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Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers' comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold.
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Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics. Our system utilizes a Garmin wearable and symptom tracker mobile app for data collection. The data consists of vitals, lifestyle, and symptom information which is fused into an online report for clinicians to review. Symptom data collected via our mobile app is used to label the recovery status of each patient daily. We propose a ML-based binary patient recovery classifier which uses wearable data to estimate whether a patient has recovered from COVID-19 symptoms. We evaluate our method using leave-one-subject-out (LOSO) cross-validation, and find that Random Forest (RF) is the top performing model. Our method achieves an F1-score of 0.88 when applying our RF-based model personalization technique using weighted bootstrap aggregation. Our results demonstrate that ML-assisted remote monitoring using automatically collected wearable data can supplement or be used in place of manual daily symptom tracking which relies on patient compliance. IEEE
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A growing number of people are using tweets about the recent coronavirus epidemic of COVID-19 as a dataset to determine how worried people are in different parts of the world. This study attempts to uncover the key sentiments expressed by Twitter users regarding the COVID-19 epidemic by categorizing the tweets into positive and negative sentiments utilizing several resources (such as the Twitter search application programming interface (API), the Tweepy Python library, and the CSV excel database), as well as some predefined search terms ('#LockdownPakistan.'). We extracted the text of English language tweets from 28th March-1st May 2020. We have performed the sentiment analysis and classified the tweets in a binary class of positive and negative. Further, we used the word frequencies of single (unigrams), double (bigrams), and three words to examine the gathered tweets (tri-grams). According to our data, the majority of tweets express a positive attitude, with the word for lockdown COVID-19 appearing frequently. When looking at frequency analysis, the word 'family and time' stood out among the other words, which suggests that tweets were mostly optimistic and sentiments of defeating SARS-COV-2 prevail. People are determined to spend the lockdown in a good way. However, a few of the negative tweets, nevertheless, should serve as a warning for healthcare officials to make appropriate arrangements. Public health crisis responses are today complicated and highly synchronized both offline and online. Social media is a significant medium that gives people the chance to communicate with healthcare authorities directly. © 2022 IEEE.
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Sustainable development, finance and related global policies and mechanisms have evolved over the decades. Today, regional initiatives for classifying sustainable activities exist, and several decades' research and development of ecosystem services and natural capital have identified and tested alternative economic models. The World Bank has the potential to finance them and sustainability at the landscape scale is achievable. But economic and environmental values can come into conflict. In developing countries, sustainable alternatives exist in business activities such as coastal and marine tourism. Financing small businesses through sound digital infrastructure is critical, as is the use of public fiscal instruments for the sustainable use of natural resources. Despite its developed status, renewable energy policies in the EU are leading to forest destruction. Financial vehicles such as green bonds have a similar potential. To avoid greenwashing, more focus needs to be on meeting the needs of those at the base of the economic pyramid, resourcing them with smart technologies and valuing civic engagement. Climate finance must be ethical and its allocation have integrity;this will foster community resilience. To avoid repeating the mistakes of terrestrial development, the world's oceans need to be protected and new business models adopted in this expanding frontier. Now is the time for all sectors to create a sustainable future for the planet and its inhabitants in the post-COVID, postcarbon era to come. © 2022 Walter de Gruyter GmbH, Berlin/Boston.
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Digitalisation has changed the delivery patterns of financial services all over the world. On August 26, 2020, the United Nations launched a task force with a mandate of catalysing and recommending ways to harness digital finance to accelerate the required finance for Sustainable Development Goals (SDGs). Digital finance has become a lifeline for billions of people to survive and to ensure financial sustainability during the COVID-19 pandemic. Social safety nets involving cash transfers through digital payments have become convenient and they enable governments to mobilise funds for emergency relief. A number of developing countries, like Bangladesh, have done tremendously well in managing the pandemic, thanks to the use of digital financing that has delivered financial services through digital platforms, including the uptake of mobile money and other payment platforms. It is also helping to channel more resources to support all SDGs through different paths. This chapter will discuss the prominent role of digital finance and its operational mechanism to finance SDGs. The outcome of the research will be to provide an assessment of digitalisation in accelerating SDGs finance so that policymakers can take some necessary actions to ensure the effective implementation of digital finance for financial sustainability. © 2022 Walter de Gruyter GmbH, Berlin/Boston.
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Background and Aim: Kawasaki Disease remains an enigma to the world to this day since first described by Dr. Tomisaku Kawasaki in 1967. In the last half a century there has been wide-spread global research elaborating the clinical aspects and patho-genesis of this disease entity. Multisystem Inflammatory Syndrome post Covid (MISC) is a relatively new disease which was described in literature in mid 2020. The striking resemblance as well as differences in spectrum of cardiac involvement of both the conditions has been elaborated in this study from a tertiary care centre in Eastern India. Method(s): The study was conducted over a period of 3 years from June 2018 to June 2021. Fiftyone patients with Kawasaki disease (including atypical and incomplete cases) and sixty children diag-nosed with MISC were included in the study. Echocardiography details were noted by a single observer. Data regarding the patient particulars, clinical aspects, lab parameters, imaging details and treatment particulars were collected and analysed. Patients were followed up for a minimum period of six months to one year. Result(s): In the Kawasaki group(51), infants(20) presented with multiple (and larger) aneurysms. Older children (gt;5 years) had more of single coronary involvement, (mostly LAD) and also had more atypical presentation(18) associated with infections like Dengue, Staphylococcal infection, Scrub Typhus. There were 4 cases of Kawasaki shock syndrome, all below 5 years. In the MISC group (60), there was also multiple coronary involvement in infants (11). But LV dysfunction was more common in older children and adolesecents (20), of whom 18 (90%) presented with severe dysfunction (LVEFlt;35%). Those with coronary involve-ment had normal function and those with dysfunction had no coronary involvement. Mild to moderate aneurysmal dilation of coronaries was found in children one to five years of age. No giant aneurysm was found in MISC. Overall, LMCA with LAD was the commonest pattern of involvement in both the conditions. Conclusion(s): KD and MISC had similar pattern of coronary involve-ment, but absence of giant aneurysm and significantly severe dys-function in older children in MISC indicates a likely different pathogenesis for myocardial involvement in MISC.
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SESSION TITLE: Impact of Health Disparities and Differences SESSION TYPE: Rapid Fire Original Inv PRESENTED ON: 10/19/2022 11:15 am - 12:15 pm PURPOSE: As of March 25, 2022, age-standardized data reported by the Centers for Disease Control and Prevention showed that Hispanic, Black and American Indian or Alaska Native are about twice as likely to die from coronavirus disease 2019 (COVID-19) as their White and Asian counterparts. However, there is paucity of data regarding the effect of race on outcomes in COVID-19 related acute respiratory distress syndrome (ARDS) patients managed with extracorporeal membrane oxygenation (ECMO). Our study aims to understand the differences in the outcome between White/Asian and other ethnically minority COVID-19 patients treated with ECMO in our intensive care unit (ICU). METHODS: Retrospective analysis of adult patients with COVID-19 related ARDS treated with ECMO in the ICUs of a quaternary care hospital between 03/01/2020 and 03/31/2022. Patients were divided into two groups: White/Asian (WA) and Other Minorities (OM). Demographics, clinical characteristics, and outcomes of the two groups were compared. RESULTS: Of the 36 COVID-19 patients managed with ECMO during the study period, 18 (50%) patients belonged to the WA group while 18 (50%) patients belonged to the OM group. In the WA group, 16 (89%) were white and 2 (11%) were Asians whereas in the OM group, 16 (89%) patients were Hispanics and 2 (11%) patients were African-American. Both groups were similar in terms of age, gender, comorbidity burden (measured by Charlson Comorbidity Index), and severity of illness at the time of ICU admission (assessed by APACHE-IV score). Mean RESP score was lower in the OM group but was not statistically significant (1.3 ± 3.9 vs 2.9 ± 2.3, p= 0.157). This was reflected in the higher hospital mortality in the OM group compared to the WA group [n= 9 (50%) vs. 15 (83%), p=0.075]. There was no significant difference between the groups in the rate of ECMO-related complications, including major bleeding requiring transfusion, transaminitis (alanine transaminase greater than 5 times of upper normal limit), stroke, myocardial dysfunction (defined as an ejection fraction < 30%), acute kidney injury requiring dialysis and positive sterile fluid cultures. CONCLUSIONS: Our study showed higher mortality in ethnically minority patients compared to the white and Asian population but the difference was not statistically significant. It is possible that the relatively small number of patients in our study led to a beta error. Higher mortality rates among people of color have been attributed to low socio-economic status, structural inequities in health care and differences in vaccination rates. CLINICAL IMPLICATIONS: Larger studies are needed to further explore differences in clinical characteristics and outcomes of COVID-19 patients of different races and ethnicities treated with ECMO. DISCLOSURES: No relevant relationships by ALEENA ARSHAD No relevant relationships by Dipak Chandy No relevant relationships by Subo Dey No relevant relationships by Oleg Epelbaum No relevant relationships by Daniel Greenberg No relevant relationships by Theresa Henson No relevant relationships by Lawrence Huang No relevant relationships by Daniel Peneyra No relevant relationships by Areen Pitaktong No relevant relationships by Hamid Yaqoob
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World wide spread of COVID-19 pandemic, is throttling the normal life nearly for two years and claiming millions of life all over the globe. Starting from Wuhan of China it crosses more than 200 countries, thereby imposing a overwhelming challenge to health care system. On the other hand, there has been unprecedented advancement of the social media, namely, Twitter, Facebook, WhatsApp and Instagram etc. in an exponential manner. The essence of this paper is to extract and elucidate the opinion or sentiments of the people all around the globe regarding Coronavirus pandemic based on Twitter data. The analysis are based on both lexicon-based approach followed by machine learning algorithms and aims to express the state-of-the-art of the sentiment analysis on the current Coronavirus epidemic prevailing in the entire world and the awareness of the people regarding the disease, its symptoms and impact followed by the preventive measures that need to be undertaken. © 2022 IEEE.
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An extensive, PRISMA-led bibliometric analysis of scientific literature from the last 20 years was conducted to extract future research agenda in the niche field of sustainable tourism and the application of virtual reality+ technologies (extended, mixed, hyper, and augmented). The field being investigated contains significant opportunities through meta-analysis methods like linear–logarithmic transformations, coupling clustering, and network analysis. Astringent keyword inclusion rule ensured that the most pertinent scientific literature entered the analysis. The authors used critical content analysis parameters like Cohen’s kappa to include the best fit papers. This bibliometric analysis convenes the need to focus on emerging areas like cyber-neurotics, telekinesis, cyber-optics, and gamification to provide impetus to sustainable consumption and production in the tourism and travel industry. The paper expresses the paradigm shift in research topics as the world enters the COVID-19-induced pandemic and its impact on future research endeavours. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The health care burden and risks to health care workers imposed by novel coronavirus disease 2019 (COVID-19) mandated the need for a simple, noninvasive, objective, and parsimonious risk stratification system predicting the level of care, need for definitive airway, and titration of the ongoing patient care. Shock index (SI = heart rate/systolic blood pressure) has been evaluated in emergency triage, sepsis, and trauma settings including different age group of patients. The ever accumulating girth of evidences demonstrated a superior predictive value of SI over other hemodynamic parameters. Inclusion of respiratory and/or neurological parameters and adjustment of the cutoffs appropriate to patient age increase the predictability in the trauma and sepsis scenario. Being reproducible, dynamic, and simple, SI can be a valuable patient risk stratification tool in this ongoing era of COVID-19 pandemic.
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To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on the severity of COVID-19 manifestation in the patient. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework, on UK BioBank data we extract novel insights on the complex interplay. The algorithm is also sensitive enough to detect the changing influence of the emergent B.1.1.7 SARS-CoV-2 (alpha) variant on disease severity, and, changing treatment protocols. The genomic component also implicates biological pathways that can help in understanding the disease etiology. Our work demonstrates that it is possible to build a robust and sensitive model despite significant bias, noise and incompleteness in both clinical and genomic data by a careful interleaving of clinical and genomic methodologies.
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Background:In routine clinical practice, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is de- termined by real time RT-PCR. In the current pandemic, the demand for a more rapid method of testing is increasing. Here, the performance of rapid COVID-19 antigen test based on lateral flow immunochromatographic assay for SARS- CoV-2 antigen was evaluated. The samples used for the antigen test were nasopharyngeal (NP) swabs from suspected COVID-19 cases. Diagnostic accuracy was compared to SARS-CoV-2 quantitative real-time RT-PCR. Methods:The study was carried out at the Virology Laboratory, Department of Microbiology, Sharda Hospital. Nasopha- ryngeal swabs were collected and subjected to COVID-19 rapid antigen testing. Thereafter both nasopharyngeal as well as oropharyngeal samples were taken from the earlier antigen positive tested patients and were then used for detection of virus particles via RT-PCR. The cycle threshold values were duly noted and a comparative was then drawn to deter- mine the range of cycle threshold values which were observed for antigen positive patients. Results:The results of the study will be revealed subsequently on the day of presentation. Conclusions:The conclusion of the study will be revealed subsequently on the day of presentation
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Keywords: Advanced Practice;Innovation;Respiratory Purpose: Scotland has seen a rising number of patients with multi-morbidity presentations and increasing health and social care needs (1). The Scottish Government has committed to delivering care closer to home by supporting better “planning, design and coordination of patient-focused care and services” (2). In early 2020, the Community Advanced Physiotherapy Practitioner (APP) role was scoped within East Lothian (EL) to support the complex rehabilitation and self-management needs of patients with long-term conditions. In March 2020, in response to COVID-19, the remit of the project rapidly changed to target patients with long-term respiratory conditions. Aims: • Early identification of patient needs to prevent hospital admissions. • Support NHS Lothian's Respiratory Services throughout pandemic. • Streamline patient pathway and enhance continuity of care. • Provide highly specialist, high frequency, high intensity rehabilitation in the community. Methods: • First Community APP was appointed March 2020. • Collaborative working with Acute Respiratory Services and Community Respiratory Teams to align service provision. • Agreement to facilitate supported discharges, through remote monitoring of oxygen saturations/supplementary oxygen usage, steroid weans and pacing/breathlessness management. • Expansion of Community APP service: additional APPs in post August 2020 and February 2021. • Development of Community Respiratory Pathway encompassing community follow-up from respiratory clinics and appropriate GP referrals. • Use of remote assessments for initial and follow up interventions as appropriate • Request for Assistance model used to enable direct self-referral (for patients known to service), allowing rapid access to expert advice and intervention. • Collection of quantitative and qualitative data through patient feedback questionnaires Results: • 98 referrals accepted (April 2020–April 2021) ∘ 30 supported discharges. ∘ 67 community follow-ups/Prevention of Admission. ∘ 2 other referrals. • Upward trend of referrals made to service. • Positive feedback received from patients and referrers regarding speed and accessibility to APP assessment/intervention in the community. • Significant functional and symptomatic improvements reported by patients following APP input alongside a holistic, supportive approach to their care. • Patients report that APP input enables them to optimise their self-management strategies and provides them with streamlined access to other rehabilitation resources and services. • Initial trends suggest an anticipated reduction in acute re-admissions/GP contacts in the 12 months post-APP contact. Conclusion(s): The Community Advanced Physiotherapy Practitioner Service provides a clinically effective, fast-tracked pathway, delivering person-centred, safe, holistic care. This service: • Delivers high quality, timely assessment, diagnosis, and early interventions. Previously these patients would not have direct access to a clinical expert in the community. • Streamlines referrals, avoiding duplication. • Promotes effective links with GP's and Secondary care to optimise patient management. • Is an integral part of the post-COVID-19 Pathway within EL. Community APPs possess expert knowledge and skills, both clinically and about EL services, and are ideally placed to optimise patient outcomes and experience. Impact: This project shows that aligning Community APP's, alongside established Integrated Rehabilitation Services and Primary/Secondary Care can provide an effective and viable model of care, enhancing the support available to individuals with Long Term Conditions in the Community. It is expected that cost benefits will be seen due to reduced hospital admissions/GP contacts. This project is now an integrated part of the EL Rehabilitation Pathway. Funding acknowledgements: This project was not externally funded.
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This article explores the gendered impact of the COVID-19 crisis in Bangladesh by analysing everyday practices within the household. Conceptually, we have followed R.W. Connell’s model of the structures of gender and Naila Kabeer’s perspective on women’s power to examine how a normative gender order involving heterosexual marital partners tends to be sustained during ‘normal’ times but can often be destabilised in the context of an unprecedented crisis. Based on an analysis of data collected through an online survey and in-depth interviews, our findings show that the COVID-19 crisis has generated an opportunity for challenging gender inequalities by diminishing the public-private divide and expanding the horizon of responsibility sharing between women and men. Facing this ‘new normal’ reality, some women have been able to consider life choices and revise unequal relationships with spouses. In contrast, others have reproduced pre-existing inequalities and continued life ‘as usual’ under the regime of men. © 2021. Journal of International Women''s Studies. All Rights Reserved.
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A detailed investigation on mass mortality of fishes was conducted in a small tropical reservoir- Derjang (20o50'32.0"N, 85o01'14.8″E), Odisha, India. Mortality mostly occurred in Systomus sarana followed by Labeo rohita , Cirrhinus mrigala , Labeo catla , Ompok bimaculatus , Labeo calbasu and Mastacembelus armatus. During 20 days of disease occurrence in May–June 2019, a total of about 3000 kg of fish died. The clinical signs in Cyprinid group were haemorrhagic spots, ulcerative lesions, rotten and pale patches in gills due to septicemic disease whereas haemorrhagic spots were the only prominent symptoms observed in Silurid group. Bacteriological isolation and identification through conventional and molecular techniques revealed that Klebsiella pneumoniae was the most common pathogen recovered from S. sarana , C. mrigala and O. bimaculatus. Further Aeromonas hydrophila , Acinetobacter baumannii were isolated from L. rohita and L. catla respectively. The role of these pathogens for this disease outbreak in multiple fish species is discussed in perspective of environmental factors. Sudden environmental alternation by the supercyclone Fani (3rd May 2019) on the coastal part of Odisha might have played a key role to translate the aquatic bacteria into the virulent infective pathogens. In the experimental challenge study, isolated bacteria showed pathogenicity in respective hosts as that in the reservoir. Thus this further revealed both bacteria and fish specific virulency with a variation in LD 50 values. All the gram negative bacterial isolates were found to resist ampicillin and amoxicillin-clavulanic acid and most of them were TEM gene positive. However, the bacteria were found to be susceptible to the rest of the nineteen antibiotics. These findings suggested that the sudden cyclone is an enormous threat to reservoir aquaculture, and should be taken into consideration before breeding, stocking and harvesting of fishes. • Disease outbreak in reservoir during Summer, 2019 caused mass mortality of many freshwater fishes with septicemic symptoms • Klebsiella pnumoniae was the most dominating pathogen recovered, besides Aeromonas hydrophila and Acinetobacter baumannii • Sudden environmental alternation by the cyclone Fani might have played a key role to flare up the virulent pathogens. • Specificity and virulence of bacteria were found host-dependent with variable LD 50 as revealed by challenge experiments [ FROM AUTHOR] Copyright of Aquaculture is the property of Elsevier B.V. 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 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 . (Copyright applies to all s.)
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COVID-19 as a pandemic has impacted many lives with continued threat to our way of life. Decision-makers are grappling with the spread without insights into whether they are in a peak or a plateau of the spread. This severely limits resource management in regions severely affected by COVID- 19. Analysis of COVID-19 cases and deaths as time-series distributions can yield insights that can aid the efforts in such regions to help estimate the curve of the spread. In this chapter, we use time-series analysis to analyze COVID- 19 spread at various locations to study the overall patterns that emerge. We utilize methods including piecewise aggregate approximation (PAA), matrix profiles (MPs), and time-series discretization to identify time periods where the number of cases and deaths reported depicted any major anomalies and where the overall time series seems to be following a trend. Our analysis can prove beneficial to understand the distributions of COVID-19 cases and deaths to quantify the data into different trends to show when the number of cases spiked and when they remained fairly consistent, even when they are in a trend of high number of cases and deaths. This type of trend analysis is particularly useful to compare locations with similar metadata such as number of assisted living communities, population densities, etc., and study if the disease spread is similar or deviating. This can also provide insights into how the policies have had an impact on the spread at these locations. © 2021 River Publishers. All rights reserved.
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Background :Coronavirus disease-2019 (COVID-19) has caused worldwide health emergencies during the last 6 months of 2020. Within very short time, severe acute respiratory coronavirus-2 (SARS-CoV-2) has infected over 64,516,333 people with 1,493,264 fatalities in 210 countries and regions. Previous studies have reported that environmental factors can affect the viability and transmission of SARS-CoV-2. This study aimed to determine the correlation of environmental factors with COVID-19 pandemic and epidemiology of COVID-19 across nine countries in five continents.
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Several carbohydrate-based drugs are currently being used to treat a number of diseases in humans worldwide. Thus, our research group has focused on the synthesis of new methyl alpha-D-mannopyranoside (MDM) derivatives and their antimicrobial evaluation through computational studies. A series of MDM derivatives (2-6) were synthesized through facile regioselective acylation, using the direct method affording 6-O-(3-chlorobenzoyl) derivatives. This isolated 6-O-derivative was further transformed to 2,3,4-tri-O-acyl derivatives, bearing a wide variety of functionalities in a single molecular framework. The structures of the newly designed molecules were elucidated with the aid of IR, H-1 NMR, mass spectroscopy, and elemental analysis. The prediction of the activity spectra for the compounds (PASS) and their in vitro antimicrobial evaluation were performed, demonstrating them to be potential antimicrobial agents. The antimicrobial tests demonstrated that the compounds 3 and 5 were the most potent with the minimum inhibitory concentration (MIC) values, ranging from 0.312 +/- 0.01 to 1.25 +/- 0.03 mg/mL, and minimum bactericidal concentration (MBC) values, ranging from 0.625 +/- 0.02 to 2.50 +/- 0.05 mg/mL. A quantum chemical study was performed to calculate the thermodynamic, molecular orbital and electrostatic potential properties of the designed compounds. Molecular docking simulation was carried out against SARS-CoV-2 M-pro protein 7BQY and 6Y84 to investigate their binding energy and binding tactics with the viral protein, and better binding affinity than that of the parent drug was observed. Also, pharmacokinetic prediction revealed an improved drug-likeness profile for all MDM derivatives.
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This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to demonstrate how the proposed methods may work in practice. © 2021 - IOS Press. All rights reserved.