Are fewer cases of diabetes mellitus diagnosed in the months after SARS-CoV-2 infection? A population-level view in the EHR-based RECOVER program.

Long-term sequelae of severe acute respiratory coronavirus-2 (SARS-CoV-2) infection may include increased incidence of diabetes. Here we describe the temporal relationship between new type 2 diabetes and SARS-CoV-2 infection in a nationwide database. We found that while the proportion of newly diagnosed type 2 diabetes increased during the acute period of SARS-CoV-2 infection, the mean proportion of new diabetes cases in the 6 months post-infection was about 83% lower than the 6 months preinfection. These results underscore the need for further investigation to understand the timing of new diabetes after COVID-19, etiology, screening, and treatment strategies.

Known *Anti Sars-Cov Protease Inhibitors* On Sars-Cov-2 Related Intracellular Reception Sites: *Docking Interaction* And Computer Aided Optimization Of Molecular Structure For *Chemioselective Interactions*

Molecular docking and molecular dynamics aided virtual search of OliveNet™ directory identified potential secoiridoids that combat SARS-CoV-2 entry, replication, and associated hyperinflammatory responses. OliveNet™ is an active directory of phytochemicals obtained from different parts of the olive tree, Olea europaea (Oleaceae). Olive oil, olive fruits containing phenolics, known for their health benefits, are indispensable in the Mediterranean and Arabian diets. Secoiridoids is the largest group of olive phenols and is exclusive to the olive fruits. Functional food like olive fruits could help prevent and alleviate viral disease at an affordable cost. A systematized virtual search of 932 conformers of 78 secoiridoids utilizing Autodock Vina, followed by precision docking using Idock and Smina indicated that Nüzhenide oleoside (NZO), Oleuropein dimer (OED), and Dihydro oleuropein (DHO) blocked the SARSCoV-2 spike (S) protein-ACE-2 interface;Demethyloleuropein (DMO), Neo-nüzhenide (NNZ), and Nüzhenide (NZE) blocked the SARS-CoV-2 main protease (Mpro). Molecular dynamics (MD) simulation of the NZO-S-protein-ACE-2 complex by Desmond revealed stability during 50 ns. RMSD of the NZO-S-protein-ACE-2 complex converged at 2.1 Å after 20 ns. During MD, the interaction fractions confirmed multiple interactions of NZO with Lys417, a crucial residue for inhibition of S protein. MD of DMO-Mpro complex proved its stability as the RMSD converged at 1.6 Å. Analysis of interactions during MD confirmed the interaction of Cys145 of Mpro with DMO and, thus, its inhibition. The docking predicted IC50 of NZO and DMO was 11.58 and 6.44 μM, respectively. Molecular docking and dynamics of inhibition of the S protein and Mpro by NZO and DMO correlated well. Docking of the six-hit secoiridoids to IL1R, IL6R, and TNFR1, the receptors of inflammatory cytokines IL1β, IL6, and TNFα, revealed the anti-inflammatory potential except for DHO. Due to intricate structures, the secoiridoids violated Lipinski's rule of five. However, the drug scores of secoiridoids supported their use as drugs. The ADMET predictions implied that the secoiridoids are non-toxic and pose low oral absorption. Secoiridoids need further optimization and are a suitable lead for the discovery of anti-SARS-CoV-2 therapeutics. For the moment, olive secoiridoids presents an accessible mode of prevention and therapy of SARS-CoV-2 infection.

Increased Screen Time as a Cause for Further Myopia BoomFollow up Study of School Health Survey

Background: Myopia is a major health issue around the world. The World Health Organization estimates that half of the population of the world may be myopic by 2050. In the present years, insufficient time spent in outdoor activities has been recognized as a major risk factor for myopia development. The duration and intensity of near work are also associated with myopia progression. Aim(s): To study the increase in myopic shift in school going children during covid 19 pandemic due to increased screen time. Material(s) and Method(s): A prospective cross sectional study was done as a follow up after 2 years (in March 2021) from a school health survey done in May 2019. 150 students, of ages 7-15 were included and spherical equivalent refraction was recorded for each child and progression of myopia was documented in dioptres. Children wearing contact lenses, with h/o any ocular surgery and children with pathological myopia were excluded from study. Result(s): Out of 145 children called for follow up, only 123 children reported in the OPD for follow up. The mean refractive error(spherical equivalent) had increased by +2D in children of ages 7-10 and by +1D in children from ages 11-13 and somewhat remained constant in older ages. The parents reported an increase in time spent on digital devices and prolonged near work and all this had a positive correlation with an increase in myopic shift. Conclusion(s): Shorter viewing distance, increased screen time and lesser outdoor activities is also associated with myopia progression, especially in younger children. Younger children's refractive status may be more sensitive to environmental changes than older children, as they are in a more important period for myopic development and progression. Copyright © 2022, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Genomic sequencing profile of SARS-CoV-2 in different parts of western Odisha, India

Genetic lineages of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) have continued to emerge and circulate around the world since the onset of the COVID-19 pandemic. There are numerous variants of SARS-CoV-2, the virus that causes corona virus disease 2019 (COVID-19). Like other viruses, SARS-CoV-2 evolves over time. Most mutations in the SARS-CoV-2 genome have no impact on viral function, but certain variants have gained worldwide attention because of their rapid emergence within populations, evidence of transmission, and clinical implications. During the pandemic, most parts of India were affected, including Odisha, leading to high rates of morbidity and mortality. For the present study, 368,303 samples were received by the COVID-19 lab i.e., medical college level (Virus Research Diagnostic Laboratory) VRDL from six districts of western Odisha, including approximately 25,000 COVID-19-positive samples. The diagnostic method of the quantitative RT-PCR cannot be used to distinguish among the variants created by mutation of the genes initially, therefore selected positive clinical samples were sent in cold chain for whole genome sequencing (WGS), using the Illumina Seq. at ILS, BBSR for variant detection. The reported observation from the next generation sequencing (NGS) based sequenced samples of western Odisha updated in the INSACOG-WGS portal confirms the presence of Delta (B.1.617.2) and Delta sublineages, Omicron (BA.2), and Omicron (B.1.1.529). Maximum infection was caused by Delta sublineages (83.5%) irrespective of age, sex, and geographic area followed by Delta and Omicron. Molecular diagnosis and WGS based study reveal the widespread transmission of the fatal virus, significantly affecting every corner of the globe. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

Molecular Medical Diagnosis of COVID-19 and Omicron Variant

Genetic lineages of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) have continued to emerge and circulate around the world since the onset of the COVID-19 pandemic. There are numerous variants of SARS-CoV-2, the virus that causes corona virus disease 2019 (COVID-19). Like other viruses, SARS-CoV-2 evolves over time. Most mutations in the SARS-CoV-2 genome have no impact on viral function, but certain variants have gained worldwide attention because of their rapid emergence within populations, evidence of transmission, and clinical implications. During the pandemic, most parts of India were affected, including Odisha, leading to high rates of morbidity and mortality. For the present study, 368,303 samples were received by the COVID-19 lab i.e., medical (Virus Research Diagnostic Laboratory) VRDL from six districts of western Odisha, including approximately 25,000 COVID-19-positive samples. The diagnostic method of the quantitative RT-PCR cannot be used to distinguish among the variants created by mutation of the genes initially. Therefore, selected positive clinical samples were sent in cold chain for whole genome sequencing (WGS), and disease severity was sequenced using the Illumina Seq at ILS, BBSR for variant detection. The reported observation from the next generation sequencing (NGS) based sequenced samples of western Odisha updated in the INSACOG-WGS portal confirms the presence of Delta (B.1.617.2) and Delta sub lineages, Omicron (BA.2), and Omicron (B.1.1.529). Maximum infection was caused by Delta sub lineages 83.5%) irrespective of age, sex, and geographic area followed by Delta and Omicron. Molecular diagnosis and WGS based study reveal the widespread transmission of the fatal virus, significantly affecting every corner of the globe. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Numerical Method to Generate and Evaluate Environmental Wind Over Hills: Comparison of Pedestrian Winds Over Hills and Plains

It is well known that the wind profile at altitudes below 10m from mean sea level (MSL) depends on the geometry of terrain, due to the boundary layer phenomenon. Hence, the profile of wind changes for hilly terrains and mountainous regions when compared with the plain regions. This phenomenon has become important to study due to the large-scale urbanisation taking place over hilly regions. The changing wind profile presents a challenge to evaluate the pedestrian winds, as depending on the aspect of the terrain an additional vertical velocity component is experienced due to the upwind climb of the winds. This creates a wind profile that is twisted in form. While wind tunnel studies have attempted to recreate this twisted wind profile (TWP), due to the inherent deficiency of wind tunnels to simultaneously map velocity and flow conditions, a lack of three-dimensional flow profile hinders pedestrian comfort evaluation. In the wind tunnel studies, it was also observed that small vertical eddies and wakes behind the interfering building were not identified which are an important factor to determine the pollution load dispersion. The authors have developed a numerical model to generate the twisted wind profile. The specialty of the numerical model lies in it’s unique boundary conditions that enable the visualization and quantification of the complete 3D wind profile, when the wind over a hilly terrain interacts with urban infrastructures. The developed model was validated with the wind tunnel experiments done previously by Tse and colleagues. The specialty of the model is that it ensures horizontal homogeneity while creating vertical heterogeneity. From the 3D flow profile hence generated the authors were able to deduce that the impact of twisted wind profile depends on the yaw angle of wind interacting with the structure and not on the wind attack angle. Also, the more the twist of the wind, more is the clockwise shifting of the far wakes behind the building. It was also seen that there are more low velocity zones in the pedestrian winds over a hill in comparison to that over the plains. The vertical eddies that aid in convective removal of pollutants were also missing in case of pedestrian winds over hilly terrains, which raises the risk of pollutant accumulation. The same was also observed in Hong-Kong during COVID 19, where due to the twisted nature of wind flow, the virus load increased and natural ventilation was inadequate in the removal of the viral load in the air near urban areas. © 2022, Penerbit Akademia Baru. All rights reserved.

Effect of SARS-CoV-2 Infection and Infection Severity on Longer-Term Glycemic Control and Weight in People With Type 2 Diabetes.

OBJECTIVE: To evaluate the association of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severity of infection with longer-term glycemic control and weight in people with type 2 diabetes (T2D) in the U.S. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using longitudinal electronic health record data of patients with SARS-CoV-2 infection from the National COVID Cohort Collaborative (N3C). Patients were ≥18 years old with an ICD-10 diagnosis of T2D and at least one HbA1c and weight measurement prior to and after an index date of their first coronavirus disease 2019 (COVID-19) diagnosis or negative SARS-CoV-2 test. We used propensity scores to identify a matched cohort balanced on demographic characteristics, comorbidities, and medications used to treat diabetes. The primary outcome was the postindex average HbA1c and postindex average weight over a 1 year time period beginning 90 days after the index date among patients who did and did not have SARS-CoV-2 infection. Secondary outcomes were postindex average HbA1c and weight in patients who required hospitalization or mechanical ventilation. RESULTS: There was no significant difference in the postindex average HbA1c or weight in patients who had SARS-CoV-2 infection compared with control subjects. Mechanical ventilation was associated with a decrease in average HbA1c after COVID-19. CONCLUSIONS: In a multicenter cohort of patients in the U.S. with preexisting T2D, there was no significant change in longer-term average HbA1c or weight among patients who had COVID-19. Mechanical ventilation was associated with a decrease in HbA1c after COVID-19.

A comprehensive review on Involvement of drones in covid 19 pandemics

The core idea of the paper is to create a multi-purpose drone/unmanned aerial vehicle which are used to counter challenges faced by COVID-19. Our work is structured to give insights into our theory method with a constructive form of qualitative research. This work's main contribution lies in the comparative study of many research articles based on the use of drone in different fields specially in sanitization of a large area in a stipulated period, Thermal screening of people and storing their temperature recorded data in cloud, AI implementation in order to detect abnormal pattern of coughing, heart rate etc and delivery of medicines and necessary essential to quarantined people and migrants this will cause a large-scale implementation of drone delivery may drastically change the business, substituting hundreds of workers, fossil-fuel-powered delivery vehicles, traffic congestion, and centralised distribution centres of drone which performs point A to point B parcel delivery. The challenges faced by drone and its operators during active mission are studied. This study will help others who are new to drones in the covid and medical field. © The Electrochemical Society

Integrating Industry 4.0 in engineering education during a global pandemic: Approach and Learning Efficacy

Given the lockdown situation due to the global pandemic in place, teaching and learning hardware-oriented hand-son skills became a major challenge for engineering education. The present study investigates some of the approaches and demonstrates an effective method of teaching modern engineering skills in a simulated environment, such as investigating the Industry 4.0 concept of 'Warehouse Automation.' As part of Project-Based Learning, we are using open source software and free services in a six-month online robotics competition with several stages and tasks. This learning activity was implemented among 1880 participants (470 teams) to teach complex engineering concepts from multidisciplinary domains such as - (1) Robot Operating System (ROS) to control two Robotic Arms in a dynamic simulator, (2) Internet of Things protocols such as MQTT and HTTP, and (3) free cloud services to log data in a database and developing an email notifications system. All the necessary resources were provided to the participants along with the troubleshooting guide that was provided via an online discussion forum. After each task performance of teams was recorded and feedback was collected from the participants. All the recorded data was passed through various statistical analyses as a part of the study to assess the effectiveness of this teaching and learning activity. The study further inspects- whether there is a correlation between participants' performance in the academic-curricular and the competition, and whether more interaction through online-discussion-forum leads to better performance. Finally, the study will help us understand the perception of participants about COVID-19 impact on their performance. © 2022 IEEE.

A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative (preprint)

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Preface for International Conference on Recent Trends in Applied Research (ICoRTAR2021) Proceedings

The 2nd International Conference on Recent Trends in Applied Research (ICoRTAR2021) is a virtual conference based on enabling platforms to present research results relating to global issues. ICoRTAR2021 is hosted by ResearchAcad HUB with channels and human resources in India, Nigeria, Kenya, Mexico, Morocco, Ghana, and Cameroon. Thus, the conference was held virtually from October 08-09, 2021.The Theme of this second series (ICoRTAR2021) is ‘Effective Research Practices Amidst Covid-19 Pandemic’. ICoRTAR202 aims to bring together faculty members, leading scientists, academicians, research & graduate scholars, industrial professionals, and decision-makers to discuss current trends in research with implications on global phenomena such as COVID-19, economic growth & development, and so on. Basically, the ICORTAR2021 participants discussed extensively vital topics, recent trends, and issues in Applied Science, Mathematical Sciences, Computational Science, Engineering Science, Physics and Nanoscience, Financial Modeling, Artificial Intelligence, and Stochastic Dynamics. Five plenary sessions with different aspects of the ICORTAR2021 Theme were held successfully. The first keynote speaker, Professor Snehashish Chakraverty from the National Institute of Technology, Rourkela, INDIA, spoke on ‘Estimating Corona Positive Cases via Uncertainty Modeling.’ The second keynote speaker, Professor/Dr. Sannan khan, from the University Putra Malaysia, spoke on ‘Online Learning: A Panacea in the Time of COVID-19 Crisis.’List of editorial board/organizing committee are available in the pdf.

Glycemic Control and Clinical Outcomes in U.S. Patients With COVID-19: Data From the National COVID Cohort Collaborative (N3C) Database.

OBJECTIVE: The purpose of the study is to evaluate the relationship between HbA1c and severity of coronavirus disease 2019 (COVID-19) outcomes in patients with type 2 diabetes (T2D) with acute COVID-19 infection. RESEARCH DESIGN AND METHODS: We conducted a retrospective study using observational data from the National COVID Cohort Collaborative (N3C), a longitudinal, multicenter U.S. cohort of patients with COVID-19 infection. Patients were ≥18 years old with T2D and confirmed COVID-19 infection by laboratory testing or diagnosis code. The primary outcome was 30-day mortality following the date of COVID-19 diagnosis. Secondary outcomes included need for invasive ventilation or extracorporeal membrane oxygenation (ECMO), hospitalization within 7 days before or 30 days after COVID-19 diagnosis, and length of stay (LOS) for patients who were hospitalized. RESULTS: The study included 39,616 patients (50.9% female, 55.4% White, 26.4% Black or African American, and 16.1% Hispanic or Latino, with mean ± SD age 62.1 ± 13.9 years and mean ± SD HbA1c 7.6% ± 2.0). There was an increasing risk of hospitalization with incrementally higher HbA1c levels, but risk of death plateaued at HbA1c >8%, and risk of invasive ventilation or ECMO plateaued >9%. There was no significant difference in LOS across HbA1c levels. CONCLUSIONS: In a large, multicenter cohort of patients in the U.S. with T2D and COVID-19 infection, risk of hospitalization increased with incrementally higher HbA1c levels. Risk of death and invasive ventilation also increased but plateaued at different levels of glycemic control.

VIRTUAL LEARNING ON SWAYAM PLATFORM BY ENGINEERING AND TECHNOLOGY STUDENTS DURING COVID-19: AN ANALYSIS

Democratization of education has formed the base for open and free education. The movement of Open Education is gaining its impetus through the emerging trends of new educational technologies. In the context of OER (Open Education Resource), the Massive Open Online Courses (MOOCs) concept is providing a framework for virtual classroom and collaborative learning. ‘Study Webs of Active Learning for Young Aspiring Minds’ (SWAYAM), as an Indian MOOC, has been providing several courses in various disciplines. The present study tries to analyse the courses in engineering and technology category on SWAYAM during the COVID-19 situation. The present study has critically analysed the courses. The analysis of present study shows the trends of learners' interest in engineering and technology discipline. The present study summarizes that the most sought after area is computer science and engineering, followed by a multidisciplinary sub-category. The National Programme on Technology Enhanced Learning (NPTEL), is the course coordinator for a maximum number of courses, and IIT Kharagpur is that institute which provides maximum courses. © 2021. All Rights Reserved.

Covid-19: Impacts of quarantine on mental health and stress

Background: The explosion of coronavirus disease Covid-19 has created as a worldwide health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the Speed, but the security measures put in situ to contain the spread of the virus also require social distancing by refraining from doing what’s inherently human, Quarantine keeps such people far away from others so that they don’t unknowingly infect anyone in their vicinity. It’s employed by Governments to stop the spread of communicable diseases. Such a survey would help me to understand the impact of psychological state and Stress during Quarantine to guard against (COVID-19). Objective: To find out the impact of Quarantine on Mental Health and Stress to protect against the coronavirus (COVID-19). Methods: 65 PEOPLE were asked by questionnaire to fill 20 different questions related to Social, distancing is a public health strategy to limit the spread of COVID-19. Result: The result of this study supports that 65 people, who they are Quarantine. At least or more than 15 days, are suffering from moderate to high-level Stress. Conclusion: The social isolation, quarantine, and lockdown can increase stress responses and generate more status of uncertainty.