Nitric Oxide as a Determinant of Human Longevity and Health Span (preprint)

The master molecular regulators and mechanisms determining longevity and health span include nitric oxide (NO) and superoxide anion radicals (SOR). L-arginine, the NO synthase (NOS) substrate, can restore a healthy ratio between the dangerous SOR and the protective NO radical to promote healthy aging. Antioxidant supplementation orchestrates protection against oxidative stress and damage—L-arginine and antioxidants such as vitamin C increase NO production and bioavailability. Uncoupling of NO generation with the appearance of SOR can be induced by asymmetric dimethylarginine (ADMA). L-arginine can displace ADMA from the site of NO formation if sufficient amounts of the amino acid are available. Antioxidants such as ascorbic acids can scavenge SOR and increase the bioavailability of NO. The topics of this review are the complex interactions of antioxidant agents with L-arginine, which determine NO bioactivity and protection against age-related degeneration.

Risk-reducing strategies and labour vulnerability during the pandemic in India.

The Covid-19 health disaster has created a labour crisis. We examine the impact of the Covid-19 induced state-level direct (such as providing free food, minimum income, and transportation services for the labourers) and indirect (such as skill mapping of the return migrants and allowing extended hour shifts in the factories) risk-reducing labour strategies on urban and rural employment rates in India. These risk-reducing labour strategies secure livelihood and discourage labourers from risking their lives by joining the workplace of high interpersonal human contact during the pandemic. This reduces employment rates. Specifically, direct risk-reducing labour strategies reduce employment in urban and rural areas, while indirect risk-reducing labour strategies lessen employment only in urban areas. The mitigating effect justifies the importance of the Keynesian interventionist resilience techniques that safeguard the labourers and reduce the risks during the disaster.

A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications. (Special Issue: Technology and innovation for a post COVID recovery and growth: the contribution of industry and academia.)

Due to the world's rapid population expansion, the demand for food is anticipated to increase significantly during the coming decade. Traditional farming practices cannot meet the need for the food crop. Conventional farming methods use resources like land, water, herbicides, and fertilisers rather inefficiently. When it comes to making the most effective and sustainable use of resources to increase production, automation in agriculture is garnering a lot of interest. How people and machines operate on farms has been changed by integrating the Internet of Things (IoT) with numerous sensors, controllers, and communication protocols. A comprehensive literature review of the key technologies involved in smart and sustainable agriculture, viz. various sensors, controllers, communication standards, IoT based intelligent machinery, were compared and presented. These sensors were continuously producing a significant quantity of data on the agricultural field. These data were transmitted to the central control unit for analysis to meet the demands for water, fertiliser, pesticides, etc. The architecture and importance of data analytics in agriculture IoT, case studies of current agricultural automation utilising IoT, key challenges and open issues in agriculture IoT technology were discussed. The findings provide support for the selection of IoT technologies for specific applications.

Technology readiness and the older citizen's acceptance of m-health services in India

PurposeThe purpose of this study was to create a theoretical model by combining the technology acceptance model (TAM) with the theory of technology readiness (TR) and then empirically test it to identify the key factors influencing older citizens' intention to adopt and use mobile health (m-health), which has emerged as a tool to facilitate health-care rights for all.Design/methodology/approachThe convenience sampling method was used to collect data from 465 respondents aged 60 and up from the Delhi-National capital region of India using a questionnaire survey method. The data collected for this study were analyzed using partial least squares structural equation modeling using SmartPLS 3.0.FindingsThe study's findings indicate that all TR components influence perceived usefulness (PU) and ease of use. The exception is discomfort, which does not affect perceived ease of use (PEOU). Furthermore, PU and PEOU influenced the older citizen's attitude toward m-health, and attitude influenced their intention to use m-health applications.Originality/valueTo the best of the authors' knowledge, this is the first study to apply the TAM in combination with TR index to examine the acceptability of m-health consulting by the older citizen in an emerging economy like India.

Is BF.7 more infectious than other Omicron subtypes: Insights from structural and simulation studies of BF.7 spike RBD variant.

Fear of a fresh infection wave and a global health issue in the ongoing COVID-19 pandemic have been rekindled by the appearance of two new novel variants BF.7 and BA.4/5 of Omicron lineages. Predictions of increased antibody evasion capabilities and transmissibility have been recognised in addition to the existing lineages (BA.1.1, BA.2, BA.2.12.1 and BA.3) as cause for worry. In comparison to Omicron, BA.4 and BF.7 share nine mutations in the spike protein, Leu371Phe, Thr376Ala, Asp405Asn, Arg408Ser, Ser446Gly, Leu452Arg, Phe486Val, Arg493Gln, Ser496Gly, whereas BF.7 contains an additional mutation, Arg346Thr, in the receptor binding domain (RBD) region. Due to the critical need for analysis and data on the BA.4 and BF.7 variants, we have computationally analyzed the interaction pattern between the Omicron, BA.4 and BF.7 RBD and angiotensin-converting enzyme 2 (ACE2) to determine the influence of these unique mutations on the structures, functions, and binding affinity of RBD towards ACE2. These analyses also allow to compare molecular models to previously reported data to evaluate the robustness of our methods for quick prediction of emerging future variants. The docking results reveal that BA.4 and BF.7 have particularly strong interactions with ACE2 when compared to Omicron, as shown by several parameters such as salt bridge, hydrogen bond, and non-bonded interactions. In addition, the estimations of binding free energy corroborated the findings further. BA.4 and BF.7 were found to bind to ACE2 with similar affinities (-72.14 and - 71.54 kcal/mol, respectively) and slightly stronger than Omicron (-70.04 kcal/mol). The differences in the binding pattern between the Omicron, BA.4 and BF.7 variant complexes indicated that the BA.4 and BF.7 RBD substitutions Asp405Asn, Ser446Gly, Leu452Arg, Phe486Val and Arg493Gln caused additional interactions with ACE2. In addition, normal mode analyses also indicate more stable conformations of BA.4 and BF.7 RBDs against human ACE2. Based on these structural and simulation analyses, we hypothesized that these changes may affect the binding affinity of BA.4 and BF.7 with ACE2.

Super Efficiency and Sensitivity Analysis of the Indian Hotels and Restaurants (preprint)

Over the course of COVID-19, the global growth of the hospitality and restaurant (H&Rs) sector has declined. During this difficult period, this sector's overall performance declined. As a result, this industry must adopt fresh approaches to seize the potential and overcome the obstacles at this pivotal moment. One of these difficulties is figuring out how to evaluate H&Rs' effectiveness using a variety of factors. This study evaluates the performance of 45 significant H&R businesses operating in India using the Data Envelopment Analysis (DEA) method. The Basic DEA models usually do not disclose the improvement in the capability of different DMUs as these models calculate only radial efficiency. In this paper, the New Slack Model (NSM) of DEA has been employed to examine the efficiency of the different 45 large-scale Indian Hotels and Restaurants (H&Rs). The NSM model cognate the input and output slacks straight away. In this study, four inputs: Capital employed, Gross fixed assets, Current assets, and Operating costs, and two outputs: Operating income and Profit before depreciation, interest, and tax (PBDIT) are considered. The data for the study has been collected from the Prowess database of the Centre for Monitoring Indian Economy (CMIE). The study indicates that only 65.58% of H&R are technically efficient which shows that some resources are unutilized and therefore there is a huge scope for an upswing. To validate the stability of results sensitivity analysis is also carried out.

Neohesperidin and spike RBD interaction in omicron and its sub-variants: In silico, structural and simulation studies.

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged first around December 2019 in the city of Wuhan, China. Since then, several variants of the virus have emerged with different biological properties. This pandemic has so far led to widespread infection cycles with millions of fatalities and infections globally. In the recent cycle, a new variant omicron and its three sub-variants BA.1, BA.2 and BA.3 have emerged which seems to evade host immune defences and have brisk infection rate. Particularly, BA.2 variant has shown high transmission rate over BA.1 strain in different countries including India. In the present study, we have evaluated a set of eighty drugs/compounds using in silico docking calculations in omicron and its variants. These molecules were reported previously against SARS-CoV-2. Our docking and simulation analyses suggest differences in affinity of these compounds in omicron and BA.2 compared to SARS-CoV-2. These studies show that neohesperidin, a natural flavonoid found in Citrus aurantium makes a stable interaction with spike receptor domain of omicron and BA.2 compared to other variants. Free energy binding analyses further validates that neohesperidin forms a stable complex with spike RBD in omicron and BA.2 with a binding energy of -237.9 ± 18.7 kJ/mol and -164.1 ± 17.5 kJ/mol respectively. Key residual differences in the RBD interface of these variants form the basis for differential interaction affinities with neohesperidin as drug binding site overlaps with RBD-human ACE2 interface. These data might be useful for the design and development of novel scaffolds and pharmacophores to develop specific therapeutic strategies against these novel variants.

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach.

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R 2 score.

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach

Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R2 score.

Analyzing Public Opinions Regarding Virtual Tourism in the Context of COVID-19: Unidirectional vs. 360-Degree Videos

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.

Neohesperidin and spike RBD interaction in omicron and its sub-variants: In silico, structural and simulation studies

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged first around December 2019 in the city of Wuhan, China. Since then, several variants of the virus have emerged with different biological properties. This pandemic has so far led to widespread infection cycles with millions of fatalities and infections globally. In the recent cycle, a new variant omicron and its three sub-variants BA.1, BA.2 and BA.3 have emerged which seems to evade host immune defences and have brisk infection rate. Particularly, BA.2 variant has shown high transmission rate over BA.1 strain in different countries including India. In the present study, we have evaluated a set of eighty drugs/compounds using in silico docking calculations in omicron and its variants. These molecules were reported previously against SARS-CoV-2. Our docking and simulation analyses suggest differences in affinity of these compounds in omicron and BA.2 compared to SARS-CoV-2. These studies show that neohesperidin, a natural flavonoid found in Citrus aurantium makes a stable interaction with spike receptor domain of omicron and BA.2 compared to other variants. Free energy binding analyses further validates that neohesperidin forms a stable complex with spike RBD in omicron and BA.2 with a binding energy of −237.9 ± 18.7 kJ/mol and −164.1 ± 17.5 kJ/mol respectively. Key residual differences in the RBD interface of these variants form the basis for differential interaction affinities with neohesperidin as drug binding site overlaps with RBD-human ACE2 interface. These data might be useful for the design and development of novel scaffolds and pharmacophores to develop specific therapeutic strategies against these novel variants. Graphical Image 1

Key supply chain strategies for post-COVID-19 recovery: evidence from an Indian smart furniture industry

Purpose - Smart furniture is an essential part of research that has been designed to best complement easy and safe human interaction. The purpose of smart furniture is to save the space of the house and make the products unique, awesome and safe, functional, strong and also make it works better so the people can live better with it. This research aims to explore the key supply chain strategies implemented by the Indian smart furniture industry to reduce the impact of a post-COVID-19 pandemic. Design/methodology/approach - This work utilized a case study and conducted semi-structured interviews with the top leadership of the smart furniture manufacturing industry to explore key supply chain strategies to reduce the influence of the post-COVID-19 pandemic. Additionally, key supply chain strategies have been analyzed using a multi-criteria decision-making technique known as grey relational analysis (GRA) to determine their ranking significance in the smart furniture industry. Findings - The results of this study discovered that "Inventory-Categorization" is essential in ensuring business continuity during the COVID-19 pandemic and helps reduce the amount of stock they have on hand. It enhanced the opportunity for employees to properly focus on their work and an opportunity for better work-life balance. The results of the study can also help supply chain stakeholders in their establishment of critical strategies. Research limitations/implications - The implications of this research work help the Indian furniture industry to make supply chain investment decisions that benefit the organization to sustain itself. Originality/value - This is the first study to explore key supply chain strategies for the post-COVID-19 era. This work will assist managers and practitioners in helping the organization decide which supply chain strategies are more critical to the betterment of the organization.

Recent advances in transition metal-catalyzed reactions of chloroquinoxalines: Applications in bioorganic chemistry.

The importance of the quinoxaline framework is exemplified by its presence in the well-known drugs such as varenicline, brimonidine, quinacillin, etc. In the past few years, preparation of a variety of organic compounds containing the quinoxaline framework has been reported by several research groups. The chloroquinoxalines were successfully used as substrates in many of these synthetic approaches due to their easy availability along with the reactivity especially towards a diverse range of metal and transition metal-catalyzed transformations including Sonogashira, Suzuki, Heck type of cross-coupling reactions. The transition metals e.g., Pd, Cu, Fe and Nb catalysts played a key role in these transformations for the construction of various CX (e.g., CC, CN, CO, CS, CP, CSe, etc) bonds. These approaches can be classified based on the catalyst employed, type of the reaction performed and nature of CX bond formation during the reaction. Several of these resultant quinoxaline derivatives have shown diverse biological activities which include apoptosis inducing activities, SIRT1 inhibition, inhibition of luciferace enzyme, antibacterial and antifungal activities, cytotoxicity towards cancer cells, inhibition of PDE4 (phosphodiesterase 4), potential uses against COVID-19, etc. Notably, a review article covering the literature based on transition metal-catalyzed reactions of chloroquinoxalines at the same time summarizing the relevant biological activities of resultant products is rather uncommon. Therefore, an attempt is made in the current review article to summarize (i) the recent advances noted in the transition metal-catalyzed reactions of chloroquinoxalines (ii) with the relevant mechanistic discussions (iii) along with the in vitro, and in silico biological studies (wherever reported) (iv) including Structure-Activity Relationship (SAR) within the particular series of the products reported between 2010 and 2022.

Best proxy to determine firm performance using financial ratios: A CHAID approach

The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm’s performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy’s efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.

Malaria vaccine: The tale of terror, triumph, tyranny, and trust

Malaria is an endemic disease in a true sense. It is an acute febrile disease caused due to the parasite Plasmodium. However, unlike COVID-19, it failed to raise an international concern or gain the scientific limelight. Most of the 200 million globally affected by malaria, half of them are from Africa. Four of the nations, Nigeria (25%), the Democratic Republic of the Congo (11%), Mozambique (5%), and Uganda (4%), account for half of the world's malaria burden and is the leading cause of illness and death. In 2019, an estimated 5-6 million people died of malaria -- most of them are young children in sub-Saharan Africa. Many of the countries affected by malaria have the lowest economic status. In the malaria-endemic region, the most vulnerable groups are young children and pregnant women. The costs of malaria are enormous to individuals, families, communities, societies, and nations. After a struggle for three decades, the much-awaited malaria vaccine, RTS, S (brand name Mosquirix), was finally launched;but it came with its controversies and allegations. This review explored the different angles of this disease, the vaccine development, and the emerging debates. [ FROM AUTHOR] Copyright of Asian Journal of Medical Sciences is the property of Manipal Colleges of Medical Sciences 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.)

Prescreening and Triage of COVID-19 Patients Through Chest X-Ray Images Using Deep Learning Model.

Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during health emergency scenarios. The Ministry of health and family welfare government of India provides guidelines from the Indian Council of Medical Research (ICMR) for triage requirements and emergency response with faster allotment of oxygen beds for COVID-19 patients requiring immediate treatment in Tamil Nadu, India. A combination of pretrained models provides a faster screening rate and finds patients with severe lung infections who need to be attended to and allotted oxygen beds. Deep learning (DL) algorithms need to be accurate in triaging undifferentiated patients entering the emergency care system (ECS). The major goal of this work is to analyze the accuracy of machine learning approaches in their application to triage the acuity of patients arriving in the ECS. The proposed triage model has an accuracy of 93% in classifying COVID/non-COVID patients. The proposed triage DL model effectively reduces the time for the triage procedure and streamlines screening and allocation of beds for patients with high risk.

SARS-CoV-2 infection is associated with increased odds of insomnia, RLS and dream enactment behavior.

Background: Literature suggests that the COVID-19 pandemic has resulted in poor sleep quality, especially among the infected population. However, literature regarding the effect of COVID-19 pandemic and SARS-CoV-2 infection on occurrence of insomnia, restless legs syndrome and dream enactment behavior is either scarce or unavailable. Methods: This study was planned to assess the effect of SARS-CoV-2 infection on the occurrence of insomnia, restless legs syndrome (RLS) and dream enactment behavior (DEB). For this cross-sectional study, a questionnaire comprising of items related to demographic details, past medical history, and information related to SARS-CoV-2 infection was distributed through social media. Insomnia was diagnosed using clinical criteria. RLS, DEB, sleep quality, depression and anxiety were assessed using a validated questionnaire. Information regarding the use of hypnotic medications was also gathered. Results: Of the 1596 respondents, 37.2% reported disturbed sleep while insomnia was reported by 22.6% respondents. 27.3% of respondents reported RLS and 17.4% suffered DEB. The odds of insomnia were greater among males (OR = 1.27; 95% CI: 1.03-1.58; P < 0.02) and among those who had SARS-CoV-2 infection (OR = 1.76; 95% CI = 1.42-2.19; P < 0.001). Similarly, SARS-CoV-2 infection was also associated with increased odds of RLS (OR = 2.48; 95% CI = 1.98-3.11; P < 0.001) and DEB (OR = 1.58; 95%CI = 1.21-2.06; P < 0.001). Insomnia, RLS and DEB were more frequent among respondents who required oxygen therapy, those who experienced loss of taste and/or smell, depression and anxiety. Prevalence of insomnia, DEB and RLS was higher than said prevalence among respondents with no history of SARS-CoV-2 infection, but lower than that of those with positive history of SARS-CoV-2 infection. 5.3% of respondents reported taking hypnotic medications before infection, 7% during infection and 5.3% after infection. Conclusion: SARS-CoV-2-infection-related factors in association with environmental factors have increased the prevalence of insomnia, DEB and RLS among subjects having infection. SARS-CoV-2-associated immunological changes, hypoxia and neurotropism may play a role in occurrence of insomnia, DEB and RLS.

Tryptophan in Nutrition and Health.

Tryptophan is a rate-limiting essential amino acid and a unique building block of peptides and proteins [...].

A visual review of artificial intelligence and Industry 4.0 in healthcare.

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain.