants endorsed clinical levels of anxiety. After controlling for demographic profiles and sociocultural values, clinical symptoms of depression, loneliness, perceived stress, anxiety, and coping strategies explained 62% of the PTSS variance. Age, history of mental illness, perceived social support, and familism were not significant predictors. This sample of college students revealed higher mental health symptoms during the COVID-19 pandemic. The high prevalence of PTSS highlights the need to develop pragmatic, cost-effective, and culturally sensitive prevention and intervention strategies to mitigate these symptoms. Implications for college administrators and clinicians are discussed. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of high-flow nasal cannula oxygen therapy (HFNC) in patients with acute respiratory failure due to COVID-19 remains uncertain. We aimed at assessing whether HFNC is associated with reduced risk of intubation or mortality in patients with acute respiratory failure due to COVID-19 compared with conventional oxygen therapy (COT). Methods: In this systematic review and meta-analysis, we searched MEDLINE, Embase, Web of Science, and CENTRAL databases for randomized controlled trials (RCTs) and observational studies comparing HFNC vs. COT in patients with acute respiratory failure due to COVID-19, published in English from inception to December 2022. Pediatric studies, studies that compared HFNC with a noninvasive respiratory support other than COT and those in which intubation or mortality were not reported were excluded. Two authors independently screened and selected articles for inclusion, extracted data, and assessed the risk of bias. Fixed-effects or random-effects meta-analysis were performed according to statistical heterogeneity. Primary outcomes were risk of intubation and mortality across RCTs. Effect estimates were calculated as risk ratios and 95% confidence interval (RR; 95% CI). Observational studies were used for sensitivity analyses. Results: Twenty studies were analyzed, accounting for 8383 patients, including 6 RCTs (2509 patients) and 14 observational studies (5874 patients). By pooling the 6 RCTs, HFNC compared with COT significantly reduced the risk of intubation (RR 0.89, 95% CI 0.80 to 0.98; p = 0.02) and reduced length of stay in hospital. HFNC did not significantly reduce the risk of mortality (RR 0.93, 95% CI 0.77 to 1.11; p = 0.40). Conclusions: In patients with acute respiratory failure due to COVID-19, HFNC reduced the need for intubation and shortened length of stay in hospital without significant decreased risk of mortality. Trial registration The study was registered on the International prospective register of systematic reviews (PROSPERO) at https://www.crd.york.ac.uk/prospero/ with the trial registration number CRD42022340035 (06/20/2022). [ABSTRACT FROM AUTHOR]

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets. [ABSTRACT FROM AUTHOR]

Measures to contain the Corona Virus Disease 2019 (COVID-19) pandemic curtailed access to prevention and treatment services for endemic infectious diseases like HIV. We used an uncontrolled before-and-after study design to compare general and HIV positive (HIV+) inpatient outcomes at a tertiary hospital in Uganda, using electronic records of medical inpatients. Data was downloaded, cleaned in Microsoft Excel, and exported to STATA for analysis. We determined the difference in number of admissions and median length of hospital stay using Mann-Whitney U test; and difference in median survival and incidence rates of mortality using Kaplan – Meier statistics, between the pre- and peri-COVID-19 groups. Of 7506 patients admitted to Kiruddu NRH, 50.8% (3812) were female and 18.7% (1,401) were aged 31–40 years, and 18.8% (1,411) were HIV+. Overall, 24.6% (1849) died. Total admissions were lower (2192 vs. 5314 patients), overall mortality rate higher (41.8% vs. 17.6%, p < 0.01), median length of hospital stay longer (6 vs. 4 days, p < 0.01) and median survival shorter (11 vs. 20 days, Chi-square = 252.05, p < 0.01) in the peri- than in pre-COVID-19 period. The adjusted hazard ratio (aHR) of death was 2.08 (95% CI: 1.85–2.23, p < 0.01) in the peri- compared to the pre-COVID-19 period. These differences were more pronounced in HIV + patients. Compared to pre-COVID-19, the peri-COVID-19 period registered lower inpatient admissions but poorer treatment outcomes for general and HIV + inpatients. Emerging epidemic responses should minimize disruption to inpatient care, especially for HIV + individuals. [ABSTRACT FROM AUTHOR]

Copyright of Pädiatrie & Pädologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Coronavirus disease (COVID-19) caused the death of millions of people and affected the lives of hundreds of millions worldwide. The WHO recommendations aimed mainly to reduce transmission, minimize infection, and get people vaccinated. Nevertheless, opinions and attitudes about the disease varied. In this study, we evaluated personal attitudes and practices of a cohort of an educated Syrian population, after several waves of infection with COVID-19 and the release of different types of vaccines. Methods: A cross-sectional internet-based survey was launched in January 2022.The survey queried the participants' personal experience, attitudes, practices towards COVID-19, and vaccination. Results: The study included 408 individuals. The respondents were mainly females (72.6%), 20–29 years old (39.2%), and college graduates (59.3%). A large proportion (89.7%) reported having been infected at least once during the pandemic; a significant association was found with age (p = 0.001). Nearly half of the respondents got vaccinated; the majority were > 40 years old. Opinions differed regarding the effectiveness and safety of the vaccines; only a small percentage of the participants (17.4%) thought all vaccines were effective and safe. Remarkably, the level of education did not significantly dominate the participants' attitudes or practices towards the COVID-19 pandemic. Approximately half of the respondents (44.9%) stated their lives were affected by the pandemic and over the third were worried (38%). A significant association was detected with gender in favour of females. Most of the participants have taken at least one precautionary measure to limit the infection. Conclusion: The level of education did not significantly dominate the participants' attitudes or practices towards the COVID-19 pandemic. Female respondents were more cautious, concerned and committed to taking precautionary measures regardless of their education level. However, their unwillingness to receive the vaccine raises significant concerns. Efforts should be made to emphasize the importance of immunization, the safety and effectiveness of vaccines, and encourage vaccination among individuals. [ABSTRACT FROM AUTHOR]

Ever since the start of the Corona pandemic, different and often conflicting views have emerged about the virus and how to appropriately deal with it. Such epistemic, societal, and economic criticisms, including those about government imposed measures, have often been dismissed as dangerous forms of conspiratorial disinformation that should be (and have been) excluded from the realm of reasonable political discussion. However, since these critiques of emerging hegemonic knowledge and policies often involve significant and complex questioning of epistemic and political claims, and since corresponding plausibilities change over time, such clear distinctions between correct knowledge and foolish, fraudulent, and/or dangerous, disinformation are not easy to draw. In fact, they can be considered political acts in these epistemic disputes over the pandemic. These conflicts, which we refer to as the "Corona Truth Wars," are not just about knowledge, but have turned into societal conflicts and even outright identity wars that run through families, circles of friends, organizations, and entire societies. In this special issue, we illuminate these dynamics by bringing together a range of scholars who have been struck by the complexity of these controversies and their far-reaching social consequences. Far from understanding these controversies as simple dichotomies between truth and disinformation, or between disinterested science and manipulative politics, these scholars are interested in the various ways in which these dimensions are intertwined. Building on a long tradition of exploring (scientific) knowledge controversies, the six contributions to this special issue show how epistemic struggles over truth are not only fought in the realm of science, but increasingly manifest and interact in everyday politics, social media platforms, daily talk shows, and family dinners. The scholars brought together in this issue, with diverse disciplinary backgrounds and from different geographical regions (Denmark, France, Germany, the Czech Republic, and Israel), present their studies on the various epistemic and social conflicts that have emerged during the Corona pandemic of the last three years. [ABSTRACT FROM AUTHOR]

Although the COVID-19 pandemic has had detrimental effects on mental health in the general population, the impact on those with schizophrenia-spectrum disorders has received relatively little attention. Assessing pandemic-related changes in positive symptoms is particularly critical to inform treatment protocols and determine whether fluctuations in hallucinations and delusions are related to telehealth utilization and treatment adherence. In the current longitudinal study, we evaluated changes in the frequency of hallucinations and delusions and distress resulting from them across three-time points. Participants included: (1) outpatients with chronic schizophrenia (SZ: n = 32) and healthy controls (CN: n = 31); (2) individuals at clinically high risk for psychosis (CHR: n = 25) and CN (n = 30). A series of questionnaires were administered to assess hallucination and delusion severity, medication adherence, telehealth utilization, and protective factors during the pandemic. While there were no significant increases in the frequency of hallucinations and delusions in SZ and CHR, distress increased from pre-pandemic to early pandemic in both groups and then decreased at the third time point. Additionally, changes in positive symptom severity in SZ were related to psychiatric medication adherence. Findings suggest that positive symptoms are a critical treatment target during the pandemic and that ongoing medication services will be beneficial. [ABSTRACT FROM AUTHOR]

Background and Objective: Interconception care (ICC) is a means of improving health outcomes for women and children by mitigating maternal risks between pregnancies. Within a pediatric medical home ICC is reliant on adherence to well-child visits (WCVs). We hypothesized that a pediatric-based ICC model would remain successful in providing access to services for adolescent women for those seen during the COVID19 pandemic. The objective of this study was to determine if the COVID19 pandemic influenced LARC use and repeat pregnancy for those seen for ICC in a dyadic pediatric medical home. Methods: The pre-COVID cohort was comprised of adolescent women seen for ICC from September 2018-October 2019. The COVID cohort was comprised of adolescent women seen for ICC from March 2020-March 2021. The two cohorts were compared across multiple characteristics including sociodemographic factors, age, education, number of visits, contraceptive choice and repeat pregnancy during the study interval. Results: The COVID cohort were significantly more likely to be primiparous, seen with a younger infant, and attend fewer visits than the pre-COVID cohort. The COVID cohort were equally likely to initiate long-acting reversible contraception but less likely to experience a repeat pregnancy. Conclusions: The COVID19 pandemic limited access to routine healthcare and likely impacted access to ICC for many women. ICC provided during WCVs allowed access to care even amid the restrictions of the COVID19 pandemic. Both effective contraception and decreased repeat pregnancy were maintained, highlighting the effectiveness of this approach for ICC within a dyadic pediatric medical home. Significance: Interconception care has been previously shown to be successful in a dyadic pediatric medical home. The COVID-19 pandemic limited reproductive healthcare access worldwide, but little is known on the impact of COVID-19 on interconception care delivered in a pediatric medical home. This study demonstrated the strength of interconception care incorporated into well child visits for those seeking care during the pandemic. What's Is Already Known on This Subject? Interconception care targets maternal risk factors to improve outcomes of subsequent pregnancies and has been effective within pediatrics. Adolescent women are at increased risk of adverse medical, educational, socioeconomic and parenting outcomes after the birth of a second child. ICC has been shown to be successful in a dyadic pediatric medical home. What This Study Adds? The COVID-19 pandemic limited reproductive healthcare access, but little is known about the impact on interconception care delivered in a pediatric medical home. This study demonstrated the strength of interconception care incorporated into well child visits during the pandemic. [ABSTRACT FROM AUTHOR]

Higher education institutions had to quickly adapt to teaching and learning during COVID19. Border closure meant students were stuck offshore and had to learn from afar. This meant a revolutionary change had to be quickly introduced by the teaching team. As for the students, this meant, experiencing teaching and learning in a new way. In this research study, the experiences of offshore students enrolled in a university in New Zealand is explored. Participants in this research study are unique as they are students who have enrolled in a university but have never step foot in New Zealand due to border closure. This setup presents a unique opportunity to gain information on what strategies were used by these students to engage with teaching and learning material. Similarly, a description of teaching and learning strategies used by the teaching team to make teaching and learning seamless for these offshore students is described. Results of this preliminary study indicates that these students had to adapt to cultural issues and technical issues. Offshore students also provide suggestions in relation to strategies that the teaching team could use to help offshore students feel included. [ABSTRACT FROM AUTHOR]

Purpose: Due to the COVID19 pandemic, the EAU has recommended to, if needed, postpone second transurethral resection of bladder tumour (TURBT) after BCG induction in selected patients. We aimed to evaluate the oncological outcomes of postponed TURBT and the potential to replace second TURBT by routine cystoscopy and cytology. Methods: A single-center, retrospective analysis of patients with TaG3/high grade (HG) or T1HG urothelial bladder cancer was performed. All patients underwent a complete TURBT between 2000 and 2013 with presence of detrusor muscle, full BCG induction and routine cystoscopy and cytology, followed by a second TURBT. Results of the cystoscopy, cytology and pathology reports of the TURBT were analyzed by descriptive characteristics, sensitivity, specificity, negative and positive predictive values, as well as survival analyses. Results: 112 patients were included. Residual tumour was present at second TURBT in 21.4%. Upstaging rate from pTaHG to pT1HG and pT1HG to pT2 was 0% and 2.7%, respectively. pT0 was confirmed in 79% of patients, but in 98% of patients with combined negative cytology and cystoscopy after BCG. With a median follow-up of 109 months, the 3-year OS was 85%, RFS 74% and PFS 89%. Sensitivity, specificity, negative predictive value and positive predictive value of cystoscopy and urinary cytology for the presence of residual tumour were 92%, 97%, 98% and 85%, respectively. Conclusion: This study underpins the recommendation of the EAU NMIBC guideline panel that, if needed and in selected patients, second TURBT may be postponed until after BCG induction treatment in pT1HG disease. Also, routine second TURBT can be omitted in pTaHG disease. Data on replacing second TURBT after BCG treatment by routine cystoscopy and cytology appear promising but require further confirmation in prospective studies. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) also named COVID-19, aggressively spread all over the world in just a few months. Since then, it has multiple variants that are far more contagious than its parent. Rapid and accurate diagnosis of COVID-19 and its variants are crucial for its treatment, analysis of lungs damage and quarantine management. Deep learning-based solution for efficient and accurate diagnosis to COVID-19 and its variants using Chest X-rays, and computed tomography images could help to counter its outbreak. This work presents a novel depth-wise residual network with an atrous mechanism for accurate segmentation and lesion location of COVID-19 affected areas using volumetric CT images. The proposed framework consists of 3D depth-wise and 3D residual squeeze and excitation block in cascaded and parallel to capture uniformly multi-scale context (low-level detailed, mid-level comprehensive and high-level rich semantic features). The squeeze and excitation block adaptively recalibrates channel-wise feature responses by explicitly modeling inter-dependencies between various channels. We further have introduced an atrous mechanism with a different atrous rate as the bottom layer. Extensive experiments on benchmark CT datasets showed considerable gain (5%) for accurate segmentation and lesion location of COVID-19 affected areas. [ABSTRACT FROM AUTHOR]

Mucormycosis is an emerging angioinvasive fungal infection caused by ubiquitous saprophytic filamentous fungus that belongs to the class Zygomycosis. Mucormycosis or black fungus infections in patients with recent COVID-19 infections has given rise to a new epidemic during the second wave of this current COVID-19 pandemic in India. Steroid misuse, poor glycemic control, use of industrial grade oxygen, improper humidification and specific variants strains have resulted in profound immunosuppression and are attributed for the high incidence of COVID associated Mucormycosis (CAM) in India. The aims of the present study were to assess the age and gender specific incidence of CAM, the utility of special stains (PAS and GMS) for diagnosing mucormycosis (false negatives and sensitivity). In the present study emphasis was laid on identifying if there exists a correlation between host response, angioinvasion and bone invasion with mortality. The Present study is a retrospective, cross sectional analytical study taken up in the Department of Pathology, Guntur Medical College for a period of 2 months between 1st of May 2021 to 30th of June 2021. Results were tabulated in Microsoft excel 2016 and SPSS software version 14 was used for calculation of odds ratio and for performing multivariate analysis. All samples were routinely fixed using 10% buffered formalin, processed and sectioned. All the sections were stained with Haematoxylin and Eosin and also special stains for fungus like periodic acid Schiff (PAS) and Gomori Methenamine silver (GMS) were used wherever required. CAM is a serious complication after recovery from COVID-19 infection due to its high morbidity and mortality (13.7% in this series). CAM epidemiology and presentation is mostly similar to non-COVID mucormycosis seen in pre-COVID era. [ABSTRACT FROM AUTHOR]

Objective: The COVID-19 pandemic altered health outcomes in populations through a host of downstream social, economic, and psychological changes, especially among those with chronic non-communicable diseases (NCDs). Some studies reveal worsened glycemic control and weight gain, while others indicate improved glycemic control and weight loss. Thus, evidence demonstrates conflicting results in this context. We aimed to conduct a study to explore changes in these metrics in an outpatient setting providing for an underserved population. Methods: We conducted a single-site observational study at a Federally Qualified Health Center (FQHC) in New York City to compare glycemic control and body weight, measured by Hemoglobin A1c (HbA1c) and body mass index (BMI) respectively, before and after the onset of the COVID-19 pandemic. Results: After the pandemic, there was a 103% increase in the annual change in average HbA1c from the years prior to the pandemic versus from early 2020 to 2021 (p < 0.005). Mean BMI increased during the pandemic, although this was not statistically significant. The slope for the change in BMI over five years prior to the pandemic is -0.09, while the slope of change in BMI before and after the onset of COVID-19 is 0.31. The difference between the two slopes is 0.48 (p = 0.37). Discussion/Conclusion: Our study reveals that the COVID-19 pandemic could have contributed to a worsening in the status of metabolic disorders due to decreased physical activity, worsened dietary habits, psychosocial stressors, and reduced access to healthcare, emphasizing the need for enhanced medical, pharmaceutical and emotional support. Concurrently, many individuals adopted healthier practices through dietary and activity modifications, with a resulting improvement in cardio-metabolic parameters. [ABSTRACT FROM AUTHOR]

The increase in using antibiotics, especially Azithromycin have increased steadily since the beginning of COVID19 pandemic. This increase has led to its presence in water systems which consequently led to its presence upon using this water for irrigation. The aim of the present work is to study the impact of irrigation using Azithromycin containing water on soil microbial community and its catabolic activity in the presence of phenolic wastes as compost. Wild berry, red grapes, pomegranate, and spent tea waste were added to soil and the degradation was monitored after 5 and 7 days at ambient and high temperatures. The results obtained show that at 30 °C, soil microbial community collectively was able to degrade Azithromycin, while at 40 °C, addition of spent tea as compost was needed to reach higher degradation. To ensure that the degradation was biotic and depended on degradation by indigenous microflora, a 25 kGy irradiation dose was used to kill the microorganisms in the soil and this was used as negative control. The residual antibiotic was assayed using UV spectroscopy and High Performance Liquid Chromatography (HPLC). Indication of Azithromycin presence was studied using Fourier Transform Infrared Spectroscopy (FTIR) peaks and the same pattern was obtained using the 3 used detection methods, the ability to assign the peaks even in the presence of soil and not to have any overlaps, gives the chance to study this result in depth to prepare IR based sensor for quick sensing of antibiotic in environmental samples. [ABSTRACT FROM AUTHOR]

Corona virus's connection to the cell is through spike protein to ACE2 from cell membrane. This connection was modeled using Density Functional Theory (DFT) computational method based on PW91 and basis set 6-31G(d). Thereafter, the assessment of the effect of diethyl zinc (Et2Zn) on bonds of COVID19 and cell membrane (C–N) showed the increased bond length up to an average of 0.82 Å. This increase in bond length may be due to the transfer of partial charge from the Zn atom with electropositive properties to σ orbital atom of N with an electronegativity property. Moreover, it was shown that an increase in bond length of connection region could weaken virus's connection to cell membrane as well as separating it. QST2 computations showed that performing the process thermodynamically and kinetically is applicable. In addition, images of TEM microscope on long of Lab Indian guinea pig with COVID19 and after using Et2Zn obviously showed lung recovery. [ABSTRACT FROM AUTHOR]

SARS, or severe acute respiratory syndrome, is caused by a novel coronavirus (COVID-19). This situation has compelled many pharmaceutical R&D companies and public health research sectors to focus their efforts on developing effective therapeutics. SARS-nCoV-2 was chosen as a protein spike to targeted monoclonal antibodies and therapeutics for prevention and treatment. Deep mutational scanning created a monoclonal antibody to characterize the effects of mutations in a variable antibody fragment based on its expression levels, specificity, stability, and affinity for specific antigenic conserved epitopes to the Spike-S-Receptor Binding Domain (RBD). Improved contacts between Fv light and heavy chains and the targeted antigens of RBD could result in a highly potent neutralizing antibody (NAbs) response as well as cross-protection against other SARS-nCoV-2 strains. It undergoes multipoint core mutations that combine enhancing mutations, resulting in increased binding affinity and significantly increased stability between RBD and antibody. In addition, we improved. Structures of variable fragment (Fv) complexed with the RBD of Spike protein were subjected to our established in-silico antibody-engineering platform to obtain enhanced binding affinity to SARS-nCoV-2 and develop ability profiling. We found that the size and three-dimensional shape of epitopes significantly impacted the activity of antibodies produced against the RBD of Spike protein. Overall, because of the conformational changes between RBD and hACE2, it prevents viral entry. As a result of this in-silico study, the designed antibody can be used as a promising therapeutic strategy to treat COVID-19. [ABSTRACT FROM AUTHOR]

Introduction: Most of COVID-19 patients present with hypoxemic respiratory failure. Proning is one of the management options proven to improve oxygenation and reduce mortality in non-COVID-19-related acute respiratory distress syndrome. As a response to COVID-19 pandemic surge, a dedicated COVID-19 respiratory ward for the management of mild to moderate ARDS patients who require oxygen therapy, non-invasive ventilation (NIV), or high-flow nasal cannula (HFNC) was established. We adopted a policy of early awake proning in such patients. Aims: To determine the physiological changes, improvement in oxygenation, the need for intubation, alongside with the duration, tolerance, and adverse effects of awake proning. Study design and methods: Single-center, prospective observational cohort study. All awake, non-intubated, spontaneously breathing patients with COVID-19, and hypoxemic acute respiratory failure requiring oxygen supplementation, NIV, or HF Results: Fifty patients were enrolled. There was a significant improvement in oxygenation when turning the patients from supine to prone position with mean PFR was 85 (SD 13.76) in supine position which increased to 124 (SD 34.08) in prone position with substantial increase in mean PFR 1-h post proning to 138 (SD 28.01) and P-value 0.0001. Prone positioning was feasible in 41 (82%) patients (mean duration 8.5 (SD 3.13) h), and 38 (76%) patients reported that it was well tolerated. Conclusion: Awake proning was feasible, tolerable, and effective in improving oxygenation in patients with COVID-19-related pneumonia and acute hypoxemic respiratory failure in this prospective study. [ABSTRACT FROM AUTHOR]

This paper examines the political economy of epidemic disease. First, it outlines the incentive and information problems facing policymakers in responding to a new epidemic. Second, it considers the existence of a tradeoff between public health and freedom. Informed by a survey of the history of public health and an analysis of the response to Covid-19, it presents evidence that such a tradeoff can obtain in the short run but that, in the long run, the negative relationship is reversed and the trade-off disappears. [ABSTRACT FROM AUTHOR]

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine–cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is the causative agent of COVID-19 which was detected in late 2019 in Wuhan, China. As of September 2022, there have been over 612 million confirmed cases of COVID-19 with over 6.5 million associated deaths. In many cases, anosmia and dysgeusia have been identified as primary symptoms of COVID-19 infection in patients. While the loss of smell (anosmia) and loss of taste (dysgeusia) due to COVID-19 infection is transient in most patients, many report that these symptoms persist following recovery. Understanding the pathogenesis of these symptoms is paramount to early treatment of the infection. We conducted a literature review of Google Scholar and PubMed to find and analyze studies discussing anosmia and dysgeusia in the context of COVID-19 to understand the progression and management of these symptoms. The mechanism for dysgeusia is largely unknown; however, pathogenesis of anosmia includes inflammation and cytokine release resulting from the infection that alters neuronal signaling, thus inducing the loss of smell that patients experience. Anosmia may be managed and potentially resolved sooner with a combination therapy of olfactory training and budesonide irrigation of the nasal cavity. It is important to note that the variants of SARS-CoV-2 are genetically distinguished from the original virion due to a mutation in their spike proteins, giving them a different symptom profile regarding anosmia and dysgeusia. This variability in symptomatology is an area of study that needs to be further explored. [ABSTRACT FROM AUTHOR]

PhOMe-salophen (1b) (salophen is N,N-bis(salycilidene)-1,2-phenylenediamine with two tert-butyl on each ring) and Cu(II) complex with PhOMe-salophen (1c) have been synthesized and characterized using various tools, including X-ray diffraction for the Cu(II)-complex (1c, C43H52CuN2O3)). The copper complex has been obtained by Cu2+ templated approach using 1b. PhOMe-salophen (1b) has been obtained in reasonably high yield using a mixture of the Schiff-base, 1a, Pd(OAc)2, PPh3, Na2CO3, 4-methoxyphenylboronic acid in benzene. We focus in this research work on the electronic and structural properties of the Cu–Schiff base complex. The tetra-coordinate τ4 index was calculated, indicating almost a perfect square planner in agreement with X-ray diffraction results. MEP reveals the maximum positive regions in 1/-associated with the azomethine and methoxyphenyl C–H bonds with an average value of 0.03 a.u. Hirshfeld surface analysis (HSA) was also studied to highlight the significant inter-atomic contacts and their percentage contribution through 2D Fingerprint plot. In a fair comparative molecular docking study, 1b and 1c were docked together with N-[{(5-methylisoxazol-3-yl)-carbonyl}alanyl}-l-valyl]-N1-((1R,2Z)-4-(benzyloxy)-4-oxo-1-[{(3R)-2-oxopyrrolidin-3-yl}methyl]but-2-enyl)-l-leucinamide, N3 against main protease Mpro, (PDB code 7BQY) using the same parameters and conditions. Interesting here to use the free energy, in silico, molecular docking approach, which aims to rank our molecules with respect to the well-known inhibitor, N3. The binding scores of 1b, 1c, N3 are –7.8, –9.0, and –8.4 kcal/mol, respectively. These preliminary results propose that ligands deserve additional study in the context of possible remedial agents for COVID-19. [ABSTRACT FROM AUTHOR]

Reliability is the probability that a system or a product fulfills its intended function without failure over a period of time and it is generally used to determine the reliability, release and testing stop time of the system. The primary objective of this study is to predict and forecast COVID19 reliabilities of the countries by utilizing this definition of the reliability. To our knowledge, this study is the first carried out in the direction of this objective. The major contribution of this study is to model the COVID19 data by considering the intensity functions with different types of functional shapes, including geometric, exponential, Weibull, gamma and identifying best fit (BF) model for each country, separately. To achieve the objective determined, cumulative number of confirmed cases are modelled by eight Non-Homogenous Poisson Process (NHPP) models. BF models are selected based on three comparison criteria, including Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Theil Statistics (TS). The results can be summarized as follows: S-shaped models provide better fit for 56 of 70 countries. Current outbreak may continue in 43 countries and a new outbreak may occur in 27 countries. 50 countries have the reliability smaller than 75%, 9 countries between 75% and 90%, and 11 countries a 90% or higher on 11 August 2021. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic represents a significant risk factor for mental distress in perinatal women. Assessment for mental health issues should therefore be an integral part of safeguarding health at every stage of pregnancy and postpartum. Considering the impact of the COVID-19 pandemic on the planning of healthcare services locally, it is important to employ information-gathering techniques such as seeking feedback from both patients and staff. E-screening conforms to stay-at-home COVID restrictions and can improve the efficiency of mental healthcare. The symptomatologic levels indicated by the cut-off points, as well as the real time concerns expressed by perinatal women through open questions, are valuable on many levels. Future studies are needed not only on the sensitivity of the e-screening routines in the context of daily clinical practice, but also on the deeper meaning of the personal concerns reported in e-screening open questions in both positive and negative screening environments. [ABSTRACT FROM AUTHOR]

COVID19 forced most education institutions to move their education provisions to digital platforms almost overnight to ensure continued learning for students. Variable factors relating to educational technologies, ease of use, accessibility and funding meant the process was more challenging for some than others. However even the most agile and well-prepared educational institutions faced a hurdle during remote learning when it came to integrity in assessing students online. This article tracks the efforts by one western university in a Middle Eastern country, tracing back the systemic changes, evolution of policies and procedures that culminated into a campus-wide response that helped redesign assessments, increased awareness among faculty towards recognising and reporting cases, and the implementation of integrity pledges. It is believed this article presents a well-rounded case study as a good practice guide for other tertiary institutions on the importance of building a culture of integrity prior to any crisis, that can help whether such situations need to arrive in the future. [ABSTRACT FROM AUTHOR]

Population size has made disease monitoring a major concern in the healthcare system, due to which auto-detection has become a top priority. Intelligent disease detection frameworks enable doctors to recognize illnesses, provide stable and accurate results, and lower mortality rates. An acute and severe disease known as Coronavirus (COVID19) has suddenly become a global health crisis. The fastest way to avoid the spreading of Covid19 is to implement an automated detection approach. In this study, an explainable COVID19 detection in CT scan and chest X-ray is established using a combination of deep learning and machine learning classification algorithms. A Convolutional Neural Network (CNN) collects deep features from collected images, and these features are then fed into a machine learning ensemble for COVID19 assessment. To identify COVID19 disease from images, an ensemble model is developed which includes, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). The overall performance of the proposed method is interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM), and t-distributed Stochastic Neighbor Embedding (t-SNE). The proposed method is evaluated using two datasets containing 1,646 and 2,481 CT scan images gathered from COVID19 patients, respectively. Various performance comparisons with state-of-the-art approaches were also shown. The proposed approach beats existing models, with scores of 98.5% accuracy, 99% precision, and 99% recall, respectively. Further, the t-SNE and explainable Artificial Intelligence (AI) experiments are conducted to validate the proposed approach. [ABSTRACT FROM AUTHOR]

The paper intends to serve two objectives. First, it revisits the celebrated Fay-Herriot model, but with homoscedastic known error variance. The motivation comes from an analysis of count data, in the present case, COVID-19 fatality for all counties in Florida. The Poisson model seems appropriate here, as is typical for rare events. An empirical Bayes (EB) approach is taken for estimation. However, unlike the conventional conjugate gamma or the log-normal prior for the Poisson mean, here we make a square root transformation of the original Poisson data, along with square root transformation of the corresponding mean. Proper back transformation is used to infer about the original Poisson means. The square root transformation makes the normal approximation of the transformed data more justifiable with added homoscedasticity. We obtain exact analytical formulas for the bias and mean squared error of the proposed EB estimators. In addition to illustrating our method with the COVID-19 example, we also evaluate performance of our procedure with simulated data as well. [ABSTRACT FROM AUTHOR]

In smart city architecture, information and communication technologies are used to improve living standards and its management by citizens and government. Most researchers have divided this structure into six main components: smart people, the smart government, smart environment, smart transportation, smart economy, and smart life. Due to the connection between smart cities and the challenges resulting from their implementation and especially its integration, there exists no perfect solution for the concept of an integrated smart city so far according to our studies. Some more general concepts such as security, ICT infrastructure, and knowledge are not seen integrative in these structures. Therefore, it seems that new sub-components and general extra-components should be added to the existing models to form an integrated structure in such a way that the executive projects are located in their proper place in this structure and create and guarantee the integration of the smart city. Therefore, the requirements engineering of the smart city can also be explained more precisely. This study presents a model of an integrated graph in such a way that besides maintaining and improving the model of the smart city and existing models, it will fully cover the integration and requirements engineering and methodologies of the smart city in the future. The present paper offers an upgraded model of a six-component smart city structure as a flexible integrated dynamic graph so that beside maintaining the features of existing smart city models, it ensures its integrity, dynamism, flexibility and performance and prevents the failure of smart operations. Due to its flexibility, adaptability and localization, the proposed model presented in this paper can create an integrated solution and facilitating the life cycle of executive systems and enable governments and communities to predict and prevent sudden events such as natural disasters, pandemics like Covid-19 and the like as well as managing and leading their target community in the best way. [ABSTRACT FROM AUTHOR]

Invasive fungal rhinosinusitis was seen to rise to epidemic levels after the 2nd wave of ongoing Covid pandemic, especially in tropical countries, maximally in India. A similar trend is being observed for cases who have recently recovered from dengue virus infection. Post dengue invasive fungal infection is a new presentation and any associations between it and Covid pandemic need to be studied in detail to help prepare for any complications. 3 patients presented to the out-patient department of E.N.T at a tertiary level teaching hospital in East India with complains similar to rhinosinusitis. These patients were then evaluated and diagnosed to be infected from Mucormycosis and Aspergillosis fungal sinusitis after which they were managed with surgical debridement and systemic antifungal therapy. All had a recent history of recovery from Dengue virus infection and a possible association could be suspected. 3 patients presented with complains of pain over upper jaw with orbital swelling and loss of vision developing over a period of 24 days. Two of them had ulceration of hard palate. They were then subjected to Contrast MRI along with CT scan of the Paranasal sinuses which depicted pansinus involvement with intracranial extension in two patients. These were then planned for diagnostic nasal endoscopies along with biopsies which turned out to be Invasive fungal sinusitis in the form of Aspergillosis and Mucormycosis. All the 3 patients had recent history of recovery from Dengue virus infection and did not have any other co-morbidities. Covid Associated Mucormycosis (CAM) is a well-known entity now but no reports of Dengue associated Invasive fungal sinusitis are yet reported. Whether this new phenomenon has anything to do with the interactions between dengue virus and coronavirus is not known at present and needs to be studied in detail so appropriate management protocols can be formulated. [ABSTRACT FROM AUTHOR]

Several existing drugs have gained initial consideration due to their therapeutic characteristics against COVID-19 (Corona Virus Disease 2019). Hydroxychloroquine (HCQ) was proposed as possible therapy for shortening the duration of COVID-19, but soon after this, it was discarded. Similarly, known antiviral compounds were also proposed and investigated to treat COVID-19. We report a pharmacophore screening using essential chemical groups derived from HCQ and known antivirals to search a natural compound chemical space. Molecular docking of HCQ under physiological condition with spike protein, 3C-like protease (3CLpro), and RNA-dependent RNA polymerase (RdRp) of SARS-CoV2 showed − 8.52 kcal/mole binding score with RdRp, while the other two proteins showed relatively weaker binding affinity. Docked complex of RdRp-HCQ is further examined using 100 ns molecular dynamic simulation. Docking and simulation study confirmed active chemical moieties of HCQ, treated as 6-point pharmacophore to screen ZINC natural compound database. Pharmacophore screening resulted in the identification of potent hit molecule [(3S,3aR,6R,6aS)-3-(5-phenylsulfanyltetrazol-1-yl)-2,3,3a,5,6,6a-hexahydrofuro[3,2-b]furan-6-yl]N-naphthalen-ylcarbamate from natural compound library. Additionally, a set of antiviral compounds with similar chemical scaffolds are also used to design a separate ligand-based pharmacophore screening. Antiviral pharmacophore screening produced a potent hit 4-[(1,5-dimethyl-3-oxo-2-phenylpyrazol-4-yl)-(2-hydroxyphenyl)methyl]-1,5-dimethyl-2-phenylpyrazol-3-one containing essential moieties that showed affinity towards RdRp. Further, both these screened compounds are docked (− 8.69 and − 8.86 kcal/mol) and simulated with RdRp protein for 100 ns in explicit solvent medium. They bind at the active site of RdRp and form direct/indirect interaction with ASP618, ASP760, and ASP761 catalytic residues of the protein. Successively, their molecular mechanics Poisson Boltzmann surface area (MMPBSA) binding energies are calculated over the simulation trajectory to determine the dynamic atomistic interaction details. Overall, this study proposes two key natural chemical moieties: (a) tetrazol and (b) phenylpyrazol that can be investigated as a potential chemical group to design inhibitors against SARS-CoV2 RdRp. [ABSTRACT FROM AUTHOR]

Pelvic exenteration is a surgery done to achieve margin negative resection in locally advanced rectal cancer infiltrating pelvic organs anterior to it. A retrospective observational study of patients undergoing pelvic exenteration for locally advanced rectal cancer was done at a single surgical unit of a tertiary care cancer centre. The period of study was from 1st January 2019 to 30th June 2021. A total of twelve patients underwent pelvic exenteration for locally advanced rectal cancer during the study period. The median duration of surgery was 310 min (range 250 to 380 min). The median duration of hospital stay was 14 days (range 12 to 30 days). Seven patients had documented postoperative complications, either major or minor, with a complication rate of 58.3%. Three patients required re-admission for complications. Two patients had COVID19 infection in the postoperative period but had uneventful recovery. Margin negative resection (R0) was achieved in eight patients (66.67%). Pelvic exenteration for locally advanced rectal cancer is a definitive surgery associated with a high morbidity rate. [ABSTRACT FROM AUTHOR]

Pandemics create survival uncertainty through infection possibilities, food scarcity, and unemployment. Being the largest democracy in the world, we have explored the response of Indian citizens on the COVID-19's lockdown and defined an anxiety response model using PLS based Structural Equation Modeling(SEM). For a comprehensive understanding, we have measured the response at two levels of individual and government. Though the types of anxieties are related, we observed that a specific response is linked with a specific type of anxiety and all responses are not anxiety-driven. We have found that the response mechanism of Health and Food anxieties follow very different paths and that the role of information is not significant in all anxieties. Our results will help policymakers in understanding how to respond to a crisis and optimize policy implementation accordingly. It will further help the scholars understand the difference in the anxieties caused by the pandemic and the layers of responses individuals take in such situations. [ABSTRACT FROM AUTHOR]

Social media platforms have become a common place for information exchange among their users. People leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights into local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, topic modelling, sentiment detection, and semantic brand score to obtain an insight into the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government-reported instances. [ABSTRACT FROM AUTHOR]

Background: The COVID19 pandemic is one of the greatest modern global healthcare crises. The resultant morbidity and mortality of public and healthcare professionals has led to psychological impacts and economic repercussions. We set out to assess the concerns of doctors in training during this crisis. Methods: A questionnaire was developed and delivered via Survey Monkey to doctors in training from 27 March to 6 April 2020, 2 weeks prior to the expected surge in Ireland and UK. The Perceived Stress Scale (PSS) was incorporated to gauge respondent stress as they prepared for the COVID19 impact. Results: A total of 285 participants engaged with 197 (69%) completing all questions. Almost 86% of respondents had been trained in donning and doffing personal protective equipment (PPE), and nearly 85% felt confident in the process. Overall, most respondents felt somewhat prepared (60%) or well prepared (20%) to treat COVID19 patients. However, 42% worried that their hospital would struggle, or not cope at all; in particular, 91% highlighted the risk of running out of PPE. Family health (86%), personal health (72%), and social life (47%) topped the list of junior doctor concerns. According to the PSS, the majority of respondents (62%) had moderate stress. Conclusions: This survey is the first to measure the concerns of doctors in training in Ireland as regards the COVID19 pandemic. Worries included PPE exhaustion and personal and family health. A significant majority had moderate stress. Additional supports for doctors in training are essential to aid stress and manage concerns better. [ABSTRACT FROM AUTHOR]