Smart Social Distancing Robot for COVID Safety

Social distancing plays an indispensable part during the ongoing pandemic. In this period, maintaining social distancing standards between people has turned into essential insurance to dial back the spreading of COVID-19. We present an original technique to recognize matches consequently of people in a jam-packed situation individuals do not maintain the social distance restriction, which calls for about 3 ft of space between them. This project assists in restricting the spread of the coronavirus by noticing the distance between disease-spreading people. Presently, it is absurd to expect to station an individual 24 × 7 at each line to screen social separating distance violations. For instance—banks, public offices, malls, schools, theatres, and so forth typically see long lines for hours consistently. To ensure social distancing in lines, this robot aids in monitoring the social distancing. Accordingly, this robot aids in maintaining the social distance between the crowd in a public environment to assist and forestall the spread of the virus. This robot serves to be an economical solution in public places where the gathering of people is significantly high. With appropriate obstacle detection, and crowd monitoring the official are also kept updated due to the Wi-Fi and IoT technology incorporated into a robot. This robot is expected to serve as a good solution in this pandemic time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An Improved Accuracy in Anticipating the User Nature using a novel logistic regression algorithm throughout the pandemic across online social media based on Indian metrics over Support Vector Clustering Algorithm

This study's primary goal is to apply machine learning classifier techniques to raise the intensity percentage of user nature detection in order to detect the impact of coronavirus on Twitter users by comparing Novel Logistic Regression and Support Vector Clustering algorithms. Materials and Methods: The accuracy percentage with a confidence interval of 95% and G-power (value =0.8) was determined many times using the LR method with test size =10 and the SVC algorithm with test size =10. The likelihood that an item belongs to one category or another is predicted using a LR model. Support Vector Clustering algorithm generates a line or hyperplane that divides the data into categories. Results and Discussion: LR model has greater efficiency (91%) when compared to Support Vector Clustering (59%). Two groups are numerically unimportant, according to the data obtained with a coefficient of determination of p=0.121 (p>0.05). Conclusion: LR performs substantially better than the Support Vector Clustering. © 2022 IEEE.

Smart Social Distancing Robot for COVID Safety

Social distancing plays an indispensable part during the ongoing pandemic. In this period, maintaining social distancing standards between people has turned into essential insurance to dial back the spreading of COVID-19. We present an original technique to recognize matches consequently of people in a jam-packed situation individuals do not maintain the social distance restriction, which calls for about 3 ft of space between them. This project assists in restricting the spread of the coronavirus by noticing the distance between disease-spreading people. Presently, it is absurd to expect to station an individual 24 × 7 at each line to screen social separating distance violations. For instance—banks, public offices, malls, schools, theatres, and so forth typically see long lines for hours consistently. To ensure social distancing in lines, this robot aids in monitoring the social distancing. Accordingly, this robot aids in maintaining the social distance between the crowd in a public environment to assist and forestall the spread of the virus. This robot serves to be an economical solution in public places where the gathering of people is significantly high. With appropriate obstacle detection, and crowd monitoring the official are also kept updated due to the Wi-Fi and IoT technology incorporated into a robot. This robot is expected to serve as a good solution in this pandemic time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Contactless Fog based Handwash Kit for COVID Safety

Recent days have changed tremendously, and rules are strictly being deployed to maintain social distancing, avoid crowding and frequent hand washing. Frequent washing of hands using our domestic water by a mass crowd result in water wastage which is a huge loss for our society. A better solution to sanitize the hands with reduced water wastage is attempted in this study. With technological advancements in engineering, several solutions and cope-up methods are being given to combat the spread of COVID-19 in this pandemic era. As an attempt, this study develops a Fog based Contactless Handwash kit which uses the Mis Spray method to sanitize the hands. The mist consists of water vapour and herbal sanitizer which is skin-friendly to humans. This kit is suggested to be deployed in public places to avoid the spreading of the virus since it is in a complete contactless manner. It is developed with an Atmega based microcontroller, NodeMCu,ultrasonic sensor and mist spray module economically. The outcomes of the developed handwash kit serve to optimally favour the preventing behaviour in this pandemic time. This study gives way for further research studies on the automatic sanitizing methods to combat the spread of the virus and its variants. © 2022 IEEE.

Added value of the measles-rubella supplementary immunization activity in reaching unvaccinated and under-vaccinated children, a cross-sectional study in five Indian districts, 2018-20.

INTRODUCTION: Supplementary immunization activities (SIAs) aim to interrupt measles transmission by reaching susceptible children, including children who have not received the recommended two routine doses of MCV before the SIA. However, both strategies may miss the same children if vaccine doses are highly correlated. How well SIAs reach children missed by routine immunization is a key metric in assessing the added value of SIAs. METHODS: Children aged 9 months to younger than 5 years were enrolled in cross-sectional household serosurveys conducted in five districts in India following the 2017-2019 measles-rubella (MR) SIA. History of measles containing vaccine (MCV) through routine services or SIA was obtained from documents and verbal recall. Receipt of a first or second MCV dose during the SIA was categorized as "added value" of the SIA in reaching un- and under-vaccinated children. RESULTS: A total of 1,675 children were enrolled in these post-SIA surveys. The percentage of children receiving a 1st or 2nd dose through the SIA ranged from 12.8% in Thiruvananthapuram District to 48.6% in Dibrugarh District. Although the number of zero-dose children prior to the SIA was small in most sites, the proportion reached by the SIA ranged from 45.8% in Thiruvananthapuram District to 94.9% in Dibrugarh District. Fewer than 7% of children remained measles zero-dose after the MR SIA (range: 1.1-6.4%) compared to up to 28% before the SIA (range: 7.3-28.1%). DISCUSSION: We demonstrated the MR SIA provided considerable added value in terms of measles vaccination coverage, although there was variability across districts due to differences in routine and SIA coverage, and which children were reached by the SIA. Metrics evaluating the added value of an SIA can help to inform the design of vaccination strategies to better reach zero-dose or undervaccinated children.

Performance & clinical utility of oropharyngeal versus nasopharyngeal swabs in COVID-19.

Background & objectives: The oropharyngeal (OP) and nasopharyngeal (NP) swab samples are the most recommended clinical specimens for detecting SARS-CoV-2 in an individual through the quantitative real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) method. The primary objective of this study was to compare the performance of NP and OP swabs for the diagnosis of COVID-19 among 2250 concomitant samples (1125 NP + 1125 OP) using rRT-PCR test. Methods: This study was conducted at a tertiary care hospital in southern India. The study compared the specificity and efficacy of the two samples (NP & OP swabs) in 1125 individuals suspected having COVID-19 infection. The rRT-PCR values from all the samples were compared based on gender, age group and viral load. The differences between unmatched proportion and matched proportion were analysed. Agreement between the two methods was assessed using Kappa statistic. Absolute sensitivity, specificity, positive and negative predictive values (PPV and NPV) for OP and NP swabs were analysed. Results: The study identified a fair degree of agreement between OP and NP swabs in diagnosis of COVID-19 (kappa = 0.275, P <0.001). There was also a fair degree of agreement between NP and OP swabs irrespective of gender, age or duration of symptoms. NP swabs had better sensitivity and NPV as compared to OP swabs, however, specificity and PPV were 100 per cent for both. Interpretation & conclusions: The present study showed that both OP and NP swabs had similar sensitivity and specificity for predicting the presence of SARS-CoV-2.

An Improved Efficiency in Envisioning the Personage Traits over Online Social Media based on Indian Metrics during Pandemic using Novel Naive Bayes Classifier Algorithm Comparing with Logistic Regression Algorithm

Aim: The primary aim of this research is to increase the intensity percentage of personage traits detection to reveal the impact of coronavirus on Twitter users by utilizing machine learning classifier algorithms by comparing Novel Naive Bayes Classifier algorithm and Logistic Regression algorithm. Material(s) and Method(s): Naive Bayes Classifier algorithm with test size=10 and Logistic Regression algorithm with test size=10 was estimated several times to envision the efficiency percentage with confidence interval of 95% and G-power (value=0.8). Naive Bayes classifier compares whether a specific feature in a class is unrelated to another feature. A logistic regression model predicts the probability of an item belonging to one group or another. Results and Discussion: Naive Bayes algorithm has greater efficiency (86%) when compared to Logistic Regression efficiency (60%). The results achieved with significance value p=0.169 (p>0.05) shows that two groups are statistically insignificant. Conclusion(s): Naive Bayes Algorithm executes remarkably greater than the Logistic Regression algorithm. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Lung-Kidney Injury Caused by Innate Derived Mediator suPAR

Background: Soluble urokinase plasminogen activator receptor suPAR is an innate-immune system derived circulating kidney disease risk factor that has its highest physiological expression at baseline in the upper airway. Critical illnesses with high mortality rates often exhibit both acute lung and kidney injury as complications. The objective of our study is to understand whether murine pulmonary airway injury can induce kidney injury, perhaps in a suPAR dependent manner. In the injured human lung, uPAR is documented in several pathological respiratory conditions including COPD, pneumonia and tuberculosis and recently also in COVID-19. Since suPAR levels increase prior to and during kidney injury, we aimed to explore a lung-kidney connection. Method(s): Injection of naphthalene, an aromatic hydrocarbon present in tobacco smoke constitutes a well-characterized model for acute airway injury in mice. SuPAR over-expressing mice (suPARTg) and UPAR knockout mice (UPARKO) were injected intraperitoneally with naphthalene. To begin to explore mechanisms of lung-kidney axis, lung lavage and serum inflammation was assessed by multi-plex ELISA and flow cytometry. Result(s): SuPAR overexpression accelerated mortality in naphthalene injured mice by 40%. Furthermore, injecting an UPAR antibody in naphthalene injured suPARTg mice increased led to significant reduction in mortality. UPARKO mice exhibited a 100% survival rate post injury. Increased survival observed in UPARKO mice could be attributed to significantly increased IL6 levels in both lung lavage and serum, thereby altering the outcome of naphthalene mediated injury. Conclusion(s): In conclusion, immune derived factors such as suPAR connect the lung with the kidney. Targeting suPAR may be beneficial in increasing survival in cases where mortality may be attributed to multi-organ failure induced by lung injury.

An Improved Efficiency in Envisioning the Personage Traits over Online Social Media based on Indian Metrics during Pandemic using Novel Naive Bayes Classifier Algorithm Comparing with Logistic Regression Algorithm

Aim: The primary aim of this research is to increase the intensity percentage of personage traits detection to reveal the impact of coronavirus on Twitter users by utilizing machine learning classifier algorithms by comparing Novel Naive Bayes Classifier algorithm and Logistic Regression algorithm. Materials and Methods: Naive Bayes Classifier algorithm with test size=10 and Logistic Regression algorithm with test size=10 was estimated several times to envision the efficiency percentage with confidence interval of 95% and G-power (value=0.8). Naive Bayes classifier compares whether a specific feature in a class is unrelated to another feature. A logistic regression model predicts the probability of an item belonging to one group or another. Results and Discussion: Naive Bayes algorithm has greater efficiency (86%) when compared to Logistic Regression efficiency (60%). The results achieved with significance value p=0.169 (p>0.05) shows that two groups are statistically insignificant. Conclusion: Naive Bayes Algorithm executes remarkably greater than the Logistic Regression algorithm.

Role of glycaemic control in predicting severity of illness in COVID 19 patients with diabetes mellitus

Introduction: Coronavirus Disease 2019 (COVID 19) pneumonia is a rapidly spreading disease and which causes morbidity and mortality of many patients. Diabetes mellitus is co-morbidity which is considered as the risk factor for COVID 19. Well-controlled diabetes is associated with better outcomes than poorly controlled diabetes. Measurement of glycated haemoglobin (HbA1c) is the standard method for assessing long term glycaemic control. Regardless of the level of hyperglycaemia, improvement in glycaemic control will lower the risk of diabetic complications. Aim: This study was conducted to identify the role of glycaemic control (HbA1c) in predicting the severity of illness in patients with COVID 19 pneumonia. Materials and Methods: This was a retrospective observational study of (51 diabetic and 51 were non diabetic) patients at Kamineni Academy of Medical Sciences, Hyderabad, India. The patients diagnosed with COVID 19 pneumonia, which includes both diabetics and non diabetics from June 2020 to September 2020. Patients age, sex, baseline HbA1c levels, and oxygen requirement during the hospital stay were analysed using Statistical Package for the Social Sciences (SPSS) software version 22.0. The Chi-Square test was used to analyse qualitative data and p-value significant at level <0.05. Results: In the study among diabetics (n=51), 20 (39.2%) were on room air, 24 (47.1%) required intermittent oxygen support, 3 (5.9%) high flow oxygen, and 4 (7.8%) non invasive ventilator support. Among non diabetics (n=51), 28 (54.9%) were on room air, 18 (35.3%) on intermittent oxygen, 2 (3.9%) high flow oxygen, and 3 (5.9%) Non Invasive Ventilator (NIV) support. It was observed that patients with HbA1c measurements with poor glycaemic control required more oxygen support during treatment in diabetics (p-value:0.469) Conclusion: In the present study, patients with poor glycaemic control required insignificantly, more oxygen support than patients with good glycaemic control.