Death registration coverage 2019-2021, India.

Objective: To investigate coverage and factors associated with death registration in India. Methods: We used data from the Indian National Family Health Survey 2019-2021. Based on responses of eligible household members, we estimated death registration in 84 390 deaths in all age groups across the country. We used multilevel logistic regression analysis to determine sociodemographic variables associated with death registration at state, district and individual levels. Findings: Nationally, 70.8% (59 748/84 390) of deaths were registered. Of 707 districts in our study period, 122 and 53 districts had death registration levels less than 40% in females and males, respectively. The likelihood of death registration was significantly lower for females than males (adjusted odds ratios, aOR: 0.61; 95% confidence interval, CI: 0.59-0.64). Death registration increased significantly with age of the deceased person, with the highest odds in 35-49-year-olds (aOR: 5.05; 95% CI: 4.58-5.57) compared with 0-4-year-olds. Death registration was less likely among rural households, disadvantaged castes, the poorest wealth quintile, Muslims and households without a below poverty level card. Higher education was associated with higher death registration with the greatest likelihood of registration in households with a member with post-secondary school education (aOR: 1.54; 95% CI: 1.42-1.66). District-level factors were not significantly associated with death registration. Conclusion: Sociodemographic characteristics of the deceased person were significantly associated with death registration. Strategies to raise awareness of death registration procedures among disadvantaged population groups and the introduction of a mobile telephone application for death registration are recommended to improve death registration in India.

Security issues and challenges in cloud of things-based applications for industrial automation.

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed.

Spectrum, Outcomes, and Mortality Predictors of Acute Kidney Injury among Non-COVID-19 Patients during COVID-19 Pandemic: Data from Four Intensive Care Units.

Introduction: The data of acute kidney injury (AKI), that is, community-acquired AKI (CA-AKI) and hospital-acquired AKI (HA-AKI) among non-COVID patients from intensive care units (ICU) during the coronavirus disease-2019 (COVID-19) pandemic are scarce. We planned to study the change in the profile of such patients compared to the pre-pandemic era. Materials and methods: This prospective observational study was conducted at four ICUs dealing with non-COVID patients at a government hospital in North India, and was aimed at assessing outcomes, and mortality predictors of AKI among non-COVID patients during the COVID-19 pandemic. Renal and patient survival at ICU transfer-out and hospital discharge, ICU and hospital stay duration, mortality predictors, and dialysis requirement at discharge were evaluated. The current or previous COVID-19 infection, previous AKI or chronic kidney disease (CKD), organ donors, and organ transplant patients were excluded. Results: Among the 200 non-COVID-19 AKI patients, diabetes mellitus (DM), primary hypertension, and cardiovascular diseases were the predominant comorbidities in descending order. The commonest cause of AKI was severe sepsis, followed by systemic infections and post-surgery patients. Dialysis requirements at ICU admission during ICU stay and above 30 days were seen in 20.5, 47.5, and 6.5% of patients, respectively. Incidence of CA-AKI and HA-AKI was 1.24:1, whereas dialysis requirement above 30 days was 0.85:1, respectively. The 30-day mortality was 42%. Hepatic dysfunction [hazard ratio (HR): 3.471], septicemia (HR: 3.342), age above 60 years (HR: 4.000), higher sequential organ failure assessment (SOFA) score (HR: 1.107; p = 0.001), anemia (p = 0.003), and low serum iron (p = 0.001) were important mortality predictors in AKI. Conclusion: Compared to the pre-COVID era, CA-AKI was more common than HA-AKI due to restricted elective surgeries during the COVID-19 pandemic. Acute kidney injury with multiorgan involvement and hepatic dysfunction, elderly age with higher SOFA score and sepsis were predictors of adverse renal and patient outcomes. How to cite this article: Singh B, Dogra PM, Sood V, Singh V, Katyal A, Dhawan M, et al. Spectrum, Outcomes, and Mortality Predictors of Acute Kidney Injury among Non-COVID-19 Patients during COVID-19 Pandemic: Data from Four Intensive Care Units. Indian J Crit Care Med 2023;27(2):119-126.

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN.

The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

Technology and Education Today

This essay probes the delusion that technology and education commonly tend to generate by projecting visions of transformation. Recognising that the total change they promise needs to be examined through extended social categories, the essay uses Ursula Franklin’s idea of the ‘real’ world of technology and investigates the implications of digitally-led education. Three spheres—work, knowledge and ethics—are identified for a focussed enquiry so as to obtain a critical view of the relationship between technology and education today. This enquiry enables us to grasp the political economy of technology-led educational planning. A brief attempt is made to indicate how the pervasive promotion of digital devices in the context of the COVID-19 pandemic has influenced institutionalised education.

Impact Of Online Based Education System

The two-way process of obtaining new information, skill sets, and beliefs and values is regarded to be learning whenever it is done. Here, Kumar et al looks at the influence of online education on Indian higher education.

Structural and Drug Screening Analysis of the Non-structural Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 Virus Extracted From Indian Coronavirus Disease 2019 Patients.

The novel coronavirus 2 (nCoV2) outbreaks took place in December 2019 in Wuhan City, Hubei Province, China. It continued to spread worldwide in an unprecedented manner, bringing the whole world to a lockdown and causing severe loss of life and economic stability. The coronavirus disease 2019 (COVID-19) pandemic has also affected India, infecting more than 10 million till 31st December 2020 and resulting in more than a hundred thousand deaths. In the absence of an effective vaccine, it is imperative to understand the phenotypic outcome of the genetic variants and subsequently the mode of action of its proteins with respect to human proteins and other bio-molecules. Availability of a large number of genomic and mutational data extracted from the nCoV2 virus infecting Indian patients in a public repository provided an opportunity to understand and analyze the specific variations of the virus in India and their impact in broader perspectives. Non-structural proteins (NSPs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus play a major role in its survival as well as virulence power. Here, we provide a detailed overview of the SARS-CoV2 NSPs including primary and secondary structural information, mutational frequency of the Indian and Wuhan variants, phylogenetic profiles, three-dimensional (3D) structural perspectives using homology modeling and molecular dynamics analyses for wild-type and selected variants, host-interactome analysis and viral-host protein complexes, and in silico drug screening with known antivirals and other drugs against the SARS-CoV2 NSPs isolated from the variants found within Indian patients across various regions of the country. All this information is categorized in the form of a database named, Database of NSPs of India specific Novel Coronavirus (DbNSP InC), which is freely available at http://www.hpppi.iicb.res.in/covid19/index.php.

Diabetes Mellitus and Hypertension Increase Risk of Death in Novel Corona Virus Patients Irrespective of Age: a Prospective Observational Study of Co-morbidities and COVID-19 from India.

Elderly people and people with co-morbidities have emerged as the most vulnerable group at risk of developing complications and succumbing to novel coronavirus (COVID-19) infection. We recorded the baseline demographic profile, baseline clinical and laboratory parameters, and prevalence of various co-morbidities and their effect on the prognosis of COVID-19 cases. We conducted a prospective observational study and analyzed baseline clinical and laboratory parameters and co-morbidities and their effect on severity and mortality in 710 COVID-19 cases. Seven hundred ten patients with laboratory-confirmed COVID-19 were recruited from the 28th of March to the 31st of August 2020. The mean age was 48.4 ± 16.4years. A total of 530 (74.6%) patients were male. Overall, the mean length of hospital stay was 12.7 days. In total, 645 patients(90.8%) were mild to moderate cases and did not require initial ICU care. Sixty-five (9.2%) cases required initial intensive care unit care. Fifty (7%) admitted patients succumbed to the illness. Diabetes mellitus and hypertension increased the risk of death in COVID-19 patients irrespective of age. Increasing age and co-morbidities adversely affect the prognosis of patients of COVID-19. Diabetes mellitus and hypertension increase the risk of death in COVID-19 patients and negate the incremental effect of age on death in these patients.

Clinical trials facing 'Serious Adverse Events' during the ongoing COVID-19 pandemic

Aims and Objectives: The ongoing COVID-19 pandemic is having a profound impact on the current clinical trials. We wanted to document the extent of the disruption amongst Indian clinical trial sites. Materials and Methods: We conducted an online survey among oncologists in India with active trials to document their experience with challenges and novel solutions. Results: A total of 60 oncologists replied of which 40 had ongoing trials with open recruitment. Majority of them had stopped screening (55%) and recruitment (62.5%). Almost half of the sites did not have adequate infrastructure (47.5%). Almost all the sites had enrolled patients worried about the impact of COVID-19 on their health outcome (up to 87.5%). The majority of sites had problems with adherence to study schedule of events (87.5%) and administration of study medication (42.5%). A total of 55% of the sites had provided the option of virtual visits. Both investigators (75%) and sponsors/contract research organizations (67.5%) had reached out to each other to maintain study integrity. More than half the centers had difficulty related to adverse events and serious adverse events (documentation and reporting;up to 75%). Discussion: Regulatory authorities in several countries have announced guidelines on the conduct of clinical trials during the COVID-19 pandemic. Whether the disruption lasts for a short or long time, its impact on clinical trials is going to be irreparable.

Exploiting Existing Molecular Scaffolds for Long-Term COVID Treatment.

Discovery and development of COVID-19 prophylactics and treatments remains a global imperative. This perspective provides an overview of important molecular pathways involved in the viral life cycle of SARS-CoV-2, the infectious agent of COVID-19. We highlight past and recent findings in essential coronavirus proteins, including RNA polymerase machinery, proteases, and fusion proteins, that offer opportunities for the design of novel inhibitors of SARS-CoV-2 infection. By discussing the current inventory of viral inhibitors, we identify molecular scaffolds that may be improved by medicinal chemistry efforts for effective therapeutics to treat current and future coronavirus-caused diseases.

Role of IoT to avoid spreading of COVID-19

Covid-19 has become pandemic, spreading all over the world. Scientists and engineers are working day and night to develop a vaccine, to evolve more testing facilities, and to enhance monitoring systems. Mobile and web-based applications, based on questionnaires, have already been developed to monitor the health of individuals. Internet of Things (IoT) can be used to avoid the spreading of Covid-19. Internet of Things is an interconnection of physical devices and the Internet. Devices are not only sensel and record, but can also monitor and respond. In this paper, we have reviewed the literature available on Covid-19, monitoring techniques, and suggested an IoT based architecture, which can be used to minimize the spreading of Covid-19.