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Mycobacterium indicus pranii (MIP) earlier known as Mw is a soil-borne, non-pathogenic, saprophytic and rapidly growing strain of mycobacteria. MIP is approved as a vaccine/ immunomodulator for various indications including mycobacterium infections like leprosy in humans. Its administration has resulted in satisfactory clinical improvement, accelerated bacillary clearance, and increased immune responses to Mycobacterium leprae antigens, thereby shortening the full recovery time of the patients. It also shares its antigens with M.tuberculosis. In the last decade, RCTs have been done establishing immunotherapeutic properties of MIP in the treatment of leprosy, tuberculosis, warts and experimently in leishmaniasis. Through its immune inducing and cytotoxic property, it has also proved beneficial for human use especially in treating lung cancer. The beneficial role of it is also being explored in breast, cervical, oral, liver, and bladder cancers. Various studies on MIP have shown that it has immune-modulating properties in humans. The curiosity of the human mind has led to it being tried in Covid treatment trials. The results have shown that administering MIP has lowered inflammatory markers in Covid 19 patients, promising us for it to be a potential treatment option. More RCTs with a larger sample size should be done to establish this. Cytokine storm seen in bacterial sepsis is also decreased with MIP administration. Considering the encouraging results in hastening recovery in various diseases it appears that MIP is perhaps not being exploited to its fullest potential. © 2023, Hind Kusht Nivaran Sangh (Indian Leprosy Association). All rights reserved.
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As we have seen in lots of newspaper, news and many magazines that robbery became the common and main issue going on over the world. My biggest concern is on secure ATM transaction, where nowadays many ATM's get misleaded and robbery are occurring very easily. So, to handle this situation Image processing has been playing a major role to identify the person who are doing the transaction via ATM. Because has the covid cases are increasing many people wear mask and helmet and do misuse the ATM for illegal transaction, here where the image processing helps to differentiate the person wearing mask/helmet and without mask/helmet for the further ATM transaction. And secondly Cloud is also playing a major in storing the datas where it stores the static data to train the model and to detect the accuracy and stores the captured real time image in different folder so that we can easily identify the person with mask/helmet and without mask/helmet. © 2022 IEEE.
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Due to COVID-19 pandemic, public health emergency was created throughout the world. So, we took the base data and perform analysis on how the effect of vaccination on the human lives in terms of recovery, severity, side effects, and deaths on the globe. We also analyzed the country wise vaccination to understand the scenarios in the world, because the COVID virus is transforming in different countries in different ways, therefore the understanding the mutations of the virus and the use of the drug analysis also very much important for the future generations and also useful to face the future COVID virus mutations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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A quick user-friendly application of any pandemic situation can reduce the huge value of mortality with producing the graph of cases. Simple database application can make sense to the people about the pandemic and transmissions. This research aimed to develop a simple application which shows the real-time cases of COVID-19 and analyzes different states condition of India and a proper graphical prediction of cases. This application notifies people to get alert about the transmission and precautions to get rid of this pandemic. This application also helps clinical doctors, ministry and decision makers to improve the gap of any unfilled section. We have used the platform of APEX Oracle to develop this application and analyzed the dataset. The accuracy of the data is 78% rather than any other existing techniques. Combining the application and advance techniques, this study can create a vital framework for the prediction of any pandemics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Coronavirus (COVID-19) is an air-borne disease that has affected the lifestyle of people all around the world. Tracing patients infected with coronavirus has become a difficult process because of the limitation of tests based on reverse transcription-polymerase chain reaction (RT-PCR). Recently, methodologies based on imaging have been proposed by various researchers especially using deep learning-based models for the detection of COVID infection. This paper analyzes the effectiveness of deep features for COVID detection from CT scan images. Deep features were extracted from the final layers of deep learning models which are then fed into machine learning frameworks for classification. Transfer learned features obtained from ResNet50, Inception V3, and EfficientNetB7 were employed for the study. A combination of Inception V3 and SVM gave the best accuracy of 86.12 and precision and recall with 83.11 and 80.44, respectively. These results are comparable to recent transfer learning approaches and architecture that is about to be discussed is having an advantage of minimized time when compared to traditional deep learning approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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With the emergence of coronavirus and the rapidly increasing number of cases, we observed that people were suffering from a lack of information about where to source critical requirements such as oxygen cylinders, beds, ambulance service, and ventilators. In this research paper and project, the authors have designed and developed an interactive information dashboard to access the availability of these resources and requirements at different locations across India from myriad sources. The information dashboard will show information for all the states across India. The dashboard visualizes the number of corona cases, hospital beds, blood bank, etc. The end-users and the COVID-19 support team can imagine all the requirements in the dashboard that is factual and credible within their vicinity from multiple information sources. The user need not search various information sites to search for different conditions. The project collates the data of other requirements from primary sources of information like Twitter and government websites and lists them on the dashboard. The authors used open-source programming and database technologies to display the data per user requirements. The dashboard was helpful to the end-users during COVID-19 times in terms of convenient accessibility and efficiency. © 2022 IEEE.
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Context: Covid 19 pandemic which evolved in successive waves had profound pyschosocial impact on affected in-dividuals. Perceptions had impact on both individual and environmental level with potential behavioural conse-quences. The aim of the research is to study the psychosocial perception and psychological impact of COVID-19 among hospitalized COVID-19 patients. Methodology: The study was a mixed method research (Quan-Qual sequential design) conducted in the Covid wards of a tertiary care hospital in Coimbatore district. The psychological impact was assessed using the General Health Questionnaire, Perceived Stress Scale. The results of quantitative analyses and qualitative analyses were expressed as proportions and done using thematic analysis using grounded theory respectively. Results: About 55% of the hospitalized Covid-19 patients had psychological impact. On multivariate analysis, the factors which emerged as independent risk factors for presence of psychological morbidi-ties due to COVID were presence of high stress level, sleep disturbances and their perception of COVID as high threat. Conclusions: Focussed Counselling with specific reference to attend to spiritual health component in addition, would go a long way in diminishing immediate and long-term psychological impact due to covid-19 illness. © 2022, MedSci Publications. All rights reserved.
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Introduction: IC14 (atibuclimab) is a monoclonal anti-CD14 antibody that may target T-regulatory (T-reg) cell function. A previous phase 1 trial of 10 participants with amyotrophic lateral sclerosis (ALS) demonstrated initial safety of IC14 for a single cycle of treatment. We provided longterm treatment with IC14 to 17 individuals with ALS via an expanded access protocol (EAP) and documented target engagement, safety, and disease endpoints. Method(s): Participants received intravenous IC14 every two weeks. Consistent with FDA guidelines, participants were ineligible for clinical trials and the EAP was inclusive of a broad population. Participants unable to travel to MGH due to the COVID-19 pandemic or disease progression, were transitioned to infusions in-home or local clinics. Blood samples for hematology, chemistry, and coagulation were collected to monitor safety. The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) was administered monthly to track disease progression. Respiratory function was measured through slow vital capacity tests -data for this is limited due to the COVID-19 pandemic. Whole blood and serum were collected to determine monocyte CD14 receptor occupancy (RO), soluble CD14, and antidrug antibodies (ADA). Ex vivo T regulatory functional assays were performed with five participants. Result(s): Participants received IC14 for up to 103 weeks (average: 30.1 weeks, range: 1-103 weeks). Treatmentemergent adverse events were uncommon, mild, and self-limited. There were 18 serious adverse events (SAEs) which were related to disease progression and unrelated (17) or likely unrelated (1) to IC14. Three participants died due to disease progression. Most participants achieved >80% monocyte mCD14 RO on a 14-day dosing schedule, although one individual required more frequent dosing (every 10 days) to achieve >80% RO. ADA were detected in only one participant and were transient, low titer, and non-neutralizing. Tregs were isolated from the available longitudinal samples and assayed for suppression of CD4 T cell proliferation and cytokine production versus baseline T-reg activity. Conclusion(s): IC14 administration to ALS patients was safe and well tolerated in this EAP, with no significant changes in laboratory tests and no drug-related SAEs. Measuring RO guided dosing frequency. Preliminary data suggest IC14 enhanced T-reg activity. Additional placebo-controlled trials are required to determine the efficacy of IC14 in ALS.
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Background: A high incidence of air leak syndromes (ALSs) has been reported in critically ill coronavirus disease 2019 (COVID-19) patients, which affects disease outcome. Objective: To evaluate the incidence, outcome, and risk factors associated with ALSs in critically ill COVID-19 patients receiving invasive or non-invasive positive pressure ventilation. Result: Out of 79 patients, 16 (20.2%) patients had ALS. The mean age of the ALS group was 48.6 ± 13.1 years as compared to 52.8 ± 13.1 (p = 0.260) years in the non-ALS group. The study group had a lower median body mass index (25.9 kg/m 2 vs 27.6 kg/m 2, P = 0.096), a higher D-dimer value (1179.5 vs 762.0, P = 0.024), lower saturation (74% vs 88%, P = 0.006), and a lower PF ratio (134 vs 189, P = 0.028) at presentation as compared to the non-ALS group. Patients with ALS had received a higher median positive end-expiratory pressure (PEEP) (10 cm vs 8 cm of water, P = 0.005). The pressure support, highest driving pressure, and peak airway pressure were not significantly different in the two groups. The ALS group had a significantly longer duration of hospital stay (17.5 vs 9 days, P = 0.003). Multiple logistic regression analyses indicated that patients who received inj. dexamethasone were less likely to develop ALS (OR: 12.6 (95% CI 1.6-95.4), P = 0.015). Conclusion: A high incidence of ALS is present in critically ill COVID-19 patients. High inflammatory parameters, severe hypoxia at presentation, and use of high PEEP are significant risk factors associated with ALS. The risk of developing ALS was lower in patients who received inj. dexamethasone. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.
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Background and Aim: The clinical assessments using the Child-Turcotte-Pugh (CTP) and model for end-stage liver disease-sodium (MELD-Na) scores are heterogeneous. The present study aims to collate most relevant markers of chronic liver disease as a new score BICEPS (Bilirubin, INR, Creatinine, Encephalopathy, Platelet and Sodium) an alternate to CTP and MELD-Na in patients diagnosed with liver cirrhosis. Methods: From January 2019 till December 2021, patients who were admitted with a diagnosis of liver cirrhosis were included. Patients were classified as per the CTP, MELD-Na and BICEPS scoring systems using a proforma. The BICEPS score is created by the authors and study intended to validate clinically. Results: In 211 patients, were evaluated during the study period including COVID pandemic period. The mean MELD-Na score was 23.49 ± 8.42, ranging 7 to 42, while CTP score ranged from 5 to 15, with a mean value of 9.65 ± 2.76. We classified patients as per scoring to 16%, 32% and 52% as Grade A, B and C on CTP respectively and 6%, 54% and 40% as Grade A, B and C on BICEPS respectively. We observed moderate level of agreement between BICEPS and CTP [Cohen’s kappa = 0.57, 95% confidence interval (CI) 0.45 to 0.69, p value < 0.01]. BICEPS correlated strongly with both MELD-Na [Pearson’s correlation coefficient (r) = 0.84, p value < 0.01] and CTP (r = 0.83, p value < 0.01). Conclusions: BICEPS yet another clinical scoring that has a moderate level of agreement with CTP and significant correlation with both CTP and MELD Na. Being a clinical scoring helps bedside estimation easy and includes all the 6 critical markers of CLD and is comparable to MELD Na without a calculator. Further studies are required with more number of patients for validating the BICEPS score.
ABSTRACT
A quick user-friendly application of any pandemic situation can reduce the huge value of mortality with producing the graph of cases. Simple database application can make sense to the people about the pandemic and transmissions. This research aimed to develop a simple application which shows the real-time cases of COVID-19 and analyzes different states condition of India and a proper graphical prediction of cases. This application notifies people to get alert about the transmission and precautions to get rid of this pandemic. This application also helps clinical doctors, ministry and decision makers to improve the gap of any unfilled section. We have used the platform of APEX Oracle to develop this application and analyzed the dataset. The accuracy of the data is 78% rather than any other existing techniques. Combining the application and advance techniques, this study can create a vital framework for the prediction of any pandemics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID+ and COVID-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.
ABSTRACT
This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data. In order to prevent cyber threats and hacking activities like SQL injection, Phishing, and DoS, this research paper has proposed a newer technique of the encryption process by using the python codes and also shown the difference between typical conventional system and proposed system for understanding both the system in a better way. © 2022 by Dr. Santosh Kumar, Dr. Rajeev Yadav, Dr. Priyanka Kaushik, S B G Tilak Babu, Dr. Rajesh Kumar Dubey and Dr. Muthukumar Subramanian.
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Health care prosperity is the most challenging task for human being in the present dangerous COVID scenario and the discovery proposes an augmented reality based personalized smart diet assistance system which provides diet recommendations, appropriate time, type, quantity and method of consumption of a food item diet based on user health parameters based on location and event activities. The augmented reality based system comprises a user data input, an image processing, food consumption assistance, transmissible disease information retrieval and diet planning modules. The system incorporates an AI based camera to scan a food item before or after cooking and utilizes augmented reality to indicate the nutritional information. The proposed system provides personalized diet recommendations to the user based on personal data such as height, weight, existing medical conditions and thereof of a user. The system retrieves existing transmissible diseases data from world health organizations and data from news articles about any viral infections or diseases to suggest immunity boosting foods to the user to thereby safeguard the user against such diseases or infections. © 2022 International Information and Engineering Technology Association. All rights reserved.
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The Internet of Things is now a groundbreaking area with the potential to influence our lives and bring major changes to the globe. Many IoT approaches have been proposed to enable data-driven and smart applications for users.;therefore, architecture, applications, and technological systems must be created for fast and prompt enforcement of guidelines, regulations, and government directives to control such future epidemics. This paper describes a new architecture, possible use-cases, and some future possibilities for building such applications utilizing Smart City. The COVID-19 epidemic is an existing pandemic induced by that of the coronavirus.It is a severe acute respiratory syndrome (SARS-CoV-2). It was discovered for the first time in December 2019 in Wuhan, China. Social distancing is a technique for controlling the transmission of infectious illnesses. It suggests that individuals must physically separate away from each other, decreasing intimate contact and therefore lowering the spreading of a deadly virus also including coronavirus. © The Electrochemical Society
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Based on several pre-defined standard symptoms, a model that can determine the coronavirus illness as positive is developed. Guidelines for these symptoms have been issued by the World Health Organization (WHO) and India's Ministry of Health and Family Welfare. In this model the various symptoms of the illnesses is given to the system. It allows users to discuss their symptoms, with the algorithm predicting a condition based on factual information. This factual information is then evaluated using the ARM based Apriori algorithm to get the most accurate results. Other conventional models such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forests (RF) are considered and have analyzed the predictions and have found that the proposed algorithm predicts a higher accuracy score. © 2022 IEEE.
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The advent of new augmented reality applications in workplaces and education centers will greatly enhance the productivity exponentially. As the world moves toward online workspace because of the COVID-19 pandemic situation, it is vital that we increase the productivity at home without compromising on the collaboration aspect. We focus on a Collaborative Interactive Workspace Environment (CIWE) as an almost ideal alternative to current office workspace setting. In CIWE, multiple users can influence a shared virtual workspace environment through an Optical Transparent Head-Mounted Device. CIWE utilizing augmented reality allows better approaches for cooperation and perception and helps to build client engagement and commitment. Furthermore, we also present a hybrid workspace solution which uses both the PC and a OTHD in sync to extend the display capabilities of the PC from conventional monitors to the digitally augmented workspace, thus reducing the need of large tables and also cutting the cost by giving the at most conveyances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Employee engagement has been a key focus area by Indian Organizations during the pandemic. This research paper focuses on the origins of Employee Engagement theories and how Indian companies are navigating these employee engagement practices due to the pandemic. There have been studies in this regard and empirical evidence indicates that improvement in Employee Engagement directly correlates to improved productivity. The authors in this paper, explore the details of the employee engagement and map the theoretical aspects to the various engagement practices undertaken by Indian companies during the pandemic. The research shows that organizations have been able to effectively manage the crisis by supporting the employee’s infrastructure requirement and address their safety needs. However, there is little evidence by these organizations, rehauling their core fundamentals in the terms of employees Roles & Responsibilities and changes in organizational processes. © IEOM Society International.
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The outbreak of Covid-19 across the world is the biggest concern for all the countries. Controlling the Covid-19 virus outbreak is very challenging and India has managed the pandemic through strict measures. This research explores the impact on employees working in companies in India. The researchers extracted data from various Research journals, Articles, and Newspapers. This study presents the Impact of Covid-19 on employees of various companies in India and measures taken by different companies in the field of safety, healthcare, Wellbeing Allowances, financial aspects, and how many companies are thinking beyond their own staff to cope up with the difficult situation and impact of Covid-19. This study answers different research questions like how companies are managing their work in this Covid-19 pandemic situation in a comprehensive manner. Based on the theoretical underpinnings of Employee Engagement, the researchers explore the various engagement aspects by companies in India. © IEOM Society International.