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The following sections are included: • Vaccination Platform for the Country • The Second and Third Waves of Covid-19 in India • Vaccine Manufacturing in India • Vaccine Service Chain Ecosystem • Institutions • Resources • Delivery Service Infrastructure • GRIP Framework • Governance • • Orchestration • • Partner Selection • • Coordination • • Execution • Risks • Innovation • Performance • India's Vaccination Ecosystem for Covid-19 • Conclusion • Acknowledgments • References. © 2023 by World Scientific Publishing Co. Pte. Ltd.
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BACKGROUND AND AIM: Clinical services were severely affected globally during the COVID-19 pandemic. This study aimed to characterize the clinical experience of using botulinum toxin (BTX) injections during the COVID-19 pandemic. Method(s): This is a retrospective chart review of patients who received BTX injections from April 2019 to January 2022. Result(s): A total of 105 patients received an BTX injections, out of which 76 (72.4%) were men. The mean age of the patients was 47.9 +/- 15.1 years. The most common indication for receiving BTX injections was dystonia (n = 79;75.2%), followed by hemifacial spasm (n = 22;21%) and miscellaneous movement disorders (n = 4;3.8%). Focal dystonia (n = 45;57%) was the most frequent form of dystonia, followed by segmental dystonia (n = 24;30%). The percentage of generalized dystonia and hemidystonia was 12% and 1%, respectively. Cervical dystonia (44.4%), blepharospasm (17.8%), and writer's cramp (15.6%) were the most frequent forms of focal dystonia. The miscellaneous group included four patients (3.8%) with trigeminal neuralgia, Holmes tremor, dystonic tics, and hemimasticatory spasm. The mean ages of patients in the dystonia, hemifacial spasm, and the miscellaneous groups were 47.7 +/- 14.9 years, 49.2 +/- 14.0 years, and 44.2 +/- 26.0 years, respectively. The mean BTX dose was 131.6 +/- 104.1 U. The mean BTX doses for the dystonia group, hemifacial spasm, and the miscellaneous group were 158.7 +/- 105.3 U, 40.1 +/- 11.3 U, and 100.0 +/- 70.7 U, respectively. Conclusion(s): Most patients in our cohort had dystonia, followed by hemifacial spasm. Among the patients with dystonia, most had focal dystonia, with cervical dystonia being the most common movement disorder. The data obtained in our study is important to increase awareness of the effectiveness of BTX injections in patients with chronic disorders.Copyright © 2023 Annals of Movement Disorders.
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This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Pharmacovigilance involves evaluation of adverse effects of drugs in the interest of patient safety. Large-scale application of pharmacovigilance generates big datasets that are mined to identify previously unknown drug–event combinations, and, as an extension, may help in identifying new indications for old drugs. The therapeutic potential of a drug using pharmacovigilance-based drug repurposing can be assessed in one of the four ways—serendipity, mechanistic profiling, signature matching, and inverse signaling. Serendipity is the phenomenon of discovery of some valuable information for an already known drug, by chance, like minoxidil. Mechanistic profiling proposed the use of sulfonylureas for diabetes mellitus, based on the observation of their hypoglycemic effect. Signature matching is puzzling out new indications of drugs based on similarity of characteristics in a network of other drugs which are already approved for any condition. Inverse signaling approach takes cues from data mining approaches, applied to pharmacovigilance databases. Currently, this approach is being tried to evaluate existing compounds for Raynaud's phenomenon, COVID-19, Alzheimer' disease, etc. In this chapter, we discuss these pharmacovigilance-based methods as they have immense translational potential for drug repurposing. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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The 2019 coronavirus (COVID-19), started in China, spreads rapidly around the entire world. In automated medical imagery diagnostic technique, due to presence of noise in medical images and use of single pre-trained model on low quality images, the existing deep network models cannot provide the optimal results with better accuracy. Hence, hybrid deep learning model of Xception model & Resnet50V2 model is proposed in this paper. This study suggests classifying X-ray images into three categories namely, normal, bacterial/viral infections and Covid positive. It utilizes CLAHE & BM3D techniques to improve visual clarity and reduce noise. Transfer learning method with variety of pre-trained models such as ResNet-50, Inception V3, VGG-16, VGG-19, ResNet50V2, and Xception are used for better feature extraction and Chest X-ray image classification. The overfitting issue were resolved using K-fold cross validation. The proposed hybrid deep learning model results high accuracy of 97.8% which is better than existing techniques.
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Background: SARS-CoV-2 is a highly contagious respiratory virus responsible for COVID-19 pandemic. To understand the role of antibodies in neutralization, our study quantified circulating levels of IgA/IgG and IgG subtypes induced at different days post onset of symptoms, in severe and non severe patients. Objective(s): To quantify circulating levels of IgA, IgG and IgG subclass in severe and non severe patients induced at different days post onset of symptoms. Material(s) and Method(s): Serum or plasma samples collected from 79 COVID-19 patients were used. Indirect SARS-CoV-2 specific IgA, IgG, and IgG subclass specific ELISAs were performed. Antibody titers between severe and non severe patients were compared at different times post onset of clinical symptoms. Titers in ELISA were correlated to neutralizing antibody titers. Results and Conclusion(s): Over 75% patients were positive for IgA and IgG antibodies in the first week. The ELISA titers did not differ during the first week of infection. However, patients with severe disease exhibited raised titers. Neutralizing antibody titers correlated with the ELISA titers in mild presentation but not in severe disease. IgA and IgG1 antibodies correlated stronger with neutralizing antibodies. The findings highlighted that IgA together with IgG play an important in SARS-CoV-2 neutralization.
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Introduction: Survival after hematopoietic cell transplantation (HCT) has improved tremendously over the last few decades. HCT survivors are at increased risk of long-term complications and secondary cancers. This poses unique challenges to the HCT-related healthcare system given the growing need for survivorship care. Developing a HCT survivorship program with a dedicated clinic to survivors ensures equitable access to care and ongoing patient education. Herein, we describe our program survivorship model and our initial experience. Method(s): The Moffitt Cancer Center (MCC) survivorship clinic (SC) planning committee was initiated in September 2019. The SC was launched in January 2021 with the mission to provide high-quality, comprehensive, and personalized survivorship care and to empower patients and community health care providers with education and a roadmap for screening for late effects. The SC initially focused on allogeneic (allo) HCT patients and later opened to autologous (auto) HCT recipients in February 2022. HCT patients are referred by primary HCT team after HCT with an emphasis on preferred timeframe of initial SC visit no earlier than 3 months but less than 12 months from HCT. SC is located at 2 physical locations: main campus and satellite, with virtual visit options to account for the distance from MCC and COVID considerations. SC applies a consultative model. SC is staffed by dedicated advanced practice professional (APP), supervised by SC faculty. The scope of SC care includes but is not limited to prevention of infections (education, vaccinations), surveillance of late effects (endocrine, pulmonary function, cardiac, bone health), and general cancer screenings (breast, colon, skin cancer). Patients' clinical data from SC inception to August 2022 were reviewed. Result(s): From January 2021 to August 2022, a total of 138 patients were seen in SC. The majority were seen in person (62% in clinic, 38% by virtual visit). Median age was 58 years (range, 19-82). Median time to first SC visit was 21 months (range, 3-1464) after HCT. Allo HCT was the most common type of HCT seen in clinic (87%, n=120). Most common diagnoses were acute myeloid leukemia (43%, n=59), myelodysplastic syndrome (17%, n=23), and acute lymphoblastic leukemia (10%, n=14). Only 17 patients (12%) were seen in 2021 but the volume increased significantly in 2022. Currently there are more than 10 patients seen in SC per month. Conclusion(s): We report successful experience in launching a contemporary HCT SC despite the challenges of an ongoing COVID pandemic. As a stand-alone cancer center, we serve a wide geographical location with subspecialty and primary care providers dispersed throughout the community. Our consultative model and experience could provide a useful guide for other programs. In 2023, we plan to expand our SC to a broader population of patients receiving other cellular immunotherapies.Copyright © 2023 American Society for Transplantation and Cellular Therapy
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Background: COVID-19 pandemic witnessed rapid development and use of several vaccines. In India, a country-wide immunization was initiated in January 2021. COVISHIELD, the chimpanzee adenoviral vectored vaccine with full length SARS-COV-2 spike insert and COVAXIN, the whole virus, inactivated vaccine, were used. Objective(s): The present study was aimed at assessment and comparison of antibody response to COVISHIELD and COVAXIN. Material(s) and Method(s): Blood samples were collected pre-vaccination, 1 month post-1/post-2 doses and 6 months post-dose-2, from healthcare workers receiving COVISHIELD and COVAXIN vaccines. The samples were tested for IgG-anti-SARS-CoV-2 (ELISA) and neutralizing antibodies (Nab, PRNT50). Result(s): In pre-vaccination-antibody negative COVISHIELD recipients (pre-negatives, n = 120), % Nab seroconversion increased from 55.1% post-dose-1 to 95.6% post-dose-2, that were independent of age/gender/BMI. Presence of co-morbidities reduced Nab titers (p = 0.004). In pre-positives (n = 67), Nab titers increased to 40.7 fold from 75 (IQR 29-129) before vaccination to 3050 (IQR 1282-3998, p<0.001) post-first-dose, but declined to 1740 (IQR 911-3116, p = 0.037) post-2nd-dose. Nab response in pre-positives was independent of age/gender/BMI/co-morbidities. Post-dose-2 seroconversion (50%, p<0.001) and Nab titers (6.75, 2.5-24.8, p<0.001) in COVAXIN recipients were lower than COVISHIELD. Diminished Nab titers were observed at 6 months post-dose-2 for both vaccines. Conclusion(s): This first-time, systematic, real-world assessment revealed generation of higher neutralizing antibody titers by COVISHIELD. Relation of dose interval and decline in Nab titers post- 2nd-dose in pre-positives need further assessment. Diminished Nab titers at 6 months emphasize early booster.
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The Corona Virus Disease of 2019 is characterized by a serious epidemic (COVID-19). The acute respiratory syndrome is caused by the coronavirus, which is followed by an inflammatory response in the host. Systemic inflammatory response syndrome (SIRS) is a condition in which the body causes acute breathing problems, multiple organ impairment disorder, and even in the early stages of multiple organ failure extreme COVID-19. Increased development of anti-inflammatory cytokines in the late stages of serious disease causes the immune system's reaction to becoming controlled, resulting in immune fatigue. Pandemics have wreaked havoc on humanity's strata, wiped out whole nations, and strengthening immunity is long overdue. A strong immune system is needed to fight a viral infection. Multivitamin-rich diets improve pathogen immunity by triggering immune responses in several immune cells, as an example. Various immune-stimulating herbs, plants, and spices like chicory, Tinospora cordifolia, Withania somnifera, myrrh, ginger, etc., must be included to counteract the pathogens. © 2022 by the authors.
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Automation has been into existence since the mid Fifties but had simplest began to gain attention lately. The RPA software program makes use of existing generation's interface to automate the human detail in the technique. So, essentially, there's no want for human intervention. web scraping is a software of robot system Automation that is used in almost all of the industries. either or not it's a e-trade internet site, commodities buying and selling web sites, or any internet site and so forth. you can scrape the information from any of them based on your hobby. Now, the problem with guide scraping by hand is that it's miles at risk of mistakes and takes numerous times. also, the facts available on websites does now not change in any respect. up to date regularly, for this reason facts saved domestically might not usually be terrible. So, industries can actually automate this mission. The main objective of the project is to save time and send the updated information to the person using RPA technology. As this COVID-19 Global Pandemic going on, we thought of creating a project around COVID-19. So, in the project we will use Data Scrapping to extract Web Table (which contains COVID-19 data such as number of affected people, recovered people etc.) from web page. And, write the extracted data into Excel then we will send that excel over the email as attachment. In this project we researched how we can send data through email using RPA and extract the data from live covid_19 website. For software automation, there are many software's that are available in market. The main RPA vendors are UiPath, Automation Anywhere, and Blue Prism. So, to complete our RPA project, we have chosen UiPath which the best in the field of automation. You should be familiar with at least one of these tools before working on the following projects. This paper aims to provide RPA reviews as technical, as well as its implementation applications. © 2022 IEEE.
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A major part of computer vision is formed by Object detection. Most of the such tasks are done with efficient object detection. This paper aims to incorporate techniques for facial mask detection to achieve an accurate and efficient mask detection algorithm. The goal is to examine various deep learning algorithms to perform mask detection in this era of Covid. This paper aims on building an application based on facial mask recognition using different deep learning algorithms and compare the results to find out the most accurate algorithm. © 2022 IEEE.
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Currently, the entire planet is terrified of a virus known as COVID-19 (coronavirus). Its effects are so deadly that the whole world has been placed on lockdown. Vaccines for this virus are being developed by scientists and physicians all over the world. Machine learning, the Internet of Things (IoT), and artificial intelligence all play a role in detecting people who have been affected by coronavirus. We have also operated in this direction and developed a system called "Health Detection System for COVID-19 Patients using IoT" which can identify coronavirus-infected people and create a database for easy monitoring. Our system named as "Health Detection System for COVID-19 Patients using IoT" can detect corona by measuring the temperature and oxygen level of the patient. The system will detect the temperature of person with the help of DHT sensor and the oxygen level with the help of MAX30100, which are interfaced with NodeMCU. Data will be uploaded on ThingSpeak server (cloud) through which it can be monitored. The system is quite simple and very effective, especially at the hospital (ICU) where doctors can monitor patient from a distant place. Complete system cost around Rs 1,000/- (Rupees One Thousand Only). © 2022 Scrivener Publishing LLC.
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This paper reports a study on the statistics for particulate matter pollution (PM2.5) and the COVID-19 lockdown in the Kathmandu valley. The PM2.5 decreased during the COVID-19 pandemic lockdown periods 2020 compared to the average value of the previous three years (2017, 2018, and 2019). Further, analysis of active fire and air mass trajectory for April and May in 2019 and 2020 shows that the particulate matter trend associated with Kathmandu is not directly influenced by the long-range transport of wind carrying aerosols from the active fire regions. Statistical tests indicate a reduction of particulate matter pollution during the period.
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The Coronavirus Disease 2019 (COVID-19), also known as a novel coronavirus (2019-n-CoV), reportedly originated from Wuhan City, Hubei Province, China. Coronavirus Disease 2019 rapidly spread all over the world within a short period. On January 30, 2020, the World Health Organization (WHO) declared it a global epidemic. COVID-19 is a Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) evolves to respiratory, hepatic, gastrointestinal, and neurological complications, and eventually death. SARS-CoV and the Middle East Respiratory Syndrome coron-avirus (MERS-CoV) genome sequences similar identity with 2019-nCoV or Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2). However, few amino acid sequences of 2019-nCoV differ from SARS-CoV and MERS-CoV. COVID-19 shares about 90% amino acid sequence simi-larity with SARS-CoV. Effective prevention methods should be taken in order to control this pandemic situation. To date, there are no effective treatments available to treat COVID-19. This review provides information regarding COVID-19 history, epidemiology, pathogenesis and molecular diagnosis. Also, we focus on the development of vaccines in the management of this COVID-19 pandemic and limiting the spread of the virus.Copyright © 2022 Bentham Science Publishers.
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This paper presents a unique time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach to quantify the connectedness and transmission mechanism of shocks of nine commodities futures returns (namely;Gold and Silver from the category of precious metals;Copper, Lead, Zinc, Nickel and Aluminium from the category of base or industry metals;Natural Gas and Brent Crude Oil from energy sector) obtained from Multi Commodity Exchange of India Limited (MCX) from January 1, 2018 to December 31, 2021. This paper employs Balcilar et al. (2021)'s TVP-VAR extended joint connectedness approach, which combines the TVP-VAR connectedness approach of Antonakakis et al. (2020) with the joint spillover approach of Lastrapes and Wiesen (2021), to investigate the dynamic connectedness among the select commodity futures of interest. Our findings show that system-wide dynamic connectedness varies over time and is driven by economic events. The pandemic shocks appear to have an impact on system-wide dynamic connectedness, which peaks during the COVID-19 pandemic. Crude oil and zinc are the primary net shock transmitters, whereas gold and silver are the primary net shock receivers. We also discovered that the role of aluminum in shock transmitters and shock receivers changed during the course of the investigation. Pairwise connectivity, on the other hand, shows that Zinc, Copper, Nickel, and Crude oil are the key drivers of gold price changes, explaining the network's high degree of interconnectivity. During the study period, it was also discovered that silver has a significant influence on gold. Furthermore, in comparison to natural gas, gold's spillover activity is still relatively modest (on a scale), indicating that gold is less sensitive to market innovations. © 2023 Elsevier Ltd
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The outbreak of SARS-CoV-2 (Covid-19) is one of the most unprecedented and devastating events that the world has witnessed so far. It was manifested in Wuhan, China in December 2019 and has spread worldwide. The rapidity at which Covid-19 is transmitted has become one of the major concerns regarding the safety of mankind. The similarity of symptoms between Covid-19 and normal flu, like cough, body ache and headache, makes it difficult to ascertain a case to be of normal flu or of Covid. Consequently, many Covid cases are unreported which further increases the risk of spread of infection. In the present chapter, by using three mathematical models, we aim to give an outline of the spread of Covid-19 in West Bengal and how lockdown has helped to reduce the number of Covid cases. The first model is an exponential model;the second model is based on Geometric Progression which shows spread of coronavirus using a tree chart. The third model, named as Model for Stay at Home, shows that due to lockdown, the number of cases is gradually attaining a constant level instead of growing exponentially;thus urging each citizen to stay at home during lockdown unless an unavoidable situation arises. © 2022 Scrivener Publishing LLC.
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Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has taken over the world, and more than 38 lakh deaths had been reported till now due to this infectious disease. It has been declared a global pandemic by the world health organization. SARS-CoV-2 causes coronavirus disease of 2019 (COVID-19), and the major problem called "Cytokine storm" is reported, which may lead to death among the COVID-19 patients. This study aimed to review the Cytokine storm and its mechanism along with few immunomodulatory therapies for SARSCoV-2 infection suppression effectively. Method(s): The recently published works of literature were selected and reviewed based on the subject of this study. The databases, including Pubmed, ScienceDirect, Scopus, and Google Scholar, were searched extensively. Result(s): The review of the literature showed that an uncontrolled immune response causes excess inflammation. Evidence from recent trials has demonstrated that cytokine storms can be an important factor in the COVID-19 severity, leading to multiple organ failure and death. Conclusion(s): This study reviewed immunomodulatory therapies and strategies for SARS-CoV-2 infected patients to suppress the immune response. Ultimately, the cytokine storm can prove to be a boon and reduce the significant death tolls to SARS-CoV-2 infection.Copyright © 2022 Bentham Science Publishers.
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As a result of the COVID-19 pandemic, safety is one of the top priorities for travellers when choosing a hotel. This work examines the effect of customers' pre-stay expectations of a hotel about its safety-focused services, shaped through its official star-rating, on the during-stay confirmation of those expectations, satisfaction, and revisit intentions. A cross-sectional research design is used spanning temporally from the pre-stay to the during-stay phases. The pre-stay phase was the peak COVID-19 period in India (June–July 2021) to stimulate the safety concerns in the travellers planning their travel, while the during-stay phase was when the planned travel was undertaken with the traveller staying at the planned hotel (October 2021–January 2022). Data were collected from 452 customers and the results supported the proposed model. Further, the star-rating, as a signal for safety-focused services, was found to have a serial effect on revisit intentions, through the pre-stay expectations of safety services, and the during-stay confirmation of expectations and satisfaction. © 2022 Elsevier Ltd
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The pandemic COVID has engulfed the entire world in general and India in particular. Almost the entire country is under lockdown now and then forcing the people to stay inside their homes for the safety of themselves and others. There is one section, i.e. Indian nurses, which is braving against all odds to ensure the proper functioning of the health care system and educate and persuade the patients and their relatives. This has necessitated the nurses to go an extra mile reflecting a sense of responsibility towards patients, colleagues, hospitals, society, and nation and discharge their duties performing activities beyond the formal job descriptions, formal reward system, or direct and explicit recognition. In the present study, the researchers have empirically investigated the nature, extent, and mechanism of the impact of variables transformational leadership, job satisfaction, and emotional intelligence that lead to nurses displaying the organizational citizenship behaviour at this unprecedented juncture of time in India. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
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The Coronavirus disease (2019) has caused massive destruction of human lives and capital around the world. The latest variant Omicron is proved to be the most infectious of all its previous counterparts – Alpha, Beta and Delta. Various measures are identified, tested and implemented to minimize the attack on humans. Face masks are one of those measures that are shown to be very effective in containing the infection. However, it requires continuous monitoring for law enforcement. In the present manuscript, a detailed research investigation using different ablation studies is carried out to develop the framework for face mask recognition using pre-trained deep convolution neural networks (DCNN) models used in conjunction with a fast single layer feed-forward neural network (SLFNN) commonly known as Extreme Learning Machine (ELM) as classification technique. The ELM is well known for its real time data processing capabilities and has been successfully applied both for regression and classification problems of image processing and biomedical domain. It is for the first time that in this paper we have proposed the use of ELM as classifier for face mask detection. As a precursor to this, for feature selection, six pre-trained DCNNs such as Xception, Vgg16, Vgg19, ResNet50, ResNet 101 and ResNet152 are tested for this purpose. The best testing accuracy is obtained in case of ResNet152 transfer learning model used with ELM as the classifier. The performance evaluation through different ablation studies on testing accuracy explicitly proves that ResNet152 - ELM hybrid architecture is not only the best among the selected transfer learning models but also proves so when it is compared with several other classifiers used for the face mask detection operation. Through this investigation, novelty of the use of ResNet152 + ELM for face mask detection framework in real time domain is established. © 2022